Wang Wei, Wang Peiren, Zhang Hanzhong, Chen Xiaoyi, Wang Guoqi, Lu Yang, Chen Min, Liu Haiyun, Li Ji
Key Laboratory of MEMS of the Ministry of Education, Southeast University, Nanjing 210096, China.
School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215400, China.
Micromachines (Basel). 2023 Dec 22;15(1):0. doi: 10.3390/mi15010028.
Nowadays, additive manufacturing (AM) is advanced to deliver high-value end-use products rather than individual components. This evolution necessitates integrating multiple manufacturing processes to implement multi-material processing, much more complex structures, and the realization of end-user functionality. One significant product category that benefits from such advanced AM technologies is 3D microelectronics. However, the complexity of the entire manufacturing procedure and the various microstructures of 3D microelectronic products significantly intensified the risk of product failure due to fabrication defects. To respond to this challenge, this work presents a defect detection technology based on deep learning and machine vision for real-time monitoring of the AM fabrication process. We have proposed an enhanced YOLOv8 algorithm to train a defect detection model capable of identifying and evaluating defect images. To assess the feasibility of our approach, we took the extrusion 3D printing process as an application object and tailored a dataset comprising a total of 3550 images across four typical defect categories. Test results demonstrated that the improved YOLOv8 model achieved an impressive mean average precision (mAP50) of 91.7% at a frame rate of 71.9 frames per second.
如今，增材制造（AM）已发展到能够交付高价值的最终用途产品，而不仅仅是单个部件。这种演变需要整合多种制造工艺，以实现多材料加工、更复杂的结构以及最终用户功能的实现。受益于这种先进增材制造技术的一个重要产品类别是3D微电子。然而，整个制造过程的复杂性以及3D微电子产品的各种微观结构显著增加了由于制造缺陷导致产品故障的风险。为应对这一挑战，本文提出了一种基于深度学习和机器视觉的缺陷检测技术，用于实时监测增材制造过程。我们提出了一种改进的YOLOv8算法来训练一个能够识别和评估缺陷图像的缺陷检测模型。为了评估我们方法的可行性，我们以挤出式3D打印过程为应用对象，定制了一个数据集，该数据集包含四个典型缺陷类别的总共3550张图像。测试结果表明，改进后的YOLOv8模型在每秒71.9帧的帧率下实现了令人印象深刻的91.7%的平均精度均值（mAP50）。
