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 (2 k-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.
An important range of existing engineered industrial parts consists of plastic materials that are reinforced with carbon fibers. Due to their excellent mechanical and thermal properties, machined mechanical parts made from reinforced polyetheretherketone (PEEK) composite materials have become standard in many high-technology engineering fields such as aerospace, automotive, and electronics. There is however a crucial need to predict the machining criteria for reinforced PEEK composite materials in order to optimize their fabrication process. In this article, the process parameters including cutting speed, feed rate, and depth of cut are investigated. A fuzzy rule-based model was derived to predict the surface roughness parameters Ra and Rt, in dry turning of reinforced PEEK with 30% of carbon fibers using TiN-coated cutting tools. The model was identified using results of experiments carried out according to Taguchi method. Predictions of the fuzzy-based model were found to fit, very well, experimental data with a correlation coefficient as high as 99%.
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