Virtual Sensor Development Based on Reduced Order Models of CFD Data Manufacturing industry is facing the challenge to cope simultaneously with the high demanding trends of the market (i.e. personalization) and the high quality standards. In this context, it is of utmost importance the implementation of zerodefect strategies relying on sensing and control systems to improve automation and minimize failures. Noninvasive physical sensing is not always feasible. In those cases, virtual sensors may stand for a great tool to support the development of enhanced control systems. In this work, a new methodology for building virtual sensors is presented. The novelty of the methodology relies on the coupling of two modeling techniques: detailed computeraided engineering (CAE) simulations and model order reduction (MOR) techniques. The result is a realtime simulation model that can provide the variables required by the control system, using other available data (physically sensed) as input. Hence, the coupling between virtual and physical sensors can provide a richer description of the process to improve the process control and performance. To demonstrate the methodology, a case study is presented, where the virtual sensor is aimed at defining the flow pattern (fluid interface position) within a flowfocusing microfluidic device. The resulting virtual sensor can predict in realtime the shape and location of a multiphase fluid interface inside the microchip, based on the volumetric flow rates measured at the chip inlets. The interface position monitoring provided by the virtual sensor is of great relevance to improve the control and performance of such a microfluidic system.

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.