Controle preditivo neural aplicado a processos de secagem e cura de pintura automotiva.

Abstract

In the automotive painting process, maintaining optimal operating conditions in drying and curing ovens is crucial to ensure high-quality paint finishes, particularly for specific car body-in-white (BIW) parts' temperature profiles. This study presents a methodology for developing and implementing a Neural Network Predictive Control (NNPC) system for painting drying and curing processes in an automotive oven used in the electrodeposition stage (Elpo oven), aiming to improve temperature control for BIW parts. To train the Artificial Neural Networks (ANN) in the various zones of the Elpo oven, a dataset was generated using a phenomenological model based on first principles. Random disturbances were applied to the input variables (conveyor speed and zone temperatures) to capture the dynamic response of the output variables (temperature in specific BIW parts). The dataset was then split into training (80%), validation (10%), and test (10%) sets, and the ANN were trained using the backpropagation algorithm. The implemented NNPC utilizes the trained ANNs to predict future temperature values in specific BIW parts (controlled variables). To determine the optimal control signals (manipulated variables), an optimizer based on the Generalized Predictive Control (GPC) model was employed, and the objective function was minimized using the Ant Colony Optimization Algorithm (ACO). Through simulations of four operational scenarios with applied disturbances to the input variables, the results demonstrate satisfactory performance of the NNPC, effectively maintaining the temperatures of BIW parts close to predefined setpoints. Thus, it is concluded that the use of NNPC offers improved temperature control for BIW parts, mitigating painting issues, reducing rework and operating costs.