Vacuum Thermoforming Process: An Approach to Modeling and Optimization Using Artificial Neural Networks
AbstractIn the vacuum thermoforming process, the group effects of the processing parameters, when related to the minimizing of the product deviations set, have conflicting and non-linear values which make their mathematical modelling complex and multi-objective. Therefore, this work developed models of prediction and optimization using artificial neural networks (ANN), having the processing parameters set as the networks’ inputs and the deviations group as the outputs and, furthermore, an objective function of deviation minimization. For the ANN data, samples were produced in experimental tests of a product standard in polystyrene, through a fractional factorial design (2k-p). Preliminary computational studies were carried out with various ANN structures and configurations with the test data until reaching satisfactory models and, afterwards, multi-criteria optimization models were developed. The validation tests were developed with the models’ predictions and solutions showed that the estimates for them have prediction errors within the limit of values found in the samples produced. Thus, it was demonstrated that, within certain limits, the ANN models are valid to model the vacuum thermoforming process using multiple parameters for the input and objective, by means of reduced data quantity. View Full-Text
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Leite, W.O.; Campos Rubio, J.C.; Mata Cabrera, F.; Carrasco, A.; Hanafi, I. Vacuum Thermoforming Process: An Approach to Modeling and Optimization Using Artificial Neural Networks. Polymers 2018, 10, 143.
Leite WO, Campos Rubio JC, Mata Cabrera F, Carrasco A, Hanafi I. Vacuum Thermoforming Process: An Approach to Modeling and Optimization Using Artificial Neural Networks. Polymers. 2018; 10(2):143.Chicago/Turabian Style
Leite, Wanderson O.; Campos Rubio, Juan C.; Mata Cabrera, Francisco; Carrasco, Angeles; Hanafi, Issam. 2018. "Vacuum Thermoforming Process: An Approach to Modeling and Optimization Using Artificial Neural Networks." Polymers 10, no. 2: 143.
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