ISSN 1854-6250 APEM journal Advances in Production Engineering & Management Volume 9 | Number 3 | September 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. 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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 3 | September 2014 | pp 107-154 Contents Scope and topics 110 Frictional characterization of teak wood dust-filled epoxy composites 111 Mishra, A. Optimal fractal dimension on grain structure robot laser-hardened tool steel 119 Babic, M.; Balic, J.; Kokol, P. Influence of welding speed on the melting efficiency of Nd:YAG laser welding 128 Tadamalle, A.P.; Reddy, Y.P.; Ramjee, E.; Reddy, V.K. Scheduling optimization of a flexible manufacturing system 139 using a modified NSGA-II algorithm Nidhiry, N.M.; Saravanan, R. Calendar of events 152 Notes for contributors 153 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 3 | September 2014 | pp 111-118 http://dx.doi.Org/10.14743/apem2014.3.180 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper Frictional characterization of teak wood dust-filled epoxy composites Mishra, A.a* aIndira Gandhi Institute of Technology, Sarang, Odisha, India A B S T R A C T A R T I C L E I N F O Composites of teak wood dust particles of 150 |m, 212 |m, and 300 |m sizes with 10 % weight fractions were developed by a hand moulding technique. A wooden mould was prepared for casting the composite pins of 8 mm diameters and 50 mm length. Sliding wear tests were conducted on a pin on disc friction and wear monitor. It was observed that the composite with mesh size 150 |m of teak wood dust exhibited the least wear rate. Furthermore, the lowest coefficient of friction was also seen in the composite with a 300 |m size wood dust filler. The composite with 212 |m size wood dust showed an increase in the coefficient of friction but at higher loads dropped down probably because of the formation of transfer film between the composite and the steel disc. The composites with 150 |m and 300 |m size dust particles were in close ranges of friction coefficients, i.e. 0.64 and 0.71, respectively. Thus though the coefficient of friction was high for a 150 |m size filler composite, the wear increased at a steady rate and may stabilize after running for more than a 5 km distance later. Out of the above three composites the wood dust of a 150 |m composite may thus be a better choice for frictional applications. © 2014 PEI, University of Maribor. All rights reserved. Keywords: Teak wood dust Epoxy Composites Friction and wear characteristics *Corresponding author: igit.antaryami@gmail.com (Mishra, A.) Article history: Received 14 March 2014 Revised 23 July 2014 Accepted 5 August 2014 1. Introduction The quest for light weight and high strength materials is never ending for due consideration owing to their wide applications. Therefore a polymeric composite material has its importance in the applications of light structures. These composite materials (notably aramid, carbon and glass fibre reinforced plastics etc.) now dominate the aerospace, automotive, construction, and sporting industries. However, these fibres have serious drawbacks such as non-renewability, non-recyclability, non-bio-degradability etc. These shortcomings have been highly exploited by proponents of natural fibre composites. Though mechanical properties of natural fibres are much inferior to those of other fibres, their specific properties, especially stiffness, are quite comparable to artificial fibres. The aim of this study is to determine the friction and wear characteristics of saw dust-epoxy composite. This study is important for thermoplastic manufacture of furniture, residential deck board, rails and balusters, transportation structures, poles and cross arm, wearing surfaces and other related industrial applications to find out suitable materials which show good friction and wear properties. Although there are several reports in the literature which discuss the mechanical behaviour of wood/polymer composites, however, very limited work has been done on the effect of wood dust types on friction and wear characteristics of polymer composites. Against this background, the present research work has been undertaken, with an objective to explore the potential of wood dust types as a reinforcing material in polymer composites and to investigate its effect on the friction and wear behaviour of the resulting composites. Most of the studies on natural fibre composites involve study of mechanical properties as a function of fibre content, effect of various treatments of fibres, and the use of external coupling agents. In the literatures, many works devoted to the properties of natural fibres from micro- to nano-scales are available. A number of investigations have been conducted on several types of natural fibres such as kenaf, hemp, flax, bamboo, and jute to study the effect of these fibres on the mechanical properties of composite materials. Mansur and Aziz [1] studied bamboo-mesh reinforced cement composites, and found that this reinforcing material could enhance the ductility and toughness of the cement matrix, and increase significantly its tensile, flexural, and impact strengths. Fracture properties and characteristics of sisal textile reinforced epoxy composites were studied by Li et al. [2]. The study concluded that proper fibre surface treatment could improve the fracture properties of this kind of eco-composite. Mechanical and fracture properties of Australian bamboo was studied by Low et al. [3]. It investigated the microstructure, mechanical, impact and fracture properties. Mosadeghzad et al. [4] studied the effect of surface treatment and filler loading on mechanical properties of the acacia saw dust unsaturated polyester resin (UPR) composite based on recycled polyethylene-terephthalate (PETP). The results showed that both tensile and flexural moduli were increased with increasing filler contents whereas the strength was decreased. This was overcome by treating the sawdust fillers with 10 % sodium hydroxide (NaOH). Study on the wear-resisting property of wood Cu/Ni electroplate coating was done by Huang et al. [5] which showed that the treated metal-wood surface has higher wear-resisting property and hardness. Biswas et al. [6] worked on the effect of ceramic fillers on mechanical properties of bamboo fibre reinforced epoxy composites. In this study, a series of bamboo fibre reinforced epoxy composites were fabricated using conventional filler (aluminium oxide A^Os) and silicon carbide (SiC) and industrial wastes (red mud and copper slag) particles as filler materials. The result showed that the inclusion of fibre in neat epoxy improved the load bearing capacity (tensile strength) and the ability to withstand bending (flexural strength) of the composites. Kranthi et al. [7] studied the wear performance of a new class of epoxy based composites filled with pine wood dust According to the study pine wood dust possesses good filler characteristics as it improves the sliding wear resistance of polymeric resin, and filler content, sliding velocity and normal loads are the important factors which affect the specific wear rate. A comparison of properties between glass-epoxy-fly ash and fly ash-epoxy composite has been made by Singla and Chawla [8]. Compression and impact tests have been carried out with varying weight fractions of fly ash and glass reinforcements in epoxy. SEM has been done to analyze the fractured surfaces. Chemical resistance to acids, alkalis and solvents to jute-glass and varying weight fractions of silica filled composites have been analzed by Kumar and Madhu [9]. It was concluded that all the composites have shown better chemical resistance to acids and alkalis except to toluene. A study on the dry sliding wear of oil palm empty fruit bunch (OPEFB) epoxy composite was done by Kasolang et al. [10]. The result showed that the mass loss was significantly higher for smallest fibre size (100 [im) examined at 30 N and at other fibre sizes, the mass loss values were relatively close due to the distribution and orientation of fibres. Wang et al. [11] studied the effect of coupling agent on bonding properties of wood/polyethylene composites. The result showed that the -OH, -C-O- and C=O functional groups were appeared on the treated surface and the surface roughness was increased after mechanical polishing treatment and coating treatment, resulting increase in the shear bonding strength for the treated sample significantly. Hisham et al. [12] studied the flexural mechanical characteristic of sawdust and chip wood filled epoxy composites and found that a good quality of saw wood (SW) and chip wood (CW) fibre composite can be used for furniture utilities. Nagieb et al. [13] investigated the effect of addition of boric acid and borax on fire-retardant properties and studied the mechanical properties of urea formaldehyde saw dust composites. The experimental results showed that the water absorption and bending strength decreased as the flame retardant increased. A study on the microstructures and properties of wood ceramics prepared from bagasse and epoxy resin composite was done by Zhang et al. [14]. The carbon yield ratio of the wood ceramics decreased with the increase of the content of ba- gasse. While the volume shrinkage ratio and volume electrical resistivity increased with the increase of the content of bagasse. Wimonsong et al. [15] worked on thermal conductivity and mechanical properties of wood sawdust/polycarbonate composites. The study showed that the Young's moduli of composites were in general higher than the neat PC except for the one with y-aminopropyl trimethoxy silane treatment. The tensile moduli of composites were increased as the filler loading increased and the addition of wood sawdust resulted in the tensile strength reduction of the composites, and also the thermal conductivity was reduced significantly with the increment of wood sawdust contents. Girisha et al. [16] have studied the mechanical performance of natural fibre reinforced (treated and untreated) hybrid composites. Tamarind fruit fibre and arecanut fibres were reinforced to epoxy. For treated fibres it was observed that tensile strength and flexural strength have increased with increase in fibre volume fractions. However beyond 40 % reinforcement the strength has decreased. Impact properties of 50 % reinforced composite has yielded the best result. Bhaskar et al. [17] worked on the evaluation of properties of polypropylene-pine wood plastic composite. Incorporation of maleated polypropylene (MAPP) coupling agent in composite formulation improved the stability. Vafaeneezhad et al. [18] considered carbonized wood from oak tree to prepare carbon/epoxy composites. From experiments it was observed that addition of epoxy has increased the sliding wear resistance. Artificial neural network was developed to validate the experimental findings. It was found that sliding distance, normal load and carbonization temperature played important role affecting the wear characteristics of the composite. Coir dust with 10 %, 20 %, and 30 % concentration both treated and untreated types were tested by Chandra Rao et al. [19] on a pin on disc type friction monitor. In order to minimize the experimental time and cost, Taguchi method was used. Abrasive wear characteristics were studied with varying load up to 30 N and varying velocities. It was seen that treated filler composites showed better wear resistance compared to untreated ones. With increase in dust content the wear rate decreased but with increase in load the wear rate has increased. Mishra [20] investigated the friction and wear characteristics of teak wood dust-filled epoxy composites of three different types of specimens. It was observed that wood dust-filled with 10 % of 300 |j.m size composite has exhibited better wear performance. Thus out of the above review an attempt has been made in this investigation to: • Development of teak wood dust-filled epoxy composites with different mesh sizes and constant volume fraction (10 % by weight). • Casting of cylindrical pins of 8 mm diameter for frictional characterization by developing a suitable wooden mould. • Carrying out short run and long run tests in a pin on disc friction and wear monitor to evaluate the coefficient of friction and wear characteristics of these materials sliding against mild steel plate. • Choosing the best out of the above three specimens for specific application. This paper describes the calculation of mechanical properties of the composite as applied to random distributed particle reinforced composites along with weight fractions and volume fractions of the reinforcement, development of a suitable mould for casting composite pins, experimental work carried out on a pin on disc machine for determining the coefficient of friction and wear rate. Lastly the results obtained have been discussed and conclusions have been drawn out of the findings. Recommendations have also been made for expected applications of these composites. 2. Mechanical properties of the composite The composite is usually prepared based on calculation of weight fractions or volume fractions. Weight fraction of the reinforcement: Weight fraction of the matrix is: Wm wm =----100 m wr + wm (2) where Wr is weight of reinforcement, and Wm weight of the matrix. Weight of the composite is: wc = wr + wm Further as per rule of mixtures, the density of the composite pc is obtained by: Pc Pm ^m ^Pr '^V (3) (4) where pm is density of the matrix, pr is density of the reinforcement, vm is volume fraction of the matrix, and Vr is volume fraction of the reinforcement. Further: V *m and Volume of the composite is: v™ =- m y +y + y "m ' vr ' vv Vr Vm + Vr + Vv 100 100 vc ^m. "^^r (5) (6) (7) where Vm is volume of the matrix, Vr is volume of the reinforcement and Vv is volume of voids. Modulus of elasticity of the composite is: Ec Er Vr ^Eyn Vm (8) where Er is modulus of elasticity of reinforcement and Em is modulus of elasticity of matrix. The properties of teak wood dust and epoxy are shown in the Table 1 and Table 2, respectively. By using the Eq. 4 and Eq. 8 the density and elastic modulus of the composites have been found out and given in Table 3. Table 1 Properties of teak wood dust [20] Properties Value Density (g/cm3) Young's modulus of elasticity (GPa) Tensile strength (MPa) 0.8 10.5 95 Table 2 Properties of epoxy [20] Properties Value Density (g/cm3) Young's modulus of elasticity (GPa) Tensile strength (MPa) 1.2*103 20 75 Table 3 Composite properties Specimens Density (kg/m3) Elastic modulus (GPa) Sp-1 (300 |m) 1120 Sp-2 (212 |m) 1140 Sp-3 (150 |m) 1160 18.58 18.58 19.05 3. Experimental investigations 3.1 Materials The teak wood dust of different sizes, i.e. 150 [im, 212 [im, and 300 [im (Fig. 1) measured through sieve shaker were considered as reinforcing material (10 % by weight) in fabrication of the composite. Epoxy (CY 230 and Hardener-HY-951 supplied by Hindustan Ceiba Geigy, Ltd.) has been used as matrix material. A wooden mould has been developed in house to cast the pins for wear testing (Fig. 2). After mixing epoxy and wood dust in the proposed ratio the composite was cast by pouring into the split mould and allowed to cure at room temperature for 24 hours. The pins were ejected out after solidification (Fig. 3). Fig. 1 Teak wood dust of three sizes: 150 |im (left), 212 |im (center), and 300 |im (right) 150M 212(1 300|i m Fig. 2 Wooden mould for pins Fig. 3 Composite pins (150 |im, 212 |im, and 300 |im) 3.2 Experimental procedure The tests were carried out in pin on disc wear and friction testing machine (Fig. 4 and Fig. 5) supplied by M/s Magnum Engineers, Bangalore, India, having the following specifications: • Load range: up to 100 N, • Friction force measurement: up to 100 N, • Wear measurement: 2000 [im (± 2 mm), • Sliding speed: 0.26-10.0 m/s, • Disc speed: 100-2000 rpm, • Diameter of track: 40-90 mm, • Disc size: diameter is 100 mm and thickness is 8 mm, disc material is EN-31 (58-60 RC), • Pin: diameter is 3-10 mm and length is 25 mm, • Software: MAGVIEW-2011 data acquisition software. For evaluation of friction coefficient under dry sliding condition the speed and time were kept constant, i.e. 400 rpm and 3 min with varying the load up to 5 kg. Similarly for estimating the wear, the pins were slid against mild steel disc for 5 km of sliding distance keeping the speed at 400 rpm (1.5 m/s sliding velocity) and load of 30 N. Fig. 4 A schematic diagram of the pin on disc apparatus Fig. 5 Friction and wear monitor [20] 4. Results and discussions The readings from the control panel of the pin on disc apparatus with respect to friction force, speed, wear, and time have been taken during conduct of the wear tests. The dead weights placed on the apparatus gave direct measurement of the normal reaction. Hence the coefficient of friction could be calculated. Thus the coefficient of friction and wear in microns were obtained for three different specimens (Table 4 and Table 5): • Specimen-1: composite with teak wood dust of 300 [im, • Specimen-2: composite with teak wood dust of 212 [im, • Specimen-3: composite with teak wood dust of 150 [im. The results have also been plotted graphically to give a better understanding as shown in Fig. 6 and Fig. 7. Table 4 Variation of coefficient of friction with load Load (kg) Specimen-1 Specimen-2 Specimen-3 1 0.52870 0.468700 0.43388 2 0.55750 0.559700 0.51084 3 0.59248 0.694020 0.57949 4 0.62229 0.843042 0.64327 5 0.63915 0.872820 0.71206 Table 5 Variation of wear with sliding distance Load (kg] Specimen-1 Specimen-2 Specimen-3 1 71.4830 33.7410 1.4080 2 72.6330 100.907 41.286 3 79.1380 145.524 79.797 4 89.0808 162.124 169.192 5 104.932 175.803 241.926 0) 'o it 0) o O 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Speed: 400 rpm Time: 3 min 300 (jm 212 (jm 150 um 1 2 3 4 5 Load (kg) Fig. 6 Variation of coefficient of friction against load 300 Load:30 N rpm: 400 Velocity: 1.5 m/s 300 (jm 212 (jm 150 (jm 2 4 6 Sliding distance (km) Fig. 7 Variation of wear with sliding distance Out of the above results it is observed that the composite with mesh size 150 nm of teak wood dust exhibited least wear rate. Further lowest coefficient of friction has also been seen in the composite with 300 nm size wood dust filler. The composite with 212 nm size wood dust showed increase in coefficient of friction and at higher loads it may drop down probably because of formation of transfer film between the composite pin and the steel disc. The composites with 150 nm and 300 nm size dust particles are in close range of coefficient of friction, i.e. 0.64 and 0.71, respectively. 5. Conclusion From the theoretical investigations it is revealed that composite with teak dust of 150 nm size has highest modulus of elasticity and density obtained by rule of mixtures. Further the co efficient of friction is also high for 150 nm size filler composite with least wear as compared to others. However it is observed that wear increases at a steady rate which may stabilize after run- ning for more than 5 km distance. Out of the above three composites thus the wood dust of 150 |j.m composite is a better choice for frictional applications. This type of composites can be used as packing materials, interior decoration of houses and buildings, light weight furniture, aircrafts interiors and automobile components etc. Long run wear tests are to be carried out for ascertaining its application in high frictional environment in industries. Acknowledgement The author is highly indebted to the authorities of Indira Gandhi Institute of Technology, Sarang, Odisha, India, for providing necessary support to conduct the experiments in the laboratories of Mechanical Engineering Department, and Metallurgy and Materials Engineering Department. References [1] Mansur, M.A, Aziz, M.A. (1983). Study of bamboo-mesh reinforced cement composites, International Journal of Cement Composites and Lightweight Concrete, Vol. 5, No. 3, 165-171, doi: 10.1016/0262-5075(83)90003-9. [2] Li, Y., Mai, Y.W., Ye, L. (2006). Fracture properties and characteristics of sisal textile reinforced epoxy composites, Key Engineering Materials, Vol. 312, 167-172, doi: 10.4028/www.scientific.net/KEM.312.167. [3] Low, I.M., Che, Z.Y., Latella, B.A., Sim, K.S. (2006). Mechanical and fracture properties of bamboo, Key Engineering Materials, Vol. 312, 15-20, doi: 10.4028/www.scientific.net/KEM.312.15. [4] Mosadeghzad, Z., Ahmad, I., Daik, R., Ramli, A., Jalaludin, Z. (2009). Preparation and properties of acacia saw-dust/UPR composite based on recycled PET, Malaysian Polymer Journal, Vol. 4, No. 1, 30-41. [5] Huang, J.T., Jia, J. Tie, K.L. (2010). Study on the wear-resisting property of wood Cu/Ni electroplate coating, Advanced Materials Research, Vol. 123-125, 1055-1058, doi: 10.4028/www.scientific.net/AMR.123-125.1055. [6] Biswas, S., Satapathy, A., Patnaik, A. (2010). 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Dry sliding wear of oil palm empty fruit bunch (OPEFB) epoxy composite, Advanced Materials Research, Vol. 308-310, 1535-1539, doi: 10.4028/www.scientific.net/AMR.308-310.1535. [11] Wang, H., Lv, X.Y., Di, M.W. (2011). Effect of coupling agent on bonding properties of wood/polyethylene composites, Advanced Materials Research, Vol. 311-313, 59-62, doi: 10.4028/www.scientific.net/AMR.311-313.59. [12] Hisham, S., Faieza, A.A., Ismail, N., Sapuan, S.M., Ibrahim, M.S. (2011). Flexural mechanical characteristic of sawdust and chip wood filled epoxy composites, Key Engineering Materials, Vol. 471-472, 1064-1069, doi: 10.4028/www. scientific.net/KEM.471-472.1064. [13] Nagieb, Z.A., Nassar, M.A., El-Meligy, M.G. (2011). Effect of addition of boric acid and borax on fire-retardant and mechanical properties of urea formaldehyde saw dust composites, International Journal of Carbohydrate Chemistry, Vol. 2011, 1-6, doi: 10.1155/2011/146763. 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Advances in Production Engineering & Management Volume 9 | Number 3 | September 2014 | pp 119-127 http://dx.doi.Org/10.14743/apem2014.3.181 ISSN 1854-625G Journal home: apem-journal.org Original scientific paper Optimal fractal dimension on grain structure robot laser-hardened tool steel Babic, M.a*, Balic, J.b, Kokol, P.c aEMO-Orodjarna d.o.o., Celje, Slovenia bFaculty of Mechanical Engineering, University of Maribor, Slovenia cFaculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia A B S T R A C T A R T I C L E I N F O In order to optimize the structure and properties of tool steel, it is necessary to take into account the effect of the self-organization of a dissipative structure with fractal properties at load. Fractal material science researches the relationship between the parameters of fractal structures and the dissipative properties of tool steel. This paper describes the application of fractal dimension in robot laser-hardening specimens. By using fractal dimensions, changes in the structure can be determined because the fractal dimension is a present indicator of the complexities of the sample forms. We hardened tool steel at different speeds and different temperatures. By researching the fractal dimensions of the microstructures of the hardened specimens we could better understand the effects of the parameters of robot cells on the material. We show the experimental results and an analysis of those fractal patterns that occur during robot laser hardening with the different parameters of temperature and speed. Finally, we present the relationship between the fractal dimensions and the parameters of temperature and speed of robot laser hardening. The hardening of various metal alloys showed that when melting occurs, fractal geometry can be used to calculate the fractal dimension. The dependence of the fractal dimension on the hardness was ascertained. This finding is important when we realize that certain alloys mix poorly because they have different melting temperatures but such alloys have a much higher hardness and better technical characteristics. Keywords: Fractal dimension Robot Laser Hardening *Corresponding author: babicster@gmail.com (Babic, M.) Article history: Received 31 October 2013 Revised 13 May 2014 Accepted 7 July 2014 © 2014 PEI, University of Maribor. All rights reserved. 1. Introduction Of all microscopic methods, electron microscopy images give the best resolution, the most accurate information of the distribution of crystals in a building, the best morphology of the various structural types, and the best structural surface topography. Fractal geometry provides a new approach in describing the structure of various illegal structures. Fractal theory has also been used in the field of materials science. Models of fractal lines and surfaces were created to describe the properties of the microstructure of materials [1]. The subject of fractals can be used to assist in the analysis of surfaces encountered in robot laser hardening. It should be noted that the morphology of a surface will change if material is hardened with robot laser cells. Analysis of fractal dimensions is a method used to study the surface properties of materials. Fractal dimension [2] is a property of fractals that is maintained over all magnifications and is therefore well defined, but in addition, it also reveals the complexity of the fractal. In general, we cannot calculate the fractal dimension for the above-mentioned procedure, as this is possible only on pure mathematical constructs, and not by nature. In practical terms, to determine the dimensions the most used method is that "of counting boxes" (box-counting dimension), which studies fractal cover using a square grid, which is then reduced and the change in the number of squares needed to cover the entire crowd observed. We face many problems in robot laser hardening [3]. Robot laser hardening [4] with an overlapping laser beam is particularly interesting. The result is, of course, an approximation, which is calculated by the desired number of places. In this paper, fractal analysis is used to determine how parameters of robot laser hardening affect the hardness of the hardened material. Robot laser surface hardening heat treatment [5-6] is complementary to conventional flame or inductive hardening. The energy source for laser hardening is the laser beam, which heats up very quickly, and works on the metal surface area of points up to 1.5 mm and a hardness of 65 HRC. Laser hardening is a process of projecting features, such as non-controlled energy intake, high-performance constancy and an accurate positioning process. A hard martensitic microstructure provides improved surface properties, such as wear resistance and high strength [7-8]. Fractal analysis [9-10] is useful when classical geometry cannot be sufficiently useful to precisely describe the results of irregular facilities. A profound feature of fractals is the fractal dimension D [11-13], which provides an important view of the physical properties of various materials. This article describes the fractal structure [14] of robot laser-hardened tool steel. Fractal patterns have been found in different mechanical properties of hardened materials (Mandelbrot 1982, Feder 1988). Fractal features have also been observed in mechanical computer simulations, which can be explained by Gauss-Marc fractal random fields. In this work, we have used a scanning electronic microscope (SEM) [15-16] to research for and analyse the fractal structure of robotic laser-hardened material. The aim of the research is to ascertain how robotic laser cell parameters for optimal tempering affect the fractal dimension of hardened material. 2. Experimental method and materials preparation The study was undertaken using tool steel standard label DIN standard 1.7225. The chemical composition of the material was 0.38-0.45 % C, maximum of 0.4 % Si, 0.6-0.9 % Mn, maximum of 0.025 % P, maximum of 0.035 % S, and 0.15-0.3 % Mo. The specimen test section was in a cylindrical form with dimensions of 25x10 mm. After hardening the test specimen was cut into smaller parts. Tool steel was forged with a laser at different speeds and at different powers. So we changed the two parameters: speed v was set to 2-5 mm/s in steps of 1 mm/s, and temperature T to 1000-1400 °C in steps of 50 °C. During all these tests, we recorded the microstructure. We recorded the hardened surface area as well as the deep hardened zone of the clips. Of interest to us was whether the robotic laser hardening parameters for different fractal structures found microparticles. Also, we wanted to know or ascertain the fractal structure of the optimal parameters of hardening. Fig. 1 shows the longitudinal and transverse cross section of hardened tool steel. In Figs. 2, 3, 4, and 5 the microstructures of tempered tool steel at different magnifications are shown. Fig. 1 Hardened tool steel Prior to testing, the specimens were subjected first to mechanical and then to electrolytic polishing [17] in H3PO4 + CrO3 at the IMT Institute of Metals and Technology, Ljubljana, Slovenia. After polishing we made images with a microscope. First, we made recordings using an optical microscope and then with an electron microscope. Images were made using a JEOL JSM-7600F field emission scanning electron microscope. Irregular surface textures with a few breaks, represented by black islands, were revealed (Fig. 2). Fig. 2 SEM image of 1000 °C and 2 mm/s at 50000x magnification on the surface Fig. 3 SEM image of 1400 °C and 5 mm/s at 10,000x magnification on the surface Fig. 4 SEM image of 1000 °C and 3 mm/s at 5000x magnification on the depth Fig. 5 SEM image of 1400 °C and 2 mm/s at 10000x magnification on the depth 3. The concept of fractals When analysing the fractal dimensions, we used the R/S method. The R/S method (adjusted rescaled range method) or the adjusted scale is a graphical method and was selected for the estimation of the Hurst exponent. Hurst, the discoverer of the exponent that bears his name, studied power laws as they related to the Nile river floods. The adjusted scale of the partial summation area space component series deviates from the mean. Following Feder, the R/S analysis is as follows. Let the time series of natural phenomena in discrete time in the space component period t be {xi, X2,..., Xn}. The calculation of the average distance m for the period t is presented in Eq. 1. n m=1ñlXi (1) u=1 Z(t) calculates as in Eq. 2. t Z(t) = ^(X¡-m) (2) U=1 R(n) calculates as in Eq. 3. R(n) = max(Z1,Z2,..,Zj -min(Z1,Z2,^,Zn) (3) S(n)2 calculates as in Eq. 4. t S(n)2 = Yd(Xi-m)^Xi-m) (4) U=1 Hurst observed the relationship R/S for a large number of natural phenomena and found the following empirical relationship in Eq. 5: j=(c t)h (5) The relationship between Hurst's exponent H and the Box-counting method for determining the fractal dimension D is very simple [10]. It is presented in Eq. 6 (in the plane) and Eq. 7 (in the space). D = 2-H (6) D = 3-H (7) 4. Results and discussions We analysed the image format (e.g., JPEG) with 256 grey level numerical matrices (level 1 for black and 256 for white) with the program ImageJ. At each point (x,y) in the image (2D plane) the value of 1 to 256 is assigned. This value is then determined by a third coordinate in the 3D coordinate system, or z-coordinate. This means that the point T = (x, y) plane is given by the third component and then forms T3D = (x, y, z). This is presented in Figs. 6, 7, 8, and 9 which show the profile of a hardened specimen with certain parameters on the surface and in the depth. The graph of grey value presents the average of all lines on the y-axis. For each specimen, we have made an image of the microstructure at 5000x, 10,000x, 20,000x, 30,000x, and 50,000x magnification. Then, when we analysed the profile graphs and profiles, we found that the graphs are similar. Distance (pixels) Fig. 6 Profile graph of surface pattern hardened by 2 mm/s at 1000 °C on surface 110 Qj g 105 I a 100 95 0 200 400 600 800 1000 1200 Distance (pixels) Fig. 7 Profile graph of depth pattern hardened by 3 mm/s at 1000 °C on depth Comparing the profiles of the graphs we show the fractality of the robot laser-hardened specimens. The comparison is analysed with Hurst parameter H. Fig. 8 Three-dimensional graph of the hardened surface of a sample of 2 mm/s at 1000 °C Fig. 9 Three-dimensional graph for of hardened surface of a sample at a depth of 3 mm/s at 1000 °C For each specimen we calculated the fractal dimension at different magnifications. The results show that the fractal dimension is equal at different magnifications. With this we show the comparison of the microstructures of robot laser-hardened specimens. Fig. 8 shows an example of the fractal structure of a robot laser-hardened specimen at 1000 °C with 2 mm/s velocity on the surface. Fig. 9 shows an example of the fractal structure of a robot laser-hardened specimen at 1000 °C with a 3 mm/s velocity at depth. 4.1 Influence of parameter temperature of robot laser cell on the fractal dimension Fig. 10 and Fig. 11 show the relationship between temperature and speed of the robot laser hardening and fractal dimensions on the surface and at depth. If we increase the temperature of the robot laser cell, then the fractal dimension also increases. Fractal dimension is higher on the surface of robot laser hardening patterns. We can see that the fractal dimension decreased in a specimen when we increased the temperature. Fig. 10 Fractal dimension at 1000 °C at different speeds of hardening 2,65 2.60 2,55 .! 2.55 tn 1 2,45 T3 ñ 22 44 u 2 2,35 u. 2233 2,25 2.20 X ♦ 34 speed (mm/s) X ♦ D on depth D on surface 2 5 Fig. 11 Dimension at 1400 °C at different speeds of hardening 4.2 Influence of parameter velocity of robot laser cell on the fractal dimension The speed of the robot laser cell impacts on hardening. We can see that the fractal dimension is higher in depth robot laser-hardened specimens. If we increase the velocity of the robot laser cell then that fractal dimension also increases. This also happens on the depth of robot laser-hardened specimens but differently. 4.3 Fractal dimension and hardness of specimen Fig. 12 and Fig. 13 present the relationship between fractal dimension and the hardness of specimens hardened with different parameters of the robot laser cell. We can see that the specimen with the least fractal dimension is the hardest. 2,30 2,34 2,32 2230 2,28 2,20 2,24 2,22 2.2,02 X 50,5 58,7 Hardness (HRc) ♦ 2 mm/s 3 mm/s X4 mm/s 5 mm/s 50 00 Fig. 12 The fractal dimension at 1000 °C at different speeds depending on hardness 2,45 2.24,04 0 2,35 U1 n ! 230 1 2,25 S 2.20 u. 2,15 2210 X 58 58.1 Hardness (HRc) ♦ 2 mm/s 3 mm/s X4 mm/s 5 mm/s 57.8 58.2 Fig. 13 The fractal dimension at 1400 °C at different speeds depending on hardness Advances in Production Engineering & Management 9(3) 2014 For Fig. 12 and Fig. 13 we calculated the correlation coefficient, showing the size of the linear connection between hardness and fractal dimension. The correlation coefficient for Fig. 12 is R = 0.0415. The correlation coefficient for Fig. 13 is R = 0.2446. We can see that the correlation coefficients are not similar. Because the correlation coefficients are not 0, the variable hardness and fractal dimensions are correlated. The purpose of this work has been to study how the parameters of robot laser cells impact on the hardness of hardened specimens. The fractal analysis of a series of digitized surface microstructures from the robot laser surface modified specimens indicated that useful correlations can be derived between the fractal dimensions and the surface microstructural features such as hardness. 5. Conclusion Fractal structures are also found in robot laser-hardened samples when viewed under sufficient magnification. The hardening of various metal alloys has shown that when melting occurs, fractal geometry can be used to calculate the fractal dimension. Using the R/S method, we analysed specimens of equal tempered metal after subjecting them to robot laser hardening using various parameters. The main findings can be summarized as follows: • A fractal structure exists in robot laser hardening. • The R/S method calculates the fractal dimensions for different parameters of laser hardening robotic cells. • The optimal fractal dimensions of different parameter robotic laser-hardened tool steel have been identified. • The fractal dimension varies between 2 and 3. By increasing the temperature of the robot laser cell, the fractal dimension becomes larger and the grain size becomes smaller. Consequently, we can use the fractal dimension as an important factor to define the grain shape. • The dependence of the fractal dimension on hardness was ascertained. This finding is important if we know that certain alloys mix poorly because they have different melting temperatures, but such alloys have much higher hardness and better technical characteristics. By varying different parameters (temperature and speed), robot laser cells produce different fractal patterns with different fractal dimensions. • Materials with higher fractal dimensions are less porous than those with lower fractal dimensions. • Fractal dimension is higher in depth robot laser-hardened specimens. • Specimens with lower fractal dimensions are the hardest. • With the correlation coefficients we show a connection between the hardness and the fractal dimension of robot laser-hardened specimens. The relationship between the microstructure and the parameters of robot laser cells may enable a better understanding of the fractal dimensions by exploring the microstructure. In the future, we want to explore fractal dimension as a function of the parameters of a robot cell for laser hardening for pinned robot laser hardening: laser parameters such as power, energy density, focal distance, energy density in the focus, focal position, the shape of the laser flash, flash frequency, temperature and speed of hardening. We want to calculate fractal dimensions for different materials to ascertain the relationship between the materials and these parameters of the robot laser cell. We are interested in calculating the fractal dimensions in: • Two-beam laser robot hardening (where the laser beam is divided into two parts). • Areas of overlap (where the laser beam covers the already hardened area). • Robot laser hardening at different angles (where the angles change depending on the x-and _y-axes). Acknowledgement The present work was supported by the European Social Fund of the European Union. 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APEM jewuial Advances in Production Engineering & Management Volume 9 | Number 3 | September 2014 | pp 128-138 http://dx.doi.Org/10.14743/apem2014.3.182 ISSN 1854-625G Journal home: apem-journal.org Original scientific paper Influence of welding speed on the melting efficiency of Nd:YAG laser welding Tadamalle, A.P.a*, Reddy, Y.P.a, Ramjee, E.b, Reddy, V.K.b aDepartment of Mechanical Engineering, Sinhgad College of Engineering, Vadgaon, Pune, India bDepartment of Mechanical Engineering, Jawaharlal Nehru Technological University, Kukatpally, Hyderabad, India A B S T R A C T A R T I C L E I N F O Melting efficiency is one of the more important measurable parameters in laser welding when assessing the performance of a process. This paper aims to study the effects of speed on the melting efficiency and energy transfer efficiency of Nd:YAG laser welding process. The weld bead on a 304L austen-itic stainless steel sheet is created by varying the welding speed. The weld samples are cut in the transverse direction by using electric discharge machining, and the cross-section is prepared for metallographic inspection. The cross-sectional dimensions and beads length are measured by using an optical microscope and image analyzer. A methodology is proposed for estimating the weld pool volume from experimental data and a generalised equation for predicting the melting efficiency and energy transfer efficiency is developed. The results obtained by the proposed method have reasonably good agreement with the models proposed by various researchers. The outcome of the result shows the significant influence of welding speed on melting efficiency and energy transfer efficiency in welding of austenitic stainless steel thin sheets. It will be seen from results that one can select appropriate welding speed and processing conditions to obtain desired melting efficiency. © 2014 PEI, University of Maribor. All rights reserved. Keywords: Nd:YAG laser welding Melting efficiency Weld pool volume Energy transfer efficiency Heat affected zone *Corresponding author: aptadmalle.scoe@sinhgad.edu (Tadamalle, A.P.) Article history: Received 22 May 2014 Revised 7 August 2014 Accepted 15 August 2014 1. Introduction The Nd:YAG laser welding process is extensively used in modern industrial applications due to increase in demand for micro products. It is very essential to understand the behavior of laser material interaction, controlling of process parameters and their effect on melting, solidification and efficiency. The fundamental figures of merits for the laser welding process can be expressed in terms of melting efficiency, coupling efficiency, process efficiency, and energy transfer efficiency. The other figures of merits considered are fusion zone size, tolerance, and changing base metal temperature. The melting efficiency quantifies the fraction of net heat input to the work piece that is used to produce the weld pool. The absorption of heat by the material is affected by many factors like process parameters, type of laser, incident power density, and base metal surface condition. The optimum melting efficiency and weld strength of the joint depend upon vaporization temperature of the work piece material. The functional characteristics of laser welding process depends upon welding speed, laser power, size of the weld pool geometry, thermal and process efficiencies. The welding efficiency can be improved by fully utilizing the power supplied to melt the unit volume of material. In literature it has been reported that the effect of very high welding speed on melting efficiency and depth of penetration has been studied [1-2]. The authors discussed the effect of weld- ing speed on weld bead geometry, and performance parameters such as variation of weld bead diameter from pulse to pulse, duty cycle, and effective pulse energy [3]. The present research work is the continuation of this research on development of new analytical equations to predict the melting efficiency. The experimental and analytical prediction shows that there is a strong dependence of energy absorption on laser power, beam size, and welding efficiency [4-5]. The dimensionless parameters Rykalin (Ry) and Christensen (Ch) are used for computing the melting efficiency of arc and CO2 laser welding. The melting efficiency of the laser welding process is determined by using experimental results, dimensionless parameter models and energy balance equations. Several researchers have formulated analytical equations to predict the melting efficiency in arc welding process [6-10]. The dimensionless parameter model is very valuable in characterizing partial penetration in laser beam welding process. The material independent model reveals that the variation in laser power and focus spot size is insensitive to the energy transfer efficiency in continuous wave laser beam welding process. A dimensionless parameter model developed for evaluating the melting efficiency in CO2 laser beam welding has been applied in gas tungsten arc welding (GTAW), plasma arc welding (PAW), and autogenous arc welding processes [11-14]. The energy transfer efficiency of arc and laser welding process is estimated by using calori-metric method and thermal expansion measurement techniques for thin sheets and the results obtained are correlated with the reflection method. The process efficiencies of laser engineered net shaping process is estimated for the tool steel and copper powder material deposits [15-19]. Transient 3D numerical model was developed to study the temperature field and molten pool shape during laser welding and volume of fluid method was employed in the calculations [18]. The influence of welding process parameters, preheating and heat absorption on different laser welding efficiencies is investigated [19-21]. It was found that the global efficiency of the laser welding process decreases slightly by varying the welding speed from 10 mm/s to 2 mm/s, but drops significantly below 1 mm/s [22]. The preheating of weld samples has an effect on absorption efficiency and other efficiencies in the beginning of the process and in the formation of the keyhole [23, 24]. The absorption efficiency and keyhole coupling efficiency are determined by using laser energy reflected from molten pool and by considering plasma effect studied under steady state condition [25]. This paper aims to study the effect of process parameters on melting efficiency (nm) of Nd:YAG laser weld joints. A semi empirical equation is proposed to estimate the weld pool volume from experimental data, a dimensionless parametric equation to estimate melting efficiency. The section 2 deals with the models proposed by various researchers to evaluate the melting efficiency in different welding process, and section 3 enumerate the experimental procedure adopted. The equations proposed for prediction of weld pool volume and melting efficiency are presented in section 4 and section 5 respectively. The results and discussions are illustrated in section 6 and the conclusions of present work are given in section 7. 2. Melting efficiency Melting efficiency is defined as the ratio of energy required to create a molten pool from heat energy supplied to the energy absorbed by the work piece. A small percentage of the total energy is used for melting the fusion zone and rest of the energy is dissipated to the surroundings by means of various modes of heat transfer. The literature review reveals that processing parameters, heat flow pattern and thermo-physical properties have significant influence on melting efficiency. A dimensionless parameter model can be used to determine the actual heat input to the metal and the melting efficiency. The dimensionless parameters Ry and Ch are derived from the Rosenthal heat flow solutions. These parameters can be evaluated by using the Eq. 1 developed for the arc welding and CO2 laser welding process [9]. The Rosenthal heat flow solution model ignores mass additions to the melt pool. Hence modifications are required in the heat flow equations that can incorporate mass addition to the Rosenthal heat flow solution model. However, a semi empirical model can provide reasonable estimates of melting efficiency for both the arc welding and CO2 laser welding process. s A Pj s ,, Ch = —— and Ry = (1) az y azSh where P, is the heat input to the substrate (W), a is the thermal diffusivity of base metal (mm2/s), Sh is the enthalpy of melting (J/mm3), A is the weld cross-sectional area (mm2), s is the welding speed (mm/s). The melting efficiency (nm) for the arc welding process [10] can also be expressed as the ratio of dimensionless parameters Ch and Ry [8] as given in Eq. 2. Ch s A Sh The dimensionless parameter Ry play an important role in estimating the melting efficiency and it is a nonlinear function of heat input and welding speed. Several researchers have developed equations to predict the melting efficiency by using 2D and 3D heat flow conditions. The 2D and 3D heat flow conditions are expressed in terms of thermo-physical properties and processing parameters for predicting the melting efficiency. The mathematical models applied for 2D and 3D heat flow conditions in arc welding process are presented in Eq. 3 and Eq. 4, respectively [10]. /8a\ V5s w) (3) 1.35 I 1 + 11 + 10.4 a2V \ (4) 0 w) where w is the bead width (mm). A model applied for predicting the melting efficiency based on power delivered to the substrate material in arc welding process is given in Eq. 5: Vm= exp[ -( 1 + ^ ^ (5) In present work, the term power input P,, supplied by the laser source to the material is replaced by the product of energy transfer efficiency (ne), voltage (V) and current (I). A semi empirical Eq. 6 is used to predict the melting efficiency based on heat input required to melt the material [7] in GTAW process: v St0cp(t)AT+AHl (6) VetPi where AH/ is the latent heat of fusion (J/mm3), v is the total volume of melted substrate (mm3), T is the temperature of the weld pool (K), To is the initial temperature (K), t is the laser on time (ms), and Cp is the heat capacity (J/mm3K). The experimental procedure adopted in this work for welding of 304L austenitic stainless steel using Nd:YAG laser welding machine is explained in the next section. 3. Experimental procedure The welding of 304L austenitic stainless steel sheets is carried out by using pulsed Nd:YAG laser welding machine, TruLaser station 5004 (Trumpf), designed to deliver a maximum laser power of 4 kW. The process parameters, thermo-mechanical properties and chemical composition of 304L stainless steel are given in Table 1 and the setup used for experimentation is shown in Fig. 1. The blank sheets are cleaned before welding by 6-8 % NaOH solution followed by 20 % HNO3 solution. The samples are cut into rectangular specimens of 30 mm by 50 mm with the help of wire cut electric discharge machine to avoid the distortion. The weld quality and aspect ratio of the WBG depends upon welding speed. The threshold value of melting front propagation was found between 1.1-1.4 mm/s and when welding speed is less than 1 mm/s the molten pool irradiates for a longer period of time which results in lower coupling efficiency [22]. The welding speed beyond 10 mm/s does not provide sufficient time to melt the material. Hence, the experiment are planned to conduct on 0.5 mm thick sheet to create a bead on plate by varying the welding speed from 2-10 mm/s in steps of 1 mm/s. Fig. 1 Specimen mounting arrangement and experimental setup Table 1 Process parameters and thermo mechanical properties and chemical composition of 304L stainless steel [3] Process parameters Thermo mechanical properties of the 304L stainless steel Parameters Values Parameters Values Parameters Values Beam diameter 0.4 mm Density 8030 kg/m3 Poisons ratio 0.29 Pulse duration 2 ms Elastic modulus 193 GPa Melting point 1723 K Frequency 25 Hz Mean coefficient of expansion 18.4 nm/m/K Refractive index 3.81 Fe Beam angle 90 ± 5° Thermal conductivity 20 W/mK Enthalpy 8.7 J/mm3 Pulse energy 2.76 J Specific heat 500 J/kgK Diffusivity 5.7 mm2/s C Mn Ni Cr Si V N Proof stress Yield strength Elongation 0.3 2.0 8-12 18-20 0.75 0.07 0.1 170 MPa 485 MPa 40 % Table 2 Weld bead geometry dimensions measured from weld samples Exp. No. Welding speed (mm/s) Bead width (|mf | Bead length (|m) Depth of penetration (|m) 1 2 895 912 500 2 4 845 845 472 3 5 y30 y30 375 4 y 691 691 233 5 8 68y 687 190 6 9 682 682 189 y 10 610 648 100 4. Estimation of weld pool volume In laser welding process, the selection of preferred levels of welding speed plays an important role to achieve higher melting efficiency. The experiments are conducted by varying welding speed and by keeping all other process parameters constant. The transverse cross-section area, bead length (BL) and bead width (BW) of the weld bead geometry (WBG) are extracted from the samples prepared for metallographic inspection, Fig. 2(a) to Fig. 2(f). The WBG and HAZ profile dimensions are measured by using Metatech (Hitachi) electron microscope. The digitized data obtained from the HAZ profile is used to generate polynomial equations at different welding speeds. This digitized data is best fitted to obtain the second order polynomial equations. A sample second order polynomial equation and corresponding curve obtained from digitized data at a welding speed of 8 mm/s is shown in Fig. 3. Similarly other second order polynomial equations are derived from experiments conducted at different welding speeds. A generalized second order polynomial equation applicable to the geometry of any heat affected zone profile is given in Eq. 7. Fig. 2 Samples prepared for metallographic inspection and measurement of WBG Fig. 3 Digitized data fitted to obtain a second order polynomial equation f(x) = Ct + C2x + C3x2 (7) The coefficients C\, C2, and C3 are constants which are evaluated by using the polynomial equations derived from digitized HAZ data and the variable x denotes the bead width. This data is measured from the cross-sectional view of the micrographs obtained at different welding speeds shown in Table 2. The digitised data is used to obtain a HAZ profile of the weld pool shown in Fig. 3 and the corresponding second order polynomial equation. The weld pool volume estimated by revolving half section of the polynomial curve about the axis of the laser beam is given in Eq. 8. d V = J(C1 + C2x + C3X2)2 dx (8) 0 A generalized equation proposed to evaluate the weld pool volume by using best fit curve obtained from the geometric mean of all corresponding coefficients. V = 0.512 + 0.028x - 0.007x2 (9) The weld pool volume and its cross-sectional area is a function of depth of penetration, bead length and bead width. The correct measurement of depth of penetration and bead length is difficult, because it requre metallographic preparation to observe cross-sectional view of the WBG. The bead width can be measured with ease hence it is proposed equation to estimate the weld pool volume in terms of bead width as given in Eq. 10. V = 0.512 + 0.028 w - 0.007w2 (10) The coefficients given in Eq. 10 are valid in the range of weld speed from 2-10 mm/s. This range is selected because it gives better results in terms of pulse overlapping factor, variation in bead diameter from pulse to pulse and other performance parameters [3] at the specified values of process parameter considered for the study. 5. Prediction of melting efficiency The dimensionless parameters Ry and Ch can be evaluated by knowing material properties, weld speed, laser power input and weld pool cross-sectional area. The values Ry and Ch are estimated by varying the welding speed and a best fit curve shown in Fig. 4 is obtained by using linear regression analysis technique. The correlation coefficient between Ch and Ry is greater than 0.943 and the relation obtained from the best fit curve is presented in Eq. 11. 0.7 0.6 0.2 Ch= 0.065 + 0.016 R, -y 0.1 0 2 4 6 Rykalin S 10 Fig. 4 Relation between Rykalin and Christensen dimensionless parameters Ch = 0.065 + 0.016 R y (11) The relationship for melting efficiency derived from Eq. 11 is given in Eq. 12: 0.065 = 0.016 + —— (12) The semi empirical Eq. 12 can be used for predicting the melting efficiency of Nd:YAG laser welding process. The melting efficiency is a function of Ry. The laser beam reflection method is used in this study to estimate energy transfer efficiency. The accurate prediction of weld size is done by considering different values of energy transfer efficiency. 6. Results and discussions The power absorbed by conduction and melting of the substrate material is more at higher welding speeds. The Fig. 5 reveals that total power absorbed by the substrate material increases with increase in welding speed. This is due to the significant difference in temperature between laser source and work piece material as a result of which more amount of heat is utilized to create and maintain molten weld pool. It is found that the power utilized for melting the material is less than that of power absorbed by conduction up to a welding speed of 7 mm/s. However beyond this limit, power absorbed for creating weld pool is greater than heat carried away by the conduction. This is due to less than 4 ms times is available for transferring heat energy to the substrate material by conduction. The stainless steel material has reflectivity 64 %, whereas for copper, aluminium and nickel it is greater than 74.20 %. Many researchers have determined the melting efficiency in welding by considering energy transfer efficiency constant at 0.37 or 0.48 [7, 10]. In this study, laser beam reflection method is employed to compute energy transfer efficiency. 40 35 30 „ 25 I I 20 o cilS 10 5 0 Power absorbed by conduction Total power absorbed Power utilised for melting 5 7 Welding speed (mm/s) 11 Fig. 5 Effect of welding speed on power absorbed by the specimen The Fig. 6 shows that there is a significant variation from 10.1-21.2 % in the energy transfer efficiency with respect to welding speed, therefore different values of energy transfer efficiencies are considered instead of a single value. The heat input to the substrate material is directly proportional to the product of energy supplied, energy transfer efficiency and pulse frequency. The energy transfer efficiency is based on materials properties, laser beam reflection, process parameters, operating conditions and power source. The analytical equations proposed by the researchers to estimate the melting efficiency predict differently for different types of welding processes. A non-linear relationship is observed between the melting efficiency, weld speed, and Ry from the results obtained by different researchers. 25 & 20 c - DO Child Fig. 6 Mutation operation 4. Results and discussions The optimization procedures developed in this work are based on the modified non-dominated sorting genetic algorithm (NSGA-II). The FMS configuration considered in this work is taken from the literature [11]. In literature, procedure is developed for 43 jobs, using combined objective optimization method. A comparison between the proposed modified NSGA-II and other algorithms namely SPT, PSO, GA, CS [13] (found in literature) and NSGA-II has been presented in Table 2 and Fig. 7. But in this work we have taken the scheduling problem with 80 parts and multi objective optimization approach as well as modified NSGA-II is implemented. The result of modified NSGA-II and existing NSGA-II relating to the problem of 80 jobs are meticulously compared. Table 2 shows the results obtained by the proposed modified NSGA-II. It performs better in terms of objective functions and computational effort, i.e. 50 % less time than the NSGA-II. The Table 3 and Fig. 8 show the comparison of both the approaches in the study. The point in the graph shows the non-dominated points after 4500 generation using NSGA-II and modified NSGA-II. Table 2 Comparison between various approaches Algorithm SPT [13] PSO [13] CS [13] NSGA-II Mod. NSGA-II Machine idle time 180100 315650 163800 109850 95900 Penalty cost 101930 298196 138025 16298 10005 20, 23, 38, 1, 9, 27 , 30, 38, 10, 8,14,28,31,3, 5,30,34, 28, 16, 39, 34, 27, 11, 26, 22, 10, 34, 18 , 15, 34, 42, 5, 42, 26, 33, 22, 24, 25, 10, 11, 30, 22, 6, 16, 28, 18, 36, 11, 25, 5, 33 , 8, 37, 23, 25, 20, 5, 24, 2, 41, 27, 36, 2, 18, 1, 23, 2, 26, 35 , 7, 16, 2, 40, 4, 41, 9, 23, 5, 43, 20, 18, 7, 10, 19, 23, 4, 29, 20, 13, 37, 25, 43, 9, 40, 36, Sequence 31, 7, 24, 28, 17, 6, 4, 36, 19, 17, 38, 4, 35, 40, 37, 17, 3, 9, 41, 12, 41, 14, 37, 3, 42, 6, 29, 35, 37, 15, 24 , 39, 31, 12, 8, 15,17, 39, 6, 2, 15, 6, 22, 7, 42, 31, 18, 10, 24, 39, 42, 27, 33, 3, 32 , 26, 6, 14, 22, 34, 1, 29, 27, 16, 38, 19, 23, 43, 20, 17, 38, 21, 43, 19, 13, 12, 3, 1, 11, 41, 9, 36, 30, 25, 32, 21, 32, 14, 33, 8, 29, 4, 32, 15, 13, 32, 30, 8, 14, 21 40 , 21, 13, 7 13, 3, 11, 10, 9 26, 35,40,31,39 33, 5, 1, 12, 8 , 19 350000 300000 250000 200000 150000 100000 50000 0 Machine idle time Penalty cost Fig. 7 Comparison between various approaches Table 3 Results after 3000 generations (80 jobs scheduling problem) Methodology Trial No. Machine idle time (min) Minimum total penalty cost (INR) 1 169835 98968.26 2 167335 99770.35 <¡ 3 166415 100289.4 a on z 4 166335 101909.6 5 160485 108597.4 6 159565 109116.5 1 121095 76757.08 2 121095 76757.08 3 129815 77308.82 4 121065 77367.78 5 121895 77645.97 I-I 6 121895 77645.97 < a on 7 126115 77719.44 Z d 0) 8 121095 77729.31 -3 9 119875 78162.64 Mo 10 119675 78275.49 11 127435 78321.32 12 119545 78347.57 13 119675 78358.47 14 123145 78366.67 15 120465 78409.79 ♦ Modified NSGA-II NSGA- 180000 e mi 170000 ti el 160000 dil e ni 150000 hci ac M 140000 130000 120000 X 50000 70000 90000 110000 130000 Total penalty cost Fig. 8 Comparison of NSGA-II and modified NSGA- Results obtained for 80 jobs scheduling problem by modified NSGA-II Global Pareto optimal front is obtained after executing 4500 generations and the details are shown in Table 4. Results are shown in Fig. 9. The software is executed on an Intel Core 2 Duo based PC with 4 GB RAM using .NET Framework. It took 15 min to complete the computation. Table 4 Results after 4500 generations (80 jobs scheduling problem) Methodology Trial No. Machine idle time (min) Minimum total penalty cost (INR) 1 116625 73660.42 2 111625 74692.36 3 114175 74729.86 4 114175 74901.74 5 114175 74901.74 II 6 114285 74916.32 AG 7 111625 75129.86 S N 8 111325 75207.99 d ei fi 9 111505 75258.33 di o 10 114175 75790.63 M 11 114175 75811.46 12 111625 76003.13 13 111505 76126.39 14 113025 76270.49 15 112675 76315.63 16 114325 76545.83 Fig. 9 Progression of Pareto-optimal fronts of modified NSGA-II 5. Conclusion In this work the optimization procedure has been developed based on the modified multi-objective non-dominated genetic algorithm. This method is implemented successfully for solving the scheduling optimization problem of FMS. Software has been written in the .NET language. FMS schedule is obtained for 80 jobs and 16 machines. The result obtained by modified NSGA-II is analyzed for two objectives, i.e. minimizing total penalty cost and minimizing total machine idle time. After 4500 generation best solution is obtained. The computational effort of FMS scheduling problem is increasing proportional to the number of components. In case of 80 components 7.1569457046263802294811533723187e+118 combinations are possible. Due to very high computational effort exhaustive search is not possible. Similarly random search also requires so much of computational effort. By implementing genetic algorithm for 4500 generations 4.5 • 105 computations needed only for getting the optimal solution. In order to reduce the computational effort further, existing NSGA-II is modified. It is found that the proposed approach consumes 50 % time only in comparing with NSGA-II and is superior in terms of objective function. The procedure developed in this work can be suitably modified to any kind of FMS with a large number of components and machines. Future work will include the availability and handling time of loading and unloading stations, robots and AGV. References [1] Guo, Z.X., Wong, W.K., Leung, S.Y.S., Fan, J.T., Chan, S.F. (2008). A genetic-algorithm-based optimization model for scheduling flexible assembly lines, The International Journal of Advanced Manufacturing Technology, Vol. 36, No. 1-2, 156-168, doi: 10.1007/s00170-006-0818-6. [2] Tiwari, M.K, Vidyarthi, N.K. (2000). Solving machine loading problems in flexible manufacturing system using a genetic algorithm based heuristic approach, International Journal of Production Research, Vol. 38, No. 14, 33573384, doi: 10.1080/002075400418298. [3] Kumar, A.V.S.S., Veeranna, V., Prasad, B.D., Sarma, B.D. (2010). 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Calendar of events • 2nd International Conference on Industrial and Production Engineering (ICIPE 2014), Chengdu, Kuala Lumpur, Malaysia, September 2-3, 2014. • International Conference on Manufacturing and Optimization (ICMO 2014), Chengdu, China, September 6-8, 2014. • 11th International Conference on High Speed Machining, Advances in Manufacturing Technology (HSM 2014), Prague, Czech Republic, September 11-12, 2014. • ETFA - 2014 IEEE Emerging Technology and Factory Automation, Barcelona, Spain, September 16-19, 2014. • The 4th International Conference on Industrial Technology and Management (ICITM 2014), Paris, France, September 17-18, 2014. • 15th International Conference MetalForming 2014, Palermo, Italy, September 21-24, 2014. • 12th Global Conference on Sustainable Manufacturing (GCSM), Malaysia, September 22-24, 2014. • 3rd Journal Conference on Innovation, Management and Technology (JCIMT 2014), Geneva, Switzerland, October 10-11, 2014. • The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014), Jeju, Republic of Korea, October 12-15, 2014. • 44th International Conference on Computers & Industrial Engineering (CIE'44), Istanbul, Turkey, October 14-16, 2014. • 9th International Symposium on Intelligent Manufacturing and Service Systems, Istanbul, Turkey, October 14-16, 2014. • International Conference on Additive Technologies, Vienna, Austria, October 15-17, 2014. • 4th International Conference on Advanced Materials and Engineering Materials, Ningbo, Hong Kong, China, October 16-20, 2014. • 22nd International Conference on Materials and Technology, Portorož, Slovenia, October 2022, 2014. • International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2014), Beijing, China, October 24-26, 2014. • The 16th International Machine Tool Engineers' Conference, Tokyo, Japan, October 31 to November 4, 2014. • International Conference on Mechatronics and Intelligent Manufacturing (ICMIM 2014), Bahrain, Bahrain, November 5-7, 2014. • ASME 2014 International Mechanical Engineering Congress & Exposition, Montreal, Canada, November 14-20, 2014. • METECH' 14 - Innovative Materials and Structures Technologies Metallurgical Engineering Conference, Istanbul, Turkey, November 17-19, 2014. • 25 th DAAAM International Symposium on Intelligent Manufacturing and Automation, Vienna, Austria, November 26-29, 2014. • The IEEE International Conference on Industrial Engineering and Engineering Management, Selangor/KL, Malaysia, December 9-12, 2014. • International Conference on Artificial Intelligence and Manufacturing Engineering, Dubai, United Arab Emirates, December 25-26, 2014. • IEEE International Conference on Industrial Technology, Seville, Spain, March 17-19, 2015. • IEEE International Conference on Technologies for Practical Robot Applications, Woburn, Massachusetts, USA, May 11-12, 2015. • IEEE International Conference on Robotics and Automation, Seattle, Washington, USA, May 25-30, 2015. • IEEE 20th Conference on Emerging Technologies & Factory Automation, Luxembourg, Luxembourg, September 8-11, 2015. 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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 9 | Number 3 | September 2014 | pp 107-154 Contents Scope and topics Frictional characterization of teak wood dust-filled epoxy composites Mishra, A. Optimal fractal dimension on grain structure robot laser-hardened tool steel Babic, M.; Balic, J.; Kokol, P. Influence of welding speed on the melting efficiency of Nd:YAG laser welding Tadamalle, A.P; Reddy, Y.P; Ramjee, E.; Reddy, V.K. Scheduling optimization of a flexible manufacturing system using a modified NSGA-II algorithm Nidhiry, N.M.; Saravanan, R. Calendar of events Notes for contributors Copyright © 2014 PEI. All rights reserved. apem-journal.org 128 139 152 153 9771854625008