Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes
Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes
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DOI：
10.1115/1.4028059
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发表时间：
2014-06
影响因子：
4
通讯作者：
S. Lee;Chenhui Shao;Tae-Hyung Kim;S. Hu;E. Kannatey-Asibu;W. Cai;J. Spicer;Jeffrey Abell
中科院分区：
文献类型：
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作者：
S. Lee;Chenhui Shao;Tae-Hyung Kim;S. Hu;E. Kannatey-Asibu;W. Cai;J. Spicer;Jeffrey Abell
汽车锂离子电池超声波焊接过程的在线监测是保证电池组组装可靠可靠的关键。已经开发了有效的质量监测算法，通过应用纯粹的统计分类方法来识别失控部件。然而，这种方法不能提供对制造过程的深入物理理解，这对于在过程失控时提供诊断能力是必要的。本研究的目的是确定超声波焊接信号特征与超声波焊接工艺条件以及最终的接头性能之间的物理相关性。对这些关系的深入了解将显著减少生产投产时间和成本，改进超声波焊接工艺设计，并通过先进的诊断方法减少运营停机时间。在这项研究中，使用焊接功率和变幅杆位移这两个过程信号来研究超声波焊接过程背后的基本物理。通过检查这些信号及其在异常工艺条件下的变化，确定了几个在线特征。通过将这些在线特征与焊接属性(如直接影响焊接性能的焊接密度和焊后厚度)相关联来预测接头质量。本研究为超声金属焊接的特征选择和高级诊断提供了指导，以实现可靠的在线质量监控系统。版权所有：ASME和通用汽车公司
Online process monitoring in ultrasonic welding of automotive lithium-ion batteries is essential for robust and reliable battery pack assembly. Effective quality monitoring algorithms have been developed to identify out of control parts by applying purely statistical classification methods. However, such methods do not provide the deep physical understanding of the manufacturing process that is necessary to provide diagnostic capability when the process is out of control. The purpose of this study is to determine the physical correlation between ultrasonic welding signal features and the ultrasonic welding process conditions and ultimately joint performance. A deep understanding in these relationships will enable a significant reduction in production launch time and cost, improve process design for ultrasonic welding, and reduce operational downtime through advanced diagnostic methods. In this study, the fundamental physics behind the ultrasonic welding process is investigated using two process signals, weld power and horn displacement. Several online features are identified by examining those signals and their variations under abnormal process conditions. The joint quality is predicted by correlating such online features to weld attributes such as bond density and post-weld thickness that directly impact the weld performance. This study provides a guideline for feature selection and advanced diagnostics to achieve a reliable online quality monitoring system in ultrasonic metal welding.Copyright © 2014 by ASME and General Motors