Datadriven modeling of semibatch manufacturing: a rubber compounding test case The continuously growing amount of available data from manufacturing processes supports the development of datadriven models. The typical target application of these models is optimal control and continuous quality management within an objective of zerodefect manufacturing. However, data obtained from batch processes are characterized by its high dimensionality that exceeds the computational capabilities of online applications and datadriven modelu0027s reliability must be guaranteed for proper industrial implementation. We explore two approaches to reduce problemu0027s size: feature extraction and feature selection; several multivariate regression methods are also compared regarding it precision and robustness. We base our analysis on an industrial rubber compounding process where natural rubber is blended in a semibatch mixer with several additives, then it is further mixed up using cylinders and it is conditioned in bands for storing. For this process, real production data is collected and stored in the manufacturing execution system of the company. The objective of the analysis is to predict mechanical properties of the rubber at the end of the processes. Based on the provided data, several datadriven models are built and tested. From the comparison among them it is concluded: models based on feature extraction and artificial neural networks yield the highest accuracy, while featureselected models provide better physical interpretability and increased robustness regarding industrial deployment.

DataDriven Fault Diagnosis in EndofLine Testing of Complex Products Machine learning approaches may support various use cases in the manufacturing industry. However, these approaches often do not address the inherent characteristics of the real manufacturing data at hand. In fact, real data impose analytical challenges that have a strong influence on the performance and suitability of machine learning methods. This paper considers such a challenging use case in the area of EndofLine testing, i.e., the final functional check of complex products after the whole assembly line. Here, classification approaches may be used to support quality engineers in identifying faulty components of defective products. For this, we discuss relevant data sources and their characteristics, and we derive the resulting analytical challenges. We have identified a set of sophisticated datadriven methods that may be suitable to our use case at first glance, e.g., methods based on ensemble learning or sampling. The major contribution of this paper is a thorough comparative study of these methods to identify whether they are able to cope with the analytical challenges. This comprises the discussion of both fundamental theoretical aspects and major results of detailed experiments we have performed on the real data of our use case.