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4 Experimental results and discussion
Following the hybrid BPNN–DFP and hybrid BPNN–GA search approaches, the final optimal process settings are shown in Table 8. To demonstrate the effectiveness of the proposed optimization system, this research performed three confirmation experiments. One experiment utilized preliminary initial process parameter settings obtained from the Taguchi method. The other two experiments utilized the final optimal initial process parameter settings obtained from the hybrid BPNN–GA and BPNN–DFP search approaches, respectively. Each experiment produced 30 product samples. The statistical averages, standard deviations, and process capability indices of all three methods were compared in order to judge the best approach for determining the final optimal process parameter settings. Comparisons of quality statistics between the Taguchi, BPNN–DFP, and BPNN–GA approaches are shown in Table 9. In addition, comparisons of quality characteristics (weight) between the Taguchi, BPNN–DFP, and BPNN-GA approaches are shown in Fig. 5. According to the experimental results, the standard deviation of the Taguchi approach was 0.0071. That is approximately two times that of the BPNN–DFP approach (0.0035) and 3.5 times that of the BPNN–GA approach (0.0021). In the practical assessment, the process capability index is a major criterion for assessing the ability of a production process to make products that meet specifications. The practical minimum process capability index (Cpk) is 1.33 in many manufacturing industries. If the process capability index (Cpk) is <1.33, then manufacturers will not achieve a high yield rate and may produce many nonconforming products. Therefore, this research utilized the process capability index as the major criterion for the quality requirement. As the results in Table 9 show, the Cpk of Taguchi’s approach was 0.585; which is roughly one third that of the BPNN–DFP approach (1.69) and one fifth that of the BPNN–GA approach (2.75). Consequently, the optimal process parameter settings
generated by the proposed two approaches definitely produced better performances than the Taguchi method. Experimental results also revealed that the BPNN–GA approach produced the highest Cpk value and the best-quality products. The BPNN–DFP approach did not perform quite as well but was still better than the original process parameter calculation method (the Taguchi method). The main reason is that the BPNN–GA approach is aglobal search methodology for determining an optimal solution, whereas the BPNN–DFP approach is a local search methodology for finding an optimal solution. The Taguchi method can only find the best set of specified process parameter level combinations which comprises discrete setting values of the process parameters. The plastic injection molding industry produces myriad products, and each product has its own optimal machine settings. An unsuitable process parameter setting can cause many defective products and unstable product quality during the injection molding process. In comparing the three methods to arrive at those parameters settings, the BPNN–GA search approach was clearly the best. Therefore, the proposed optimization system is practical and effective for parameter optimization in the plastic injection molding process.
5 Conclusions
Costs of production in plastic injection molding are directly affected by strategies for choosing parameter settings, especially when setting up production runs. Setup strategies have traditionally relied on some combination of skilled trial and error, plus the Taguchi method. These traditional strategies, however, often produce less than optimal results. In seeking to alleviate some of those shortcomings, this research made use of the Taguchi method, adding backpropagation neural networks, genetic algorithms, the Davidon–Fletcher–Powell method, and engineering optimization concepts to determine efficient strategies that optimize both the setup process and product quality. Test results showed that measurably better performance was obtained using a tailored combination of approaches than with the Taguchi method alone. Specifically, the Taguchi method with BPNN plus DFP and BPNN plus GA and statistical techniques optimally predicted process parameter settings for MISO plastic injection molding setup procedures. Application of these simple techniques produced dramatic improvements in productivity by: (1) improving the quality of the parts produced; (2) reducing the number of rejects produced; (3) reducing waste or the regrinding of rejects; (4) reducing inspection times required during production; and (5) improving the scheduling of production. Thus, the proposed system is a feasible and effective method for process parameter optimization of MISO plastic injection molding and can result in significant quality and cost advantages.