Determining Best Hardware, Software and Data Structures for Worker Guidance during a Complex Assembly Task

A widespread challenge in the industrial domain is the modernization and digitization of assembly processes involving human workers to increase production efficiency and thus stay competitive with rival companies. Specifically, in assembly processes involving low lot sizes, human workers are required to deal with variations to individual assembly work processes due to product customization. In case of complex tasks this leads to mistakes and further expensive dis- and reassembly steps. This paper investigates which are the quantifiably best data sources, pre-procession steps, features, and machine learning algorithms to determine the correct execution of a specific work process in the manufacturing environment. To answer this question, a wearable sensor system consisting of multiple heterogeneous sensor devices was developed. The data used for this work was specifically collected from the actual production environment in multiple recording sessions, and particular focus was given to achieve this in a realistic yet controlled way. An assistance provisioning pipeline for industrial workers consisting of (i) an activity recognition system, (ii) a work flow correlation engine, (iii) a wrench activity estimator and a (iv) feedback system was developed. These systems were designed and evaluated using authentic, task-specific expert knowledge and using a grid search study to determine the best selection of data sources, pre-procession steps, features, and machine learning algorithms. This study was able to answer the given research question and reifies the final results in the form of a guidance system to be deployed in an industrial manufacturing line.