Optimization with artificial intelligence in additive manufacturing: a systematic review

被引：45

作者：

Ciccone, Francesco ^{[1
]}

Bacciaglia, Antonio ^{[1
]}

Ceruti, Alessandro ^{[1
]}

机构：

[1] Univ Bologna, Dept Ind Engn DIN, Bologna, Italy

来源：

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING | 2023年 / 45卷 / 06期

关键词：

Additive manufacturing; Artificial intelligence; Optimization; Machine learning; Deep learning; Review; TOPOLOGY OPTIMIZATION; MECHANICAL-PROPERTIES; SUPPORT;

D O I：

10.1007/s40430-023-04200-2

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In situations requiring high levels of customization and limited production volumes, additive manufacturing (AM) is a frequently utilized technique with several benefits. To properly configure all the parameters required to produce final goods of the utmost quality, AM calls for qualified designers and experienced operators. This research demonstrates how, in this scenario, artificial intelligence (AI) could significantly enable designers and operators to enhance additive manufacturing. Thus, 48 papers have been selected from the comprehensive collection of research using a systematic literature review to assess the possibilities that AI may bring to AM. This review aims to better understand the current state of AI methodologies that can be applied to optimize AM technologies and the potential future developments and applications of AI algorithms in AM. Through a detailed discussion, it emerges that AI might increase the efficiency of the procedures associated with AM, from simulation optimization to in-process monitoring.

引用

页数：22

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[2]

Ali MH, 2018, PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL CONFERENCE ON ADVANCED MANUFACTURING (IEEE ICAM), P365, DOI 10.1109/AMCON.2018.8614886

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