Abstract
Ultrasonic metal welding (UMW) is a key joining technology with widespread industrial applications. Condition monitoring (CM) capabilities are critically needed in UMW applications because process anomalies significantly deteriorate the joining quality. Recently, machine learning models emerged as a promising tool for CM in many manufacturing applications due to their ability to learn complex patterns. Yet, the successful deployment of these models requires substantial training data that may be expensive and time-consuming to collect. Additionally, many existing machine learning models lack generalizability and cannot be directly applied to new process configurations (i.e., domains). Such issues may be potentially alleviated by pooling data across manufacturers, but data sharing raises critical data privacy concerns. To address these challenges, this paper presents a Federated Transfer Learning with Task Personalization (FTL-TP) framework that provides domain generalization capabilities in distributed learning while ensuring data privacy. By effectively learning a unified representation from feature space, FTL-TP can adapt CM models for clients working on similar tasks, thereby enhancing their overall adaptability and performance jointly. To demonstrate the effectiveness of FTL-TP, we investigate two distinct UMW CM tasks, tool condition monitoring and workpiece surface condition classification. Compared with state-of-the-art FL algorithms, FTL-TP achieves a 5.35%--8.08% improvement of accuracy in CM in new target domains. FTL-TP is also shown to perform excellently in challenging scenarios involving unbalanced data distributions and limited client fractions. Furthermore, by implementing the FTL-TP method on an edge-cloud architecture, we show that this method is both viable and efficient in practice. The FTL-TP framework is readily extensible to various other manufacturing applications.
Abstract (translated)
超声金属焊接（UMW）是一种广泛应用于工业领域的连接技术。在UMW应用中，状态监测（CM）功能至关重要，因为过程异常会显著恶化连接质量。最近，机器学习模型在许多制造应用中显示出成为有前景的工具，因为它们能够学习复杂的模式。然而，这些模型的成功部署需要大量的训练数据，这可能昂贵且耗时。此外，许多现有机器学习模型缺乏泛化能力，不能直接应用于新的过程配置（即领域）。这些问题可能通过汇集制造商的数据得到缓解，但数据共享会引发关键的数据隐私问题。为了应对这些挑战，本文提出了一个分散学习任务个性化（FTL-TP）框架，在分布式学习过程中提供领域泛化能力，同时确保数据隐私。通过从特征空间有效地学习统一表示，FTL-TP可以适应在类似任务上工作的客户端CM模型，从而提高它们的整体适应性和性能。为了证明FTL-TP的有效性，我们研究了两个不同的UMW CM任务，即工具状况监测和工件表面状况分类。与最先进的FL算法相比，FTL-TP在CM在新目标域中的准确度提高了5.35%--8.08%。FTL-TP在涉及不平衡数据分布和有限客户端份额的具有挑战性的场景中也表现出色。此外，通过在边缘云架构上实现FTL-TP方法，我们证明了这种方法在实践中是可行且高效的。FTL-TP框架易于扩展到各种其他制造应用。
URL
https://arxiv.org/abs/2404.13278
