In-situ process evaluation for continuous fiber composite additive manufacturing using multisensing and correlation analysis

Lu Lu, Shangqin Yuan, Xiling Yao, Yamin Li, Jihong Zhu, Weihong Zhang

科研成果: 期刊稿件文章同行评审

13 引用 (Scopus)

摘要

Multi-sensing and correlation analyses are essential for online process evaluation and optimization to improve the quality of as-fabricated components. Defect-free process control is important for additively manufactured (AM) continuous fiber-reinforced composites (CFRP) because the number of defects and poor-quality control in AM-fabricated CFRP restrict their mechanical performance and product service life. In this study, a framework of multi-sensor fusion for CFRP additive manufacturing is proposed for in-situ process evaluation and to establish correlations between process parameters/pattern features with layer wise defects and surface quality. Infrared (IR), visual cameras, force, and laser-displacement sensors were integrated with the printing head to obtain online datasets. Multiple signal denoising, feature extraction, and classification were performed to incorporate deep-learning neural networks and correlation analyses using feature-level fusion approaches. The critical features of these signals were extracted for a quantitative analysis of the layer wise surface roughness, level of fiber misalignment (LoM), and number of defects. Multi-sensor fusion is an effective approach to online monitoring and process evaluation. The established knowledge base is helpful for predicting and adjusting the localized process parameters during the fabrication process.

源语言英语
文章编号103721
期刊Additive Manufacturing
74
DOI
出版状态已出版 - 25 7月 2023

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