Sheet temperature in thermoforming

Michaud, Ryan A; Giacomin, A. Jeffrey

doi:10.1177/8756087911411134

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…The processing methods for ship-hull plates are generally divided into two types: hot forming and cold forming. With the advancement of shipbuilding intelligence, and due to the high labor intensity and harsh working environment of water-fire processing [1][2][3], large multipoint cold-forming processing equipment gradually began to replace water-firebending equipment. Multipoint cold-forming processes have many advantages in terms of efficiency and labor intensity.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Spherical Sheet Springback Based on a Sparrow-Search-Algorithm-Optimized BP Neural Network

Zhang²,

Xu³

2022

Metals

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Springback is an unavoidable problem in cold-forming processes and affects the efficiency and quality of the processing of outer sheets for ships. Therefore, effective control and prediction of sheet-forming springback is particularly important in the field of cold-bending processes. To this end, this paper presents research on cold-bending springback prediction based on a study of the multipoint cold-bending process combined with intelligent algorithms, as well as research on the multipoint cold-bending production of ship-hull plates. The forming process of spherical sheets was simulated by a finite element simulation. The amount of springback under different processes was studied, and the forming state and springback state were briefly analyzed. Then an in-depth study of machine learning was carried out, and the sparrow search algorithm (SSA) was introduced based on a back-propagation neural network (BPNN). The purpose of this integration was to prevent the BP neural network model from falling into local optimal solution problems. Then simulation data were obtained with the help of a simulation to build a backpropagation neural network prediction model, which was optimized based on the sparrow search algorithm, and training tests were conducted. Then the prediction results of the model were compared with the simulation data to verify that the prediction accuracy performance of the sparrow-search-algorithm-optimized BPNN model was improved. Finally, the prediction model based on the SSA–BPNN algorithm was compared with the prediction models of different algorithms, and the prediction results showed that SSA–BPNN outperformed other algorithms in prediction accuracy and speed; its prediction error was within 4%, which meets on-site processing requirements. The sparrow-search-algorithm-based optimization of BPNN was confirmed to have strong applicability in springback prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of Spherical Sheet Springback Based on a Sparrow-Search-Algorithm-Optimized BP Neural Network

Zhang²,

Xu³

2022

Metals

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“…They have also derived the heat flux distributions over both faces of a bowing or sagging sheet, along with the corresponding temperature profiles through and across the sheet. [12][13][14][15] An ideal thermoforming material should not sag much during heating. However, it should flow well during forming.…”

Section: Introductionmentioning

confidence: 99%

Studies on the stretching behaviour of medium gauge high impact polystyrene sheets during positive thermoforming

Kommoji

Banerjee

Bhatnagar

et al. 2014

Journal of Plastic Film & Sheeting

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High impact polystyrene sheets (HIPS) of 1.5 mm and 2.5 mm thicknesses were thermoformed on moulds of different parameters at sheet heating temperatures of 130 C and 140 C. Formability studies indicated no difference in wall thickness distribution at different sheet temperatures. Due to non-uniform stretching of the sheet, wall thickness distribution along the slant length was found to have three regions. In the regions near the mould corner and clamp point, wall thickness was found to decrease with increase in distance from the corner, in these zones. The decrease near the corner was much greater and sharper than that near the clamp point. The decrease in both regions was found to increase at higher depths of draw and decrease at higher draft angles. In the intermediate portion of the slant length, no significant variation in wall thickness was observed.

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“…Once softened, the melt plastic sheet descends under its own weight and we call this sag . This affects the position and orientation of each sheet element with respect to the heater bank, causing variations in the heat flux distribution across the sheet, and, hence, complicating the subsequent shaping of the sheet . Understanding of the sheet's sag behavior for a given temperature and time helps thermoforming engineers choose their thermoforming processing parameters.…”

Section: Introductionmentioning

confidence: 99%

Sag in commercial thermoforming

2013

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in Wiley Online Library (wileyonlinelibrary.com) Our previous analytical solution gives sag advancing implicitly as H5 1 2 arctan R2 R= R 2 11 À Á È É Â Ã , or for R 2 5 , sag advances with the cube root of time for a thin wide rectangular Newtonian isothermal sheet. This previous analytical work applies to sheets that are pinned along just two edges, and not all the way around. Corresponding sagometer experimental results confirmed this cube root relation. This work compares the H5Ã prediction with measured commercial thermoforming behavior on rectangular sheets that are, of course, pinned all the way around. Then sag parallel superposition is used to extend H5for a sheet pinned all the way around. We evaluate sag parallel superposition using a finite element method (FEM) employing ANSYS Polyflow. The equation H5 1 2 arctan R2 R= R 2 11 À Á È É Â Ã assumes sagging sheet cylindricity, and from our FEM we find that this assumption is reliable when R 3 2 . We compare sag measured in commercial thermoforming, using high-impact polystyrene (HIPS) sheets that are pinned all the way around, by extending H5 1 2 arctan R2 R= R 2 11 À Á È É Â Ã with parallel superposition. It is found that the time evolution of the commercial sag follows nearly exactly the same shape as the isothermal prediction. We measure sag runaway, and although the isothermal analysis H5 1 2 arctan R2 R= R 2 11 À Á È É Â Ã , predicts the sag runaway time accurately, our isothermal theory overpredicts the amount of sag in the nonisothermal commercial operation by as much as a factor of 14. It is also shown how to use sheet sag measurements from commercial thermoforming to deduce the Newtonian viscosity of a thermoforming resin at a temperature that is above its softening point. V C 2013 American Institute of Chemical Engineers AIChE J, 60: [1529][1530][1531][1532][1533][1534][1535] 2014

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Sheet temperature in thermoforming

Cited by 6 publications

References 17 publications

Prediction of Spherical Sheet Springback Based on a Sparrow-Search-Algorithm-Optimized BP Neural Network

Prediction of Spherical Sheet Springback Based on a Sparrow-Search-Algorithm-Optimized BP Neural Network

Studies on the stretching behaviour of medium gauge high impact polystyrene sheets during positive thermoforming

Sag in commercial thermoforming

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