A novel experimental methodology is developed for the characterization of the vulcanization and foaming processes of an ethylene propylene diene (EPDM) cellular rubber and for establishing the relationship of its physical and mechanical property evolution with vulcanization and foaming process temperature. To establish this relationship, the vulcanization and foaming reaction kinetics and their coupling have been determined, as well as important parameters in the behaviour of the material, such as conductivity, specific heat capacity and coefficients of expansion and foaming. This aforementioned strategy allows the setting of a material model that can be implemented into finite-element (FE) codes to reproduce the material changes during the vulcanization and foaming processes. The material model developed reproduces with enough accuracy the coupling of chemical kinetics of vulcanization and foaming reactions. The results provided by the numerical material model fit a similar trend, and values with an accuracy of 90–99% to those observed in the experiments conducted for the determination of the cellular rubber expansion in function of the temperature. Moreover, the cellular rubber expansion values agree with the structural analysis of vulcanized and foamed samples at different isothermal temperatures and with the proportional loss of mechanical properties in the function of the vulcanization and foaming degree.

Industrial manufacturing management can benefit from the use of modeling. For a correct representation of the manufacturing process and the subsequent management, the models must incorporate the effect of the uncertainty propagation throughout the stages considered. In this paper, the proposed methodology for uncertainty management uses a nonintrusive method that is based on building a deterministic physics-informed real-time model for the a posteriori computation of output uncertainties. This model is built using tensor factorization as the Model Order Reduction technique. It includes as model parameters: material properties, process operations, and those random and epistemic uncertainties of known variables. The resulting model is used off-line to identify sensitivities and therefore to unify uncertainty management across the material transformation process. This method is presented by its direct application to an automotive door seal manufactured by continuous co-extrusion of several rubbers and reinforcement (metal strip and glass fiber thread).

Reaching a zero-defect 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 non-intrusive method for uncertainty management that relies on a Monte Carlo approach building on a deterministic parametric real-time simulation model. The real-time 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 door/body 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.