针对传统模压成型工艺能耗高、生产效率低、产品质量不稳定等问题,提出一种基于自适应遗传算法的模压成型工艺优化方法,用来优化模压成型过程中保温时间、模压压力以及温度等参数,该方法将实验得到的工艺数据作为输入层神经元,以成型质量翘曲变形量作为输出层神经元,构建BP神经网络,由此得到翘曲变形与模压压力、保温时间、温度之间的函数关系,然后运用自适应遗传算法对多工艺参数进行优化,经过二进制编码、选择、交叉、变异等步骤,最后解码得到优化后的结果.研究结果表明,自适应遗传算法能够对模压成型过程中因保温时间、模压压力以及温度三者不平衡引起的翘曲变形量有很好的改善效果,能提高产品成型质量.
Aiming at the problems of high energy consumption, low production efficiency and unstable product quality of traditional molding process, the adaptive genetic algorithm was proposed to optimize the multi-parameters of holding time, molding pressure and temperature in the molding process. And the molding pressure, holding time and temperature of process data obtained by experiments are taken as input layer neurons, and the warping deformation of molding mass is taken as output layer neurons. BP neural network was constructed to obtain the functional relationship between warping deformation and molding pressure, holding time and temperature. Then adaptive genetic algorithm is used to optimize multiple process parameters. After the steps of binary coding, selection, crossover and mutation, the optimized results were obtained. The results show that the adaptive genetic algorithm can improve the warping deformation caused by the imbalance of holding time, molding pressure and temperature in the molding process, and improve the molding quality of the product.
[1] 吴雪平,代建建,汤瑛召,等.模压成型环氧树脂/玄武岩纤维复合材料研究[J].工程塑料应用, 2016, 44(1): 16-21. DOI:10.3969/j.issn.1001-3539.2016.01.004.
[2] 武海鹏,于柏峰.复合材料螺旋弹簧的刚度预测及分析[J].纤维复合材料,2019, 36(2): 19-21.
[3] 张文武.纤维增强热塑性复合材料模压成型与低速冲击性能研究[D].长沙: 湖南大学, 2020. DOI: 10.27135/d.cnki.ghudu.2020.001325.
[4] DI F D, SUN L M, CHEN L. Optimization of hybrid cable networks with dampers and cross-ties for vibration control via multi-objective genetic algorithm[J]. Mech Syst Signal Process, 2022, 166: 108454. DOI: 10.1016/j.ymssp.2021.108454.
[5] SONG D, SHEN J X, MA T C, et al. Multi-objective acoustic sensor placement optimization for crack detection of compressor blade based on reinforcement learning[J]. Mech Syst Signal Process, 2023, 197: 110350. DOI: 10.1016/j.ymssp.2023.110350.
[6] ZHANG T Y, CHEN L, WANG J. Multi-objective optimization of elliptical tube fin heat exchangers based on neural networks and genetic algorithm[J]. Energy, 2023, 269: 126729. DOI: 10.1016/j.energy.2023.126729.
[7] 韩宇泽,刘雁鹏,任中杰,等.基于NSGA-Ⅱ算法的缠绕过程多目标工艺参数优化[J].复合材料学报, 2024, 41(10): 5622-5633. DOI: 10.13801/j.cnki.fhclxb.20231218.005.
[8] 秦国华,赖晓春,金杨杨,等.碳纤维复合材料超声振动辅助钻孔工艺参数多目标优化方法[J/OL].计算机集成制造系统[2025-01-06]. http://doi.org/10.13196/j.cims.2023.0220.
[9] CHEN Z H, WU H Y, CHEN Y, et al. Patrol robot path planning in nuclear power plant using an interval multi-objective particle swarm optimization algorithm[J]. Appl Soft Comput, 2022, 116: 108192. DOI: 10.1016/j.asoc.2021.108192.
[10] ZHANG X H, PANG X Y, YU S B, et al. A MOPSO-based spectrum modification method for shallow surface electromagnetic detection transmitting scheme[J]. Measurement, 2023, 218: 113129. DOI: 10.1016/j.measurement.2023.113129.
[11] FENG H, JIANG J Y, DING N, et al. Multi-objective time-energy-impact optimization for robotic excavator trajectory planning[J]. Autom Constr, 2023, 156: 105094. DOI: 10.1016/j.autcon.2023.105094.
[12] CHEN L Z, LIU W L, ZHONG J H. An efficient multi-objective ant colony optimization for task allocation of heterogeneous unmanned aerial vehicles[J]. J Comput Sci, 2022, 58: 101545. DOI: 10.1016/j.jocs.2021.101545.
[13] YAGMAHAN B. Mixed-model assembly line balancing using a multi-objective ant colony optimization approach[J]. Expert Syst Appl, 2011, 38(10): 12453-12461. DOI: 10.1016/j.eswa.2011.04.026.
[14] CUI J G, WU L, HUANG X D, et al. Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning[J]. Knowl Based Syst, 2024, 288: 111459. DOI: 10.1016/j.knosys.2024.111459.
[15] ZHANG J R, LI Z Y, WANG B B. Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing[J]. Energy, 2021, 223: 120027. DOI: 10.1016/j.energy.2021.120027.
[16] YANNIBELLI V, AMANDI A. Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem[J]. Expert Syst Appl, 2013, 40(7): 2421-2434. DOI: 10.1016/j.eswa.2012.10.058.
[17] MATAI R. Solving multi objective facility layout problem by modified simulated annealing[J]. Appl Math Comput, 2015, 261: 302-311. DOI: 10.1016/j.amc.2015.03.107.
[18] 张欢.一种改进的自适应免疫遗传算法[J].工业控制计算机, 2022, 35(12): 61-63. DOI: 10.3969/j.issn.1001-182X.2022.12.023.
[19] 朱楠,彭德功,李军,等.复合材料模压成型工艺研究[J].纤维复合材料, 2020, 37(2): 33-35. DOI: 10.3969/j.issn.1003-6423.2020.02.010.
[20] 肖翠蓉,唐羽章.复合材料工艺学[M].长沙: 国防科技大学出版社, 1991.
[21] 张学军.基于MATLAB GUI的环境温湿度检测上位机设计[J].电子制作, 2021, 29(22): 77-78, 17. DOI: 10.16589/j.cnki.cn11-3571/tn.2021.22.027.
[22] 宋杰.基于FLTK的工程车显示屏GUI系统设计与实现[J].电力机车与城轨车辆, 2013, 36(4): 28-30. DOI: 10.16212/j.cnki.1672-1187.2013.04.011. (
