Speaker
Description
For automotive companies, continuous improvement of the manufacturing process is a must in order to achieve optimal product quality and cost. The traditional approach for this improvement process is Model Based Engineering, where hypotheses, and cause-effect chains are discovered purely by considering the laws of physics.
At Robert Bosch, engineers and data scientists are working on a concept which utilizes data mining methods to create hypothesis of failure causes (grey-box modeling).
In this presentation, we present the general approach of combination of data analytics and classical engineering and demonstrate the feasibility of the concept by a pilot big data project about a plastic encapsulation process optimization.
In order to provide timely data for a wide range of reporting and predictive tasks, at the Robert Bosch Engineering Center, a cluster running the Apache Hadoop stack was set up as storage and analysis infrastructure for the excessive amount of production data. Data pipelines were developed in order to enable storing different data sources in the Hadoop Distributed File System (HDFS) from the Manufacturing Execution System (MES), the main data source.
Among other sources, sensor data of molding press tool were used for modeling, in order to discover the most probable failure cause for the primary defect phenomenon, called delamination. Algorithms of modeling, feature importance measures and physical meaning of major important factors are also presented.
