Semantic ML for Manufacturing Monitoring at Bosch

Motivation. Technological advances that come with Industry 4.0, in e.g. sensoring and communication, unlock unprecedented large volumes of manufacturing data. This opens new horizons for data-driven methods like Machine Learning (ML) in analysis of manufacturing processes for a wide range of industries. An important scenario here is monitoring of manufacturing processes, including e.g. analysing the quality of the manufactured products and predicting the health state of machines and equipment. Consider an example of welding quality monitoring at Bosch, where welding is performed with automated machines that connect pieces of metal together by pressing them and passing high current electricity through them. Development of ML approaches for welding quality monitoring used in Bosch follows an iterative workflow that includes data collection (Step 1), task negotiation (Step 2), data preparation (Step 3), ML model development (Step 4), result interpretation and model selection (Step 5), model deployment (Step 6).