Smart quality control powered by machine learning algorithms

This paper addresses the achievements of the QU4LITY project, funded by the Swiss Innovation Agency. Within this research project, the automation back-bone developed for the SUPSI MiniFactory [1], coupled with adaptive Machine Learning (ML) algorithms, is exploited to test numerous families of bearings. The MiniFactory is associated with an evolving digital profile, a digital twin, based on constant synchronization through IoT devices, that encompasses a massive, real-time, real-world data, gathered from the different sensors interfaced via OPC-UA protocol. This representation feeds a ML algorithm capable of 1) detecting defective bearings and 2) continually tuning the quality testing process parameters based on the analysis performed on the gathered data. Specifically, the identification of defective bearings is performed by a voting classifier fed by statistical metrics measured from the collected experiments. Our approach also aims at continually learning from the tests performed with the cell: the ML algorithm is tuned and adjusted every time a new set of tests is executed. Tests show that the proposed ML classifier accurately distinguishes damaged and uncorrupted bearings (accuracy ∼ 100%, precision ∼100%, recall ∼100%, TNR ∼100%). The digitalization of the quality control process is furtherly enriched by the development of a data aggregator platform. The platform fetches, cleans and aggregates data from every different machine belonging to the quality cell, enabling, also remotely, the pattern-tracking, the significant KPIs displaying and the comprehensive system behavior monitoring. The platform features also as HMI for the operator, who can monitor parameters and schedule new test campaigns.