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Energy component in the density of selective laser melting fabricated prototype

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Abstract

Due to recent trends in the growing demand for customized and better product quality, inappropriate selection of linear energy density values during the selective laser melting (SLM) process will not only result in a decrease in productivity but will also have negative implications on environmental performance. The SLM process is complex and involves multiple inputs; therefore, the physical-based models are difficult to be formulated. In this context, the present work proposes two variants of the evolutionary approach using genetic programming (GP) in the formulation of a functional expression between the density of fabricated parts and seven inputs of the SLM process. These variants are proposed by introducing two model selection criteria from statistical learning theory to be used as fitness functions in the GP framework. The performance of the proposed energy-based density models is evaluated against the actual experimental data based on five statistical metrics and hypothesis testing. The relationships between the system parameters are unveiled and can be used for effectively monitoring the economic and environmental performance of SLM processes. It was found that the hatching space and linear energy density have the highest impact on the SLM density component. A major contribution of the study is that the optimum values of the inputs can be selected to curtail the energy usage from the SLM process.

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References

  1. Luo, R.C., Chang C.L.,Pan and Tzou, J. H. 2005. Rapid tooling using laser powered direct metallic manufacturing process,” Industrial Electronics Society.IECON 2005.31st Annual Conference of IEEE.

  2. Kumar S (2009) Manufacturing of WC–Co moulds using SLS machine. J Mater Process Technol 209:3840–3848

    Article  Google Scholar 

  3. Shen Y, Mckown S, Tsopanos S, Sutcliffe CJ, Mines RAW, Cantwell WJ (2010) The mechanical properties of sandwich structures based on metal lattice architectures. J Sandw Struct Mater 12(2):159–180

    Article  Google Scholar 

  4. Zhong W, Li F, Zhang Z, Song L, Li Z (2001) Short fiber reinforced composites for fused deposition modeling. Mater Sci Eng 301:125–130

    Article  Google Scholar 

  5. Masood SH, Song WQ (2004) Development of new metal/polymer materials for rapid tooling using fused deposition modelling. Mater Des 25:587–594

    Article  Google Scholar 

  6. Stampfl J, Liska R (2005) New materials for rapid prototyping applications. Macromol Chem Phys 206:1253–1256

    Article  Google Scholar 

  7. Badrossamay M, Childs THC (2007) Further studies in selective laser melting of stainless and tool steel powders. Int J Mach Tools Manuf 47(5):779–784

    Article  Google Scholar 

  8. Kruth JP, Froyen L, Van Vaerenbergh J, Mercelis P, Rombouts M, Lauwers B (2004) Selective laser melting of iron-based powder. J Mater Process Technol 149(1):616–622

    Article  Google Scholar 

  9. Paul R, Anand S (2012) Process energy analysis and optimization in selective laser sintering. J Manuf Syst 31(4):429–437

    Article  Google Scholar 

  10. Anitha R, Arunachalam S, Radhakrishnan P (2001) Critical parameters influencing the quality of prototypes in fused deposition modelling. J Mater Process Technol 118:385–388

    Article  Google Scholar 

  11. Paul B, Voorakarnam V (2001) Effect of layer thickness and orientation angle on surface roughness in laminated object manufacturing. J Manuf Process 3:94–101

    Article  Google Scholar 

  12. Pandey PM, Venkata Reddy N, Dhande SG (2003) Improvement of surface finish by staircase machining in fused deposition modeling. J Mater Process Technol 132:323–331

    Article  Google Scholar 

  13. Khan ZA, Lee BH, Abdullah J (2005) Optimization of rapid prototyping parameters for production of flexible ABS object. J Mater Process Technol 169(1):54–61

    Article  Google Scholar 

  14. Wang RJ, Wang L, Zhao L, Liu Z (2007) Influence of process parameters on part shrinkage in SLS. Int J Adv Manuf Technol 33:498–504

    Article  Google Scholar 

  15. Garg A, Tai K, Savalani M (2014) State-of-the-art in empirical modelling of rapid prototyping processes. Rapid Prototyp J 20(2):164–178

    Article  Google Scholar 

  16. Yang H-J, Hwang P-J, Lee S-H (2002) A study on shrinkage compensation of the SLS process by using the Taguchi method. Int J Mach Tools Manuf 42:1203–12

    Article  Google Scholar 

  17. Savalani MM, Hao L, Dickens PM, Zhang Y, Tanner KE, Harris RA (2012) The effects and interactions of fabrication parameters on the properties of selective laser sintered hydroxyapatite polyamide composite biomaterials. Rapid Prototyp J 18(1):16–27

    Article  Google Scholar 

  18. Liao H-T, Shie J-R (2007) Optimization on selective laser sintering of metallic powder via design of experiments method. Rapid Prototyp J 13(3):156–162

    Article  Google Scholar 

  19. Kruth JP, Kumar S (2005) Statistical analysis of experimental parameters in selective laser sintering. Adv Eng Mater 7(8):750–755

    Article  Google Scholar 

  20. Beal VE, Paggi RA, Salmoria GV, Lago A (2009) Statistical evaluation of laser energy density effect on mechanical properties of polyamide parts manufactured by selective laser sintering. J Appl Polym Sci 113(5):2910–2919

    Article  Google Scholar 

  21. Chatterjee AN, Kumar S, Saha P, Mishra PK, Choudhury AR (2003) An experimental design approach to selective laser sintering of low carbon steel. J Mater Process Technol 136(1):151–157

    Article  Google Scholar 

  22. Garg A, Lam JSL (2015) Improving environmental sustainability by formulation of generalized power consumption models using an ensemble evolutionary approach. J Clean Prod 102(1):246–263

    Article  Google Scholar 

  23. Borges, C. E., Alonso, C. L. & Montana, J. L. Model selection in genetic programming. In Proceedings of 12th Annual Conference on Genetic and Evolutionary Computation (GECCO-2010), pp. 985–986

  24. Garg A, Tai K (2015) Evolving genetic programming models of higher generalization ability in modelling of turning process. Eng Comput 32(8):2216–2234

    Article  Google Scholar 

  25. Garg, A. and Tai, K. (2013) “Genetic programming for modeling vibratory finishing process: role of experimental designs and fitness functions”, in Panigrahi, B.K., Suganthan. P.N., Das, S. and Dash, S.S. (Eds), SEMCCO 2013–Proceedings of the 4th International Conference on Swarm, Evolutionary and Memetic Computing–Part II, Chennai, India, 19–21 December 2013, Lecture Notes in Computer Science Vol.8298, Springer, pp.23-31.

  26. Sun J, Yongqiang Y, Di W (2013) Parametric optimization of selective laser melting for forming Ti6Al4V samples by Taguchi method. Optics Laser Technol 49:118–124

    Article  Google Scholar 

  27. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. Mit Press, USA

    MATH  Google Scholar 

  28. Garg, A. & Tai, K. Review of genetic programming in modeling of machining processes. Modelling, Identification & Control (ICMIC), 2012 Proceedings of International Conference on, 2012. IEEE, 653–658.

  29. Jenkins G.M. Watts D.G “Spectral analysis and its applications”, Holden-Day, 1968.

  30. W.S. Wei “Time series analysis” Addison Wesley, 1990.

  31. M.B. Priestley “Spectral analysis and time series” Academic Press, 1981.

  32. Garg A, Lam JSL, Gao L (2015) Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach. J Clean Prod 108(Part A, 1):34–45

    Article  Google Scholar 

  33. Yao, P., Xue, J., & Zhou, K. (2015). Study on the wire feed speed prediction of double-wire-pulsed MIG welding based on support vector machine regression. The International Journal of Advanced Manufacturing Technology, 1–10.

  34. Deng C, Xie SQ, Wu J, Shao XY (2014) Position error compensation of semi-closed loop servo system using support vector regression and fuzzy PID control. Int J Adv Manuf Technol 71(5–8):887–898

    Article  Google Scholar 

  35. Pasandideh SHR, Niaki STA, Atyabi SM (2014) A new approach to solve multi-response statistical optimization problems using neural network, genetic algorithm, and goal attainment methods. Int J Adv Manuf Technol 75(5–8):1149–1162

    Article  Google Scholar 

  36. Zhang H, Liu H, Li L (2014) Research on a kind of assembly sequence planning based on immune algorithm and particle swarm optimization algorithm. Int J Adv Manuf Technol 71(5–8):795–808

    Article  Google Scholar 

  37. Zhang G, Liu M, Li J, Ming W, Shao X, Huang Y (2014) Multi-objective optimization for surface grinding process using a hybrid particle swarm optimization algorithm. Int J Adv Manuf Technol 71(9–12):1861–1872

    Article  Google Scholar 

  38. Lam JSL (2015) Designing a sustainable maritime supply chain: a hybrid QFD-ANP approach. Transp Res E 78:70–81

    Article  Google Scholar 

  39. Lam JSL, Dai J (2015) Environmental sustainability of logistics service provider: an ANP-QFD approach. Int J Logist Manag 26(2):313–333

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    A. Garg & Jasmine Siu Lee Lam

  2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    M. M. Savalani

Authors
  1. A. Garg
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  2. Jasmine Siu Lee Lam
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  3. M. M. Savalani
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Correspondence to M. M. Savalani.

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Garg, A., Lam, J.S.L. & Savalani, M.M. Energy component in the density of selective laser melting fabricated prototype. Int J Adv Manuf Technol 86, 603–611 (2016). https://doi.org/10.1007/s00170-015-8162-3

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  • DOI: https://doi.org/10.1007/s00170-015-8162-3

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