Multiobjective Optimization of Injection Molding Process Parameters for the Precision Manufacturing of Plastic Optical Lens

Abstract

Injection molding process parameters (IMPP) have a significant effect on the optical performance and surface waviness of precision plastic optical lens. This paper presents a set of procedures for the optimization of IMPP, with haze ratio (HR) reflecting the optical performance and peak-to-valley 20 (PV 20 ) reflecting the surface waviness as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the results were analyzed by ANOVA to screen out the IMPP having a significant effect on the objectives. Then, the 3 4 full-factor experiment was conducted on the key IMPP, and the experimental results were used as the training and testing samples. The BPNN algorithm and the M-SVR algorithm were applied to establish the mapping relationships between the IMPP and objectives. Finally, the multiple-objective optimization was performed by applying the nondominated sorting genetic algorithm (NSGA-II), with the built M-SVR models as the fitness function of the objectives, to obtain a Pareto-optimal set, which improved the quality of plastic optical lens comprehensively. Through the experimental verification on the optimization results, the mean prediction error (MPE) of HR and PV 20 is 7.16% and 9.78%, respectively, indicating that the optimization method has high accuracy.

Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials

“…Table 9 , Table 10 and Table 11 present the results of the validation test for shrinkage in the x and y directions and tensile strength of the specimens by applying the optimal settings of processing parameters as suggested from the RSM and PSO analysis for R0, R30, R40 and R50 specimens. The validation data is quite revealing in such a way that the percentage differences between the results achieved from the validation test in comparison with the optimisation method has minimal error of between 0.21% to 8.07%, indicating that optimal settings of processing parameters produced by optimisation methods were acceptable to mould parts made of recycled LDPE, since the percentage errors are all below 10% [ 17 , 48 ].…”

Intelligent Injection Molding on Sensing, Optimization, and Control

“…For example, the GA approach was combined with BPNN [149], RSM [134,150], and the Kriging model [148] as surrogate models.…”

Energy-Efficient Enhancement for Viscoelastic Injection Rheomolding Using Hierarchy Orthogonal Optimization

“…Analysis of Variance (ANOVA) of 0 , , is listed in Tables 4-6, respectively. The analysis results show that the corresponding P-value is not more than 0.05, indicating a significant impact on the objectives, which must be considered in the optimization process [20]. Of the seven parameters, the most significant effect on forming time is A and C; the most significant effect on warping deformation is A, B, and E; the most significant effect on energy consumption of injection molding is A and B.…”

“…Zhai et al [19] proposed a computationally efficient scheme based on flow path to locate the optimum gate for achieving balanced flow; the range of filling time is employed as objective function. Liu et al [20] presented a set of procedures for the optimization of IMPP, the multiple-objective optimization was performed by applying the nondominated sorting genetic algorithm (NSGA-II), optimization results indicate that the optimization method has high accuracy.…”