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.
Highlights
Thermoforming of polymers is a generic term for a group of processes that involves the forming or stretching of a preheated polymer sheet on a mold producing the specific shape
The process which uses the vacuum negative pressure force to stretch this heated polymer sheet on a mold is called vacuum forming or vacuum thermoforming [2]. This is the forming technique and/or stretching where a sheet of thermoplastic material is preheated by a heating system (Figure 1a,b), and forced against the mold surface by means of the negative vacuum pressure produced in the space
The network inputs in this work included the thickness distribution at different positions various parts, and the output or optimal process parameters were obtained by artificial neural networks (ANN)
Summary
Thermoforming of polymers is a generic term for a group of processes that involves the forming or stretching of a preheated polymer sheet on a mold producing the specific shape. The network inputs in this work included the thickness distribution at different positions various parts, and the output or optimal process parameters were obtained by ANNs. Küttneret et al [3] and Martin et al [17] presented the development of a methodology that uses an ANN to optimize the production technologies together with the product design. First of all, the current work studied both the values of manufacturing parameters and the quality of samples produced by the vacuum thermoforming process on a laboratory scale These initial experimental results were used to investigate the computational modeling of the process through several ANN models that aimed to correctly present the deviation values given a set of manufacturing parameters. Validation tests and confirmation are carried out with the objective of evaluating the ability of each model to simulate the process under new experimental conditions and, estimate deviations, verify the efficiency of the approach, and validate the proposed methodology
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