ISSN 1854-6250 APEM journal Advances in Production Engineering & Management Volume 9 | Number 4 | December 2014 ü 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 Editors Tomaz Irgolic deski@apem-journal.org Matej Paulic desk2@apem-journal.org Website Master_ Lucija Brezocnik lucija.brezocnik@student.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 Kanji Ueda, The University of Tokyo, Japan 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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Copyright © 2014 PEI, University of Maribor. All rights reserved. APEM journal is indexed/abstracted in 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), and TEMA (DOMA). Listed in Ulrich's Periodicals Directory and Cabell's Directory. The APEM journal was positively evaluated for inclusion in the Scopus (Elsevier) database. journal University of Maribor Production Engineering Institute (PEI) Advances in Production Engineering & Management Volume 9 | Number 4 | December 2014 | pp 155-204 Contents Scope and topics 158 Laser cladding of Ti-6Al-4V alloy with vanadium carbide particles 159 El-Labban, H.F.; Mahmoud, E.R.I.; Al-Wadai, H. Parametric study of die sinking EDM process on AISI H13 tool steel 168 using statistical techniques Bose, G.K.; Mahapatra, K.K. Effect of welding variables on mechanical properties of low carbon steel welded joint 181 Talabi, S.I.; Owolabi, O.B.; Adebisi, J.A.; Yahaya, T. A Petri net model for the integration of purchasing, production and packaging 187 using Kanban system Ullah, H. Calendar of events 201 Notes for contributors 203 Journal homepage: apem-journal.org ISSN 1854-6250 ISSN 1855-6531 (on-line) ©2014 PEI, University of Maribor. All rights reserved. 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 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 Advances in Production Engineering & Management Volume 9 | Number 4 | December 2014 | pp 159-167 http://dx.doi.Org/10.14743/apem2014.4.184 ISSN 1854-625G Journal home: apem-journal.org Original scientific paper Laser cladding of Ti-6Al-4V alloy with vanadium carbide particles El-Labban, H.F. a, Mahmoud, E.R.I.a*, Al-Wadai, H.a aFaculty of Engineering, King Khalid University, Abha, Saudi Arabia A B S T R A C T A R T I C L E I N F O The tribological properties of Ti-6Al-4V alloy are generally poor. This study was an attempt to produce a hardened surface layer on this alloy for longer service life during severe wear conditions. For this purpose, laser surface cladding of this alloy with vanadium carbide (VC) powder was performed using a YAG Fiber laser at power strengths of 1000 W, 1500 W, and 2000 W and a travelling speed of 4 mm/s. Surface cladded layers of Ti-6Al-4V alloy metal matrix composite reinforced with VC particles were produced on the substrate under all processing conditions. The size of the cladding layer was increased by increasing the processing power. The cladding layer was well bonded to the substrate, especially at higher processing powers. The VC particles were homogenously distributed within the cladding layer at processing powers of 2000 W and 1500 W, whilst it showed some clusters at a power of 1000 W. Some of the VC particles were melted and re-solidified as fine long dendritic structures during the laser treatment. The cladding layer produced under all processing conditions exhibits remarkable improvement of hardness and wear resistance (almost twice). As the processing powers decreased, the surface of the cladding layers showed higher hardness. The cladding layer also showed improved corrosion resistance. © 2014 PEI, University of Maribor. All rights reserved. Keywords: Laser cladding Ti-6Al-4V alloy VC powder Surface microhardness Wear and corrosion resistance *Corresponding author: emahoud@kku.edu.sa (Mahmoud, E.R.I.) Article history: Received 2 August 2014 Revised 29 October 2014 Accepted 10 November 2014 1. Introduction Titanium and its alloys are used for manufacturing of some components in automobile, aerospace, marine, medicine, chemical and energy industries, due to their improved properties such as high strength-to-weight ratio, excellent corrosion resistance, high temperature strength, high Young's modulus and high cycle fatigue properties [1-3]. Ti-6Al-4V alloy is considered the most used alloy in these applications. However, the uses of this alloy in the severe environments, where the wear is the main failure mode, are limited due to its poor wear resistance [4]. To overcome this problem, it is necessary to improve the surface wear resistance. Many different traditional surface modifications such as surface hardening [5] and surface cladding [6, 7] are applied to improve wear and erosion characteristics of the surfaces of Ti alloys. Various techniques such as thermal spraying [8], plasma spraying [9], traditional arc welding and focused energy technologies like electron beam [10] and laser [11-13] have been employed. The excessive energy input from the traditional welding processes such as shielded metal arc welding or even gas tungsten arc welding may cause some undesirable distortion and residual thermal stresses which may cause cracks in the hardened layer [6, 7]. Laser surfacing has been suggested as a potential technique to produce a hard surface layer of Ti alloys for a number of reasons. The most important one arises from the fact that laser beam has rapid heating and cooling, which can easily produce special types of microstructure with novel properties that cannot be produced by other conventional processing technique [14, 15]. Generally, the obtained microstructure in the laser treated area is dependent on the heating and cooling cycles that take place during the process, which in consequence depends on the laser parameters [16]. Other merits of the laser surfacing are to produce a hard layer with low dilution and deformation, relative cleanliness, lack of quenching medium and limited grain growth during the heating [11]. Laser cladding process is considered one of the laser surfacing techniques that can produce Ti alloy-based composites clad layer where hard particles such as carbides, borides, and nitrides are used to reinforce the Ti alloy [17]. In this case, the wear properties can be improved by the combination of embedded hard carbide particles and the rapid heating and cooling which forms hard structure matrix. The widely used carbide particles as reinforcement are titanium carbide (TiC) and vanadium carbide (VC). VC possess many favorable properties, such as high hardness (2460-3150 HV0.05) [18], high melting temperature (2830 °C) [19], low heat conductivity [20], certain plasticity, and good wettability to metal bonding. Moreover, VC has a low-friction coefficient [21]. Besides, when VC is used in high temperatures, it is oxidized to vanadium oxide (V2O5), which is characterized by self-lubrication performance [22, 23]. This advantageous combination can create a protective coating layer on the surface of the composite material with enhanced resistance against thermal, corrosion and mechanical wear [24, 25]. Thus, in the present study, we aim to investigate the effects of main laser parameters and rapid solidification on the microstructure, hardness and wear behaviour of Ti-6Al-4V alloy surface cladded by VC powder. Microstructural changes in the build-up, melted, and heat affected zones are examined in details. 2. Experimental work Specimens of Ti-6Al-4V alloy were used as substrate with dimensions of 100 mm x 50 mm x 3 mm. The surfaces of specimens were cleaned and the oxides were removed by grinding using emery papers. In order to avoid the oxidation of the strip during the treatment, argon with the flowing rate of 15 L/min was used as a shielding gas during and after the treatment. The cladding treatment was carried out using VC powder with 40-50 [im particle size as a cladding material and YAG Fiber laser (Ytterbium laser system, YLS-3000 SM, 3 kW). The powder was pre-placed on the top surface of the strip with 0.5 mm height and then emitted by laser beam. The treatments were conducted at different laser power strenghts of 1000 W, 1500 W, and 2000 W, and at fixed travelling speed of 4 mm/s. The process was conducted at a defocusing distance (Df) of 24 mm. The microstructures of the coated layer and substrates were investigated using optical microscope and scanning electron microscope equipped with EDS (Energy-dispersive X-ray spectroscopy) analyser. The micro-Vickers hardness in the coated layer cross-section and the substrate were measured with an indentation load of 9.8 N and loading time of 15 s at room temperature. The wear behaviour of the laser cladded zone was evaluated using a pin-on-disk dry sliding wear tester in air at room temperatures. A stationary sample with a diameter of 2.5 mm was slid against a rotating disk with a rotational speed of 265 rpm for 15 min. The tests were carried out at a fixed load of 2 kg applied to the pin. Before the test, all the specimens were ground on emery paper up to # 600 to get smooth and flattened surface. The specimens were weighted before and after the test with a sensitive electronic balance with an accuracy of 0.001 g. The differences in average weight before and after the wear test were measured and accounted. Three specimens of each condition were chosen for wear tests. The untreated base metal was selected as the reference material for the wear test. The corrosion behavior of the substrate and the cladding layer were evaluated by the corrosion current density and the corrosion potential obtained from polarization curves in a 3 wt. % NaCl solution at room temperature with an IM-6 electrochemical workstation. The scanning potential can be in the range of -1.0 V to +2 V, and the scanning rate was 5 mV/s. 3. Results and discussion 3.1 Macro and micro-structure analysis Fig. 1 shows the macrographs of the cross-sections of the surface laser treated layer at different processing powers. The treated layers in all conditions appeared as nearly half moon shape inside the Ti alloy substrate. This is may be due to the higher defocusing distance that penetrate the heat into deeper areas and increase the dilution of the cladding materials with the substrate. It was clear from these figures that the area of the cladding layer is in direct proportion to the laser power. At laser power of 2000 W, the cladding layer appeared as a deeper complete half moon above the substrate, while it appears as a narrow band at laser power of 1000 W. The dimensions of laser treated zones were 2.99 mm width and 0.52 mm depth for 1000 W, 3.05 mm width and 0.61 mm depth for 1500 W, and 3.1 mm width and 0.73 mm depth for 2000 W. This is due to the more heat input that produced at higher powers which melts the cladding materials together with more areas from the substrate. The microstructures of the cladding layer treated by power of 2000 W are shown in Figs. 2 and 4. Many (white color) fine long dendrites with short secondary arms were precipitated inside the Ti alloy as clearly shown in Fig. 2. EDS demonstrated that these dendrites were VC, as shown in Fig. 3(b), while the matrix was Ti alloy substrate, Fig. 3(a). This means that a surface composite consisted of Ti alloy reinforced with VC dendrites was produced in the cladded layer. In addition, the dendrite morphology of the high melting point VC particles means that they were melted and then solidified during the laser processing. As it is well known that laser technology is characterized by a high energy density and ceramics have a much higher capability to absorb laser energy than metals [26]. Therefore, VC particles were melted (or partially melted) in spite of its extremely high melting point, and then solidified by the self-quenching effect of the very high cooling rate after laser surface treatment. Fig. 1 Macro-views of the cross-sections of the surface laser cladding layer at different processing powers of 2000 W, 1500 W, and 1000 W Fig. 2 Micrographs of the top portion of the laser cladding layer near the free surface produced by processing power of 2000 W EDS Quantitative Results Element Wt% At% CK 1.83 2.58 A1K 8.37 26.46 TiK 84.22 66.79 VK 5.58 4.16 0 u EDS Quantitative Results Element Wt» At% CK 22.00 15.50 ÏL 0.34 0.14 TiK 11.68 34.91 VK 65.98 49.45 E Fig. 3 EDS analysis of: (a) matrix, and (b) dendritic structure that appeared in the cladding layer produced at processing power of 2000 W Fig. 4 Micrographs of the lower portion of the laser cladding layer produced by processing power of 2000 W showing the interface with the substrate By going down through the cladding layer, near the interface, some fine VC particles are appeared distributed homogenously inside the Ti alloy matrix as shown in Fig. 4. In this area, the higher heat input melts the course VC particles. The relatively lower cooling rate at this embedded area was not fast enough to form VC dendrites. So, it appeared as fine VC particles. It is obviously to note here that the cladding layer was tightly bonded to the substrate without any defects as shown in Fig. 4. At processing power of 1500 W, the amounts of VC dendrites are reduced and it concentrates at the top portion of the cladding layer (near the free surface) as shown in Figs. 5(a) and 5(b). The VC morphology is appeared as some dendrites mixed with particles. At the lower portion of the cladding layer (near the interface), the VC particles appeared as their original course particles shape, as clearly shown in Figs. 5(c) and 5(d). The heat generated is not enough to melt most of the added VC particles. Most of the VC dendrites are concentrated at the top center of the cladding layer where the heat is concentrated. When the laser processing power was reduced to 1000 W, the generated heat is not enough to melt the added VC particles. There was almost no dendritic VC morphology in the cladding layer produced at this condition as shown in Fig. 6. Moreover, the VC particles accumulated in clusters as shown in Fig. 6(c). The lower heat input at this condition reduces the dilution process of the VC particles in the Ti alloy matrix. This causes that the VC particles to concentrate in small area in their original shape. Fig. 5 Micrographs of the laser cladding layer produced by processing power of 1500 W: (a) and (b) - top portion near the free surface, (c) and (d) - lower portion Fig. 6 Micrographs of the laser cladding layer produced by processing power of 1000 W: (a) - right side of the cladding layer, (b) - left side, (c) - lower center, (d) - center of the cladding layer 3.2 Surface and subsurface microhardness evaluation Fig 7 shows the hardness distribution along the depth direction of the laser-cladded areas at different powers. The substrate has an average microhardness value of approximately 360 HV. At all condition, high microhardness values (almost twice as the substrate) were obtained at the surface and a certain subsurface layer and decreased towards the substrate. This is due to the presence of hard VC particles with a great amount in these areas. These results also indicate that the increase in processing power cause a decrease in the free surface hardness improvement and an increase in the hardened zone depth. The decrease in processing power decreases the amount of the heat input and consequently the dilution is decreased. As a result, the volume fractions of VC in the cladded layer are increased. This represents a main reason for the high hardness values resulted in case of the low processing power. Conversely, the increase of processing power increases the heat input and consequently the dilution is increased. As a result, the volume fractions of unmelted VC in the cladded layer are decreased. Moreover, during the re-solidification, some carbon came from the melted VC particles can be pushed by the solidification front due to it has low solubility in Ti [20, 21]. For that reason, the percentage of carbon in this region can be increased. This can be represents one of the main reasons for the high hardness at this region. The hardness distributions at powers of 2000 W and 1500 W showed almost homogenous trend, which that at power of 1000 W showed inhomogeneous distribution. This may be due to the homogeneity of the VC particles inside the cladding layer, which confirm the microstructure analysis. Distance from free surface, mm Fig. 7 Microhardness profiles through the depth of the laser treated zone obtained at different laser powers 3.3. Wear rate of the developed surface layer The wear rates were calculated for the cladded layer and the substrate material as described in the experimental work. From Fig. 8 it is clear that the addition of VC powder on the Ti alloy substrate with the aid of laser improved the overall wear resistance of the MMC produced in the cladding zone on the surface. The three conditions of 2000 W, 1500 W, and 1000 W processing powers gave high reduction in wear rate. Generally, the improved wear resistance in the laser cladded zone can be mainly attributed to the higher hardness of this zone due to: i) the presence of VC in the form of refined particles and dendrites, ii) the carbon diffusion in the matrix and iii) the strong interface bonding between the Ti alloy matrix and the VC reinforcement. The hard reinforcing phase (VC) act as load-bearing compounds and resist the plastic deformation of the matrix phase. With the increase of power, the volume fraction of unmelted VC was decreased (due to the increase of dilution) and as a result the improvements in hardness and the wear resistance of the cladded zone were decreased. In the same time, the non-homogeneous distribu- tion of VC particles inside the cladding layer at power of 1000 W increases the weight loss, and in consequence, reduces the wear resistance. Regarding the corrosion resistance evaluation, the sample treated at processing power of 1500 W was chosen due to that it gave the best results regarding the dimensions, microstructure, hardness, and wear resistance of the resulted zone. Fig. 9 shows the polarization curves of Ti alloy substrate and the treated layer. From this figure, it is clear that the corrosion potential of the treated sample was shifted to more positive than that of the Ti alloy substrate. Also, the corrosion current of the treated layer showed lower values than that of the Ti alloy substrate. It is well known that when the potential is increased and the current is decreased, the polarization resistance is increased and the material show improved corrosion resistance. Thus, it is clearly evident that the laser melting of Ti alloy had a positive influence on its the corrosion behavior. 1.4 - 1.2 - - 1.0 - tlO » 0.8- t/i C) £ 0.6 -M Main effect plot for S/N ratios: Ra Fig. 1 S/N ratio plot for MRR, Ra, and overcut It is observed from the S/N ratio graph that the MRR attains its peak with the parametric combination of POT (16 us), POF (12 us), GI (11 A), SG (0.16 mm). For smaller is better for Ra is obtained at POT (24 us), POF (16 us), GI (7 A), SG (0.20 mm). Similarly for smaller is better for OC is obtained at POT (16 us), POF (16 us), GI (7 A), SG (0.18 mm). ANOVA results as exhibited from F-values and percentage contribution of the process variables states that the F-values of gap current assume value 22.337 with a yield of 82.28 % in case of MRR. This implies that the variable have significant effects on MRR. Whereas in case of Ra, pulse on time (POT) alone is the major contributor having F-value of healthy 5.34 and having percentage contribution of 47.24 %, which is widely followed by gap current having F-value of approximately 4. Finally in case of overcut the spark gap (SG) alone is the major contributor having F-value of healthy 4.0 with percentage contribution of 65.60 %. Other factors here remain insignificant. 4. Results analysis using response surface methodology (RSM) The response surface (output) can be related with the number of controllable variables X1, X2,... , Xk as y = f(x1,x2,...,xk) + E (4) A second order model is used to establish input-output relationship efficiently that takes the generic form y = P 0+ ^ft^+^fti^+^fty^ + e (5) ¿=i ¿=i ¿=i The predicted response for the model is 9 = fio + (6) ¿=i ¿=i ¿=i In the present work, Box-Behenken design is followed which is based on 2k (k = 4) factorials with incomplete designs and found to be very efficient [17]. The process variables combinations and the corresponding responses are presented in Table 2. Table 2 Combination of factors and responses for RSM Expt. POT POF GI SG MRR Ra Overcut No. (Ms) (Ms) (A) (mm) (mm3/s) (Mm) (mm2) 1 20 12 11 0.18 1.2578 9.467 2.529 2 24 20 9 0.18 0.1572 2.067 3.498 3 24 16 11 0.18 0.832 7.6 5.3177 4 20 20 7 0.18 0.0956 2.267 2.7668 5 16 16 11 0.18 2.0271 9.067 2.892 6 20 16 7 0.16 0.07652 5.467 3.739 7 16 20 9 0.18 0.4193 7.733 4.9574 8 20 20 11 0.18 1.1941 11.367 5.6864 9 20 16 11 0.2 1.6 12.667 5.2014 10 24 16 9 0.16 0.0969 3.067 3.4982 11 20 16 9 0.18 0.0479 11.467 3.2556 12 16 16 7 0.18 0.0367 8.133 2.166 13 20 12 9 0.2 0.1581 7.6 4.4686 14 16 16 9 0.16 0.17158 8.867 3.376 15 20 16 9 0.18 0.1383 8.867 4.5915 16 20 16 11 0.16 0.2064 9.4 4.3488 17 20 20 9 0.16 0.08905 9.467 2.2852 18 20 16 9 0.18 0.095 8.667 3.2536 19 20 20 9 0.2 0.0771 9.333 5.4462 20 20 12 9 0.16 0.0773 9.333 1.4424 21 20 16 7 0.2 0.00877 8 1.6827 22 16 16 9 0.2 0.0892 11.6 2.8896 23 16 12 9 0.18 0.17357 9.867 2.0444 24 24 12 9 0.18 0.0324 3.933 1.9248 25 24 16 9 0.2 0.116 11.733 3.6187 26 24 16 7 0.18 0.00636 5.333 3.498 27 20 12 7 0.18 0.01333 6.6 3.376 4.1 Analysis of test results for material removal rate (MRR) The estimated regression surface equation for MRR is: MRR = -2.59 - 0.0349 POT + 0.0032 POF + 0.287 GI + 5.55 SG (7) The details of the regression analysis result are presented in Table 3. R-square as well as R-square (adjusted) assumes a value of 90.9 % and 80.2 %, respectively, that implies the model is poised to explain 90.9 % variability with process variable POT, POF, GI and SG . From the T values of the process variables it can be concluded that GI is the most significant process variables followed by SG, POF and POT. Table 3 Estimated regression coefficients for material removal rate (MRR) Term Coef. SE Coef. T P Constant 0.0937 0.13894 0.675 0.513 POT -0.1397 0.06947 -2.011 0.067 POF 0.0129 0.06947 0.186 0.855 GI 0.5733 0.06947 8.253 0.000 SG 0.1110 0.06947 1.597 0.136 POT*POT 0.0974 0.10421 0.935 0.368 POF*POF 0.0457 0.10421 0.439 0.669 GI*GI 0.4970 0.10421 4.769 0.000 SG*SG -0.0765 0.10421 -0.734 0.477 POT*POF -0.0302 0.12033 -0.251 0.806 POT*GI -0.2912 0.12033 -2.420 0.032 POT*SG 0.0254 0.12033 0.211 0.837 POF*GI 0.0046 0.12033 0.039 0.970 POF*SG -0.0232 0.12033 -0.193 0.850 GI*SG 0.3653 0.12033 3.036 0.010 Notes: S = 0.2407 R-Sq = 90.9 % R-Sq(adj) = 80.2 % The response surface plots of MRR with respect to GI, SG, POT and POF are shown in Fig. 2. It is observed that high levels of the two variables out of four yield maximum responses. The GI and SG have the significant effect on MRR. Since the response is proportional to the variables, there can not have any stationary point as evident from the surface plots. Further, the effect of GI is more pronounced than other three parameters. It is observed that high levels of the two variables out of four yield maximum responses. The GI and SG have the significant effect on MRR. Since the response is proportional to the variables, there can not have any stationary point as evident from the surface plots. Further, the effect of GI is more pronounced than other three parameters. Hold values: POT: 24.0; GI: 11.0 Hold values: POF: 20.0; GI: 11.0 Hold values: POF: 20.0; SG: 0.2 Hold values: GI: 11.0; SG: 0.2 Fig. 2 Wire frame surface plot for MRR 4.2 Analysis of test results for surface roughness (Ra) The estimated regression surface equation for Ra is: RA = -4.70 - 0.449 POT + 0.085 POF + 0.990 GI + 63.9 SG (8) The particulars of the regression analysis outcome are presented in Table 4. R-square as well as R-square (adjusted) furnishes a value of 71.4 % and 38.0 %, respectively, that implies the model is balanced to explain 71 % variability with process variable POT, POF, GI and SG. From the T values of the process variables, it can be concluded that GI is the most significant process variables followed by SG, POF and POT. The response surface plots of Ra with respect to GI, SG, POT and POF are shown in Fig. 3. It is seen that high levels of the two variables out of four capitulate utmost responses. The GI and SG have the considerable effect on Ra. Since the response is proportional to the variables, there can not have any stationary point as evident from the surface plots. Further, the effect of GI is more pronounced than other three parameters. Table 4 Estimated regression coefficients for surface roughness (flQ) Term Coef. SE Coef. T P Constant 9.667 1.3010 7.430 0.000 POT -1.795 0.6505 -2.759 0.017 POF 0.342 0.6505 0.525 0.609 GI 1.981 0.6505 3.045 0.010 SG 1.278 0.6505 1.964 0.073 POT*POT -1.624 0.9758 -1.664 0.122 POF*POF -1.620 0.9758 -1.660 0.123 GI*GI -0.828 0.9758 -0.848 0.413 SG*SG 0.568 0.9758 0.582 0.571 POT*POF 0.067 1.1267 0.059 0.954 POT*GI 0.333 1.1267 0.296 0.772 POT*SG 1.483 1.1267 1.316 0.213 POF*GI -0.608 1.1267 -0.540 0.599 POF*SG 0.400 1.1267 0.355 0.729 GI*SG 0.184 1.1267 0.163 0.873 Notes: S = 2.253 R-Sq = 71.4% R-Sq(adj) = 38.0% Hold values: GI: 7.G; SG: G.16 Hold values: POF: 12.G; SG: G.16 6 □ 4 3 RA 2 i 0 RA 12 13 -15 16 ,7 Hold values: POF: 12.0; GI: 7.0 Fig. 3 Wire frame surface plot for R, POF "" " 18 is Hold values: POT: 16.G; SG: G.16 Hold values: POT: 16.0; GI: 7.0 Hold values: POT: 16.0; POF: 12 Fig. 3 Wire frame surface plot for Ra (continuation) 4.3 Analysis of test results for overcut (OC) The estimated regression surface equation for overcut (OC) is: OC =-4.70 - 0.449 POT + 0.085 POF + 0.990 GI + 63.9 SG (9) The particulars of the regression analysis are presented in Table 5. R-square furnishes a value of 61.4 % that implies the model is balanced to explain 61 % variability with process variable POT, POF, GI and SG. From the T values of the process variables, it can be concluded that POF is the most significant process variables followed by GI, SG and POT. Table 5 Estimated regression coefficients for overcut (OC) Term Coef. SE Coef. T P Constant 3.7002 0.6344 5.833 0.000 POT 0.2525 0.3172 0.796 0.441 POF 0.8394 0.3172 2.646 0.021 GI 0.7289 0.3172 2.298 0.040 SG 0.3848 0.3172 1.213 0.248 POT*POT -0.3339 0.4758 -0.702 0.496 POF*POF -0.2409 0.4758 -0.506 0.622 GI*GI 0.1065 0.4758 0.224 0.827 SG*SG -0.0444 0.4758 -0.093 0.927 POT*POF -0.3350 0.5494 -0.610 0.553 POT*GI 0.2734 0.5494 0.498 0.628 POT*SG 0.1517 0.5494 0.276 0.787 POF*GI 0.6371 0.5494 1.160 0.269 POF*SG 0.0337 0.5494 0.061 0.952 GI*SG 0.7272 0.5494 1.324 0.210 Notes: S = 1.099 R-Sq = 61.4% R-Sq(adj) = 16.4% Fig. 4 Wire frame surface plot for overcut Hold values: POF: 12.0; SG: 0.16 Hold values: GI: 7.0; SG: 0.16 Fig. 4 Wire frame surface plot for overcut (continuation) The response surface plots of Ra with respect to GI, SG, POT and POF are shown in Fig. 4. It is seen that high levels of the two variables out of four capitulate utmost responses. Since the response is proportional to the variables, there can not have any stationary point as evident from the surface plots. It is observed that the two variables out of four yield maximum responses. It clears that the POF and GI are the significant parameter for O/C. 5. Multi response optimization 5.1 Overlaid contour plots High MRR and low Ra are the two major attributes of EDM machining process. These two responses are conflicting in nature and hence achieving the both simultaneously by a set of optimum variables combination is difficult. In this section the multi response optimization is conceded out so that two conflicting goals are fulfilled concurrently. We resort to overlay contour plots which are comparatively simple approach to review the levels of operating parameters that satisfy two constrained objectives. It is considered that Ra in the range of 1.067 |j.m to 5 |j.m found to be reasonably good and acceptable for most of the applications. MRR has been set between a lower bound of 0.1 mm3/min and upper bound of 2.0 mm3/min. Thus constrained equation become: 1.067 < Ra < 5.0 (10) 0.1 < MRR < 2.0 (11) The overlaid contour plots of MRR and Ra is shown in Fig. 5. - Lower Bound -----Upper Bound White area: feasible region 1.067 5.000 Hold values: GI: 9.0; SG: 0.18 30 — CD - Lower Bound ------------- Upper Bound White area: feasible region MRR RA 0.1 2.0 1.067 5.000 Hold values: POT: 20.0; SG: 0.18 O 40 30 — 20 — 10 — 10 20 POT 30 - Lower Bound ------------- Upper Bound White area: feasible region MRR — RA 0.1 2.0 1.067 5.000 Hold values: POF: 16.0; SG: 0.18 40 Fig. 5 Overlaid contour plot for MRR and Rc The overlaid contour plot of MRR and Ra with respect to POT, POF, and GI are portrayed. The bounded white areas (unshaded) as indicated in the figure are the region that simultaneously satisfies global objectives along with possible combinations of process variables. The plots advocate that combination of moderate POT and medium POF help achieve the targets. Corresponding value of GI and POF can be predicted from the curve with the hold value of POT and SG. The white area in the figure highlights for optimum MRR and Ra and corresponding value of GI and POT can be predicted from the curve with the hold value of POF and SG. 5.2 Desirability functions Response optimizer helps to help recognize the factor settings that optimize a single response or a set of responses. For multiple responses, the necessities for all the responses in the set must be fulfilled. Response optimization is frequently helpful in product development when it is required to establish operating conditions that will effect in a product with desirable properties. Here the goal, lower, target, upper, and weight characterize the desirability function for each individual response. The importance (Import) parameters decide how the desirability functions are combined into a single composite desirability. The response optimization is shown in Table 6. From the S/N ratio plot of Taguchi design we get highest MRR at combination of POT (16 [is), POF (12 |j.s), GI (11 A), SG (0.16 mm) and lowest Ra at combination of POT (24 ^s), POF (16 ^s), GI (7 A), SG (0.2 mm). Hence an optimized combination of POT (20 ^s), POF (16 ^s), GI (9 A), SG (0.18 mm) can be taken as starting point. Table 6 Desirability function results Parameters Goal Lower Target Upper Weight Import MRR Maximum 0.100 0.5 2 1 1 Ra Minimum 1.067 3.0 5 1 1 Predicted responses MRR = 0.34789, desirability is 0.61972 (61.972 %) Ra = 3.00017, desirability is 0.99992 (99.9992 %) Composite desirability is 0.78719 (78.719 %) Global solution POT = 22.0652 POF = 20.0000 GI = 7.0000 SG = 0.1600 Fig. 6 represents the optimization plot of the responses (MRR and Ra) with the process variables. It shows how the factors affect the predicted responses and allows to modify the factor settings interactively. Optimal D Jr'_ ■D.7&7-3 KU 1 24.0 le.o KJh 23.3 [20.0] 12.0 11 0 7.5 3.29 [0.160] 0.160 MRR T.arg: 0. M y =0.3479 = 9.61972 -—-- V/ RA Targ: 3.0 y = l. 0002 = D. 99992 \ — Fig. 6 Plot showing responses (MRR and Ra) against process variables The figure shows the goal for the response, the predicted response, y, at the current factor settings, and the individual desirability score. The composite desirability, D, is displayed in the upper left corner of the graph. The label above the composite desirability refers to the current setting. When the optimization plot is created, the label is optimal. The vertical red lines on the graph represent the current factor settings. The horizontal dotted blue lines represent the current response values. From the earlier limit of MRR and Ra and assigning unbiased weight to the dual responses, the desirability of MRR becomes 0.91672 having predicted response of 0.34892 mm3/min. The same for Ra is dna = 0.99992 with the predicted response of 3.00017 [im. Finally the dual desirability is 0.78719 having POT = 2.0652, POF =20.0000, GI = 7.0000, SG = 0.1600 is the near optimal combination. 6. Discussion and conclusion The experimental study indicates that in while machining AISI H13 tool steel using die sinking EDM process the responses are dependent on pulse on time, pulse off time, gap current and spark gap. The S/N ratio analysis along with ANOVA is a simple method to ascertain implication of several input parameters that administers multiple responses of the process. For higher MRR, GI is the most significant parameter and having contribution of 82.28 %. MRR increases with respect to increase of GI. In case of lower Ra, the POT is having the most significant effect and contributes 47.24 %. Ra decreases with the increase of POT and however Ra increases with increase of GI. For smaller overcut, SG is the most significant parameter and contributed 65.6 % and OC decreases with the increase of SG initially up to 0.18 mm then it increases with respect to SG. The present work is carried out with a view to optimize MRR (maximize) and Ra (minimize) concurrently by employing a near optimal set of process variables. Since the optimization is carried out for a single pass machining, the due importance is given to the surface finish considering quality characteristics in a cost effective manner (enhanced productivity harnessing high MRR). This optimization is carried out by RSM that is promised to offer near optimal solution with little effort. The regression models are found to be worthy to express input-output relationship with a very high degree of predictability. The inferences drawn from the regression analysis is accentuated with the desirability functions. Gap current is found to be the most significant in comparison to the responses. The near optimal combinations of process variables are high POT, POF and low GI and SG to satisfy both the responses (MRR and Ra) simultaneously. This set of inputs can be used to further optimize other functions like machining cost and can form the backbone of adaptive control strategies (adaptive control with optimization and geometric adaptive control). The overlaid contour plot is a good visual aid to identify the feasible region in regard to a set of input variables. The individual desirability for each predicted responses are calculated. The individual desirability values are then combined into the composite desirability. The closer the predicted responses are to your target requirements, the closer the desirability will be to 1. The composite desirability combines the individual desirability into an overall value, and reflects the relative importance of the responses. The higher the desirability the closer it will be to 1. Here MRR has an intermediate desirability score of 0.61972 because the predicted response for MRR of 0.34789 is approximately two-thirds of the way between the target of 2 and the lower bound of 0.100. The goal for MRR was to maximize; therefore higher values are more desirable. Similarly Ra has a desirability score of 0.9999 because the predicted response of 3 is nearer to the target of 3. The experiment was less successful optimizing overcut than MRR and Ra, respectively. The composite desirability of 0.78719 places greater emphasis on MRR (importance is 2) than on Ra and uvercut (importance is 1). The RSM being a powerful tool, its potential can be extended to other areas of machining such as tool life, power and cutting force modeling. The experimental investigation for evaluating the optimal parametric combination and the subsequent effect of the parameters over the responses can act as an efficient and useful guideline for machining and manufacturing various metallic products. The future work in this emerging area can be considered with other parameters and different responses such as cutting force, tool life etc. to capture the process in full perspective. The estimation of the reduction of the cost using multi-response optimized EDM process with respect to non-optimized die sinking EDM process can be further investigated. The average cost of energy consumption vs. cost of electrode material (and cost for electrode manufacturing) for the typical product manufactured by EDM process gives a scope for future work. References [1] Selvakumar, G., Sarkar, S., Mitra, S. (2013). 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Benefits of using real-time pulse discriminating system in micro-EDM monitoring and control system, International Journal of Mechatronics and Manufacturing Systems, Vol. 3, No. 5/6, 466-481, doi: 10.1504/IIMMS.2010.036070. [5] Liu, H.S., Tarng, Y.S. (1997). Monitoring of the electrical discharge machining process by abductive networks, The International Journal of Advanced Manufacturing Technology, Vol. 13, No. 4, 264-270, doi: 10.1007/ BF01179608. [6] Ayesta, I., Izquierdo, B., Sánchez, J.A., Ramos, J.M., Plaza, S., Pombo, I., Ortega, N., Bravo, H., Fradejas, R., Za-makona, I. (2013). Influence of EDM parameters on slot machining in C1023 aeronautical alloy, In: Lauwers, B., Kruth, J.-P. (eds.), Procedia CIRP, Proceedings of the Seventeenth CIRP Conference on Electro Physical and Chemical Machining (ISEM), Vol. 6, Elsevier, 129-134, doi: 10.1016/j.procir.2013.03.059. [7] Nipanikar, S.R. (2012). 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Prediction of surface roughness in wire electric discharge machining (WEDM) process based on response surface methodology, International Journal of Engineering and Technology, Vol. 2, No. 4, 708-719. [12] Kohli, A., Wadhwa, A., Virmani, T., Jain, U. (2012). Optimization of material removal rate in electrical discharge machining using fuzzy logic, World Academy of Science, Engineering and Technology, Vol. 6, No. 12, 1509-1514. [13] Mohanty, C.P., Sahu, J., Mahapatra, S.S. (2013). Thermal-structural analysis of electrical discharge machining process, In: Mehta, U. (ed.), Procedia Engineering, Chemical, Civil and Mechanical Engineering Tracks of 3rd Nirma University International Conference on Engineering, Vol. 51, 508-513, doi: 10.1016/j.proeng.2013.01.072. [14] Arikatla, S.P., Krishnaiah, A., Mannan, K.T. (2013). 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Advances in Production Engineering & Management Volume 9 | Number 4 | December 2014 | pp 181-186 http://dx.doi.Org/10.14743/apem2014.4.186 ISSN 1854-625G Journal home: apem-journal.org Original scientific paper Effect of welding variables on mechanical properties of low carbon steel welded joint Talabi, S.I.a*, Owolabi, O.B.b, Adebisi, J.A.a, Yahaya, T.a Department of Materials and Metallurgical Engineering, University of Ilorin, Ilorin, Nigeria bNational Engineering Design Development Institute, Nnewi Anambra State, Nigeria A B S T R A C T A R T I C L E I N F O This paper discussed the effect of welding variables on the mechanical properties of welded 10 mm thick low carbon steel plate, welded using the Shielded Metal Arc Welding (SMAW) method. Welding current, arc voltage, welding speed and electrode diameter were the investigated welding parameters. The welded samples were cut and machined to standard configurations for tensile, impact toughness, and hardness tests. The results showed that the selected welding parameters had significant effects on the mechanical properties of the welded samples. Increases in the arc voltage and welding current resulted in increased hardness and decrease in yield strength, tensile strength and impact toughness. Increasing the welding speed from 40-66.67 mm/min caused an increase in the hardness characteristic of the welded samples. Initial decrease in tensile and yield strengths were observed which thereafter increased as the welding speed increased. An electrode diameter of 2.5 mm provided the best combination of mechanical properties when compared to the as received samples. This behaviour was attributed to the fact that increased current and voltage meant increased heat input which could create room for defect formation, thus the observed reduced mechanical properties. © 2014 PEI, University of Maribor. All rights reserved. Keywords: Welding Low carbon steel Welding variables Mechanical properties *Corresponding author: isaacton@yahoo.com (Talabi, S.I.) Article history: Received 18 September 2014 Revised 3 November 2014 Accepted 10 November 2014 1. Introduction Steel is an important engineering material. It has found applications in many areas such as vehicle parts, truck bed floors, automobile doors, domestic appliances etc. It is capable of presenting economically a very wide range of mechanical and other properties. Traditionally mechanical components has been joined through fasteners, rivet joints etc. In other to reduce time for manufacturing, weight reduction and improvement in mechanical properties, welding process is usually adopted. Today, a variety of different welding processes are available, such that welding is extensively used as a fabrication process for joining materials in a wide range of compositions, part shapes and sizes. Welding is an important joining process because of high joint efficiency, simple set up, flexibility and low fabrication costs [1]. Welding is an efficient, dependable and economical process. Welded joints are finding applications in critical components where failures are catastrophe. Hence, inspection methods and adherence to acceptable standards are increasing. These acceptance standards represent the minimum weld quality which is based upon test of welded specimen containing some discontinuities. Welding involves a wide range of variables such as time, temperature, electrode, pulse frequency, power input and welding speed that influence the eventual properties of the weld metal [2-9]. Welding of steel is not always easy. There is the need to properly select welding parameters for a given task to provide a good weld quality. Therefore, the use of the control system in arc welding can eliminate much of the "guess work" often employed by welders to specify welding parameters for a given task [10]. There is therefore need for experimental research to generate data for the design of a welding control system that can give optimized properties. The effect of welding variables on the mechanical properties of low carbon steel arc welded joints was studied in this research. The experiment was carried out with the object of knowing how these individual variables affect the mechanical properties of the welded steel sample. 2. Materials and methods The composition of the sample is shown in Table 1. Fig. 1 shows, the welded plate and the geometry of the 10 mm plates butt welded with a weld gap of 3 mm. Specimens of dimensions 60 mm x 40 mm x 10 mm were prepared as suggested by Agarwal [11]. Work piece surfaces and edges were suitably prepared using wire brush prior to the welding processes. The plates were welded together by the SMAW process employing basic coated electrodes. A 7018 low hydrogen electrode rod was used for the welding operation. A high voltage DC generators with rectifiers, capable of supplying current of up to 600 A, air and water cooled electrode holder was used for the welding operation. Pair of prepared metal plates were abutted leaving a gap of about 3 mm in between, while the gap is filled completely, putting into consideration the root, hot pass, fill, cap and bead. The welding was done under controlled and varying welding variables. The welded samples were allowed to cool and tapped with hammer to remove the slag in other to ensure the gap was perfectly filled. The completely filled welded joints were thereafter ground with grinding machine to standard dimension. Four independent process variables, i.e. welding current, welding voltage, welding speed and electrode diameter were selected for study. Impact tests were conducted using the Avery-Dennison impact-testing machine. Each experiment was repeated at least three times and the average values recorded. Brinell hardness tester under a static load of 3000 kg with a ball indenter of 10 mm diameter was used for the determination of the hardness of the welded joint specimens at a dwell time of 15 s. The diameter of indentation on the specimen was measured with the aid of a calibrated microscope and determined according to ASTM E 10-08 standard and the corresponding hardness obtained. A transverse tensile test specimen was cut from a welded butt joint to determine its transverse tensile strength according to BS EN 895 standard. A Mosanto tensiometer was used to determine the ultimate tensile strength and yield strength of the welded specimen using ISO 6892 standard. Table 1 Chemical analysis result of as-received low carbon steel (LCS) Element C Si Mn P S Cr Nb Ni Al Cu V Percentage 0.08 0.35 1.49 0.013 0.002 0.03 0.004 0.17 0.047 0.03 0.001 £ e o u> Fig. 1 Welded Plate and the geometry of the 10 mm plates butt welded with a weld gap of 3 mm 3. Result and discussions 3.1 Hardness Fig. 2 shows the effect of the welding variables on the hardness of welded joint of low carbon steel samples. Fig. 2(a) shows that the hardness of the welded samples changed slightly with changes in voltage values between 20 V and 35 V. The sample welded at 20 V shows a considerable increase in hardness as compared to the unwelded sample which decreased slightly above this voltage value. As seen in Fig. 2(b), increase in the welding current from 95 A to 155 A resulted in increase in hardness. This is similar to the effect of the welding voltage. In Figs. 2(c) and 2(d), the hardness of the samples increased with increasing welding speed while the highest hardness value was obtained with 3.5 mm electrode diameter. Increasing the welding speed from 40 mm/min to 66.67 mm/min caused an increase in the hardness characteristic of the welded samples. This phenomenon can be related to structural changes of weld metal during solidification and chances of formation of defect in the various welding conditions. The weld-ment increased hardness value may be due to carburization. These increased hardness values indicates that the welded joint will be prone to brittleness than the base metal; hence post-welding heat treated will be required to optimize the mechanical property [12]. The results obtained are similar to the work of other researchers [12-14]. w, Vy, (2) V PiPj,PiMj,PiIj,PiAj,PiPaj EIN0 (3) V BCi,MCi,PCi,ICi,PaCi,SBi,Spi,STi,S¡i,SAi,Spai ElN0 (4) where M(y) is the sum of tokens in the places of circuit y. Place names represent the number of tokens in the places which belong to that circuit. Some simple rule to do this can be given: all the parts/kanban circuits should contain one token. The rule of thumb is to put a token in a kanban place instead of a part place because it does not increase WIP. The total transition time r(y) in each elementary circuit y is determined as the sum of the transition firing times in that elementary circuit: m T(y) = ^T(ti) (5) i=l The total number of tokens M(y) in each elementary circuit y is obtained as the sum of the number of tokens in that circuit: n X 7 = 1 ; (6) where M0 stands for initial marking. The cycle time C(y) of each elementary circuit is the ratio between the total transition time of the circuit r(y) and the total number of tokens M(y) in that circuit: r(y) 3.2 Optimal marking of the Petri net model Let C(yc) be the largest cycle time of an elementary circuit. This elementary circuit will be called a critical circuit. The cycle time in steady-state is given by the maximum cycle time taken over all elementary circuits. Increasing the number of tokens in each elementary circuit reduces the cycle time of the elementary circuits. The machining server circuit (also called machining sequencing circuit) with the largest cycle time will limit the maximum throughput. In other words, this station will be the bottleneck station. It is possible to increase the number of tokens in nonserver circuits in such a way that the machining server circuit becomes the critical circuit. Hence, the objective is keeping WIP minimum corresponding to the maximum throughput The maximum throughput with minimum WIP is formulated as a linear programming problem: MIN ^{PiMj + PiPj +PiTj +PiIj +PiAj +PiPaj) i,j where i = 1, 2, 3,... shows the number of part in the model, whereas j = 1, 2, 3,... shows the number of the activity/operation (manufacturing, movement, and assembly etc.). The place names stand for the number of tokens in that place subject to: C(y) C(yc)} (10) Elementary circuits with cycle times greater than the cycle time of the machining server circuit are selected for optimization, since the cycle time of the machining server circuit merely represents the capacity of the corresponding station. 3.3 Calculation of station utilization and lead time for the Petri net model The utilization Uj of each station j can be calculated as the ratio of the cycle time of the server circuit j and the cycle time of the critical circuit. „ C(Ysj) Ui=-Frr (11) where, ysj represents the server circuit for station j. The lead time LT can be determined by using Little's law. The WIP and critical cycle time are known so the lead time can be calculated: LTi = C(yc)Y{PiMj + PiPj +PiTj +PiIj +PiAj +PiPaj) [12] ij The number of kanban cards in kanban places is determined by the following formula: Nkc = Yj(Pi+^Ci) (13) where Nkc shows the number of kanban cards. The part places Pi stand for the number of kanban cards attached with parts, subassemblies, or final assembly. The kanban places KCi show the number of kanban cards in the kanban places. 4. Case study The proposed PN model is applied to a ball bearing. The designation of the ball bearing is SKF TAM 6208. The bearing consists of four parts: outer race, inner race, balls, and cage. The two races are almost made in the same way, i.e. by CNC lath machines. First, the inner race is inserted inside the outer race, with some eccentricity. Then balls are inserted between the two races. At the final stage of the assembly, the cages are riveted on both sides to equally distribute the balls around the inner race, and lock the balls between the races. The assembly sequence of the ball bearing is shown in Fig. 8. The INA (Integrated network analyzer) software by Starke is a tool package supporting the analysis of Place/Transition Nets (PNs) and Colored PNs (http://www2.informatik.hu-berlin.de) and is used to determine the elementary circuits in the PN model. The elementary circuits given by INA, their cycle times, and initial marking are shown in Table 2. Part-1 (Inner race) Part-2 (Outer race) Subassembly-1 Part-3 (Balls) Subassembly-2 Part-4 (Cage) Ball Bearing Fig. 8 Assembly sequence of a ball bearing Advances in Production Engineering & Management 9(4) 2014 197 Table 2 Elementary circuits and their corresponding cycle times for PN model shown in Fig. 7 (a split window is shown) TM 0 0 0 2 0 0 1 0 0 20 0 0 20 TRAN KR KB KB TB KM1 KM1 TT1 KP KP TP1 KM2 KP TP2 TOK 0 1 1 0 0 1 0 0 1 0 0 1 0 PLACE MCR PMA BC PB PM1 MC1 PT1 PP PC1 P1P1 P1M2 PC2 P2P2 No. 1 2 3 4 5 6 7 8 9 10 11 12 13 M(g) t(g) C(g) 1 0 1 0 1 1 0 1 1 0 1 0 0 0 1 61 61 2 0 1 0 1 1 0 1 1 0 1 0 0 0 1 60 60 3 0 1 0 1 1 0 1 1 0 1 0 0 0 1 60 60 4 0 1 0 1 1 0 1 1 0 1 0 0 0 1 59 59 5 0 1 0 1 1 0 1 1 0 1 0 0 0 1 59 59 6 0 1 0 1 1 0 1 1 0 1 0 0 0 1 59 59 7 0 1 0 1 1 0 1 1 0 1 0 0 0 1 58 58 8 0 1 0 1 1 0 1 1 0 1 0 0 0 1 58 58 9 0 1 0 1 1 0 1 1 0 1 0 0 0 1 57 57 10 0 1 0 1 1 0 1 1 0 1 0 0 0 1 55 55 11 0 1 0 1 1 0 1 1 0 1 0 0 0 1 54 54 12 0 1 0 1 1 0 1 1 0 1 0 0 0 1 53 53 13 0 1 0 1 1 0 1 1 0 1 0 0 0 1 52 52 14 0 1 0 1 1 0 1 1 0 1 1 0 0 1 43 43 15 0 1 0 1 1 0 1 1 0 1 1 0 0 1 42 42 16 0 1 0 1 1 0 1 1 0 1 1 0 0 1 42 42 17 0 1 0 1 1 0 1 1 0 1 1 0 0 1 42 42 After initial marking, the next step is to optimize the PN for keeping WIP minimum corresponding to the maximum throughput. For this purpose, the machining server circuit is made the bottleneck station because the cycle time of this circuit merely represents the capacity of the corresponding station. To do this, all those part circuits are considered of which cycle time is greater than the cycle time of the machining server circuit, after the initial marking stage. These part circuits result in constraints. Total twenty seven (27) elementary circuits appear as constraints. The cycle time of these circuits is made equal to or lower than the cycle time of the machining server circuit by putting more tokens in these circuits. LINGO is used for optimization of the PN model. The objective function for the system optimization based on equation (8) is as follows. MIN: PMA + PB + PM1 + PT1 + PP + P1P1 + P1M2 + P2P2 + P2M3 + P1T2 + P1i1 + P2T3 + P2i1 + P1I1 + P1M4 + P2I2 + P2M5 + P1T4 + P1A11 + P2T5 + P2A21 + P1A1 + P2A1 + PM6 + PT6 + PA2P + P3T7 + P3A32 + PA2 + P3A2 + PM8 + PT8 + PA3P + P4T9 + PA34 + PA3 + P4A3 + PM10 + PT10 + PPa + PP + PR Each place name represents the number of tokens in that place. 5. Results, discussion, and managerial implications Total twenty seven (27) elementary circuits appear as constraints. The tokens to be added to the part circuits can be determined by dividing the cycle time of the part circuit with the cycle time of the critical circuit. The number of tokens to be added to the part circuits should be greater than or equal to 1.525, 1.5, 1.475, 1.45, 1.425, 1.375, 1.35, 1.325, 1.3, 1.075, 1.05, and 1.025. After optimization, the performance measures are calculated for the system as shown in Tables 3 and 4. Table 3 shows the total WIP in the system of four (4), the cycle time and lead time of 40 time unit, the throughput (or rate of production) of 0.025 product per unit time, and the total cycle time of the system of 65 time unit. Table 4 shows the optimal values of machine utilization for each station, calculated by dividing the cycle time of a corresponding station by the critical cycle time of the system. This table shows 100 % utilization for the machining station while that of all other stations is lower than 100 %, the minimum being for purchasing station. It is because the machining server circuit with the largest cycle time determines the bottleneck station. The production is bounded by the utilization of this bottleneck station. Table 5 shows the number of kanban cards in the system as follow: buying card is 1, productions cards are 2, move cards are 13, inspection cards are 2, assembly cards are 6, and packaging card is 1. Total number of kanban cards is twenty five (25). Using the model, there can be a better coordination among all the functional areas involved in the system. The model can also provide managers a better coordination both with the suppliers and the end users. It can help them in coordination and cooperation of the enterprise's overall operation. This coordination will lead to JIT activities in the system. It will result in minimum WIP, less lead time, more throughput, and better product quality. Managers can choose among desired performance measures in order to achieve production management and control. The determination of the total WIP, total number of stations in the system, and the number of servers at each station will help in factory floor management. It will result in greater production efficiency along with ease of supervision. Table 3 Performance measures for the system WIP Cycle time (time unit) Lead time (time unit) Throughput Total cycle time of system (product/time unit) (time unit) 4 40 40 0.025 65 Table 4 Stations utilization in the system Station's name Station's utilization Purchasing station Machining station Inspection station Assembly station Packaging station (2/40) x 100 = 5 % (40/40) x 100 = 100 % (4/40) x 100 = 10 % (6/40) x 100 = 15 % (3/40) x 100 = 7.5 % « 1 Table 5 Number of kanban cards in the system given by Lingo Purchasing cards Production cards Move cards Inspection cards Assembly cards Packaging cards 1 2 13 2 6 1 Total kanban cards 25 6. Conclusion A generic deterministic PN model for the integration of purchasing, production, and packaging is developed in a pull environment using kanban. The performance evaluation of the PN model is based on solution of a linear programming problem. The optimization of the PN model is influenced by the utilization of the bottleneck station. 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Calendar of events • The IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2014), Selangor/KL, Malaysia, December 9-12, 2014. • International Conference on Artificial Intelligence and Manufacturing Engineering (ICAIME 2014), Dubai, United Arab Emirates, December 25-26, 2014. • International Conference on Trends in Mechanical Engineering (MEC 2014), Chennai, India, December 27-28, 2014. • 2nd International Conference on Recent Trends in Engineering and Technology (ICRTET 2015), Cochin, Kerala, India, January 10-11, 2015. • 4th International Conference on Operations Research and Enterprise Systems (ICORES 2015), Lisbon, Portugal, January 10-12, 2015. • 3rd International Conference on Laser and Plasma Application in Materials Science (LAPAMS 2015), Kolkata, India, January 15-17, 2015. • International Conference on Advances in Mechanical Engineering (AME 2015), Dubai, United Arab Emirates, January 23-24, 2015. • XIII International Conference on Industrial Engineering and Management Systems (ICIEMS 2015), Paris, France, January 23-24, 2015. • 39th International Conference and Expo on Advanced Ceramics and Composites, Daytona Beach, Florida, USA, January 25-30, 2015. • XIII International Conference on Industrial Engineering and Operations Management (ICI-EOM 2015), Istanbul, Turkey, January 26-27, 2015. • The 2nd International Materials, Industrial, and Manufacturing Engineering Conference (MIMEC2015), Bali, Indonesia, February 4-6, 2015. • International Conference on Design, Manufacturing and Mechatronics, Pune, Maharashtra, India, February 11-13, 2015. • 6th International Conference on Automation, Robotics and Applications (ICARA 2015), Queenstown, New Zealand, February 17-19, 2015. • XIII International Conference on Sustainable Intelligent Manufacturing (ICSIM 2015), Paris, France, February 23-24, 2015. • International Symposium Additive Manufacturing, Dresden, Germany, February 25-26, 2015. • International Conference on Industrial Engineering and Operations Management (IEOM 2015), Dubai, United Arab Emirates, March 3-5, 2015. • 6th International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2015), Melaka, Malaysia, March 6-7, 2015. • IEEE International Conference on Industrial Technology, Seville, Spain, March 17-19, 2015. • 4th International Conference on Manufacturing and Industrial Engineering (ICMIE 2015), Singapore, March 21-23, 2015. • The Seventh International Conference on Adaptive and Self-Adaptive Systems and Applications (ADAPTIVE 2015), Nice, France, March 22-27, 2015. • 20th International Conference on Wear of Materials, Toronto, Canada, April 12-16, 2015. • The 4th International Conference on Manufacturing Engineering and Process (ICMEP 2015), Paris, France, April 13-14, 2015. • 2nd International Conference on New Technologies (NT 2015), Mostar, Bosnia and Herzegovina, April 24-25, 2015. • IEEE International Conference on Technologies for Practical Robot Applications, Woburn, Massachusetts, USA, May 11-12, 2015. • IFAC Symposium on Information Control in Manufacturing (INCOM 2015), Ottawa, Canada, May 11-13, 2015. • 14th International Conference on Tribology (SERBIATRIB 2015), Belgrade, Serbia, May 13-15, 2015. • International Conference on Advances in Mechanical Engineering (ICAME 2015), Istanbul, Turkey, May 13-15, 2015. • IEEE International Conference on Robotics and Automation, Seattle, Washington, USA, May 25-30, 2015. • 2nd International Conference on Industrial Engineering, Management Science and Applications (ICIMSA 2015), Tokyo, Japan, May 26-28, 2015. • 6th International Conference on Modeling, Simulation and Applied Optimization, Istanbul, Turkey, May 27-29, 2015. • 10th International Conference on Additive Manufacturing & 3D Printing, Nottingham, UK, July 7-9, 2015. • 27th European Conference on Operational Research (EURO 2015), Glasgow, UK, July 12-15, 2015. • The 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2015), Colmar, France, July 21-23, 2015. • XXIV International Materials Research Congress (IMRC 2015), Cancon, Mexico, August 16-20, 2015. • IEEE 20th Conference on Emerging Technologies & Factory Automation, Luxembourg, Luxembourg, September 8-11, 2015. • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), Hamburg, Germany, September 28 - October 2, 2015. 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APEM journal Production Engineering Institute (PEI) University of Maribor APEM homepage: apem-journal.org Advances in Production Engineering & Management Volume 9 | Number 4 | December 2014 | pp 155-204 Contents Scope and topics 158 Laser cladding of Ti-6Al-4V alloy with vanadium carbide particles El-Labban, H.F.; Mahmoud, E.R.I.; Al-Wadai, H. 159 Parametric study of die sinking EDM process on AISI H13 tool steel using statistical techniques Bose, G.K.; Mahapatra, K.K. 168 Effect of welding variables on mechanical properties of low carbon steel welded joint Talabi, S.I.; Owolabi, O.B.; Adebisi, J.A.; Yahaya, T. 181 A Petri net model for the integration of purchasing, production and packaging using Kanban system Ullah, H. 187 Calendar of events 201 Notes for contributors 203 Copyright © 2014 PEI. All rights reserved. apem-journal.org 9771854625008