GENETIC ALGORITHM-BASED OPTIMIZATION OF THE LASER-BEAM PATH IN ADDITIVE MANUFACTURING

This study presents a methodology of genetic-algorithm-based optimization of the laser-beam path for improving laser-based additive manufacturing (AM). A simple thermal model was developed to simulate the effects of laser-induced heat input on the temperature distribution within the substrate during the fabrication of one layer. The optimization approach aims to find solutions with more homogeneous temperature properties that minimize the thermal gradient on the substrate caused by laser-based AM. The laser beam, i.e., the tool-path planning, is formulated as the search for the optimal sequence of cell depositions that minimize the fitness function, which is composed of two components, i.e., the thermal fitness and process fitness. The thermal fitness is expressed as the average thermal gradient, and the process fitness regulates the suitability of the proposed tool path for the implementation of the AM process. Various tool-path generators are proposed to initialize the initial population of tool-path solutions. Genetic-algorithm-based tool-path optimization is proposed, where custom initialization, crossover and mutation operators are developed for application in laser-based AM. Simulation studies demonstrate the effectiveness of the genetic-algorithm-based optimization in finding solutions that minimize the fitness function and therefore provide both thermally and, for the AM process implementation, more suitable laser-beam-path solutions.