ANN Based Model for Estimation of Transformation Hardening of AISI 4340 Steel Plate Heat-Treated by Laser

Guillaume Billaud, Abderazzak El Ouafi, Noureddine Barka

Mathematics, Computer Science and Engineering Department, University of Quebec at Rimouski, Rimouski, Canada.

DOI: 10.4236/msa.2015.611098                

Abstract

Quality assessment and prediction becomes one of the most critical requirements for improving reliability, efficiency and safety of laser surface transformation hardening process (LSTHP). Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. This paper presents a structured and comprehensive approach developed to design an effective artificial neural network (ANN) based model for quality estimation and prediction in LSTHP using a commercial 3 kW Nd:Yag laser. The proposed approach examines laser hardening parameters and conditions known to have an influence on performance characteristics of hardened surface such as hardened bead width (HBW) and hardened depth (HD) and builds a quality prediction model step by step. The modeling procedure begins by examining, through a structured experimental investigations and exhaustive 3D finite element method simulation efforts, the relationships between laser hardening parameters and characteristics of hardened surface and their sensitivity to the process conditions. Using these results and various statistical tools, different quality prediction models are developed and evaluated. The results demonstrate that the ANN based assessment and prediction proposed approach can effectively lead to a consistent model able to accurately and reliably provide an appropriate prediction of hardened surface characteristics under variable hardening parameters and conditions.

Keywords

Laser Hardening Process, AISI 4340 Steel, Case Depth, Hardened Bead Width, Artificial Neural Network

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Kannatey-Asibu, E. (2009) Principles of Laser Materials Processing. Wiley, Hoboken. http://dx.doi.org/10.1002/9780470459300
[2]	Steen, W.M. (2010) Laser Material Processing. 4th Edition, Springer, London. http://dx.doi.org/10.1007/978-1-84996-062-5
[3]	Patwa, R. and Shin, Y.C. (2007) Predictive Modeling of Laser Hardening of AISI5150H Steels. International Journal of Machine Tools and Manufacture, 47, 307-320. http://dx.doi.org/10.1016/j.ijmachtools.2006.03.016
[4]	Miokovic, T., Schulze, V., Vöhringer, O. and Löhe, D. (2006) Prediction of Phase Transformations during Laser Surface Hardening of AISI 4140 including the Effects of Inhomogeneous Austenite Formation. Materials Science and Engineering: A, 435-436, 547-555. http://dx.doi.org/10.1016/j.msea.2006.07.037
[5]	Ashby, M.F. and Easterling, K.E. (1984) The Transformation Hardening of Steel Surfaces by Laser Beams—I. Hypo-Eutectoid Steels. Acta Metallurgica, 32, 1935-1948. http://dx.doi.org/10.1016/0001-6160(84)90175-5
[6]	Antony, J. and Jiju Antony, F. (2001) Teaching the Taguchi Method to Industrial Engineers. Work Study, 50, 141-149. http://dx.doi.org/10.1108/00438020110391873
[7]	Chen, S.L. and Shen, D. (1999) Optimisation and Quantitative Evaluation of the Qualities for Nd-YAG Laser Transformation Hardening. International Journal of Advanced Manufacturing Technology, 15, 70-78. http://dx.doi.org/10.1007/s001700050041
[8]	Badkar, D., Pandey, K. and Buvanashekaran, G. (2011) Parameter Optimization of Laser Transformation Hardening by Using Taguchi Method and Utility Concept. International Journal of Advanced Manufacturing Technology, 52, 1067-1077. http://dx.doi.org/10.1007/s00170-010-2787-z
[9]	Benyounis, K.Y., Olabi, A.G. and Hashmi, M.S.J. (2005) Effect of Laser Welding Parameters on the Heat Input and Weld-Bead Profile. Journal of Materials Processing Technology, 164-165, 978-985. http://dx.doi.org/10.1016/j.jmatprotec.2005.02.060
[10]	Sathiya, P., Abdul Jaleel, M.Y. and Katherasan, D. (2010) Optimization of Welding Parameters for Laser Bead-on-Plate Welding Using Taguchi Method. Production Engineering-Research and Development, 4, 465-476. http://dx.doi.org/10.1007/s11740-010-0234-5
[11]	Hagan, M.T., Demuth, H.B. and Beale, M.H. (1996) Neural Network Design: Pws Boston.
[12]	Ciurana, J., Arias, G. and Ozel, T. (2009) Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel. Materials & Manufacturing Processes, 24, 358-368. http://dx.doi.org/10.1080/10426910802679568
[13]	Pan, Q.Y., Huang, W.D., Song, R.G., Zhou, Y.H. and Zhang, G.L. (1998) The Improvement of Localized Corrosion Resistance in Sensitized Stainless Steel by Laser Surface Remelting. Surface and Coatings Technology, 102, 245-255. http://dx.doi.org/10.1016/S0257-8972(98)00358-2
[14]	Munteanu, A. (2012) Surface Hardness Prediction Using Artificial Neural Networks in Case of Electron Beam Machining Process. Romanian Association of Nonconventional Technologies.
[15]	Doane, D. (1979) Application of Hardenability Concepts in Heat Treatment of Steel. Journal of Heat Treating, 1, 5-30. http://dx.doi.org/10.1007/BF02833206
[16]	Haykin, S. (2010) Neural Networks: A Comprehensive Foundation, 1994. Mc Millan, New Jersey.
[17]	Principe, J., Euliano, N. and Lefebvre, W. (2000) Neural and Adaptive Systems: Fundamentals through Simulations. John Wiley and Sons, New York.