Multi-objective optimization of injection molding process parameters in two stages for multiple quality characteristics and energy efficiency using Taguchi method and NSGA-II

2016 ◽  
Vol 89 (1-4) ◽  
pp. 241-254 ◽  
Author(s):  
Maosheng Tian ◽  
Xiaoyun Gong ◽  
Ling Yin ◽  
Haizhou Li ◽  
Wuyi Ming ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Taguchi Method ◽  
Process Parameters ◽  
Quality Characteristics ◽  
Injection Molding Process ◽  
Multi Objective Optimization ◽  
Molding Process ◽  
Nsga Ii ◽  
Multiple Quality Characteristics ◽  
Two Stages

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Junhui Liu ◽  
Xindu Chen ◽  
Zeqin Lin ◽  
Shipu Diao
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Orthogonal Experiment ◽  
Fitness Function ◽  
Injection Molding Process ◽  
Optical Performance ◽  
Surface Waviness ◽  
Molding Process ◽  
Nsga Ii ◽  
Optical Lens

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 (PV20) 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 34 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 PV20 is 7.16% and 9.78%, respectively, indicating that the optimization method has high accuracy.

scholarly journals DETERMINING NOMINAL VALUES OF INPUT PARAMETERS FOR PRODUCTION PROCESS USING DOE

2021 ◽  
Vol 24 (1) ◽  
pp. 29-34
Author(s):  
Anastasija Ignjatovska ◽  
◽  
Mite Tomov ◽  
◽  
◽  
...  
Keyword(s):  
Mathematical Model ◽  
Quality Improvement ◽  
Production Process ◽  
Process Parameters ◽  
Injection Molding Process ◽  
Molding Process ◽  
Control Stage ◽  
Molded Parts ◽  
Tolerance Field ◽  
Two Stages

Application of statistical methods in quality improvement of molded parts is presented in this paper. Implementation of two stages of DMAIC improvement cycle in a pre-production process is analysed in detail. DOE method is performed to define nominal values of process parameters of injection molding process. A fraction-factorial design with a single central point is used. A linear mathematical model with included elements of second-order interaction is defined. Finally, control stage is performed using prior defined nominal values. Process capability test is conducted in order to determine whether the process is capable of producing parts within specified tolerance field.

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