A Fast and Cost-Effective Imaging System for Fine-Scale Tool Condition Monitoring in Ultrasonic Metal Welding
A Fast and Cost-Effective Imaging System for Fine-Scale Tool Condition Monitoring in Ultrasonic Metal Welding
复制标题
用于超声波金属焊接中精细工具状态监测的快速且经济高效的成像系统
DOI：
10.1115/msec2023-104906
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Shao, Chenhui
中科院分区：
文献类型：
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作者：
Dong, Zhiqiao;Chen, Qianmeng;Lu, Kuan-Chieh;Shao, Chenhui
在超声波金属焊接 (UMW) 的工业规模应用中，工具状态监测 (TCM) 是最重要的维护任务之一，因为工具健康状况会迅速恶化，而且工具状态会显着影响工艺物理和接头质量。此外，工具更换构成维护成本的很大一部分。在 UMW 中，刀具健康状况恶化主要以刀具表面几何形状的变化形式发生。在线TCM利用现场传感数据间接对工具状况进行分类，已被证明是有效的；然而，这种间接方法无法提供工具表面轮廓的详细表征。另一方面，直接测量工具表面需要昂贵且耗时的高分辨率 3D 计量，这大大增加了质量成本并延迟了维护决策。为了克服这些挑战，本文开发了一种快速且经济高效的 UMW 精细 TCM 成像系统。成像系统主要由微距镜头和安装在步进电机驱动的线性导轨上的 Raspberry Pi (RPI) 高品质相机组成，整个系统通过 RPI GPIO（通用输入/输出）进行控制。该成像系统与同轴照明结合使用，可增强工具表面特征，以捕获再现工具表面几何形状的铸件照片。然后，开发图像处理技术来表征工具表面轮廓和特征。我们在三种不同条件下使用工具证明了所提出的中医策略的有效性。结果表明，TCM 成像系统可以有效地重建工具的关键精细几何特征，从而使 UMW 的 TCM 更具响应性、可解释性和可靠性。
In industrial-scale applications of ultrasonic metal welding (UMW), tool condition monitoring (TCM) is one of the most important maintenance tasks because tool health degrades quickly, and tool condition impacts the process physics and joint quality significantly. Moreover, tool replacement constitutes a notable portion of maintenance costs. In UMW, tool health degradation occurs mainly in the form of changes in tool surface geometry. Online TCM, which uses in-situ sensing data to indirectly classify tool conditions, has demonstrated to be effective; however, such indirect methods cannot provide a detailed characterization of tool surface profiles. On the other hand, direct measurements of tool surfaces require expensive and time-consuming high-resolution 3D metrology, which substantially increases the cost of quality and delays maintenance decision-making. To overcome these challenges, this paper develops a fast and cost-effective imaging system for fine-scale TCM in UMW. The imaging system mainly consists of a macro lens and a Raspberry Pi (RPI) high quality camera mounted over a linear rail driven by a stepper motor, and the full system is controlled through RPI GPIO (general-purpose input/output). The imaging system is used in conjunction with coaxial illumination, which enhances tool surface features, to capture a photo of a cast reproducing tool surface geometry. Then, image processing techniques are developed to characterize tool surface profiles and features. We demonstrate the effectiveness of the proposed TCM strategy using tools in three distinct conditions. Results show that the TCM imaging system can effectively reconstruct critical fine-scale geometric features of tools, thus enabling more responsive, interpretable, and reliable TCM for UMW.