Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Laser power based surface characteristics models for 3-D printing process

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Selective laser melting (SLM) is one of the important 3-D Printing processes that builds components of complex 3D shapes directly from the metal powder. It is widely used in manufacturing industries and is operated on significant amount of laser power drawn from the electric grid. The literature reveals that the properties such as surface roughness, waviness, tensile strength and dimensional accuracy of an SLM fabricated parts, depend on the laser power and can be improved by its appropriate adjustment. Determination of accurate values of laser power and the other inputs could lead to an improvement in energy efficiency and thus contributing to a clean and healthy environment. For determining the accurate value of laser power in achieving the required surface characteristics, the formulation of generalized mathematical models is an essential pre-requisite. In this context, an artificial intelligence approach of multi-gene genetic programming (MGGP) which develops the functional expressions between the process parameters automatically can be applied. The present work introduces an ensemble-based-MGGP approach to model the SLM process. Experiments on the SLM process with measurement of surface characteristics, namely surface roughness and waviness, based on the variations of laser power and other inputs are conducted, and the proposed ensemble-based-MGGP approach is applied. Statistical evaluation concludes that the performance of the proposed approach is better than that of the standardized MGGP approach. Sensitivity and parametric analysis conducted reveals the hidden relationships between surface characteristics and the laser power, which can be used to optimize the SLM process both economically and environmentally.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Ahn, D., Kweon, J. H., Kwon, S., Song, J., & Lee, S. (2009). Representation of surface roughness in fused deposition modeling. Journal of Materials Processing Technology, 209, 5593–5600.

    Article  Google Scholar 

  • Aijun, L., Zhuohui, Z., Daoming, W. & Jinyong, Y. (2010). Optimization method to fabrication orientation of parts in fused deposition modelling rapid prototyping. In IEEE International Conferenceon Mechanic Automation and Control engineering (pp. 416–419).

  • Anitha, R., Arunachalam, S., & Radhakrishnan, P. (2001). Critical parameters influencing the quality of prototypes in fused deposition modelling. Journal of Materials Processing Technology, 118, 385–388.

    Article  Google Scholar 

  • Badrossamay, M., & Childs, T. H. C. (2007). Further studies in selective laser melting of stainless and tool steel powders. International Journal of Machine Tools and Manufacture, 47(5), 779–784.

    Article  Google Scholar 

  • Bernard, A., & Fischer, A. (2002). New trends in rapid product development. CIRP Annals Manufacturing Technology, 51, 635–652.

    Article  Google Scholar 

  • Byun, H. S., & Lee, K. H. (2006). Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making. Robotics and Computer Integrated Manufacturing, 22, 69–80.

    Article  Google Scholar 

  • Chang, D. Y., & Huang, B. H. (2011). Studies on profile error and extruding aperture for the RP parts using the fused deposition modelling process. The International Journal of Advanced Manufacturing Technology, 53, 1027–1037.

    Article  Google Scholar 

  • Emami, M., Sadeghi, M. H., Sarhan, A. A. D., & Hasani, F. (2014). Investigating the minimum quantity lubrication in grinding of \(\text{ Al }_2 \text{ O }_3\) engineering ceramic. Journal of Cleaner Production, 66, 632–643.

    Article  Google Scholar 

  • Gandomi, A. H., Alavi, A. H., Arjmandi, P., Aghaeifar, A., & Seyednoor, M. (2010). Genetic programming and orthogonal least squares: A hybrid approach to modeling the compressive strength of Cfrp-confined concrete cylinders. Journal of Mechanics of Materials and Structures, 5, 735–753.

    Article  Google Scholar 

  • Garg, A., & Lam, J. S. L. (2015). Improving environmental sustain ability by formulation of generalized power consumption models using an ensemble evolutionary approach. Journal of Cleaner Pro-duction, 102(1), 246–263.

    Article  Google Scholar 

  • Garg, A., Lam, J. S. L., & Gao, L. (2015). Energy conservation in manufacturing operations: Modelling the milling process by a new complexity-based evolutionary approach. Journal of Cleaner Production, 108A, 34–45.

    Article  Google Scholar 

  • Garg, A., & Tai, K. (2015). Evolving genetic programming models of higher generalization ability in modelling of turning process. Engineering Computations, 32(8), 2216–2234.

    Article  Google Scholar 

  • Garg, A., Tai, K., & Savalani, M. M. (2014). State-of-the-art in empirical modeling of rapid prototyping processes. Rapid Prototyping Journal, 20(2), 164–178.

    Article  Google Scholar 

  • Hinchliffe, M., Hiden, H., Mckay, B., Willis, M., Tham, M., & Barton, G. (1996). Modelling chemical process systems using a multi-gene genetic programming algorithm. In Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28–31, 1996 (pp. 28–31).

  • Hopkinson, N., Hague, R., & Dickens, P. (2006). Rapid manufacturing. New York: Wiley.

    Google Scholar 

  • Jin, S., Xiaobo, C., Haihong, L., & Juntong, X. (2013b). Template-based framework for nasal prosthesis fabrication. Rapid Prototyping Journal, 19(2), 68–76.

    Article  Google Scholar 

  • Jin, S., Yaoyang, X., Xiaobo, C., & Juntong, X. (2013a). Imperfect symmetry transform for orbital prosthesis modelling. Rapid Prototyping Journal, 9(3), 180–188.

    Google Scholar 

  • Khan, Z. A., Lee, B. H., & Abdullah, J. (2005). Optimization of rapid prototyping parameters for production of flexible ABS object. Journal of Materials Processing Technology, 169(1), 54–61.

    Article  Google Scholar 

  • Krause, F. L., Chiesla, M., Stiel, C., & Ulbrich, A. (1997). Enhanced rapid prototyping for faster product development processes. CIRP Annals Manufacturing Technology, 46(1), 93–96.

    Article  Google Scholar 

  • Kruth, J. P., Froyen, L., Van Vaerenbergh, J., Mercelis, P., Rombouts, M., & Lauwers, B. (2004). Selective laser melting of iron-based powder. Journal of Materials Processing Technology, 149(1), 616–622.

    Article  Google Scholar 

  • Kumar, S. (2009). Manufacturing of WC-Co moulds using SLS machine. Journal of Materials Processing Technology, 209, 3840–3848.

  • Li, C. L., Fu, G. Y., & Guo, K. B. (2011). Study on forecast of forming temperature of ABS resign during fused deposition manufacturing by fuzzy comprehensive evaluation. Key Engineering Materials, 464, 264–267.

    Article  Google Scholar 

  • Luo, R.C., Chang C.L., Pan & Tzou, J. H. (2005). Rapid tooling using laser powered direct metallic manufacturing process. Industrial Electronics Society. In IECON 2005 31st Annual Conference of IEEE.

  • Masood, S. H., & Song, W. Q. (2004). Development of new metal/polymer materials for rapid tooling using fused deposition modelling. Materials and Design, 25, 587–594.

    Article  Google Scholar 

  • Ng, C. C., Savalani, M. M., Man, H. C., & Gibson, I. (2010). Layer manufacturing of magnesium and its alloy structures for future applications. Virtual and Physical Prototyping, 5(1), 13–19.

    Article  Google Scholar 

  • Pandey, P. M., Venkata Reddy, N., & Dhande, S. G. (2003). Improvement of surface finish by staircase machining in fused deposition modeling. Journal of Materials Processing Technology, 132, 323–331.

    Article  Google Scholar 

  • Paul, B., & Voorakarnam, V. (2001). Effect of layer thickness and orientation angle on surface roughness in laminated object manufacturing. Journal of Manufacturing Processes, 3, 94–101.

    Article  Google Scholar 

  • Sarıkaya, M., & Güllü, A. (2014). Taguchi design and response surface methodology based analysis of machining parameters in Cnc turning under mql. Journal of Cleaner Production, 65, 604–616.

    Article  Google Scholar 

  • Searson, D. P., Leahy, D. E., & Willis, M. J. (2010). GPTIPS: An opensource genetic programming toolbox formultigene symbolic regression. International Multiconference of Engineers and Computerscientists, 1, 77–80.

    Google Scholar 

  • Shen, Y., Mckown, S., Tsopanos, S., Sutcliffe, C. J., Mines, R. A. W., & Cantwell, W. J. (2010). The mechanical properties of sandwich structures based on metal lattice architectures. Journal of Sandwich Structures and Materials, 12(2), 159–180.

    Article  Google Scholar 

  • Shiomi, M., Osakada, K., Nakamura, K., Yamashita, T., & Abe, F. (2004). Residual stress within metallic model made by selective laser melting process. CIRP Annals Manufacturing Technology, 53(1), 195–198.

    Article  Google Scholar 

  • Sood, A. K., Equbal, A., Toppo, V., Ohdar, R., & Mahapatra, S. S. (2011a). An investigation on sliding wear of FDM built parts. CIRP Journal of Manufacturing Science and Technology, 54, 1–48.

    Google Scholar 

  • Sood, A., Ohdar, R., & Mahapatra, S. (2010a). A hybrid ANN-BFOA approach for optimization of FDM process parameters.Swarm. Evolutionary and Memetic Computing, 6466, 396–403.

    Article  Google Scholar 

  • Sood, A., Ohdar, R., & Mahapatra, S. S. (2010b). Parametric appraisal of fused deposition modelling process using the grey Taguchi method. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224, 135–145.

    Article  Google Scholar 

  • Stampfl, J., & Liska, R. (2005). New materials for rapid prototyping applications. Macromolecular Chemistry and Physics, 206, 1253–1256.

    Article  Google Scholar 

  • Vijayaraghavan, V., Garg, A., Lam, J. S. L., Panda, B., & Mahapatra, S. S. (2015). Process characterisation of 3D-printed FDM components using improved evolutionary computational approach. The International Journal of Advanced Manufacturing Technology, 78(5), 781–793.

    Article  Google Scholar 

  • Wang, R. J., Wang, L., Zhao, L., & Liu, Z. (2007). Influence of process parameters on part shrinkage in SLS. The International Journal of Advance Manufacturing Technology, 33, 498–504.

    Article  Google Scholar 

  • Wiedemann, B., & Jantzen, H. A. (1999). Strategies and applications for rapid product and process development in Daimler-Benz AG. Computers in Industry, 39, 11–25.

    Article  Google Scholar 

  • Yadroitsev, I., Bertrand, P., & Smurov, I. (2007). Parametric analysis of the selective laser melting process. Applied Surface Science, 253(19), 8064–8069.

    Article  Google Scholar 

  • Yan, J., & Li, L. (2013). Multi-objective optimization of milling parameters—The trade-offs between energy, production rate and cutting quality. Journal of Cleaner Production, 52, 462–471.

    Article  Google Scholar 

  • Yildiz, A. R. (2009a). A novel hybrid immune algorithm for global optimization in design and manufacturing. Robotics and Computer-Integrated Manufacturing, 25(2), 261–270.

    Article  Google Scholar 

  • Yildiz, A. R. (2009b). An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. Journal of Materials Processing Technology, 209(6), 2773–2780.

    Article  Google Scholar 

  • Yildiz, A. R. (2012a). A comparative study of population-based optimization algorithms for turning operations. Information Sciences, 210, 81–88.

    Article  Google Scholar 

  • Yildiz, A. R. (2012b). Comparison of evolutionary based optimization algorithms for structural design optimization. Engineering Applications of Artificial Intelligence, 26(1), 327–333.

    Article  Google Scholar 

  • Yildiz, A. R. (2013a). A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Applied Soft Computing, 13(3), 1561–1566.

    Article  Google Scholar 

  • Yildiz, A. R. (2013b). A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Applied Soft Computing, 13(5), 2906–2912.

    Article  Google Scholar 

  • Yildiz, A. R. (2013c). Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Information Sciences, 220, 399–407.

    Article  Google Scholar 

  • Yildiz, A. R. (2013d). Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Applied Soft Computing, 13(3), 1433–1439.

    Article  Google Scholar 

  • Yildiz, A. R. (2013e). Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. The International Journal of Advanced Manufacturing Technology, 64(1–4), 55–61.

    Article  Google Scholar 

  • Zhong, W., Li, F., Zhang, Z., Song, L., & Li, Z. (2001). Short fiber reinforced composites for fused deposition modeling. Materials Science and Engineering, 301, 125–130.

    Article  Google Scholar 

Download references

Acknowledgments

The study was supported by Nanyang Technological University’s funding, Reference Number M060030008.

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jasmine Siu Lee Lam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. M. Savalani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. M. Savalani.

Appendix

Appendix

$$\begin{aligned}&\textit{Surface roughness}_{\textit{MGGP}} =8.8498+( {0.015232})\nonumber \\&\quad *\,( ( {\hbox {x}3} )\,*\,( ( \hbox {tan}( ( \hbox {tan}({\hbox {x}2} ) )\,*\,( {\hbox {x}1} )))\,*\,(( \hbox {tan}( {\hbox {x}2} ))\,*\,({\hbox {x}1})))) \nonumber \\&\quad +\,( {-4.3936} )\,{*}\,((\hbox {tan}(\hbox {sin}(( \hbox {tan}({\hbox {x}2} ))\,{*}\,( {\hbox {x}1} ))))\,{*}\,( ( (\hbox {cos}( {\hbox {x}3} ))\nonumber \\&\quad {*}\,({\hbox {x}3} ))\,{*}\,( \hbox {plog}(\hbox {tan}(\hbox {x}3)))))+(-0.012043)\,{*}\,((\hbox {tan}(\hbox {x}3))\nonumber \nonumber \\&\quad {*}\,(( \hbox {x}3)\,{*}\,( (\hbox {tan}( {\hbox {x}2} ) )\,{*}\,( {\hbox {x}1} ) ) ) )+( {0.036929})\,{*}\,(\hbox {tan}(((({\hbox {x}2}) \nonumber \\&\quad -\,( {\hbox {x}1} ))+( ( {( {-5.249071})} )-( {\hbox {x}1} )))\,{*}\,( ( {\hbox {x}2} )\nonumber \\&\quad -\,({\hbox {tan}( {\hbox {tan}( {\hbox {x}2} )} )}) )))+( {-0.39776} )\,{*}\,( \hbox {tan}( \hbox {x}2 ) ) \nonumber \\&\quad +\,( 9.888\hbox {e}-005 )\,{*}\,(( {\hbox {x}3} )\,{*}\,(( ( \hbox {tan}( {\hbox {x}2} ) )\nonumber \\&\quad {*}\,( \hbox {sin}( \hbox {plog}( {( {8.353597} )} ) ) ) )\,{*}\,( {\hbox {tan}( {\hbox {x}2} )} ))) \nonumber \\&\quad +\,( {-0.095928} )\,{*}\,( \hbox {tan}(( {\hbox {x}3} )\,{*}\,( ( {\hbox {x}2} )-( {\hbox {x}3} ) ) ) )\nonumber \\&\quad +\,( {0.064227} )\,{*}\,((( {\hbox {tan}( {\hbox {x}2} )} ) \,{*}\,( {\hbox {x}1} ))\,{*}\,( {\hbox {tan}( {\hbox {x}2} )} )) \end{aligned}$$
(5)
$$\begin{aligned}&\textit{Waviness}_{\textit{MGGP}} =-136.8083+(-2.4736)\,{*}\,(\hbox {x}3)\nonumber \\&\quad +\,(155.1688 )\,{*}\,(\hbox {cos}(\hbox {tanh}( \hbox {tan}(( \hbox {x}2)-(\hbox {x}1)))))\nonumber \\&\quad +\,({-0.07331})\,{*}\,(({\hbox {x}2})\,{*}\,(\hbox {sin}({\hbox {x}3})))+( {6.4672} ){*}( ( {\hbox {x}1} )\nonumber \\&\quad {*}\,( {\hbox {x}3} ))+( {22.7613} )\,{*}\,(\hbox {cos}((({\hbox {x}1})\,{*}\,( ( {\hbox {x}2} )\nonumber \\&\quad -\,( {\hbox {sin}( {\hbox {x}3} )} ) ))+( {\hbox {x}1} )))+( {117.5742} )\,{*}\,( \hbox {tanh}(( {\hbox {x}3} )\nonumber \\&\quad {*}\,( {\hbox {x}1} ) ) )+( {78.3211} )\,{*}\,( {\hbox {x}1}) +( {-0.1337} )\,{*}\,( {\hbox {x}2} ) \end{aligned}$$
(6)
$$\begin{aligned}&\textit{Surfaceroughness}_{\textit{EN}{\text {-}}{} \textit{MGGP}} =8.6796+( {0.27771} )\nonumber \\&\quad {*}\,(( ({\hbox {x}1} )\,{*}\,( {( {( {\hbox {x}1} )\,{*}\,( {\hbox {tan}( {\hbox {x}2} )} )} )\,{*}\,( {\hbox {x}3} )} ) ) \,{*}\,(\hbox {cos}(( ({\hbox {x}1} )\,{*}\,( \hbox {tan}( {\hbox {x}2} )))\nonumber \\&\quad {*}\,( {\hbox {x}3} ))))+( {10.578} )\,{*}\,( \hbox {sin}( ( ( {\hbox {x}1} )\,{*}\,( {\hbox {tan}( {\hbox {x}2} )} ) )\,{*}\,({\hbox {tan}( {\hbox {x}2} )} ) ) )\nonumber \\&\quad +\,( {0.0058965} )\,{*}\,( {\hbox {x}2}) +( {8.0947} )\,{*}\,( \hbox {sin}(( {( {\hbox {x}1} )\,{*}\,( {\hbox {x}1} )} )-( ( {\hbox {x}3} )\nonumber \\&\quad +\,( {( {-4.029785} )} ) ) ) )+( {2.5051} )\,{*}\,(( {\hbox {x}1})\nonumber \\&\quad {*}\,( {\hbox {tan}( {\hbox {x}2} )} ))+( {-1.1768} )\,{*}\,( ( ( {\hbox {x}1} ) \,{*}\,( {\hbox {x}3} ) ){*}( \hbox {tan}( ( {\hbox {x}1} )\nonumber \\&\quad {*}\,( {\hbox {tan}( {\hbox {x}2} )} ) ) ) )+( {6.9443} )\,{*}\,( ( {\hbox {cos}( {\hbox {x}3} )} )\,{*}\,( \hbox {tan}(( {\hbox {x}1} )\nonumber \\&\quad {*}\,( {\hbox {tan}( {\hbox {x}2} )} ) ) ) )\quad +\,( {0.0061773} )\,{*}\,( {( {\hbox {tan}( {\hbox {x}2} )} )\,{*}\,( {\hbox {x}3} )} ) \end{aligned}$$
(7)
$$\begin{aligned}&\textit{Waviness}_{\textit{EN}{\text {-}}{} \textit{MGGP}} =1092.6173+( {0.11925} )\nonumber \\&\quad {*}\,( \hbox {tan}( ( {\hbox {tan}( {\hbox {x}3} )} )+( ( {\hbox {x}2} )-( {\hbox {x}1} ) ) ) )+( {23.4796} )\,{*}\,(\hbox {tan}(( {\hbox {x}3} )\nonumber \\&\quad +\,( {\hbox {x}1} )))+( {-1.0985} )\,{*}\,( {\hbox {x}3} )+( {-418.4781} )\nonumber \\&\quad {*}\,( \hbox {plog}( \hbox {plog}( ( {\hbox {x}2} )\,{*}\,( {\hbox {tan}( {\hbox {x}3} )}))))+( {-201.3874} )\nonumber \\&\quad {*}\,( \hbox {tan}( {\hbox {cos}( {\hbox {cos}( {\hbox {sin}( {( {\hbox {x}2} )-( {\hbox {x}1} )} )} )} )} ) ) +({-30.4952} )\,{*}\,( {\hbox {tan}( {\hbox {x}3} )})\nonumber \\&\quad +\,( {-31.5096} )\,{*}\,( {\hbox {cos}( {\hbox {x}2} )} )+( {45.6679} )\,{*}\,( {\hbox {plog}( {\hbox {x}1} )} ) \end{aligned}$$
(8)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garg, A., Lam, J.S.L. & Savalani, M.M. Laser power based surface characteristics models for 3-D printing process. J Intell Manuf 29, 1191–1202 (2018). https://doi.org/10.1007/s10845-015-1167-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-015-1167-9

Keywords

Search

Navigation