A model predictive controller of plastic sheet temperature for a thermoforming process

Chy, Muminul Islam; Boulet, Benoît; Haidar, Ahmad

doi:10.1109/acc.2011.5991551

Cited by 14 publications

(9 citation statements)

References 12 publications

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“…In these studies, the in-plane temperature differences are minimized based on a physical model instead of estimating or directly measuring temperatures at specific locations (Chy and Boulet, 2010). Although these types of process control show some potential, the currently required computational effort is too high to implement the method on an industrial scale (Chy and Boulet, 2011a, 2011b, 2012Modirnia and Boulet, 2013).…”

Section: Introductionmentioning

confidence: 99%

Pulsed IR Heating of Thermoplastic Sheets for Thermoforming Applications

Buffel

Leeman

Desplentere

2021

International Polymer Processing

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This study presents the pulsed heating strategy as an advancement of the current state of the art in industry towards the theoretically fastest method of heating a thermoplastic sheet. Experimental temperature measurements are combined with an explicit finite difference numerical model to describe the pulsed heating method and indicate its added value in IR heating of thermoplastic sheets. Different process settings are evaluated and indicate the effect of the applied heat flux and the time interval tOFF during pulsed heating. When switched off, the residual heating of the heater elements is able to partially compensate for the convective heat losses at the surface of the sheet. This results in a more uniform temperature distribution through thickness without slowing down the overall heating process. The study shows that this effect is lost when the time interval in which the heater element is switched off, increases. Applying pulsed heating opens up a large processing window to control the through-thickness temperature difference. When the total amount of applied thermal energy is taken into account, pulsed heating is able to increase the overall heating rate and simultaneously keep the temperature difference through thickness limited.

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Section: Introductionmentioning

confidence: 99%

Pulsed IR Heating of Thermoplastic Sheets for Thermoforming Applications

Buffel

Leeman

Desplentere

2021

International Polymer Processing

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“…Chy ve ark. [7] plastik termoform prosesinde, plastik levha sıcaklığını kontrol etme üzerine çalışmıştır. O'Connor ve ark.…”

Section: Introductionunclassified

Optimization of plastic thermoform process with adaptive response surface methodology: a case study at Sahlan Plastic Co.

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et al. 2021

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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In this study, optimization of plastic thermoforming process is considered. For this purpose, thermoform cup forming process of Sahlan Plastic Company operating in Balıkesir Industrial Zone was optimized by using Adaptive Response Surface Methodology (ARSM). This method is an optimization method by dealing with controllable variables and uncontrollable variables together. During the application phase of the study, the production of 200 ml white buttermilk glasses was discussed. Zone temperatures (4 different zones), mold speed, inlet water temperature, ambient temperature, coil cooling time are used as the factors, while the cup weights were measured as the output (response) and 48 observation values were taken. The relationship between weight and these factors affecting the weight is modeled with the regression equation. To determine whether the model is significant, the results for the coefficient of determination (R 2 ) and ANOVA analysis were examined. Then, for this model -which is found to be significant -the optimized production process parameters were determined, giving the weight of 5 g.

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“…The accuracy of the developed controller and its prospective real-time application is evidenced by the results. Some studies focused on modeling, simulation, and optimization of the heating system by different methods and techniques [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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 (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.

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A model predictive controller of plastic sheet temperature for a thermoforming process

Cited by 14 publications

References 12 publications

Pulsed IR Heating of Thermoplastic Sheets for Thermoforming Applications

Pulsed IR Heating of Thermoplastic Sheets for Thermoforming Applications

Optimization of plastic thermoform process with adaptive response surface methodology: a case study at Sahlan Plastic Co.

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

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