Online process monitoring with near-zero misdetection for ultrasonic welding of lithium-ion batteries: An integration of univariate and multivariate methods
Online process monitoring with near-zero misdetection for ultrasonic welding of lithium-ion batteries: An integration of univariate and multivariate methods
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DOI：
10.1016/j.jmsy.2016.01.001
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发表时间：
2016-01-01
影响因子：
12.1
通讯作者：
Wang, Hui
中科院分区：
文献类型：
--
作者：
Guo, Weihong;Shao, Chenhui;Wang, Hui
超声波金属焊接用于电动汽车锂离子电池的连接。电池连接过程的监控需要几乎零误检，以防止任何低质量连接的电池接头进入下游组件。在许多过程监控系统中广泛使用的传统控制图技术是基于预先设定的虚警率设计的。为了确保焊接质量，同时减少人工检查，最重要的是实现接近零的误检率，同时实现低误报率。本文将单变量控制图与马氏距离法相结合，提出了一种针对近零误检的监测算法。该算法能够以灵活的控制范围监测非正态多变量观测值，在保持低虚警率的同时实现接近零的误检率。通过将该算法应用于电池制造超声波焊接过程，验证了该算法在过程监控中实现近零误检的有效性，保证了电池焊接质量。开发的算法还显示出监测其他过程的巨大潜力，这些过程的目标是接近零的误检。(C) 2016制造工程师学会。Elsevier Ltd.出版。版权所有。
Ultrasonic metal welding is used for joining lithium-ion batteries of electric vehicles. The monitoring of battery joining processes requires near-zero misdetection in order to prevent any battery joints with a low quality connection going into the downstream assembly. The conventional control chart techniques widely used in many process monitoring systems were designed based on a pre-specified false alarm rate. To ensure weld quality and reduce manual inspection at the same time, a near-zero misdetection rate is desired foremost while achieving a low false alarm rate. A monitoring algorithm targeting near-zero misdetection is developed in this article by integrating univariate control charts and the Mahalanobis distance approach. The proposed algorithm is capable of monitoring non-normal multivariate observations with flexible control limits to achieve a near-zero misdetection rate while keeping a low false alarm rate. By implementing this algorithm on the ultrasonic welding process of battery manufacturing, the developed algorithm proves to be effective in achieving near-zero misdetection in process monitoring to ensure battery weld quality. The developed algorithm also shows great potential for monitoring other processes that target at near-zero misdetection. (C) 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.