In manufacturing sectors such as textiles and electronics, manual processes are a fundamental part of production. The analysis and monitoring of the processes is necessary for efficient production design. Traditional methods for analyzing manual processes are complex, expensive, and inflexible. Compared to established approaches such as Methods-Time-Measurement (MTM), machine learning (ML) methods promise: Higher flexibility, self-sufficient & permanent use, lower costs. In this work, based on a video stream, the current motion class in a manual assembly process is detected. With information on the current motion, Key-Performance-Indicators (KPIs) can be derived easily. A skeleton-based action recognition approach is taken, as this field recently shows major success in machine vision tasks. For skeleton-based action recognition in manual assembly, no sufficient pre-work could be found. Therefore, a ML pipeline is developed, to enable extensive research on different (pre-) processing methods and neural nets. Suitable well generalizing approaches are found, proving the potential of ML to enhance analyzation of manual processes. Models detect the current motion, performed by an operator in manual assembly, but the results can be transferred to all kinds of manual processes.

翻译：在纺织和电子等制造业中，手工过程是生产的基本组成部分。对这些过程的分析与监控对于高效的生产设计至关重要。传统的手工过程分析方法复杂、昂贵且缺乏灵活性。与方法-时间-测量（MTM）等成熟方法相比，机器学习（ML）方法提供了更高的灵活性、自主且持续的使用以及更低的成本。本研究基于视频流，检测手工装配过程中的当前动作类别。利用当前动作信息，可以轻松推导出关键绩效指标（KPI）。由于该领域近期在机器视觉任务中取得了显著成功，本文采用了基于骨骼的动作识别方法。针对手工装配中的基于骨骼动作识别，目前尚未发现充分的先前工作。因此，我们开发了一套机器学习流水线，以便对不同（预）处理方法及神经网络进行广泛研究。研究发现了几种具有良好的泛化能力的方法，证明了机器学习在增强手工过程分析方面的潜力。模型能够检测操作员在手工装配中执行的当前动作，但其结果可推广至所有类型的手工过程。