Estimating Rubber Covered Conveyor Belting Cure Times Using Multiple Simultaneous Optimizations Ensemble

Abstract

Multiple response surface methodology (MRSM) has been the favorite method for optimizing multiple response processes though it has two weaknesses which challenge the credibility of its solutions. The first weakness is the use of experimentally generated small sample size datasets, and the second is the selection, using classical model selection criteria, of single best models for each response for use in simultaneous optimization to obtain the optimum or desired solution. Classical model selection criteria do not always agree on the best model resulting in model uncertainty. The selection of single best models for each response for simultaneous optimization loses information in rejected models. This work proposes the use of multiple simultaneous optimizations to estimate multiple solutions that are ensembled in solving a conveyor belting cure time problem. The solution is compared with one obtained by simultaneous optimization of single best models for each response. The two results were different. However, results show that it is possible to obtain a more credible solution through ensembling of solutions from multiple simultaneous optimizations.

Defect classification of radius shaping in the tire curing process using Fine-Tuned Deep Neural Network

“…Green tires produced from the assembly process are then fed into the curing area for vulcanization. The curing process is vulcanization with high temperature and pressure [1] [2] with polymer (rubber), carbon black, and sulfur with the help of chemical compounds to obtain the required characteristics so that it becomes a quality tire product [3].…”

“…The network parameters are constrained in the training process by introducing a physicsbased loss function. Transfer learning demonstrates improvement in PINN training and demonstrates its extension to a surrogate modeling setting by including heat transfer coefficients as input parameters [3].…”