An evolutional algorithm for automatic 2D layer segmentation in laser-aided additive manufacturing

Liu, Ning; Ren, Kai; Zhang, W.; Zhang, Y.F.; Chew, Youxiang; Bi, Guijun; Fuh, Jerry Ying Hsi

doi:10.1016/j.addma.2021.102342

Cited by 2 publications

(1 citation statement)

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“…Over successive generations, the population evolves toward an optimal solution. GA-based methods have been proposed for solving various challenges in AM [13,[33][34][35]. In this study, customized GA operators for LB path optimization are proposed and described in the following sections.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Genetic Algorithm-Based Framework for Optimization of Laser Beam Path in Additive Manufacturing

Potočnik,

Jeromen,

Govekar

2024

Metals

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In this study, a genetic algorithm-based laser beam (LB) path optimization method is presented to improve laser-based additive manufacturing (LBAM). To emulate the LBAM process, LB irradiation of a thin metal substrate is applied. The LB path generation is formulated as the search for the optimal sequence of LB irradiation into the cells on the substrate that minimizes the fitness function, which is composed of two components, i.e., thermal fitness and process fitness. The thermal fitness is expressed by the average thermal gradient, and a simple thermal model is developed to simulate the effects of laser-induced heat input on the temperature distribution in the substrate. The process fitness regulates the suitability of the proposed LB path for the implementation of the LBAM process. In addition to standardized tool paths (i.e., raster, spiral, etc.), novel LB path generators are proposed to define the initial population of LB path solutions. To implement a genetic algorithm-based LB path optimization, a framework is proposed, and custom initialization, crossover, and mutation operators are developed for application in LBAM. The effectiveness of the proposed approach is demonstrated through a simulation case study aiming to identify LB paths that minimize the fitness function and thus provide more suitable LB path solutions with respect to the defined fitness function. Compared with the traditional trial-and-error LB path formulations, the proposed approach provides an improved and automated method for an efficient laser beam path selection in LBAM.

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Section: Genetic Algorithmmentioning

confidence: 99%