提高增材制造精度:无缺陷连续碳纤维增强聚合物的智能检测和优化

IF 5.3 Q2 MATERIALS SCIENCE, COMPOSITES Composites Part C Open Access Pub Date : 2024-03-06 DOI:10.1016/j.jcomc.2024.100451
Md Hasib Zubayer , Yi Xiong , Yafei Wang , Haque Md Imdadul
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引用次数: 0

摘要

人工智能(AI)已成为管理大量数据集、实现模式识别和推导解决方案的重要工具,尤其是在增材制造(AM)领域带来了革命性的变化。本研究旨在开发人工智能深度机器学习图像处理技术,用于实时检测增材制造连续碳纤维增强聚合物(cCFRP)试样中的缺陷。本研究利用 YOLOv8(一种最先进的单阶段物体检测算法),重点研究打印参数与缺陷发生(特别是不对齐误差)之间的关系。该研究将检测到的缺陷与参数优化联系起来,在方法上取得了进步,从而显著提高了 cCFRP 试样的质量。通过微调喷嘴温度,错位检测的准确率达到了令人印象深刻的 94%,从而显著降低了错位误差,同时观察到打印床温度、进料量和进料速度/秒对完善所提出的最佳参数识别模型的影响微乎其微。
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Enhancing additive manufacturing precision: Intelligent inspection and optimization for defect-free continuous carbon fiber-reinforced polymer

Artificial intelligence (AI) has emerged as a pivotal tool in managing extensive datasets, enabling pattern recognition, and deriving solutions, particularly revolutionizing additive manufacturing (AM). This study intends to develop AI deep machine learning image processing techniques for real-time defects detection in additively manufactured continuous carbon fiber-reinforced polymer(cCFRP) specimens. Leveraging YOLOv8- a state-of-the-art, single-stage object detection algorithm, this study focuses on the relationship between printing parameters and defect occurrences, specifically misalignment errors. The research delineates a methodological advancement by correlating detected defects with parameter optimization, leading to significant quality improvements in cCFRP specimens. An impressive 94 % accuracy in detecting misalignments was achieved through fine-tuning the nozzle temperature adjustment, resulting in significant reductions in misalignment errors, while minimal impact is observed from print bed temperature, feed amount, and feed rate/sec on refining the proposed model for identifying optimal parameters.

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来源期刊
Composites Part C Open Access
Composites Part C Open Access Engineering-Mechanical Engineering
CiteScore
8.60
自引率
2.40%
发文量
96
审稿时长
55 days
期刊最新文献
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