Multi-Objective Optimization of Plastics Thermoforming

被引:7
作者
Gaspar-Cunha, Antonio [1 ]
Costa, Paulo [1 ]
Galuppo, Wagner de Campos [1 ]
Nobrega, Joao Miguel [1 ]
Duarte, Fernando [1 ]
Costa, Lino [2 ]
机构
[1] Univ Minho, IPC Inst Polymer & Composites, P-4800050 Guimaraes, Portugal
[2] Univ Minho, ALGORITMI Ctr, P-4800050 Guimaraes, Portugal
基金
欧盟地平线“2020”;
关键词
plastics thermoforming; sheet thickness distribution; evolutionary algorithms; multi-objective optimization; WALL THICKNESS DISTRIBUTION; TEMPERATURE; PARAMETERS; SELECTION; POLYSTYRENE; SIMULATION; ALGORITHM; STRAIN; SHEETS; TIME;
D O I
10.3390/math9151760
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The practical application of a multi-objective optimization strategy based on evolutionary algorithms was proposed to optimize the plastics thermoforming process. For that purpose, in this work, differently from the other works proposed in the literature, the shaping step was considered individually with the aim of optimizing the thickness distribution of the final part originated from sheets characterized by different thickness profiles, such as constant thickness, spline thickness variation in one direction and concentric thickness variation in two directions, while maintaining the temperature constant. As far we know, this is the first work where such a type of approach is proposed. A multi-objective optimization strategy based on Evolutionary Algorithms was applied to the determination of the final part thickness distribution with the aim of demonstrating the validity of the methodology proposed. The results obtained considering three different theoretical initial sheet shapes indicate clearly that the methodology proposed is valid, as it provides solutions with physical meaning and with great potential to be applied in real practice. The different thickness profiles obtained for the optimal Pareto solutions show, in all cases, that that the different profiles along the front are related to the objectives considered. Also, there is a clear improvement in the successive generations of the evolutionary algorithm.
引用
收藏
页数:20
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