Efficient Process Parameters Optimization of Rapid Heat Cycling Molding Technology Based on Response Surface Methodology and Improved Genetic Algorithm

Abstract

Abstract: An independently developed vehicle-borne high-gloss bluetooth product is taken as an example in the research of optimization of rapid heat cycle molding (RHCM) technology. Coupling with the experiment and numerical simulation technology, the center composite design (CCD) method is employed in arranging the experimental points. With an interference factor introduced artificially, the simulation results are kept in good consistency with experimental result. And the samples qualitative data is processed to more truly response the product quality by integrating entropy-based weight (EBW) method and technique for order preference by similarity ideal solution (TOPSIS) method. Then a predictive response surface model (RS) for comprehensive quality index is created by using response surface methodology (RSM) based on analysis of variance (ANOVA) method. The RS model is interfaced with an effective improved genetic algorithm (IGA) to find the optimum process parameters values. And the actual production achieves good result, which shows that the process control technology is feasible and effective in processing quality optimization of high-gloss plastic part.

Key words: Improved genetic algorithm, Parameters optimization, Rapid heat cycle molding, Response surface methodology

CLC Number:

TQ320

LIU Donglei;SHEN Changyu;LIU Chuntai;XIN Yong;SUN Ling;WU Xiaoyu. Efficient Process Parameters Optimization of Rapid Heat Cycling Molding Technology Based on Response Surface Methodology and Improved Genetic Algorithm[J]. , 2011, 47(14): 54-61.

[1]	ZHANG Tong, LI Decai, LI Yanwen. Design and Optimization of Combined Magnetic Fluid Seal and Labyrinth Seal [J]. Journal of Mechanical Engineering, 2022, 58(9): 172-181.
[2]	XING Lei, LI Xinya, JIANG Minghu, ZHAO Lixin, CAI Meng. Structure Optimization of Micro-hydrocyclone for Ultra-low Inlet Flow Rate [J]. Journal of Mechanical Engineering, 2022, 58(23): 251-261.
[3]	YU Bin, XIE Min, GAO Bo, SUN Hui, DING Jinjin, ZHANG Feng, WANG Peng, XIAO Huafeng. DC Current Component Suppression and Parameters Design for Grid-connected Inverter [J]. Journal of Electrical Engineering, 2021, 16(3): 47-53.
[4]	TIAN Yong, ZHU Ze, TIAN Jindong, XIANG Lijuan. Parameters Optimization of Electric Vehicles Dynamic Wireless Power Transfer System Based on LCC-S Compensation Topology [J]. Journal of Mechanical Engineering, 2021, 57(14): 150-159.
[5]	ZHAO Xiong, ZHENG Lianyu, FAN Wei, YU Lu. Real-time Machining Vibration Data Driven Milling Process Parameters Adaptive Optimization [J]. Journal of Mechanical Engineering, 2020, 56(23): 172-184.
[6]	LI Jing, ZHU Kai, SHEN Nanyan, GAO Huayu, WU Yang. Optimization of Crankshaft Grinding Process Using CNC Tracing Grinding Machine with Double Grinding Carriages [J]. Journal of Mechanical Engineering, 2019, 55(17): 222-232.
[7]	WEI Xin, ZHANG Zequn, TANG Dunbing, YANG Changqi, JIN Yongqiao, QIN Wei. Energy-saving Oriented Multi-objective Shop Floor Scheduling for Mixed-line Production of Missile Components [J]. Journal of Mechanical Engineering, 2018, 54(9): 45-54.
[8]	HOU Wenqing, YE Ming, LI Weihua. Rolling Element Bearing Fault Classification Using Improved Stacked De-noising Auto-encoders [J]. Journal of Mechanical Engineering, 2018, 54(7): 87-96.
[9]	ZHAO Qingqing, DENG Zhengquan, ZHU Yongwei. Parameters On-line Measurement and Control and Optimization Experiments of Ultrasonic Compound Electric Machining [J]. Journal of Mechanical Engineering, 2018, 54(17): 255-264.
[10]	XIA Xuhui, YANG Yi, WANG Lei, CAO Jianhua, LIU Xiang. Spraying Method and Optimization of Process Parameters for Remanufacturing Profiled Workpieces Based on Coating Model [J]. Journal of Mechanical Engineering, 2018, 54(13): 215-224.
[11]	Jikai Si, Suzhen Zhao, Haichao Feng, Ruiwu Cao, Yihua Hu. Multi-Objective Optimization of Surface-Mounted and Interior Permanent Magnet Synchronous Motor Based on Taguchi Method and Response Surface Method [J]. Chinese Journal of Electrical Engineering, 2018, 4(1): 67-73.
[12]	LIU Qiong, ZHOU Yingdong, ZHANG Yi. Integrated Optimization of Cutting Parameters and Scheduling for Reducing Carbon Emissions [J]. Journal of Mechanical Engineering, 2017, 53(5): 24-33.
[13]	LI Min, YANG Mengyao, CHEN Ze, ZHAO Qidong. Supervised Manifold Learning Method Based on Local Feature Extraction [J]. Journal of Mechanical Engineering, 2017, 53(24): 211-220.
[14]	SONG Shouxu, WANG Wei, KE Qingdi. Optimization Design of Predecisional Remanufacturing Based on Structural Function Derivative Coefficient [J]. Journal of Mechanical Engineering, 2017, 53(11): 175-183.
[15]	CHEN Peng, CAO Huajun, ZHANG Ying, ZHU Libin, YANG Yong. The Process Parameters Optimization Model of Gear High-speed Dry Hobbing and Its Application System Development [J]. Journal of Mechanical Engineering, 2017, 53(1): 190-197.

Efficient Process Parameters Optimization of Rapid Heat Cycling Molding Technology Based on Response Surface Methodology and Improved Genetic Algorithm

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments