New Paper: Automated Quality Assurance for Hand-held Tools via Embedded Classification and AutoML
In this paper we motivate, describe and evaluate the Machine Learning enabled tool tracking system, that is jointly developed with Fraunhofer IIS.
Despite the ongoing automation of modern production processes, manual labor continues to be necessary due to its flexibility and ease of deployment. Automated processes assure quality and traceability, yet manual labor introduces gaps into the quality assurance process. This is not only undesirable but even intolerable in many cases. We introduce a system that monitors the process using inertial, magnetic field and audio sensors that we attach as add-ons to hand-held tools. The sensor data is analyzed via embedded classification algorithms and our system directly provides feedback to workers during the execution of work processes. We outline the special requirements caused by the usage of vastly different tools and show how our system automatically trains and deploys new machine learning models based on new user data.
This work was presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020, 14th-18th September 2020). It was supported by the Bavarian Ministry of Economic Affairs, Infrastructure, Energy, and Technology as part of the Bavarian project Leistungszentrum Elektroniksysteme (LZE) and through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II”.
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