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Polymers 2018, 10(2), 143; https://doi.org/10.3390/polym10020143

Vacuum Thermoforming Process: An Approach to Modeling and Optimization Using Artificial Neural Networks

1
Departamento de Mecânica, Instituto Federal de Educação, Ciência e Tecnologia de Minas Gerias—Campus Betim, Rua Itaguaçu, No. 595, São Caetano, 32677-780 Betim, Brazil
2
Escola de Engenharia, Departamento de Engenharia Mecânica, Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, No. 6627, Pampulha, 31270-901 Belo Horizonte, Brazil
3
Escuela de Ingeniería Minera e Industrial de Almadén, Departamento Mecánica Aplicada e Ingeniería de Proyectos, Universidad de Castilla-La Mancha, Plaza Manuel Meca No. 1, 13400 Ciudad Real, Spain
4
Escuela de Ingeniería Minera e Industrial de Almadén, Departamento de Filología Moderna, Universidad de Castilla-La Mancha, Plaza Manuel Meca No. 1, 13400 Ciudad Real, Spain
5
Ecole Nationale des Sciences Appliquées d’Al Hoceima (ENSAH), Département of Civil and Environmental Engineering, 32000 Al Hoceima, Morocco
*
Author to whom correspondence should be addressed.
Received: 16 December 2017 / Revised: 23 January 2018 / Accepted: 31 January 2018 / Published: 2 February 2018
(This article belongs to the Special Issue Model-Based Polymer Processing)
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Abstract

In 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
Keywords: vacuum thermoforming process; modeling and optimization; artificial neural networks; deviations and process parameters; multi-criteria optimization vacuum thermoforming process; modeling and optimization; artificial neural networks; deviations and process parameters; multi-criteria optimization
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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.

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