Optimal design of laser solid freeform fabrication system and real-time prediction of melt pool geometry using intelligent evolutionary algorithms

  • Authors:

  • Ahmad Mozaffari;Alireza Fathi;Amir Khajepour;Ehsan Toyserkani

  • Affiliations:

  • Department of Mechanical Engineering, Babol University of Technology, Iran;Department of Mechanical Engineering, Babol University of Technology, Iran;Department of Mechanical and Mechatronics Engineering, University of Waterloo, Canada;Department of Mechanical and Mechatronics Engineering, University of Waterloo, Canada

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2013

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Abstract

With the rapid growth of laser applications and the introduction of high efficiency lasers (e.g. fiber lasers), laser material processing has gained increasing importance in a variety of industries. Among the applications of laser technology, laser cladding has received significant attention due to its high potential for material processing such as metallic coating, high value component repair, prototyping, and even low-volume manufacturing. In this paper, two optimization methods have been applied to obtain optimal operating parameters of Laser Solid Freeform Fabrication Process (LSFF) as a real world engineering problem. First, Particle Swarm Optimization (PSO) algorithm was implemented for real-time prediction of melt pool geometry. Then, a hybrid evolutionary algorithm called Self-organizing Pareto based Evolutionary Algorithm (SOPEA) was proposed to find the optimal process parameters. For further assurance on the performance of the proposed optimization technique, it was compared to some well-known vector optimization algorithms such as Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA 2). Thereafter, it was applied for simultaneous optimization of clad height and melt pool depth in LSFF process. Since there is no exact mathematical model for the clad height (deposited layer thickness) and the melt pool depth, the authors developed two Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to estimate these two process parameters. Optimization procedure being done, the archived non-dominated solutions were surveyed to find the appropriate ranges of process parameters with acceptable dilutions. Finally, the selected optimal ranges were used to find a case with the minimum rapid prototyping time. The results indicate the acceptable potential of evolutionary strategies for controlling and optimization of LSFF process as a complicated engineering problem.