A preliminary step toward intelligent forming of fabric composites: Artificial intelligence-based fiber distortions monitoring

被引:4
作者
Kazemi, Sorayya [1 ]
Milani, Abbas S. [1 ]
机构
[1] Univ British Columbia, Compos Res Network Okanagan Lab, Kelowna, BC, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Fabric-forming; Quality monitoring; Fiber distortions; Intelligent composite manufacturing;
D O I
10.1016/j.engappai.2024.108262
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This work presents a prototype of an intelligent quality inspection tool for application to fibers distortion monitoring in the fabric composites forming processes. To this end, a series of hemisphere draping tests on a typical commingled fiberglass/polypropylene twill weave were conducted at the dry form, and the defects (inplane and out-of-plane) for each formed part were inspected via camera vision. By gathering data from around 30 samples and over 1200 images from different forming regions, different machine-learning algorithms were trained and validated. As an application scenario in a smart factory, the developed simple AI tool would be used by an operator, or a robot, to scan different areas of the formed parts and identify 'defected' (fail) versus 'nondefected' (pass) scenarios. It was found that the K-nearest neighbors and Support Vector Machine models detect the defects with an error rate of less than 5% in the present case study, regardless of the background noise in the images such as external objects, marks on samples, or blurriness.
引用
收藏
页数:11
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