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A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process

Selective laser melting (SLM) process was optimized in this work using multi-objectives genetic algorithm. Process parameters involved in the printing process have an obvious impact on the quality of the printed parts. As the relationship between process parameters and the quality of different parts...

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Autores principales: Xia, Qingfeng, Han, Jitai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267891/
https://www.ncbi.nlm.nih.gov/pubmed/35806734
http://dx.doi.org/10.3390/ma15134607
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author Xia, Qingfeng
Han, Jitai
author_facet Xia, Qingfeng
Han, Jitai
author_sort Xia, Qingfeng
collection PubMed
description Selective laser melting (SLM) process was optimized in this work using multi-objectives genetic algorithm. Process parameters involved in the printing process have an obvious impact on the quality of the printed parts. As the relationship between process parameters and the quality of different parts are complex, it is quite essential to study the effect of process parameter combination. In this work, the impact of four main process parameters, including defocusing amount, laser power, scan speed and layer thickness, were studied on overhanging surface quality of the parts with different inner structures. A multiple-factor and multiple-level experiment was conducted to establish a prediction model using regression analysis while multi-objective genetic algorithm was also employed here to improve the overhanging surface quality of parts with different inner shapes accordingly. The optimized process parameter combination was also used to print inner structure parts and compared with the prediction results to verify the model we have obtained before. The prediction results revealed that sinking distance and roughness value of the overhanging surface on a square-shape inner structure can reduce to 0.017 mm and 9.0 μm under the optimal process parameters combination, while the sinking distance and roughness value of the overhanging surface on a circle-shape inner structure can decrease to 0.014 mm and 10.7 μm under the optimal process parameters combination respectively. The testing results showed that the error rates of the prediction results were all within 10% in spite of random powder bonding in the printing process, which further proved the reliability of the previous results.
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spelling pubmed-92678912022-07-09 A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process Xia, Qingfeng Han, Jitai Materials (Basel) Article Selective laser melting (SLM) process was optimized in this work using multi-objectives genetic algorithm. Process parameters involved in the printing process have an obvious impact on the quality of the printed parts. As the relationship between process parameters and the quality of different parts are complex, it is quite essential to study the effect of process parameter combination. In this work, the impact of four main process parameters, including defocusing amount, laser power, scan speed and layer thickness, were studied on overhanging surface quality of the parts with different inner structures. A multiple-factor and multiple-level experiment was conducted to establish a prediction model using regression analysis while multi-objective genetic algorithm was also employed here to improve the overhanging surface quality of parts with different inner shapes accordingly. The optimized process parameter combination was also used to print inner structure parts and compared with the prediction results to verify the model we have obtained before. The prediction results revealed that sinking distance and roughness value of the overhanging surface on a square-shape inner structure can reduce to 0.017 mm and 9.0 μm under the optimal process parameters combination, while the sinking distance and roughness value of the overhanging surface on a circle-shape inner structure can decrease to 0.014 mm and 10.7 μm under the optimal process parameters combination respectively. The testing results showed that the error rates of the prediction results were all within 10% in spite of random powder bonding in the printing process, which further proved the reliability of the previous results. MDPI 2022-06-30 /pmc/articles/PMC9267891/ /pubmed/35806734 http://dx.doi.org/10.3390/ma15134607 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xia, Qingfeng
Han, Jitai
A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process
title A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process
title_full A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process
title_fullStr A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process
title_full_unstemmed A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process
title_short A Multi-Objectives Genetic Algorithm Based Predictive Model and Strategy Optimization during SLM Process
title_sort multi-objectives genetic algorithm based predictive model and strategy optimization during slm process
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267891/
https://www.ncbi.nlm.nih.gov/pubmed/35806734
http://dx.doi.org/10.3390/ma15134607
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