ISSN 1854-6250 APEM journal Advances in Production Engineering & Management Volume 11 | Number 1 | March 2016 Published by PEI apem-journal.org University of Mari bor Advances in Production Engineering & Management Identification Statement APEM ISSN 1854-6250 | Abbreviated key title: Adv produc engineer manag | Start year: 2006 ISSN 1855-6531 (on-line) Published quarterly by Production Engineering Institute (PEI), University of Maribor Smetanova ulica 17, SI - 2000 Maribor, Slovenia, European Union (EU) Phone: 00386 2 2207522,Fax: 00386 2 2207990 Language of text: English APEM homepage: apem-journal.org UniversityofMaribor University homePage: WWW.um.si APEM Editorial Editor-in-Chief Miran Brezocnik editor@apem-journal.org, info@apem-journal.org University of Maribor, Faculty of Mechanical Engineering Smetanova ulica 17, SI - 2000 Maribor, Slovenia, EU Desk Editor Tomaz Irgolic deski@apem-journal.org Website Master Lucija Brezocnik lucija.brezocnik@um.si Editorial Board Members Eberhard Abele, Technical University of Darmstadt, Germany Bojan Acko, University of Maribor, Slovenia Joze Balic, University of Maribor, Slovenia Agostino Bruzzone, University of Genoa, Italy Borut Buchmeister, University of Maribor, Slovenia Ludwig Cardon, Ghent University, Belgium Edward Chlebus, Wroclaw University of Technology, Poland Franci Cus, University of Maribor, Slovenia Igor Drstvensek, University of Maribor, Slovenia Illes Dudas, University of Miskolc, Hungary Mirko Ficko, University of Maribor, Slovenia Vlatka Hlupic, University of Westminster, UK David Hui, University of New Orleans, USA Pramod K. Jain, Indian Institute of Technology Roorkee, India Isak Karabegovic, University of Bihac, Bosnia and Herzegovina Janez Kopac, University of Ljubljana, Slovenia Iztok Palcic, University of Maribor, Slovenia Krsto Pandza, University of Leeds, UK Andrej Polajnar, University of Maribor, Slovenia Antonio Pouzada, University of Minho, Portugal Rajiv Kumar Sharma, National Institute of Technology, India Katica Simunovic, J.J. Strossmayer University of Osijek, Croatia Daizhong Su, Nottingham Trent University, UK Soemon Takakuwa, Nagoya University, Japan Nikos Tsourveloudis, Technical University of Crete, Greece Tomo Udiljak, University of Zagreb, Croatia Ivica Veza, University of Split, Croatia Limited Permission to Photocopy: Permission is granted to photocopy portions of this publication for personal use and for the use of clients and students as allowed by national copyright laws. 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Advances in Production Engineering & Management is indexed and abstracted in the WEB OF SCIENCE (maintained by THOMSON REUTERS): Science Citation Index Expanded, Journal Citation Reports - Science Edition, Current Contents - Engineering, Computing and Technology • Scopus (maintained by Elsevier) • Inspec • EBSCO: Academic Search Alumni Edition, Academic Search Complete, Academic Search Elite, Academic Search Premier, Engineering Source, Sales & Marketing Source, TOC Premier • ProQuest: CSA Engineering Research Database -Cambridge Scientific Abstracts, Materials Business File, Materials Research Database, Mechanical & Transportation Engineering Abstracts, ProQuest SciTech Collection • TEMA (DOMA) • The journal is listed in Ulrich's Periodicals Directory and Cabell's Directory journal University of Maribor Production Engineering Institute (PEI) Advances in Production Engineering & Management Volume 11 | Number 1 | March 2016 | pp 1-72 Contents Scope and topics 4 Announcement and acknowledgement 4 Assessment of mechanical and wear properties of epoxy-based hybrid composites 5 Agunsoye, J.O.; Bello, S.A.; Bello, L.; Idehenre, M.M. A knowledge-based system for end mill selection 15 Prasad, K.; Chakraborty, S. Thermal analysis on a weld joint of aluminium alloy in gas metal arc welding 29 Ismail, M.I.S.; Afieq, W.M. A bi-objective inspection policy optimization model for finite-life repairable systems using 38 a genetic algorithm Ramadan, S. Integration of SWOT and ANP for effective strategic planning in the cosmetic industry 49 Al-Refaie, A.; Sy, E.; Rawabdeh, I.; Alaween, W. Aluminium hot extrusion process capability improvement using Six Sigma 59 Ketan, H.; Nassir, M. Calendar of events 70 Notes for contributors 71 Journal homepage: apem-journal.org ISSN 1854-6250 ISSN 1855-6531 (on-line) ©2016 PEI, University of Maribor. All rights reserved. 3 Scope and topics Advances in Production Engineering & Management (APEM journal) is an interdisciplinary refer-eed international academic journal published quarterly by the Production Engineering Institute at the University of Maribor. The main goal of the APEM journal is to present original, high quality, theoretical and application-oriented research developments in all areas of production engineering and production management to a broad audience of academics and practitioners. In order to bridge the gap between theory and practice, applications based on advanced theory and case studies are particularly welcome. For theoretical papers, their originality and research contributions are the main factors in the evaluation process. General approaches, formalisms, algorithms or techniques should be illustrated with significant applications that demonstrate their applicability to real-world problems. Although the APEM journal main goal is to publish original research papers, review articles and professional papers are occasionally published. Fields of interest include, but are not limited to: Additive Manufacturing Processes Advanced Production Technologies Artificial Intelligence Assembly Systems Automation Cutting and Forming Processes Decision Support Systems Discrete Systems and Methodology e-Manufacturing Fuzzy Systems Human Factor Engineering, Ergonomics Industrial Engineering Industrial Processes Industrial Robotics Intelligent Systems Inventory Management Joining Processes Knowledge Management Logistics Announcement and acknowledgement I am very pleased to announce that in December 2016, the journal Advances in Production Engineering & Management was positively evaluated for inclusion in the WEB OF SCIENCE scientific citation indexing service maintained by THOMSON REUTERS. The journal will be indexed and abstracted in the Science Citation Index Expanded, Journal Citation Reports - Science Edition, and Current Contents - Engineering, Computing and Technology. The coverage will begin with Vol. 8, No. 1, 2013. I would like to thank the leadership of the Production Engineering Institute on Faculty of Mechanical Engineering - University of Maribor, and the laboratories' leaders for the financial support to the journal. My appreciation for the financial support given in the last two years is also directed to the Slovenian Research Agency - ARRS. I gratefully acknowledge the journal's founding team members, the current editorial team and the editorial board members for their support, suggestions, and a lot of good advice regarding the journal contents and the technical details. I gratefully acknowledge the reviewers for the time and expertise devoted to reviewing the manuscripts. Many thanks also to the English abstract proofreading experts and to the office personnel in the Production Engineering Institute for their contributions to the journal's success. Miran Brezocnik Editor-in-Chief Machine Tools Machining Systems Manufacturing Systems Mechanical Engineering Mechatronics Metrology Modelling and Simulation Numerical Techniques Operations Research Operations Planning, Scheduling and Control Optimisation Techniques Project Management Quality Management Queuing Systems Risk and Uncertainty Self-Organizing Systems Statistical Methods Supply Chain Management Virtual Reality 4 APEM jowatal Advances in Production Engineering & Management Volume 11 | Number 1 | March 2016 | pp 5-14 http://dx.doi.Org/10.14743/apem2016.1.205 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper Assessment of mechanical and wear properties of epoxy-based hybrid composites Agunsoye, J.O.a, Bello, S.A.ab- , Bello, L.a, Idehenre, M.M.a aDepartment of Metallurgical and Materials Engineering, University of Lagos, Akoka, Yaba, Lagos Nigeria bDepartment of Materials Science and Engineering, Kwara State, University, Malete, Kwara State Nigeria A B S T R A C T A R T I C L E I N F O Discarded florescent tubes and graphite rods obtained from dumped primary cells have been processed to obtain glass and graphite particles. 80 |m glass and graphite particles were used as reinforcements in epoxy resin, LY 556 cured with HY 931 hardener to produce epoxy resin hybrid composites. The morphology, mechanical properties, thermal stability and wear resistance characteristics of the epoxy resin glass/graphite hybrid composites were studied. The thermogravimetric analyser TGA 701 was used to examine the thermal stability of the epoxy resin glass particle/graphite composites. Addition of graphite and glass particles enhanced the strength, thermal stability and wear resistance of the epoxy resin. However, tensile strain and impact energy absorption of the epoxy resin hybrid composites started declining at 6 wt% of glass particle addition. The increase in wear rate of the composites with an increment in applied loads is attributable to increase in the normal reaction between the examined sample surfaces and the emery paper. Furthermore, the increase in wear resistance with an increment in wt% of glass particle additions is attributable to good interfacial adhesion between matrix and the fillers. The textural and appearance differences between the scanning electron micrographs of the control and epoxy resin hybrid composites is attributable to the presence of new phases due to exothermic and cross linking reaction between the matrix and the fillers. Hence, new vital engineering composites peculiar to automobile, aerospace and building industries have been produced. © 2016 PEI, University of Maribor. All rights reserved. Keywords: Epoxy resin Composite Glass particle Graphite particle Mechanical properties Wear properties Corresponding author: sefiu.bello@kwasu.edu.ng; adekunle_b@yahoo.com (Bello, S.A.) Article history: Received 11 June 2015 Revised 8 January 2016 Accepted 19 January 2016 1. Introduction Epoxy resins belong to a class of polymer under the aegis of thermoset [1]. They possess excellent mechanical strength, electrical and chemical resistance; good thermal characteristic and fine adhesion to many substrates after cure. They are used as matrix resins for reinforced composites, in aerospace industry, adhesives in car, as insulating materials for electrical and electronic industry. However, when compared to other light materials like aluminium, they have low mechanical strength and thermal resistance. Hence, filling of epoxy resin for improved mechanical, wear and thermal resistance properties is imperative. The mechanical performance of the fiber-reinforced composites usually depends on the properties of the matrix and fiber materials [2-4]. Reinforcement such as glass has good thermal resistance, wear resistance and high strength but they have low fracture toughness. Also, in the development of wide variety of composites for application in areas such as aerospace industry, automobile industries and sporting goods, carbon fiber has been used as the reinforcing material [5-6]. Carbon fibre reinforced polymer com- 5 Agunsoye, Bello, Bello, Idehenre posites are being used in a wide range of engineering applications because of increase in the impact energy absorption per unit weight, reduce noise and vibrations and excellent resistance to fatigue [7]. Hybridization of particles as a filler for a polymer gives rise to a new polymer based composite with enhanced mechanical properties [8]. Hence, the composites of these three engineering materials, i.e. polymer-matrix composites with small amount and size of glass and carbon particle reinforcements, could improve strength and impact energy of the composites. This is the thrust of this research i.e. to use the good properties offered by both glass and carbon particles to reinforce epoxy resin to develop composites with improved properties. Such composites made from high-performance particles (e.g., carbon and glass particles) embedded in compliant polymeric resins can be used in a wide range of fields such as aerospace engineering and sports utilities. The developed composites are expected to have high specific strength and toughness, superior manufacturability, as well as excellent corrosion resistance and fatigue tolerance. Agunsoye et al. (2014) worked on the development and characterization of aluminium dross/epoxy resin composite materials [9]. Their results revealed that additions of particulate aluminium dross to epoxy resin enhanced significantly, the thermal and wear resistance of the epoxy resin aluminium dross composites. Hassan et al. (2012) worked on development of polyester/eggshell particulate composites [10]. Their results show that the addition of eggshell to the polyester slightly improved the mechanical properties of the produced composites. Allaouis et al. (2002) fabricated and studied mechanical and electrical properties of epoxy resin/carbon nanotube composites [11]. Results of the experimental examination revealed that the Young's modulus and the yield strength of the composites have been doubled and quadrupled for composites with respectively 1 wt% and 4 wt% nanotubes, compared to the pure resin matrix samples. Conductivity measurements on the composite samples showed that the insulator-to-conductor transition took place for nanotube concentration between 0.5 wt% and 1 wt%. Vala-sek et al. 2015 studied two-body abrasive wear of polymeric composites using waste abrasive Al2O3 particles as a filler [12]. Their results indicated 16 % improvement in hardness with composite made from 284 [im sized Al2O3 particles. Many worked have been focused on enhancement of thermoset properties, the use of flo-rescent tube glass particles and graphite particles obtained from discarded primary cell as reinforcements in epoxy is rare or not found. However, in this work, thermoset hybrid composites have been produced from epoxy resin (LY 556) reinforced with 80 [im glass and graphite particles. The mechanical properties, wear behaviour, phase morphology and distribution in the epoxy matrix of the produced composites were investigated. The glass particles used were obtained from processed discarded fluorescent tubes which have the potential of causing serious environmental harm due to mercury. The mercury from just one fluorescent tube is enough to pollute 30,000 l of underground water so that the water is no longer safe to drink [13]. Hence, this research was also aimed at eliminating this challenge by making use of this potential harmful material (discarded fluorescent tube) as reinforcement in the production of composites for engineering applications. 2. Materials and methodology Materials used in this work are discarded florescent tube procured from Waste Dumping Centre of University of Lagos; graphite rods were obtained from discarded primary cells. Epoxy resin (LY 556), HY931 hardener and distilled water were purchased from Tony Nigeria Chemicals, Ojota Lagos. The major equipment used includes scanning electron microscope (SEM), Instron extensometer, model 3319; hot press, X-ray diffractometer and Avery Denison Universal Impact Testing Machine. Broken glasses obtained from florescent tubes were submerged in a 100 l of boiling water contained in a plastic drum covered with 0.5 kg ground sulphur powder. This attempt was made to remove mercury from the florescent tube. The resulted suspension containing black particles was decanted off the drum after 24 h, leaving behind the broken glasses. The broken glasses were sundried at average daily temperature of 25 °C for 2 days. They were ground into powder using 87002 LIMOGES planetary ball mill, model 28A20 92 in accordance with [14]. Glass pow- 6 Advances in Production Engineering & Management 11(1) 2016 Assessment of mechanical and wear properties of epoxy-based hybrid composites ders were classified using a set of sieves arranged in descending order of mesh sizes ranging from 50-300^m. Sieves were vibrated for 30 min using a sine shaker. Solid graphite electrodes were ground and sieved. Fig. 1 and Fig. 2 present glass and graphite particles. Epoxy resin (LY 556) and hardener (HY 931) were mixed inside a beaker at 3:1 in accordance with manufacturer specification. The mixture was stirred by a hot plate and stirrer, model Jen-way 1000 at 120 °C for 1 h and then poured into a metallic mould coated with petroleum jelly. Epoxy resin/hardener blends were left in the mould for 2 days at room temperature to obtain epoxy/hardener preforms. Epoxy preforms were placed in a mould with different cavities patterned to the standard shapes for mechanical property analyses. The preforms were heated to 150 °C, held at the temperature for 1 h and then forged to obtain the standard samples for analyses (in accordance with ASTM D 3039 M959 and ASTM D 790-90), using a hot press. For composite production, a mixture containing 80 [im sized graphite and glass particles were dispersed in 50 cm3 of water in a beaker and then stirred manually with a glass rod for a period of 10 min. The mixture was added to 250 cm3 epoxy resin (LY 556) in another beaker and then stirred for 15 min. 30 % HY931 hardener was added to the epoxy resin/filler mixture. The resulting mixture was stirred using magnetic mantle shaker for 1 h at a temperature of 120 °C to evaporate water molecules. The epoxy resin mixture was poured into a mild steel mould (coated with petroleum jelly as a dispatching/releasing agent) and then left in the mould for 2 days at room temperature. Epoxy graphite/glass particles hybrid composite preforms were placed in a forming mould. They were heated to 150 °C, held at the temperature for 1 h and then forged to obtain standard samples for mechanical property investigation. Six different composite samples produced contained 2 wt% graphite particles with increasing wt% of glass particles from 2-12 % at 2 % interval. Phase identification of the produced epoxy samples were carried out using a Panalyti-cal Empyrean X- ray diffractometer (XRD) in accordance with [15]. Morphology of the samples was examined with the aids of scanning electron microscope, ASPEX 3020 in line with [16]. Tensile samples of 80 mm gauge length and 7 mm width were subjected to tensile test using Instron extensometer, model 3369. Samples were gradually loaded at a strain rate of 103 1/s [17]. This resulted in the simultaneous elongation and reduction in the cross section of the sample until the samples became fractured. The flexural test was carried out on the bended 100 mm square samples. The samples were loaded at the bended centre point until fracture occurred at the point. 150 mm diameter samples were subjected to compressive load using Instron exten-someter. The impact energies of the produced samples were determined with the aids of Avery Den-ison Universal Impact Testing Machine. The notched 60 x 10 x 10 mm3 dimensioned samples were subjected to the Charpy impact test. First, the hammer pendulum was released to set the scale to zero point. Each sample was impacted with hammer pendulum of weight 300 J, released from the upper position of the machine. The impact energy absorbed by each sample was read and then recorded. Fig. 1 Photograph of glass particles Fig. 2 Photograph of graphite particles Advances in Production Engineering & Management 11(1) 2016 7 Agunsoye, Bello, Bello, Idehenre The wear resistance of 60 x 10 x 10 mm3 dimensioned samples of the control, epoxy resin hybrid composites containing 2, 4 and 12 % glass particles were investigated using Pin on Disc wear machine. Densities and the initial masses of the examined samples are expressed in two Cartesian system as follows (1.33 g/cm3, 8 g); (1.28 g/cm3, 7.7 g); (1.27 g/cm3, 7.64 g) and (1.24 g/cm3, 7.43 g) respectively. The wear test was carried out on a 200 mm circular rotating disc with attached emery paper of 220 grit size according to [2]. The surface of the test sample was placed against the rotating disc for a period of 60 s under different loads at a speed of 2.36 m/s. The final mass after each test was measured and recorded. The mass loss during the investigation was calculated and recorded using the Eq. 1. The thermal stability of the control and the epoxy resin hybrid composites containing 2 and 12 % glass particles were examined using TGA 701. Each sample was heated from room temperature to a maximum of 900 °C for 200 min. The thermal stability was studied as a function of weight loss with heating temperature and time. 3. Results and discussion 3.1 X-ray diffractograms X-ray diffractogram in Fig. 3 reveals the phases present in the epoxy resin (control) sample. It was observed from the result obtained that aluminium manganese titanium (Al3Ti0.78Mn0.25), burnt lime (CaO), pyrolusite (P2O5), sodium aluminium sulphide (Na4AlS5) were identified at diffraction angles of 44.23°, 46.24°, 27.81° and 14.02° respectively. Fig. 4 shows the X-Ray diffractogram of the epoxy resin hybrid composite containing 12 % glass particles. There are two peaks with many shoulders at their sides. This indicates higher degree of phase segregation and or straining within the matrix. They are attributable to chemical interaction among epoxy resin, glass and graphite particles. It was observed from the result that the phases were Iron titanium (FeTi), anatase (TiO2), Titanium Zinc (Zn0.6Ti0.4X These phases were identified at diffraction angles of 50.01°, 28.23° and 85.23° respectively. Mass loss = initial mass - final mass (1) C o 0 0 0 5 0 0 2 0 3 0 5 0 7 0 Position [°2Theta] Fig. 3 X-ray diffractogram of the unfilled epoxy resin [2] 8 Advances in Production Engineering & Management 11(1) 2016 Assessment of mechanical and wear properties of epoxy-based hybrid composites 3.2 SEM micrographs Microstructure of epoxy resin control sample is presented in Fig. 5. The structure reveals the white phases within the black matrix. The white phases are attributable to Al3Ti0.7sMn0.25, CaO and Na4AlS5 as indicated by the diffractogram in Fig. 3. Fig. 6 presents the microstructure of the filled epoxy resin. The facial difference of the microstructure in Fig. 7 from that in Fig. 6 is attributable to glass and graphite particle additions. The microstructure appears rocky which indicates a good interfacial adhesion between the matrix and reinforcements. Fig. 5 Unfilled epoxy resin SEM microgragh Fig. 6 Epoxy resin hybrid composite micrograph obtained from SEM Advances in Production Engineering & Management 11(1) 2016 9 Agunsoye, Bello, Bello, Idehenre 3.3 Mechanical properties It was observed from Fig. 7, Fig. 8, and Fig. 9 that filling of epoxy resin with graphite and glass particles produced epoxy resin hybrid composites with enhanced tensile, flexural and compressive strengths. Strength of the epoxy hybrid composites increased with an increment in wt% of glass particle additions up to 10 wt% when the ultimate tensile and flexural strength start declining. This indicated epoxy resin saturation level. However, the increase in compressive strength beyond 10 wt% of glass particles may be attributable to brittleness of the phases within the matrix which enhance the strength in compression. Fig. 10 and Fig. 11 depicted that impact energy and tensile strain started declining at 6 wt% glass particle addition. This indicated a critical glass filling level such that glass particle addition above this level embrittled the epoxy hybrid composites. The decline in impact energy and tensile strain could be associated with brit-tleness of glass and graphite particles. During loading, the hybridized particles allowed the crack propagation in a more rapid manner than that in the case of epoxy hybrid composites at lower wt% of glass particle additions. This caused the 2 % graphite-6 % glass particle epoxy composites to fail at lower absorbed energy and percentage elongation. 70 145 r 40 5 10 %wt of glass particles 15 115 0 5 10 %wt of glass particle addition 15 Fig. 7 Ultimate tensile with wt% of glass particles 160 Q- H e eu +J m cu > t/3 m cu u a o u 150 140 130 120 s 3 4000 as aj | 3200 £ 2400 1600 800 0 4 8 12 16 20 24 28 32 36 40 Time t s Fig. 5 Temperature histories of the three evaluated points transverse to the direction (Voltage: 22 V, Speed: 30 mm/s] 8000 7200 6400 5600 ® 4800 CD 3 4000 re (D ^ 3200 £ 2400 1600 800 0 4 8 12 16 20 24 28 32 36 40 Time t s Fig. 6 Temperature histories of the three evaluated points of the cross-section of plate (Voltage: 22 V, Speed: 30 mm/s] In Fig. 8, by having different heat input even though the similar welding speed is used due to different in the welding voltage during the welding process. The faster the welding is having lower heat input compare to the slower welding speed which is having higher heat input. This is because the welding speed is inversely proportional with the heat input. The different in the temperature distribution can be only seen on the top surface of the plate since the heat are been distributed throughout a large volume. The depth of the penetration for all parameter are similar due to the plate are thin, which the heat only throughout small volume. In order to verify the developed model, the simulation and experimental weld geometry were compared. Fig. 9 shows the comparison of bead profile between the simulation and experimental. From this comparison, the highest heat input makes the bead formation wider when comparing parameter used in the similar welding speed. Whereas at a higher speed with the similar power usage the bead was reduce in its size. The bead profile from the experimental was viewed by using optical microscope. The height of the bump of the welding bead depended on the voltage usage and the speed of welding. The voltage of 18 V showed the highest bump formation for each speed. This was due to the low power usage to melt the filler so that it could bond completely with the plate. The usage of voltage 20 V and 22 V showed that the bump formations were smaller in sizes where the filler was completely bonded with the plate. Thus those power were more preferable to be used since the quality of welding process was been referred by the formation of bumps. Point A (94mm, 0, 0) •-- Point B (94mm, 4mm, 0) _ Point C (94mm, 8mm, 0) ----------- 34 Advances in Production Engineering & Management 11(1) 2016 Thermal analysis on a weld joint of aluminium alloy in gas metal arc welding (a) ^^^^^ r ^ >' ^^^^^^ /= 1.3S (b) ^^^^^ (c) ^^^^^ ^^^^^ t = (d) ^^^^ (e) ^^^^ 293 323 373 855 2000 3000 4000 6000 8000 K Fig. 8 Temperature distributions for welding conditions with the speed of 30 mm/s and different voltage of (a) 18 V, (b) 20 V, and (c) 22 V Most of the percentage of error is less than 20 % and the least error is at the voltage of 18 V speed of 40 mm/s with the percentage of error of 0.05 %. The results from the analysis become the theoretical result which was used as the reference to calculate the percentage of errors. The percentage of error varies among the parameter due to some problems that occurred during the experimental which affect the results. Voltage: 18V Speed: 30mm/s Voltage: 20V Speed: 30mm/s Voltage: 22V Speed: 30mm/s Voltage: 18V Speed: 40mm/s Voltage: 20V Speed: 40mm/s Voltage: 22V Speed: 40mm/s 293 323 373 855 2000 3000 4000 6000 8000 K Fig. 9 Comparison between experimental and simulated weld bead profiles Advances in Production Engineering & Management 11(1) 2016 35 Ismail, Afieq The voltage that was used during the welding process may differ from the analysis since the welding machine could not maintain the power output. The power used during the welding was set by setting the arc voltage with the tolerance of ± 1 V so that the result could be acceptable. The welding process was done manually where the height of the nozzle was not static, affecting the torch arc diameter which lead to the error in the size of bead. 5. Conclusion A three-dimensional finite element model has been developed to simulate the thermal history during gas metal arc welding of aluminium alloy sheet. Main conclusions obtained in this study are as follows: • The developed numerical model using a Gaussian heat source can well represent the real welding as the heat source penetrates into the material. • Arc voltage and welding speed have a significant effect on the temperature distribution, weld pool size and shape, and weld bead geometry. • Heat input to the weld pool is transferred rapidly first in the thickness direction of the sheet and then in the width direction to reach uniformed distribution. • Temperature distributions obtained from the developed model can be used as inputs for the thermo-mechanical analysis of aluminium alloy in gas metal arc welding. Acknowledgement The authors would like to acknowledge the technical support provided by Mr. Mohd Saiful Azuar Md. Isa in carrying out the experimental work at the Faculty of Engineering, Universiti Putra Malaysia (UPM). References [1] Benyounis, K.Y., Olabi, A.G., Hashmi, M.S.J. (2005). 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Advances in Production Engineering & Management 11(1) 2016 37 APEM jowatal Advances in Production Engineering & Management Volume 11 | Number 1 | March 2016 | pp 38-48 http://dx.doi.Org/10.14743/apem2016.1.208 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper A bi-objective inspection policy optimization model for finite-life repairable systems using a genetic algorithm Ramadan, S.a* Department of Mechanical and Industrial Engineering, Applied Science Private University, Shafa Badran, Amman, Jordan A B S T R A C T A R T I C L E I N F O This paper presents a bi-objective optimization model for finding the optimal number and optimal aperiodic times for the inspections of finite-life repairable systems when the availability of the component and the total maintenance cost are under consideration. The model utilizes the delay-time concept under perfect inspection assumption. The defect arrival process is modelled using the nonhomogeneous Poisson process and the failure times are probabilistic. The solution to this problem is NP-hard, therefore, a mutation-based genetic algorithm has been designed to solve the model. The effectiveness of the model was demonstrated using seven illustrative examples and compared to an existing classical periodic inspection model that uses a fixed number of inspections. The results showed that the proposed model did better (in all of the attributes) than the aperiodic model that using a fixed number of inspections. Furthermore, the results showed that the proposed model gave better results than a single-objective aperiodic model. The proposed model is a general model that can be implemented with different rates of occurrence of defects and different delay-time distributions. Also this model can be extended easily to cover complex systems and imperfect inspection cases. © 2016 PEI, University of Maribor. All rights reserved. Keywords: Maintenance Aperiodic inspection Periodic inspection Delay-time Multi-objective optimization Genetic algorithms *Corresponding author: s_ramadan@asu.edu.jo (Ramadan, S.) Article history: Received 14 September 2015 Revised 29 September 2015 Accepted 19 October 2015 1. Introduction As equipment age, the failure and deterioration related maintenance costs and interruptions increase; hence, the need for effective maintenance policies become more obvious. Traditionally, corrective maintenance is the most prevailing maintenance type practiced. It was estimated that 80 % of the industry dollars is spent on maintaining chronic failures of machines, systems, and people. Despite this huge figure, corrective maintenance cannot improve the reliability of the machines/systems as the maintenance action is taken after the failure. In the other hand, it was estimated that eliminating many of those chronic failures by implementing an effective maintenance policies can reduce this percentage between 40 % and 60 % [1]. Preventive maintenance (PM) is one of the most widely used maintenance types that can reduce the cost of maintaining machines and systems due to its ability of discovering hidden failures that may constitute up to 40 % of the failure modes in complex industrial systems [2]. Many inspection models were developed in literatures to optimize the inspection process in order to reduce the number of chronic failures. Earlier inspection models aimed to optimize the number of inspections per unit of time by minimizing the total downtime or maximizing the profit which were expressed as a function of number of inspections [3-7]. These models did not discuss the periodicity of the inspections but rather found the optimum number of inspections per unit of time. More recent inspection models were developed based on delay-time concept introduced by [8] which is very 38 A bi-objective inspection policy optimization model for finite-life repairable systems using a genetic algorithm similar to the Potential Failure interval in reliability centered maintenance developed later [2]. Delay-time concept divides the failure process into two stages: defect initialization stage and failure stage and defines the time elapsed between the defect initialization and the corresponding actual failure as the delay-time. This concept is very important in preventive maintenance PM because it shows that there is a time window (equals to the delay-time) that the maintenance crew can detect and fix the defect before it turns into a chronic failure. This concept inspired many researchers to develop optimization inspection models to reduce the number of chronic failures. The essence of those inspection models is to find the optimal periodicity of the inspections that will reduce the expected number of chronic failures. Christer et al. and Baker, used the delay-time concept in the industrial plant to find the periodicity of the inspections where the value of the delay-time was considered probabilistic [9-13]. Wang and Majid [14] used the concept of delay-time in offshore oil platform plant to optimize the periodicity of the inspections by minimize the system downtime. The work of Dawotola et al. [15] used the concept of delay-time in very long cross-country petroleum pipeline system where the periodicity of the inspections was determined by minimizing the total economic loss of failure while taking the human risk and maintenance budget as constraints. Abdel-Hameed [16] implemented increasing jump Markov process to optimize the periodicity of inspections. Okumura et al. [17, 18] proposed a stochastic-process free method for optimizing the discrete time point inspections for single unit system using stochastic processes. Wang [19] proposed two models one for single component and another one for complex component based on delay-time concept and in [20] the author extended the delay-time concept and instead of assuming that the failures can be detected only by inspections, he assumes that the failures can be revealed by themselves. Based on this extension, he proposed an inspection model for two types of inspections and repairs to determine the optimal constant periodicity of the inspections. Later, Wang et al. [21] extended the work of Wang [20] to multi-component multi-failure mode inspection model. Unfortunately, very little work was devoted to consider the multi-objective optimization of the inspection models under delay-time concept. Under delay-time concept, most of the literatures aimed to optimize the inspection policy based on a single objective namely, minimizing some form of maintenance cost [17, 22-27]. Other objectives are also found in the literatures such as maximizing the availability or the reliability of the system [6, 28, 29]. Few of the studies in the literatures considered both the number of inspections and the timing of these inspections in there models. The majority of them optimized either the number of inspection per unit time [4-7] or considered a constant number of inspections and optimized the times at which the inspections were made [30]. Moreover, a lot of the optimization models in the literatures were solved by a special designed algorithms that can be used only to the corresponding inspection model or algorithms that were time inefficient like enumeration. In this paper a bi-objective inspection optimization model is considered to optimize the number and the timing of inspections utilizing two objectives: maximizing the availability and minimizing the maintenance cost of the system. The model utilized the delay-time concept under perfect inspection assumption. The defect arrival process is modelled using nonhomogeneous Poisson process and the failure times are probabilistic. Genetic algorithm, which is a generic and efficient optimization algorithm, was used to optimize this model. The paper contains the following sections: Section 2 shows the notations and the assumptions of the model. Section 3 presents the model formulation based on delay-time concept. Section 4 presents the details of the genetic algorithm used. Section 5 presents the experimentations and discussion, and finally, section 6 concludes. 2. Assumptions and notations This section lists the assumptions and notations used in this paper. The following assumptions and notations can be explained on the light of Fig. 1 which shows a typical defect-failure -inspection relation under delay-time concept. Advances in Production Engineering & Management 11(1) 2016 39 Ramadan O Initial pMOt of a defect # Failure pouit | Defect identified 31 inspection O O Q à O |0 "i n¿ u^ t-i lUt^u* Time Fig. 1 The relationship between defects, failures, and inspections under the delay-time concept Consider a system with a finite life L, the objective of this proposed model is to find the optimal inspection policy; i.e., the optimal number of inspections n and the optimal timing for the inspections t to achieve the highest possible availability and the lowest possible maintenance cost Cm for the system. The assumptions underlying the proposed model are as follows: • The system is treated as single unit. • One mode of failures (defects) is analyzed and the defects are assumed to be independent. • The defects arise as a nonhomogenous Poisson process with Rate of Occurrence of Defects (ROCOD) A(u) at time u. • A failure happens after the initialization of a defect and the corresponding delay-time h is passed. • The delay-time distribution is independent of the time origin u. • The probability density function for the delay-time h is f(K) with cumulative density function F(h). • Inspections are carried out at t = {t1,t2,t3,^, tn}, hence the decision variables are t and n where t takes discrete values. • Only one type of inspection is considered and thus the inspections are identical. • Inspections are perfect in that all the defects present at the time of inspection will be recognized. • The mean inspection time is dins during which the system is down. • The mean time to rectify a defect is dr during which the system is down. • The mean time to repair a failure is df during which the system is down. • The average inspection cost is cins. • The average rectification cost is cr. • The average repairing cost is df. • E[Nd(ti_1,ti)] represents the expected number of defects in the interval (ti_1,ti). ti) |t(_i] represnts the expected number of failures in the interval (ti_1,ti). • E[Nr(ti-1,ti)] represents the expected number of rectified defects by inspection i at time • As denotes the nonparametric availability of the system during its life L. • Cm denotes the expected maintenance cost of the system during its life L. • Bm denotes the maintenance budget allocated for the system during its life L. • SLAgis the satisfaction level at As. • SLr is the satisfaction level at Cm. cm m 3. Model formulation Consider a nonhomogeneous defect arrivals process with arrival rate given by A(u), then the number of defects in the infinitesimal time S(u) is A(u)S(u). Integrating A(u)5(u) over the interval (ti_1,ti) gives the expected number of defects in that interval. Mathematically, the expected number of defects in the interval (ti_1,ti) is 40 Advances in Production Engineering & Management 11(1) 2016 A bi-objective inspection policy optimization model for finite-life repairable systems using a genetic algorithm E[Nd(ti_1,ti)]= i 1 A(u)du (1) Jti-1 The probability that any of these defects who arose in time u and is in the interval (ti_1,ti) will develop into a failure in the interval (u,u + 5(u)) is A(u)F(u)8(u). Integrating A(u)F(u)S(u) over the interval (ti_1,ti) will give the expected number of failures over that interval. Mathematically, the expected number of failures in the interval (ti_1,ti) is rti E[Nf(tí.1,tí)]= I A(u)F(t[ — Jti-1 u)du (2) Since perfect inspection is assumed, at the ith inspection which is conducted at time tu the expected number of rectifications is simply the difference between the expected number of defects arrived in the interval (ti_1,ti) and the expected number of defects developed into failures, i.e., the expected number of failures, in the same interval. Mathematically the expected number of rectifications in the interval (ti_1,ti) is E[Nr(ti)] =E[Nd(ti_1,ti)] -E[Nf(ti_1,ti)] (3) The nonparametric availability of the system can be seen as the ratio between the uptime and the down time. Mathematically the nonparametric availability As can be given as Uptime As = Uptime + Downtime (4) The uptime of the system is simply the life time of the system, L, minus the downtime of the system during the system's life. This means that the uptime plus the downtime is the L, the life of the system. The system downtime is calculated as the sum of four components, namely: the total expected rectification time corresponding to the n inspections; the total expected correction time corresponding to the n inspections, the total time for the n inspections, and finally, the expected correction time corresponding to the period between the last inspection time tn and the life of the system,!. Mathematically, the expected availability of the system during its life L can be given as As = L- [Tl=1(drE[Nd(ti.í,ti)] +dfE[Nf(ti.i,ti)]) +ndins + dfE[Nf(tn,L)]] (5 The system corrective maintenance cost during its life L, is also the sum of four components namely: the total expected rectification cost corresponding to the n inspections; the total expected correction cost corresponding to the n inspection periods, the total cost for the n inspections, and finally, the expected correction cost corresponding to the period between the last inspection time tn and the life of the system, L. Mathematically, the expected maintenance cost of the system during its life L can be given as n Cm = ^{crE[Nd(ti-1,ti)] + cfE[Nf(ti-1,ti)]) +ncins + cfE[Nf(L, tj] (6) i=i The two objective functions of the proposed model can be expressed as the total satisfaction level TSL about the inspection policy. The total satisfaction level can be calculated as the weighted average of the maintenance cost satisfaction level SLCm and the availability satisfaction level SLAs. To develop the two satisfaction levels, two membership functions were defined: one for As (Fig. 2) and one for Cm (Fig. 3). Advances in Production Engineering & Management 11(1) 2016 41 Ramadan Fig. 2 Membership function for the Fig. 3 Membership function for the C, Using those two membership functions, the SLAs and SLCjn are given by Si, = As SLr =1-^ (7) Bm (8) TSL is TSL = wSLAs + (1- w)SLCm (9) Putting all this together, gives the proposed inspection model as max TSL subject to Cm < Bm (10) — ti-1 ^^ins In this model the decision variables are the number of inspections,n, and the inspection times, t.The objective function of this mode will maximize the total satisfaction level for the inspection policy, (n, t), i.e, find (n, t) corresponding to the highest possible availability (highest SLAs) and lowest possible maintenance cost (highest SLCjn). The constraint >ti^1 + dins dictates that the ith inspection should be at least dins apart from the previous inspection, i.e., the inspection times are discrete. The constraint Cm 0 and exponential delay-time /(t) given by ye~yt, y > 0 are used traditionally in the literatures such as references [30-33]. For such ROCOD and f(t), the expected number of defects, rectifications, and failures are given as follows: E[Nd(ti_1,ti)] = Í ' aeßtdt = ^[e^ Jtí-! P ] (12) rti E[Nf(t¿_1,t¿)]= I A(u)F(t¿ —u)du = ''ti-i rti = 1 ae?u(l - e-Y^-^du Jtí-i -f = l[eßtl_eßtl.1]_ ßu -ae^e'Y^-^du a ~[eßtl -e/?tí"1] -ae~yti ae^e-vtie^du e(ß+Y)ti _e(ß+Y)ti-i (ß + Y) (13) EiNsití)] = E[Nd(ti.1,ti)] -E[Nf(ti.1,ti)] e(ß+Y)ti _e(iS+y)tí-i"l = ae -Yti (ß + Y) Table 1 shows the parameters used in the 7 examples. (14) Table 1 Parameters used in Examples 1-7 Example # À(u) m w Life (year) Bm 1 X(u) = 0.025e(18e"2)u f(h) = 0.0625e"°.°625h w = 0.5 20 $5.0E6 2 X{u) = 0.025 f(h) = 0.0625e-°.°625h w = 0.5 20 $5.0E6 3 Ku) = 0.025e(18e"2)u f(h) =0.1e"olh w = 0.5 20 $5.0E6 4 X{u) = 0.025 f{h) = 0.0625e"°.°625h w = 0.5 10 $5.0E6 5 X(u) = 0.025e(18e"2)u f(h) = 0.0625e-°.°625h w = 0,0 20 $5.0E6 6 À(u) = 0.025e(18e"2)u f(h) = 0.0625e-°0625h w = 1.0 20 $5.0E6 7 A(u) = 0.025e(18e"2)u f(h) = 0.0625e-°.°62Sh w = 0.5 20 $2.5E6 The results for the first example will be discussed in details to show how the model works. The results for the rest of the examples will be listed in Table 2 for comparison. Fig. 5 shows the evolution of the TSL values throughout the generations using the proposed model. The figure shows that the algorithm converged to a value of 0.9400. This convergence happened after 120 generations and stayed for the rest of the generations through the generation number 150. The processing time was 0.57 seconds with population size of 10 chromosomes and 150 generations. 44 Advances in Production Engineering & Management 11(1) 2016 A bi-objective inspection policy optimization model for finite-life repairable systems using a genetic algorithm Generation Number Fig. 5 The evolution of the TSL value throughout the generations using the proposed model The best inspection policy produced by the proposed model for Example 1 consisted of 123 inspections at the following timing (in days): 156 350 537 742 893 1095 1288 1437 1547 1703 1887 1960 2080 2165 2266 2341 2486 2530 2613 2678 2747 2839 2917 3016 3097 3170 3291 3365 3435 3509 3559 3627 3712 3775 3859 3924 3960 4025 4061 4140 4195 4261 4295 4355 4418 4460 4509 4571 4606 4682 4748 4820 4880 4921 4971 5001 5043 5085 5125 5170 5193 5254 5273 5332 5373 5417 5465 5524 5563 5613 5651 5696 5745 5769 5813 5834 5869 5891 5927 5952 5978 6011 6035 6064 6092 6103 6149 6180 6209 6236 6276 6317 6342 6364 6396 6426 6445 6467 6497 6514 6530 6571 6608 6625 6654 6681 6694 6735 6762 6800 6832 6846 6886 6911 6936 6982 6999 7020 7048 7075 7087 7112 7127 } Fig. 6 shows a histogram for the number of inspections in each of the twenty years. The histogram shows that the number of inspections increased with the life of the system. For example in the first 1200 days of the system life, the model suggested 6 inspections while in the last 1200 days of the system life the model suggested to have 51 inspections. This increase in the number of inspections coincides with the fact that the system is aging. As the system ages, the number of defects increases and the delay-time of the defects decreases which force the model to assign more inspections toward the end of the system life. Table 2 shows the results of the 7 examples for the proposed model along with the results for the aperiodic model with fixed number of inspections where the number of inspections was 30 inspections. The table shows that the proposed model is better, in all of the attributes, than the aperiodic model with fixed number of periods except for Example 2 where the number of inspections is equal. Basically, in Example 2, the two models are equivalent. Example 4 shows that the proposed model chose 12 inspections with lower maintenance cost and higher TSL than the aperiodic model with the fixed number of inspections 30. Moreover, the rest of the examples (except Example 2) show that even the number of inspections is higher in the proposed model than the number of inspections in the aperiodic model with fixed number of inspections, both the maintenance cost and the availability is better in the proposed model. These results show that treating the number of inspections as a variable, that need to be optimized in the inspection model, is better than treating it as a constant in the model as this will enhance the maintenance cost and the availability of the system simultaneously. 0.94 0.92 0.88 0.86 0.84 0.82 0 50 100 Advances in Production Engineering & Management 11(1) 2016 45 Ramadan 10 ¡p a 6 4 3000 4000 5000 System life (Days) Fig. 6 A histogram for the number of inspections in the twenty years of life o Comparing the results of Examples 1, 5, and 6 for the proposed model, one can see that the number of inspections chosen by the model is significantly different In Example 5, where the objective of the model was to maximize the availability of the system alone, the number of inspections was significantly higher than the number of inspections in Example 6 where the objective was to minimize the maintenance cost only. Moreover the TSL for example 5 was lower than the TSL in Example 6. The average of TSLs of Example 5 and Example 6 is almost the same as the TSL in Example 1 where the two objectives were considered. Moreover the average number of inspections for the two examples was almost the same as the number of inspections in Example 1 but the average cost of the two examples was higher than the average cost in Example 1. To better understand what happened in Examples 1, 5, and 6 and why it happened. Consider Fig 7 which shows the relation between the As and Cm. The figure shows that there may be more than one value of As for the same value of Cm. This result can be understood on the light of that different inspection policies may have the same cost but different effect on the availability of the system. For this reason, it is not wise to use maintenance cost as the only objective in the inspection models. On the same taken, using availability as the only objective in the inspection model may result in choosing an expensive inspection policy when we can have the same availability using other inspection policies that have lower costs. This result emphasizes the importance of treating the inspection-policy optimization problem as a multi-objective optimization problem rather than a single objective problem. By comparing the results of Example 1 and the results of Example 3, it is easy to see that the increase in the delay-time rate caused an increase in the number of inspections (to increase the availability of the system) but this increase also increased the maintenance cost, the matter that caused a decrease in the TSL. This result is expected because the increase in the delay-time rate means that the defects will turn into failures faster and thus more inspections are needed to prevent the defects from turning into failures and hence reducing the availability of the system. Table 2 The results of the 7 examples for the proposed model along with the results for the aperiodic model with 30 inspections Example 1 Example 2 Example 3 Example 4 Example 5 Example 6 Example 7 Results for the proposed model As,SL^s 0.9305 0.9903 0.9231 0.9908 N/A 0.9313 0.9295 r 2.53e+05 4.58e+04 2.99e+05 2.298e+04 2.52e+05 2.67e+05 2.54e+05 Sir 0.9495 0.9908 0.9401 0.9954 0.9496 N/A 0.8985 TSL 0.9400 0.9906 0.9316 0.9931 0.9496 0.9313 0.9140 n 120 30 134 12 138 91 127 Results for the aperiodic model with fixed number of inspections (30 inspections) As,SLAs 0.9096 0.9898 0.8882 0.9875 N/A 0.9085 0.9086 r 4.75e+05 4.87e+04 6.48e+05 2.57e+04 4. 80e+05 4.89e+05 4.84e+05 Sir 0.9050 0.9903 0.8703 0.9949 0.9040 N/A 0.8064 TSL 0.9073 0.9900 0.8792 0.9912 0.9040 0.9085 0.8575 n 30 30 30 30 30 30 30 46 Advances in Production Engineering & Management 11(1) 2016 A bi-objective inspection policy optimization model for finite-life repairable systems using a genetic algorithm 0.86r 0.850.84-y 0.830.820.81 -0.80.79-0.78^ "'s* .'""S» 1.1 1.2 1.3 Maintenance Cost ($) 1.4 1.5 1.6 x 106 Fig. 7 The relation between the and C„ The proposed model responded to the increase in the delay-time rate by increasing the number of inspections but this also increased the maintenance cost as well. The model chose the optimal inspections number that compromised between the availability of the system and the maintenance cost of the system 6. Conclusion In this paper an aperiodic inspection model is proposed and solved using mutation-based Genetic algorithm. The proposed inspection model is based on delay-time concept and nonhomogene-ous Poisson process of defect arrivals rather than renewal theory and periodic inspection modelling that are used classically. The proposed model also optimizes the number of inspections and the timing of inspections simultaneously rather than optimizing either the number of inspections or the timing of inspections as in the case of the majority of the available inspection models. Moreover, the proposed model uses two objectives, namely: system availability and maintenance cost, to optimize the inspection policy whereas the available inspection models use only one objective. The results showed that the proposed model is better (in all of the attributes) than the aperiodic model that uses fixed number of inspections. Moreover, the results showed that using two objectives (system availability and maintenance cost) in the inspection models rather than one objective, can improve the quality of the inspection policy in terms of system availability and maintenance cost. The proposed model is a general model that can be implemented with different ROCOD and different delay-time distributions. Also this model can be extended easily to cover complex systems and imperfect inspection cases. Acknowledgement The author is grateful to the Applied Science Private University, Amman, Jordan, for the full financial support granted to this research (Grant No. DRGS-2015). References [1] Dhillon, B.S. (2002). Engineering maintenance: A modern approach, CRC press, Boca Raton, USA, doi: 10.1201/ 9781420031843. [2] Moubray J. (1997). 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Reliability data analysis and modelling of offshore oil platform plant, Journal of Quality in Maintenance Engineering, Vol. 6, No. 4, 287-295, doi: 10.1108/13552510010346824. 48 Advances in Production Engineering & Management 11(1) 2016 APEM jowatal Advances in Production Engineering & Management Volume 11 | Number 1 | March 2016 | pp 49-58 http://dx.doi.Org/10.14743/apem2016.1.209 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper Integration of SWOT and ANP for effective strategic planning in the cosmetic industry Al-Refaie, A.a*, Sy, E.b, Rawabdeh, I.a, Alaween, W.c department of Industrial Engineering, The University of Jordan, Amman, Jordan bAteneo de Manila University, Metro Manila, Philippines cDepartment of Industrial Engineering, The University of Jordan, Jordan A B S T R A C T A R T I C L E I N F O Typically, the decision making processes in cosmetics firms are greatly affected by internal and external factors, which as a result affect firms' success. In this research, the Strengths, Weakness, Opportunities, and Threat (SWOT) analysis was used to identify those factors that affect a cosmetics firm's success and consequently lists the feasible strategy alternatives. The analytic network process (ANP) was adopted for calculating the relative importance for each SWOT factors and sub-factors, while taking into consideration the dependency among SWOT factors, as well as among sub-factors. Utilizing the importance values in the super-matrix, the most preferred strategy in a cosmetic industry is identified, which is to open-up new markets on European market. In conclusion, the SWOT and ANP integration may provide great assistance to strategic planners in determining the best strategy alternative that fulfils the firm's desired objectives. © 2016 PEI, University of Maribor. All rights reserved. Keywords: Cosmetic industry Analytic network process (ANP) SWOT analysis Strategic planning *Corresponding author: abbas.alrefai@ju.edu.jo (Al-Refaie, A.) Article history: Received 9 January 2015 Revised 10 September 2015 Accepted 5 January 2016 1. Introduction Strategic management is a collection of actions and decisions taken in order to achieve organization's goals and objectives. Decision making process is greatly affected by internal and external factors. Systematic identification and analysis of the effects of such factors on organization success has received significant research attention [1-8]. The Strengths-Weakness-Opportunities-Threats (SWOT) technique is frequently used to analyse internal and external factors, assess the feasible alternative strategies, and then to determine the best one that helps an organization in achieving its desired objectives and goals. Nevertheless, the SWOT analysis as a qualitative tool does not numerically evaluate the effect of each factor on selected strategies [9-11]. The analytic hierarchy process (AHP) method [12-14] is a powerful technique which assists analysts in selecting the best decision among multiple decisions by structuring the decision problem in a hierarchically structure at different levels. In AHP, each level consists of finite number of decision elements, where the upper level of the hierarchy represents the overall goal, while the lower level represents all possible alternatives and the intermediate levels shape the decision criteria and sub-criteria [15-17]. The AHP allows the assessment of factors, which considered as criteria and the alternative strategies by giving them relative weights. Next, pairwise comparisons are carried out between all factors by assigning weights between one (equal importance) to nine (absolutely more important), whereas reciprocal values are assigned to the inverse comparison. Then, for each factor a pairwise comparison is performed between strate- 49 Ai-Refaie, Sy, Rawabdeh, Aiaween gies using a scale between one and nine. Finally, the integration between relative weight of factors and strategies are utilized to identify the overall weight of each strategy [18]. The AHP method assumes that there are unidirectional relationships between elements of different decision levels along the hierarchy and uncorrelated elements within each cluster as well as between clusters [19]. As a result, AHP is not appropriate for models that deal with interdependent relationships in AHP. The analytic network process (ANP) is introduced to solve this problem [20-23]. The comparison between AHP and ANP tools is depicted in Fig. 1. ANP method is an improved version of AHP, which provides more accurate results in complicated problems. In the ANP method and after clearly defined factors, the pairwise comparisons are performed as done by the AHP method; in addition, the dependencies among factors should be examined in pairwise manner. As a final step, the weighted score for each strategy is determined and then used to identify the best strategy. This research integrates SWOT analysis and ANP technique to determine the best strategy that results in improving the performance of a Jordanian cosmetics sector. The remaining of this research is organized as follows. Section two presents SWOT analysis. Section three introduces the ANP technique. Implementation of the integrated approach is performed in section four. Finally, conclusions are summarized in section five. a) b) Fig. 1 Hierarchy and network structure: a) AHP, and b) ANP 2. SWOT analysis The SWOT matrix treats an organization's strengths and weaknesses as internal factors, whereas the threats and opportunities, as external factors. These factors are utilized to identify and formulate strategies by matching the key internal and external factors. The matching between internal and external factors, what is called TOWS, is the most difficult and challenging part in SWOT analysis. TOWS matrix is utilized to develop four types of strategies. These strategies are shown in Fig. 2. ^Internal External Strengths (S) 1,..., s Weakness (W) 1,., w Opportunities (0) 1,..., o SO strategies WO Strategies Threats (T) 1,., t ST Strategies WT Strategies Fig. 2 SWOT matrix 50 Advances in Production Engineering & Management 11(1) 2016 Integration of SWOT and ANP for effective strategic planning in the cosmetic industry The Strengths-opportunities (SO) strategies utilize internal strengths of an organization to take advantage of external opportunities, weaknesses-opportunities (WO) strategies improve internal weaknesses by taking advantage of external opportunities, strengths-threats (ST) strategies use strengths of organization to avoid or minimize the effect of external threats, and weaknesses-threats (WT) strategies are defensive tactics aimed at reducing internal weaknesses and avoiding external threats. 3. ANP analysis The ANP is used to determine the dependencies and interrelations among factors using four main steps: Step 1: Clearly state and define the decision model as a network structure shown in Fig. 1.b. Once the goal or objective of the decision model is stated, it would further be decomposed into criteria, sub-criteria, and so on until alternatives level is reached. Step 2: Establish pairwise comparison matrices and priority vectors. In each factor pairs of decision elements are compared with respect to their relative importance. Then, the factors themselves are compared pairwise with respect to their contribution to the main goal. Furthermore, the interdependencies among elements of each factor are examined pairwise. The pairwise comparison is done by assigning relative importance values (ap) as shown in Table 1. However, the reciprocal (ap = 1/ap) of this value is assigned to the inverse comparison. Table 1 Preference scale as represented by Saaty (1996) Weight Definition Description 1 Equal importance Factor i and j are of equally important 3 Moderate importance Factor i is weakly more important than j 5 Strong importance Factor i strongly more important than j 7 Very strong importance Factor i is very strongly more important than j 9 Absolute importance Factor i is absolutely more important than j 2, 4, 6, 8 Intermediate values Represent compromise between the priorities The pairwise comparison matrix A, is represented as follows: A = 1 1/^21 «12 1 al(n-l) a2(n-l) aln a2n 1/al(n-l) ••• n 1/a2 r, 1 a(n-l~)n 1/a(n-l)n 1 (1) An estimate of the relative importance of the compared factors is determined using Eq. 2. = Àmaxw (2) where w is the desired to estimate eigenvector and Amax is the largest Eigen value of A. Step 3: Determine the relative importance of all components with dependency effects and then create the super-matrix. The super-matrix adjusts the relative weights in individual matrices to form a new ''overall'' matrix with the eigenvectors of the adjusted relative weights. That is, the eigenvectors obtained in step 2 are grouped and placed in the appropriate positions in the super matrix in a hierarchy manner as goal, factors, sub-factors and alternatives as follows: Advances in Production Engineering & Management 11(1) 2016 51 Ai-Refaie, Sy, Rawabdeh, Aiaween 0 0 0 0 W21 0 0 0 0 W32 0 0 0 0 w43 I where each entry in W is a matrix. The W21 is a matrix which represents the impact of the goal on the factors, W32 is a matrix that represents the impact of the factors on each of the sub-factor, W43 represents the impact of the sub-factors on each of the alternatives, and I is the identity matrix. If there is any dependency among the factors of W, then W22 would be non-zero matrix, and so on. All interdependences can be represented in the same manner. Step 4: Calculate the weights of alternatives from the normalized super-matrix. Step 5: Select the alternative that corresponds to the largest priority as the most preferred alternative. 4. Cosmetics industry The integration of the SWOT and ANP analysis was implemented in cosmetics industry in Jordan and is described as follows. The key internal factors (strengths and weakness) and the most external factors (opportunities and threats) are listed in Table 2. The corresponding ANP structure for cosmetics is shown in Fig. 2. The pairwise comparisons between these factors are presented in Table 3. Then, the matrix Wi, represents the Eigenvector that represents for the SWOT factors is expressed as: W-, = 0.547 0.135 0.272 0.047. (4) The dependency among the SWOT factors is analysed by identifying the impact of each factor on the others in pairwise comparison as shown in Table 4. Consequently, the dependency matrix W2, of the SWOT factors is written as: W, = 1.000 0.649 0.768 0.768 0.587 1.000 0.153 0.153 0.324 0.295 1.000 0.079 0.089 0.057 0.079 1.000 (5) Utilizing Eqs. 4 and 5, the matrix, Wfactors, contains the relative importance of the SWOT factors is determined by multiplying the relative importance matrix Wi, under the assumption of independency by the relative importance matrix W2, considering the dependency among factors. That is: ^factors =W1XW2 = 1.000 0.649 0.768 0.768 0.547 0.880 0.587 1.000 0.153 0.153 X 0.135 0.505 0.324 0.295 1.000 0.079 0.272 0.493 0.089 0.057 0.079 1.000 0.047 0.125 (6) In Eq. 6, it is noted that the largest importance weight (= 0.880) corresponds to the strengths factor, whereas the smallest weight (0.125) associated with the threats. There is significant difference between the relative weight for each factor with and without considering the dependencies. 52 Advances in Production Engineering & Management 11(1) 2016 Integration of SWOT and ANP for effective strategic planning in the cosmetic industry Table 2 TOWS matrix for the cosmetic company Internal Factors Strength Weakness 1. Human expertise and financial resources. 2. Strong and well-known brand name. 3. Depending on neutral material. 1. Loss of trust from different supply chain parties. 2. Falling in utilizing e-commerce capabilities. 3. Price is expensive. 4. Innovation skills and strong research and development. 5. Better products quality relative to rivals. USL 2005.50 PPM Total 536804.00 Fig. 6 Process capability of internal dimension of section (X1) before improvement 2.3. Analysis phase This phase includes causes and effect diagram tool for analysis the previous results obtained from measure phase. Cause and effect analysis This step expresses the possible causes identified which have the most impact on the extrusion process. Fig. 7 for dimensional deflection defect presents a chain of causes and effects. Cause and Effect diagram of Dimensional deflection Dies Machines Deviation in backer part of die ^Weakness in the process o fins tailing die parts Ins u fficent refininE d i es Materials Difference in extrusionprocess tençserature • .from the s tand ard temperature (500*0) Difference in extrusion speed from the standard extrusion speed (25.4mm-'sec) rod u et fartúlv to family variation Supplier to supplier variation \ Tools used away fiom allowing Further decline in die and the front face of die is broken v The mandrel part in hollow extrusion is not properly centered Old equipment Machine vibration Unskilled worker in extrusion factory Not familiar with the procès s and equipment Absence of engineering staff responsible for quality control in extrusion factory Man power Dim en sional ~~^ d eflection Inspector to inspectorvariation Measurement system ins äbiHtv Difference in machining allow an ce Measurements Fig. 7 Process capability of internal dimension of section (X1) before improvement 2.4 Improve phase The improve phase is the fourth step in DMAIC methodology phases and its objective is to implement and find measures that would solve the aluminium products defects. Cause and suggested solution are shown in Table 5. Advances in Production Engineering & Management 11(1) 2016 65 Ketan, Nassir Table 5 Cause and suggested solution Cause Suggested solution 1 - Difference in extrusion process temperature from the standard temperature. 2 - Unskilled workers in extrusion factory. 3 - Absence of engineering staff to monitor the production line in every step of the extrusion process. 4 - Further decline in die and the front face of die is broken. 5 - Deviation in backer. 6 - Weakness in the process of assembly die parts. 7 - Insufficient refining dies. 1 - Monitoring the extrusion process temperature (control the die temperature and billet preheating temperature) by thermocouple device as shown in Fig. 8. 2 - Workers must engage in training sessions before overseeing the extrusion process. 3 - Creating a staff of quality control specialist. 4 - Replacement of the old die with a new die and check the front face of the die. 5 - Checking the process of assembly and grinding of die parts (mandrel and backer) as shown in Fig. 9, Fig. 10, and Fig. 11. Fig. 8 Thermocouple device Fig. 9 Parts of corner section die Fig. 10 Corner section die after assembly Fig. 11 Corner section die after grinding process 2.5 Control phase The extrusion process will be test by finding the values of PCIs (Process capability indices), Sigma level, DPMO, Yield (Y) and profit after improvement. Therefore, new data of 15 samples with sample size 5 have been collected from the aluminium extrusion process. Then the entire steps in measure phase are repeated. The collected data and the details of the steps and calculations are shown in Table 6 and Figs. 12, 13, and 14. Fig. 12 X-R charts for internal dimension of section (X1) after improvement 66 Advances in Production Engineering & Management 11(1) 2016 Aluminium hot extrusion process capability improvement using Six Sigma Normality Test for internal dimension of section (XI) Normal - 9S% CI Fig 13 Normality test for internal dimension of section (X1) after improvement Process Capability of Internal dimension of section (X1) USL Process Data LSL 36.24 Target 36.7 USL 37.16 Sample Mean 36.4297 Sample N 75 StDev 0.212094 Process Capability Cp 0.72 CPL 0.30 CPU 1.15 Cpk 0.30 36.0 36.2 36.4 36.6 36.8 37.0 Exp. Overall Performance PPM < LSL 185507.58 PPM > USL 287.51 PPM Total 185795.09 Fig 14 Process capability of internal dimension of section (X1) after improvement Table 6 Results for calculations extrusion process measures of internal dimension of section (X1) after improvement Extrusion process measures Measure value Cp Cpk Sigma level DPMO Yield (Y) Defect per 1000 kg Profit per 1000 kg E X 0.72 0.3 2.4 185795.09 81 % 0.18579509 223.000 0.212094 36.4297 Advances in Production Engineering & Management 11(1) 2016 67 Ketan, Nassir 3. Results and discussion The results of extrusion process measures PCIs (Process capability indices), sigma levels, and DPMO values before and after improvement shown in Table 4 and Table 6. The improvement of performance measures are as following: Cp value has been increased from 0.5 to 0.74 which means that the process capability is sufficient and the specification width greater than the process spread. The value of Cpk has increased from -0.032 to 0.306 which means that the standard deviation has decreased from 0.3082 to 0.208125. The process yield is increase to 36 % items without defects. The value of sigma level has increased from 1.4 to 2.42 which means reduction in defect products, so that DPMO value has been reduced from 536804 to 185795.09 and the profit increased from ID 127.000 to ID 226.000 per 1000 kg. 4. Conclusions and recommendations for future work The conclusions and recommendations that are drawn from this work are as follows: • Profits of implementation DMAIC methodology are accomplished in expression of cost decrease and remove aluminium products defects. • The values for process capability measures (Cp, Cpk) indicate the ability to process improves or not. If the values are less than 1.0 as for CQC (X1) this situation point out the process mean deviation for aluminium product design specification (target value). • The extrusion process mean increased, the extrusion process dispersion decreased and the process extrusion very nearer to target value. • Based on the results, the sigma levels values increased depending on the implemented suggested solution. Therefore, this improvement is not sufficient to reach the value of six sigma level. • The results prove that the DMAIC methodology is effective in estimation, analysis and improvement process capability of data that are normally distributed. • Study the process capability improvement (DMAIC methodology) by using simulation technique to test and improve the effectiveness of suggested solution before they are implemented. • The possibility of the DMAIC methodology application in the other aluminium products and other product defects were not able to study in this work due to the limitation of research time. Acknowledgement Our grateful to the staff members of quality control, dies manufacturing departments and staff of extrusion factory in UR state company for engineering industries in Iraq for their support to accomplish this research work. 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Advances in Production Engineering & Management 11(1) 2016 69 Calendar of events • 5th International Conference on Industrial Technology and Management, Paris, France, March 17-18, 2016. • 18th International Conference on Engineering Systems Modeling, Simulation and Analysis, Boston, USA, April 25-26, 2016. • 3rd International Conference New Technologies NT-2016, Mostar, Bosnia and Herzegovina, May 13-14, 2016. • 6th CIRP Conference on Assembly Technologies and Systems, Gothenburg, Sweden, May 1617, 2016. • 49th CIRP Conference on Manufacturing Systems, Stuttgart, Germany, May 25-27, 2016. • 14th Annual Industrial Simulation Conference, Bucharest, Romania, June 6-8, 2016. • 3rd CIRP Conference on Surface Integrity, Charlotte, NC, USA, June 8-10, 2016. • International Symposium on Green Manufacturing and Applications, Bali, Indonesia, June 2125, 2016. • 8th IFAC Conference on Manufacturing Modelling, Management and Control, Troyes, France, June 28-30, 2016. • 28th European Conference on Operational Research, Poznan, Poland, July 3-6, 2016. • 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Gulf of Naples, Italy, July 20-22, 2016. • International Conference on Design and Production Engineering, Berlin, Germany, July 25-26, 2016. • 27th DAAAM International Symposium, Mostar, Bosnia and Herzegovina, October 26-29, 2016. • 3rd International Conference on Mechatronics, Automation and Manufacturing, Tokyo, Japan, October 29-31, 2016. 70 Advances in Production Engineering & Management 11(1) 2016 Notes for contributors General Articles submitted to the APEM journal should be original and unpublished contributions and should not be under consideration for any other publication at the same time. 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The following decisions can be made: accepting the paper, reconsidering the paper after changes, or rejecting the paper. Accepted papers may not be offered elsewhere for publication. The editor may, in some circumstances, vary this process at his discretion. Proofs Proofs will be sent to the corresponding author and should be returned within 3 days of receipt. Corrections should be restricted to typesetting errors and minor changes. Offprints An e-offprint, i.e., a PDF version of the published article, will be sent by e-mail to the corresponding author. Additionally, one complete copy of the journal will be sent free of charge to the corresponding author of the published article. APEM journal Production Engineering Institute (PEI) University of Maribor APEM homepage: apem-journal.org Advances in Production Engineering & Management Volume 11 | Number 1 | March 2016 | pp 1-72 Contents Scope and topics Announcement and acknowledgement Assessment of mechanical and wear properties of epoxy-based hybrid composites Agunsoye, J.O.; Bello, S.A.; Bello, L.; Idehenre, M.M. A knowledge-based system for end mill selection Prasad, K.; Chakraborty, S. Thermal analysis on a weld joint of aluminium alloy in gas metal arc welding Ismail, M.I.S.; Afieq, W.M. A bi-objective inspection policy optimization model for finite-life repairable systems using a genetic algorithm Ramadan, S. Integration of SWOT and ANP for effective strategic planning in the cosmetic industry Al-Refaie, A.; Sy, E.; Rawabdeh, I.; Alaween, W. Aluminium hot extrusion process capability improvement using Six Sigma Ketan, H.; Nassir, M. Calendar of events Notes for contributors Copyright © 2016 PEI. 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