@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 = {<p style="text-align: justify;">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.</p>},
issn = {},
doi = {https://doi.org/10.4208/aam.OA-2023-0023},
url = {http://global-sci.org/intro/article_detail/aam/22084.html}
}