Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection
Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection
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DOI：
10.1016/j.compstruct.2020.112514
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发表时间：
2020-10-15
影响因子：
6.3
通讯作者：
Gregory, Elizabeth
中科院分区：
文献类型：
--
作者：
Sacco, Christopher;Radwan, Anis Baz;Gregory, Elizabeth
复合材料在工业中的大规模采用使得结构及其各自组件的设计和功能具有更大的自由度。然而，材料选择的自由导致制造的复杂性增加。目前正在探索机器学习（ML）和人工智能（AI）在许多先进制造应用中的应用，它们的适用性已开始扩展到复合材料制造领域。在本文档中，将全面概述复合材料制造中的机器学习应用，并讨论南卡罗来纳大学利用 ML 视觉系统为自动纤维铺放 (AFP) 流程开发的新型检测软件。该视觉系统允许将缺陷数据完全集成到制造过程中，从而使机器学习检查系统能够影响复合材料产品生命周期管理中的多个链条。
The large-scale adoption of composite materials in industry has allowed for a greater freedom in design and function of structures and their respective components. However, the freedom of material choice has resulted in increased complexity in manufacturing. Machine learning (ML) and Artificial Intelligence (AI) are currently being explored for a number of advanced manufacturing applications, and their applicability has begun to extend into the composites manufacturing realm. In this document, a comprehensive overview of machine learning applications in composites manufacturing will be presented with discussions on a novel inspection software developed for the Automated Fiber Placement (AFP) process at the University of South Carolina utilizing an ML vision system. This vision system allows for defect data to be fully integrated into the manufacturing process, allowing for the ML inspection system to influence several chains in the composites product lifecycle management.