Volume 39, Issue 4
A Review of Process Optimization for Additive Manufacturing Based on Machine Learning

Xiaoya Zhai & Falai Chen

Ann. Appl. Math., 39 (2023), pp. 493-543.

Published online: 2023-11

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  • 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.

  • AMS Subject Headings

68V99, 90C90

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COPYRIGHT: © Global Science Press

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@Article{AAM-39-493, author = {Zhai , Xiaoya and Chen , Falai}, title = {A Review of Process Optimization for Additive Manufacturing Based on Machine Learning}, journal = {Annals of Applied Mathematics}, year = {2023}, volume = {39}, number = {4}, pages = {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.

}, issn = {}, doi = {https://doi.org/10.4208/aam.OA-2023-0023}, url = {http://global-sci.org/intro/article_detail/aam/22084.html} }
TY - JOUR T1 - A Review of Process Optimization for Additive Manufacturing Based on Machine Learning AU - Zhai , Xiaoya AU - Chen , Falai JO - Annals of Applied Mathematics VL - 4 SP - 493 EP - 543 PY - 2023 DA - 2023/11 SN - 39 DO - http://doi.org/10.4208/aam.OA-2023-0023 UR - https://global-sci.org/intro/article_detail/aam/22084.html KW - Additive manufacturing, 3D printing, machine learning, process optimization. AB -

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.

Zhai , Xiaoya and Chen , Falai. (2023). A Review of Process Optimization for Additive Manufacturing Based on Machine Learning. Annals of Applied Mathematics. 39 (4). 493-543. doi:10.4208/aam.OA-2023-0023
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