Towards Data Driven Process Control in Manufacturing Car Body Parts

The manufacturing process of car body parts is a complex industrial process where many machine parameters and material measurements are involved in establishing the quality of the final product. Data driven models have shown great advantages in helping decision makers to optimize this kind of complex processes where good physical models are hard to build. In this paper a framework for on-line process monitoring and predictive modeling is proposed to optimize a car body part production process. Anomaly detection plays an important role in this framework as it can provide an early alert for operators on the production line using a complex set of machine parameters and material properties. In this paper an anomaly detection algorithm, GLOSS, that is successfully implemented as the first module in the process, is introduced. GLOSS finds local outliers in high dimensional mixed data-sets using a relative density measure that takes the global neighborhood into account while searching for outliers in subspaces of the data. An overview of the application and implementation of the algorithm in the car body part press shop is presented.