Year: 2023
Author: Xiaoya Zhai, Falai Chen
Annals of Applied Mathematics, Vol. 39 (2023), Iss. 4 : pp. 493–543
Abstract
Additive manufacturing (AM), also known as 3D printing, has emerged as a groundbreaking technology that has transformed the manufacturing industry. Its ability to produce intricate and customized parts with remarkable speed and reduced material waste has revolutionized traditional manufacturing approaches. However, the AM process itself is a complex and multifaceted undertaking, with various parameters that can significantly influence the quality and efficiency of the printed parts. To address this challenge, researchers have explored the integration of machine learning (ML) techniques to optimize the AM process. This paper presents a comprehensive review of process optimization for additive manufacturing based on machine learning, highlighting the recent advancements, methodologies, and challenges in this field.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/aam.OA-2023-0023
Annals of Applied Mathematics, Vol. 39 (2023), Iss. 4 : pp. 493–543
Published online: 2023-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 51
Keywords: Additive manufacturing 3D printing machine learning process optimization.
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