Integration of a fuzzy neural network and multi-objective genetic algorithm for optimisation of BLU light guide plate injection moulding parameters

Wang, Shen Tsu

doi:10.1504/ijmpt.2012.051344

Cited by 1 publication

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“…They took the warpage Processes 2024, 12, 36 3 of 20 and birefringence of symmetric biconvex Fresnel lenses as the optimization objectives, and based on the Taguchi method, they established the gray correlation for each experiment using the gray correlation analysis technique to figure out the optimal molding process parameters to achieve the two trade-off objectives. Wang et al [22] took the dimension of the backlight panel axis as the optimization target to solve the difficult injection molding problem of the ultrathin backlight panel of LCD modules. They applied the gray relational sorting method to obtain the main control process parameters, and then a fuzzy neural network approximation model between process parameters and objectives was established based on orthogonal experiments.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Injection Molding Process Parameters for Moderately Thick Plane Lens Based on PSO-BPNN, OMOPSO, and TOPSIS

Liu,

Pang,

2023

Processes

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Injection molding (IM) is an ideal technique for the low-cost mass production of moderately thick plane lenses (MTPLs). However, the optical performance of injection molded MTPL is seriously degraded by the warpage and sink marks induced during the molding process with complex historical thermal field changes. Thus, it is essential that the processing parameters utilized in the molding process are properly assigned. And the challenges are further compounded when considering the MTPL molding energy consumption. This paper presents a set of procedures for the optimization of injection molding process parameters, with warpage, sink marks reflecting the optical performance, and clamping force reflecting the molding energy consumption as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the S/N response shows that these three objectives cannot reach the optimal values simultaneously. Second, considering the experimental data scale, the back propagation neural network updated by the particle swarm optimization method (PSO-BPNN) was applied to establish the complex nonlinear mapping relationship between the process parameters and these three trade-off objectives respectively. Then, the Pareto optimal frontier of the multi-objective optimization problem was attained by multi-objective particle swarm optimization using a mutation operator and dominance coefficient algorithm (OMOPSO). And the competitive relationship between these objectives was further confirmed by the corresponding pairwise Pareto frontiers. Additionally, the TOPSIS method with equal weights was employed to achieve the best optimal solution from the Pareto optimal frontier. The simulation results yielded that the maximum values of warpage, sink marks, and clamping force could be reduced by 7.44%, 40.56%, and 5.56%, respectively, after optimization. Finally, MTPL products were successfully fabricated.

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Section: Introductionmentioning

confidence: 99%