Multi-objective optimization of cellular scanning strategy in selective laser melting

The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study is to develop a multi-objective approach to optimize the cellular scanning strategy such that the two aforementioned defects are minimized. The decision variable in the chosen problem is a combination of the sequence in which cells are processed and one of six scanning strategies applied to each cell. Thus, the problem is a combination of combinatorial and choice optimization, which makes the problem difficult to solve. On a process simulation domain consisting of 32 cells, our multi-objective evolutionary method is able to find a set of trade-off solutions for the defined conflicting objectives, which cannot be obtained by performing merely a local search. Possible similarities in Pareto-optimal solutions are explored.

[1]  Rémy Glardon,et al.  Study and simulation of different scanning strategies in SLM , 2009 .

[2]  Jesper Henri Hattel,et al.  Reducing residual stresses and deformations in selective laser melting through multi-level multi-scale optimization of cellular scanning strategy , 2016, SPIE LASE.

[3]  Wei Qingsong,et al.  Notice of Retraction Comparison of two scan strategies applied to the selective laser melting , 2010 .

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  P. Fox,et al.  The development of a scanning strategy for the manufacture of porous biomaterials by selective laser melting , 2009, Journal of materials science. Materials in medicine.

[6]  Q. Wei,et al.  The helix scan strategy applied to the selective laser melting , 2012 .

[7]  Padmavathi Kora,et al.  Crossover Operators in Genetic Algorithms: A Review , 2017 .

[8]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[9]  Jorge Morales,et al.  Evidence of a false thumb in a fossil carnivore clarifies the evolution of pandas. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Jesper Henri Hattel,et al.  Cellular Scanning Strategy for Selective Laser Melting: Capturing Thermal Trends with a Low-Fidelity, Pseudo-Analytical Model , 2014 .

[11]  Cem Celal Tutum,et al.  Cellular scanning strategy for selective laser melting: evolution of optimal grid-based scanning path and parametric approach to thermal homogeneity , 2013, Photonics West - Lasers and Applications in Science and Engineering.

[12]  Jesper Henri Hattel,et al.  Cellular scanning strategy for selective laser melting: Generating reliable, optimized scanning paths and processing parameters , 2015, Photonics West - Lasers and Applications in Science and Engineering.

[13]  Rémy Glardon,et al.  On the effect of scanning strategies in the selective laser melting process , 2010 .

[14]  T. Childs,et al.  Raster scan selective laser melting of the surface layer of a tool steel powder bed , 2005 .

[15]  Kalyanmoy Deb,et al.  An improved fully stressed design evolution strategy for layout optimization of truss structures , 2016 .

[16]  Kalyanmoy Deb,et al.  Breaking the Billion-Variable Barrier in Real-World Optimization Using a Customized Evolutionary Algorithm , 2016, GECCO.

[17]  A. Messac,et al.  Normal Constraint Method with Guarantee of Even Representation of Complete Pareto Frontier , 2004 .

[18]  Jean-Pierre Kruth,et al.  Optimization of Scan Strategies in Selective Laser Melting of Aluminum Parts With Downfacing Areas , 2014 .

[19]  Yusheng Shi,et al.  Consecutive sub-sector scan mode with adjustable scan lengths for selective laser melting technology , 2009 .

[20]  Robert C. Plumb,et al.  Prince Rupert's drops , 1973 .

[21]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .