Reduced order models for uncertainty management and zerodefect control in seal manufacturing Reaching a zerodefect manufacturing is one of the biggest challenge for the current manufacturing industry. One of the barriers to overcome is to handle appropriately the uncertainty propagation across manufacturing lines, that hinder the development of accurate control systems. Within this framework, we introduce a nonintrusive method for uncertainty management that relies on a Monte Carlo approach building on a deterministic parametric realtime simulation model. The realtime simulation model is a Reduced Order Model (ROM) based on a generalized Canonical Polyadic Decomposition. The method is introduced using an industrial test case as demonstrator, namely car doorbody seals manufacturing by means of continuous coextrusion of a metal strip and various types of rubber. The resulting model is used to unify uncertainty management of: (i) aleatory and epistemic origin, and (ii) material characterization and process parameters.

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.