人工智能在注射成型参数设置及优化中的研究进展

doi:10.19491/j.issn.1001-9278.2022.01.013

中国塑料 ›› 2022, Vol. 36 ›› Issue (1): 84-91.DOI: 10.19491/j.issn.1001-9278.2022.01.013

人工智能在注射成型参数设置及优化中的研究进展

郑方莉¹, 傅南红², 焦晓龙³, 杨卫民¹, 谢鹏程¹()

^1.北京化工大学机电工程学院，北京 100029
^2.宁波长飞亚塑料机械制造有限公司，浙江宁波 315000
^3.海天塑机集团有限公司，浙江宁波 315801

收稿日期:2021-06-17 出版日期:2022-01-26 发布日期:2022-01-21
基金资助:
大容量塑料内胆可靠性设计技术、可控成型与焊接工艺研究(2020YFB1506102)

Research progress in applications and optimization of artificial intelligence technology in injection molding parameters setting

ZHENG Fangli¹, FU Nanhong², JIAO Xiaolong³, YANG Weimin¹, XIE Pengcheng¹()

^1.College of Mechanical and Electrical Engineering，Beijing University of Chemical Technology，Beijing 100029，China
^2.Zhafir Ningbo Plastic Machinery Manufacturing Co，Ltd，Ningbo 315000，China
^3.Haitian Plastics Machinery Group Co，Ltd，Ningbo 315801，China

Received:2021-06-17 Online:2022-01-26 Published:2022-01-21
Contact: XIE Pengcheng E-mail:xiepc@mail.buct.edu.cn

摘要/Abstract

摘要：

对专家系统及案例推理、进化计算和机器学习这3类注射成型工艺参数设置及优化的人工智能技术发展现状进行了综述，并对今后的研究方向提出了建议。

关键词: 注射成型, 工艺参数设置, 工艺参数优化, 人工智能

Abstract:

In this paper， the development status of artificial intelligence technology for the three types （expert system and case?based reasoning， evolutionary computation and machine learning） of injection molding parameter setting and optimization was reviewed， and some suggestions for future research directions were proposed.

Key words: injection molding, process parameter setting, process parameter optimization, artificial intelligence

中图分类号:

TQ320.66⁺2

郑方莉, 傅南红, 焦晓龙, 杨卫民, 谢鹏程. 人工智能在注射成型参数设置及优化中的研究进展[J]. 中国塑料, 2022, 36(1): 84-91.

ZHENG Fangli, FU Nanhong, JIAO Xiaolong, YANG Weimin, XIE Pengcheng. Research progress in applications and optimization of artificial intelligence technology in injection molding parameters setting[J]. China Plastics, 2022, 36(1): 84-91.

图/表 2

参考文献 60

1	KUO C F J， DEWANTORO G，HUANG C C. Optimization of injection⁃molded light guide plate with microstructures by using reciprocal comparisons［J］. Journal of Intelligent Manufacturing， 2013， 26（4）： 677⁃690.
2	KASHYAP S，DATTA D. Process parameter optimization of plastic injection molding： a Review［J］. International Journal of Plastics Technology， 2015， 19（1）： 1⁃18.
3	CHEN Z，TURNG L S. A review of current developments in process and quality control for injection molding［J］. Advances in Polymer Technology， 2005， 24（3）： 165⁃182.
4	LI D， ZHOU H， ZHAO P， et al. A real⁃time process optimization system for injection molding［J］. Polymer Engineering & Science， 2009， 49（10）： 2 031⁃2 040.
5	GAO Y，WANG X. Surrogate⁃based process optimization for reducing warpage in injection molding［J］. Journal of Materials Processing Technology， 2009， 209（3）： 1 302⁃1 309.
6	TIAN M， GONG X， YIN L， et al. Multi⁃objective optimization of injection molding process parameters in two stages for multiple quality characteristics and energy efficiency using taguchi method and nsga⁃ii［J］. The International Journal of Advanced Manufacturing Technology， 2016， 89（1⁃4）： 241⁃254.
7	郭进.全球智能制造业发展现状、趋势与启示［J］.经济研究参考，2020（5）：31⁃42.
	GUO J. Intelligent manufacturing industry：global status，development trend and its inspiration to China. ［J］. Review of Economic Research，2020（5）：31⁃42.
8	李德群，周华民，赵朋，等.智能型塑料注射机的研究与开发［J］.电加工与模具，2014（S1）：54⁃55，63.
	LI D Q ， ZHOU H M ， ZHAO P ， et al. Research and development of intelligent plastic injection machines［J］. Electromachining & Mould，2014（S1）：54⁃55，63.
9	MOK S L ，KWONG C K ， LAW W S. Review of research in the determination of process parameters for plastic injection molding［J］. Advances in Polymer Technology， 1999， 18（3）：225⁃236.
10	BERMAN A F， MALTUGUEVA G S，YURIN A Y. Application of case⁃based reasoning and multi⁃criteria decision⁃making methods for material selection in petrochemistry［J］. Proceedings of the Institution of Mechanical Engineers， Part L： Journal of Materials： Design and Applications， 2015， 232（3）： 204⁃212.
11	DENG Z， ZHANG H， FU Y， et al. Research on intelligent expert system of green cutting process and its application［J］. Journal of Cleaner Production， 2018， 185：904⁃911.
12	CHAVES M L， MÁRQUEZ J J， PÉREZ H， et al. Intelligent decision system based on fuzzy logic expert system to improve plastic injection molding process［C］// International Workshop on Soft Computing Models in Industrial and Environmental Applications Computational Intelligence in Security for Information Systems Conference International Conference on EUropean Transnational Education. Leon， Spain：Speinger ，2018：57⁃67.
13	SHELESH⁃NEZHAD K ， SIORES P E. An intelligent system for plastic injection molding process design［J］. Journal of Materials Processing Technology， 1997， 63（ 1⁃3）：458⁃462.
14	TAY⁃CHERNG J， O'b KEITH T. A user⁃friendly， interactive expert system for the injection moulding of engineering thermoplastics［J］. The International Journal of Advanced Manufacturing Technology，1993，8（1）：42⁃51.
15	辛勇，赵果.薄壳注塑制品多指标工艺参数优化［J］.中国塑料，2009，23（1）：59⁃63.
	XIN Y ， ZHAO G. Optimization of multi⁃target parameters on injection molded thin⁃wall products［J］. China Plastics，2009，23（1）：59⁃63.
16	DUVERLIE P，CASTELAIN J M. Cost estimation during design step： parametric method versus case based reasoning method［J］. International Journal of Advanced Manufacturing Technology，1999，15（12）：895⁃906.
17	ZHOU H， ZHAO P，FENG W. An integrated intelligent system for injection molding process determination［J］. Advances in Polymer Technology， 2007， 26（3）： 191⁃205.
18	YU S ， ZHANG T ， ZHANG Y ， et al. Intelligent setting of process parameters for injection molding based on case⁃based reasoning of molding features［J］. Journal of Intelligent Manufacturing， 2020（13）， DOI： 10.1007/s10845-020-01658-y.
19	NASIRI S，KHOSRAVANI M R. Faults and failures prediction in injection molding process［J］. The International Journal of Advanced Manufacturing Technology， 2019， 103（5⁃8）： 2 469⁃2 484.
20	LU N Y， GONG G X， LU J H. Multi⁃objective process parameter optimization for energy saving in injection molding process［J］. Journal of Zhejiang University Science A， 2012， 13（5）：382⁃394.
21	DENG Y M， LAM Y C，BRITTON G A. Optimization of injection moulding conditions with user⁃definable objective functions based on a genetic algorithm［J］. International Journal of Production Research， 2007， 42（7）： 1 365⁃1 390.
22	WU C Y， KU C C， PAI H Y. Injection molding optimization with weld line design constraint using distributed multi⁃population genetic algorithm［J］. The International Journal of Advanced Manufacturing Technology， 2010， 52（1⁃4）： 131⁃141.
23	ZHAO J， CHENG G， RUAN S， et al. Multi⁃objective optimization design of injection molding process parameters based on the improved efficient global optimization algorithm and non⁃dominated sorting⁃based genetic algorithm［J］. The International Journal of Advanced Manufacturing Technology， 2015， 78（9⁃12）： 1 813⁃1 826.
24	DENG Y M， ZHANG Y，LAM Y C. A Hybrid of mode⁃pursuing sampling method and genetic algorithm for minimization of injection molding warpage［J］. Materials & Design， 2010， 31（4）： 2 118⁃2 123.
25	MEIABADI M S ， VAFAEESEFAT A ， SHARIFI F. Optimization of plastic injection molding process by combination of artificial neural network and genetic algorithm［J］. Journal of Optimization in Industrial Enginee⁃ring， 2013，13：49⁃54.
26	ZOU T， WU S，ZHANG R. Improved State Space Mo⁃del Predictive Fault⁃Tolerant Control for Injection Mol⁃ding Batch Processes with Partial Actuator Faults Using Ga Optimization［J］. ISA Trans， 2018， 73：147⁃153.
27	OZCELIK B，ERZURUMLU T. Comparison of the warpage optimization in the plastic injection molding using anova， neural network model and genetic algorithm［J］. Journal of Materials Processing Technology， 2006， 171（3）： 437⁃445.
28	XU G， YANG Z T，LONG G D. Multi⁃objective optimization of mimo plastic injection molding process conditions based on particle swarm optimization［J］. The International Journal of Advanced Manufacturing Technology， 2011， 58（5⁃8）： 521⁃531.
29	KUMAR S，SINGH A K. Warpage and shrinkage analysis and optimization of rapid tooling molded thin wall component using modified particle swarm algorithm［J］. Journal of Advanced Manufacturing Systems， 2019， 18（1）： 85⁃102.
30	CHEN W C ， NGUYEN M H ，TAI P H. An intelligent manufacturing system for injection molding［J］. Procee⁃dings of Engineering and Technology Innovation，2018，8：09⁃14.
31	XU Y， ZHANG Q， ZHANG W， et al. Optimization of injection molding process parameters to improve the mechanical performance of polymer product against impact［J］. The International Journal of Advanced Manufactu⁃ring Technology， 2014， 76（9⁃12）： 2 199⁃2 208.
32	MTSAI K，LUO H J. An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm［J］. Journal of Intelligent Manufacturing， 2014， 28（2）： 473⁃487.
33	GUO F， ZHOU X， LIU J， et al. A Reinforcement Learning Decision Model for Online Process Parameters Optimization from Offline Data in Injection Molding［J］. Applied Soft Computing， 2019，85：105 828.
34	CHEN J C， GUO G，WANG W N. Artificial neural network⁃based online defect detection system with in⁃mold temperature and pressure sensors for high precision injection molding［J］. The International Journal of Advanced Manufacturing Technology， 2020， 110（7⁃8）： 2 023⁃2 033.
35	DAI H C ，COLIN M. Effects of learning parameters on learning procedure and performance of a BPNN［J］. Neural Networks，1997，10（8）：1 505⁃1 521.
36	CAI K， WANG Y，LU S. Research on optimization design of injection mold for automobile filter shell model based on bp neural network［J］. IOP Conference Series： Materials Science and Engineering， 2019， 612：032014.
37	CHEN W C， HLIOU P，CCHOU S. An integrated parameter optimization system for mimo plastic injection molding using soft computing［J］. The International Journal of Advanced Manufacturing Technology， 2014， 73（9⁃12）： 1 465⁃1 474.
38	LEE H， LIAU Y，RYU K. Real⁃time parameter optimization based on neural network for smart injection molding［J］. IOP Conference Series： Materials Science and Engineering， 2018， 324：012076.
39	YAN T， MEI Y，WANG L， Optimization of Fhb5.10 electrical appliance shell injection molding process based on moldflow and bp neural network［C］// 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing （WCMEIM）. Shanghai， China： IEEE Computer Society. 2020：217⁃225.
40	SONG Z， LIU S， WANG X， et al. Optimization and prediction of volume shrinkage and warpage of injection⁃molded thin⁃walled parts based on neural network［J］. The International Journal of Advanced Manufacturing Technology， 2020， 109（3⁃4）： 755⁃769.
41	LI K， YAN S， PAN W， et al. Warpage optimization of fiber⁃reinforced composite injection molding by combining back propagation neural network and genetic algorithm［J］. The International Journal of Advanced Manufacturing Technology， 2016， 90（1⁃4）： 963⁃970.
42	EKTA S M J，RAJESH K B. Simulation and optimization of warpage of fiber reinforced using human behavior based optimization［J］.International Journal of Innovative Technology and Exploring Engineering （IJITEE），2019，8（10）： 296⁃302.
43	CHEN W C， WANG M W， CHEN C T， et al. An integrated parameter optimization system for miso plastic injection molding［J］. The International Journal of Advanced Manufacturing Technology， 2008， 44（5/6）： 501⁃511.
44	YIN F， MAO H，HUA L. A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters［J］. Materials & Design， 2011， 32（6）： 3 457⁃3 464.
45	KITAYAMA S， ARAKAWA M，YAMAZAKI K. Sequential approximate optimization using radial basis function network for engineering optimization［J］. Optimization and Engineering， 2010， 12（4）： 535⁃557.
46	HASHIMOTO S， KITAYAMA S， TAKANO M， et al. Simultaneous optimization of variable injection velocity profile and process parameters in plastic injection molding for minimizing weldline and cycle time［J］. Journal of Advanced Mechanical Design， Systems， and Manufacturing， 2020， 14（3）：29.
47	KITAYAMA S， YOKOYAMA M， TAKANO M， et al. Multi⁃objective optimization of variable packing pressure profile and process parameters in plastic injection molding for minimizing warpage and cycle time［J］. The International Journal of Advanced Manufacturing Techno⁃logy， 2017， 92（9⁃12）： 3 991⁃3 999.
48	KITAYAMA S， ONUKI R，YAMAZAKI K. Warpage reduction with variable pressure profile in plastic injection molding via sequential approximate optimization［J］.The International Journal of Advanced Manufacturing Techno⁃logy， 2014， 72（5⁃8）： 827⁃838.
49	KITAYAMA S， MIYAKAWA H， TAKANO M， et al. Multi⁃objective optimization of injection molding process parameters for short cycle time and warpage reduction using conformal cooling channel［J］. The International Journal of Advanced Manufacturing Technology， 2016， 88（5⁃8）： 1 735⁃1 744.
50	KITAYAMA S，NATSUME S. Multi⁃objective optimization of volume shrinkage and clamping force for plastic injection molding via sequential approximate optimization［J］. Simulation Modelling Practice and Theory， 2014， 48：35⁃44.
51	KITAYAMA S， TAMADA K， TAKANO M， et al. Numerical optimization of process parameters in plastic injection molding for minimizing weldlines and clamping force using conformal cooling channel［J］. Journal of Manufacturing Processes， 2018， 32：782⁃790.
52	FENG Q Q， LIU L Z ，ZHOU X H. Automated multi⁃objective optimization for thin⁃walled plastic products using taguchi， anova， and hybrid ann⁃moga［J］. The International Journal of Advanced Manufacturing Technology， 2019， 106（1/2）： 559⁃575.
53	OUNPRASEUTH S T. Gaussian processes for machine learning［J］. Journal of the American Statistical Association， 2012， 103（481）： 429⁃429.
54	ZHOU J，STURNG L. Process optimization of injection molding using an adaptive surrogate model with gaussian process approach［J］. Polymer Engineering & Science， 2007， 47（5）： 684⁃694.
55	XIA W， LUO B，LIAO X P. An enhanced optimization approach based on gaussian process surrogate model for process control in injection molding［J］. The International Journal of Advanced Manufacturing Technology， 2011， 56（9⁃12）： 929⁃942.
56	WANG X， LI H， GU J， et al. Pressure analysis of dynamic injection molding and process parameter optimization for reducing warpage of injection molded products［J］. Polymers （Basel）， 2017， 9（3）：85.
57	LUO L， YAO Y， GAO F， et al. Mixed⁃effects gaussian process modeling approach with application in injection molding processes［J］. Journal of Process Control， 2018， 62：37⁃43.
58	ZHAO P， DONG Z， ZHANG J， et al. Optimization of injection⁃molding process parameters for weight control： converting optimization problem to classification problem［J］. Advances in Polymer Technology， 2020：7 654 249.
59	YU S， ZHANG Y， YANG D， et al. Offline prediction of process windows for robust injection molding［J］. Journal of Applied Polymer Science， 2014， 131（18）：40 804.
60	TRIPATHI S ， CHRISRIANMITTERMAYR，MUHR D，JODLBAUER H. Large scale predictability analysis of process variables from injection molding machines［J］. Procedia Computer Science，2021，180：545⁃560.

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人工智能在注射成型参数设置及优化中的研究进展

Research progress in applications and optimization of artificial intelligence technology in injection molding parameters setting

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