面向智能制造的真空注型工艺质量控制方法
|
摘要
针对真空注型(VC)产品质量依赖于工艺设计人员经验,造成VC装备自动化程度低、加工柔性差、产品质量难以控制等问题,基于计算机图形学和人工智能技术,建立一种面向VC工艺的质量控制模型。通过构建体素化模型,近似求解壁厚、均匀程度、体积等模具型腔几何特征参数;在此基础上,融合案例推理和神经网络推理技术,建立型腔几何特征参数与成形工艺间的关系模型,充分利用历史案例,实现初始工艺参数的智能推荐;利用基于规则的模糊逻辑推理方法,挖掘工艺设计人员经验,对试模后的产品缺陷进行智能修正。生产实例说明,基于上述思想方法和推理机制的质量控制模型有较好的推理和质量控制能力。
According to the problems that the VC product quality was difficult to be controlled,low automation of VC equipment and poor flexible processing,depended on the experience of process designers,a quality control model for VC processes was established based on the computer graphics and artificial intelligence.By constructing the voxel model,the geometric parameters of the cavity,such as wall thickness,uniformity and volume,were approximated.On the basis of this,case-based reasoning and network-based reasoning technology were used to establish the relationship model between cavity geometrical parameters and forming processes,which made full use of the historical processing cases to achieve intelligent recommendation of initial processing parameters; then technicians' experiences were excavated to correct product defects by fuzzy inference after trial mode.The examples show that the quality control model based on the above thought method and reasoning mechanism has better reasoning and quality control ability.
According to the problems that the VC product quality was difficult to be controlled,low automation of VC equipment and poor flexible processing,depended on the experience of process designers,a quality control model for VC processes was established based on the computer graphics and artificial intelligence.By constructing the voxel model,the geometric parameters of the cavity,such as wall thickness,uniformity and volume,were approximated.On the basis of this,case-based reasoning and network-based reasoning technology were used to establish the relationship model between cavity geometrical parameters and forming processes,which made full use of the historical processing cases to achieve intelligent recommendation of initial processing parameters; then technicians' experiences were excavated to correct product defects by fuzzy inference after trial mode.The examples show that the quality control model based on the above thought method and reasoning mechanism has better reasoning and quality control ability.
引文
[1] 刘洪军,李亚敏,曹驰.快速模具制造技术分析与发展趋势[J].模具工业,2010,36(3):63-66.LIU Hongjun,LI Yamin,CAO Chi.Development Trends and Demand Analysis of Rapid Tooling Technology[J].Die&Mould Industry,2010,36(3):63-66.
[2] 欧志华.硅胶快速模具真空注型过程CAE分析[D].天津:天津大学,2008.OU Zhihua.CAE Analysis of the Processing of Vacuum Casting for Rapid Tooling[D].Tianjin:Tianjin University,2008.
[3] 周济.智能制造——“中国制造2025”的主攻方向[J].中国机械工程,2015,26(17):2273-2284.ZHOU Ji.Intelligent Manufacturing—Main Direction of “Made in China 2025”[J].China Mechanical Engineering,2015,26(17):2273-2284.
[4] 罗晨,王欣,苏春,等.基于案例推理的夹具设计案例表示与检索[J].机械工程学报,2015,51(7):136-143.LUO Chen,WANG Xin,SU Chun,et al.Case Representation and Retrieval under a Case-based Reasoning System for Fixture Design[J].Journal of Mechanical Engineering,2015,51(7):136-143.
[5] 刘伟,邓朝晖,万林林,等.基于正交试验-遗传神经网络的陶瓷球面精密磨削参数优化[J].中国机械工程,2014,25(4):451-455.LIU Wei,DENG Chaohui,WAN Linlin,et al.Parameter Optimization on Precision Grinding of Ceramic Sphere Using Orthogonal Experiment and Genetic Neural Network[J].China Mechanical Engineering,2014,25(4):451-455.
[6] 曾莎莎,彭卫平,雷金.基于混合算法的薄壁件铣削加工工艺参数优化[J].中国机械工程,2017,28(7):842-845.ZENG Shasha,PENG Weiping,LEI Jin.Optimization of Milling Process Parameters Based on Hybrid Algorithm for Thin-walled Workpieces[J].China Mechanical Engineering,2017,28(7):842-845.
[7] CHEN W C,FU G L,TAI P H,et al.An Integrated Parameter Optimization system for MIMO Plastic Injection Molding Using Soft Computing[J].International Journal of Advanced Manufacturing Technology,2014,73 (9/12):1465-1474.
[8] CHEN W C,KURNIAWAN D.Process Parameters Optimization for Multiple Quality Characteristics in Plastic Injection Molding Using Taguchi Method,BPNN,GA,and Hybrid PSO-GA[J].International Journal of Precision Engineering and Manufacturing,2014,15(8):1583-1593.
[9] ZHAO J,CHENG G.An Innovative Surrogate-based Searching Method for Reducing Warpage and Cycle Time in Injection Molding[J].Advances in Polymer Technology,2016,35(3):288-297.
[10] 时培明,梁凯,赵娜,等.基于深度学习特征提取和粒子群支持向量机状态识别的齿轮智能故障诊断[J].中国机械工程,2017,28(9):1056-1061.SHI Peiming,LIANG Kai,ZHAO Na,et al.Intelligent Fault Diagnosis for Gears Based on Deep Learning Feature Extraction and Particle Swarm Optimization SVM State Identification[J].China Mechanical Engineering,2017,28(9):1056-1061.
[11] 张海光,宋晨霞,胡庆夕.调压式真空注型充型速度智能调控方法研究[J].机械工程学报,2015,51(21):165-173.ZHANG Haiguang,SONG Chenxia,HU Qingxi.Study on the Intelligent Control Method of Filling Velocity Oriented to Adjustable Pressure Vacuum Casting[J].Journal of Mechanical Engineering,2015,51(21):165-173.
[12] 张壮雅,张海光,万福平,等.面向真空注型技术的双组分材料混合脱泡方法[J].机械工程学报,2013,49(23):28-35.ZHANG Zhuangya,ZHANG Haiguang,WAN Fuping,et al.Methodology of Two-components Material Agitating Deaeration for Vacuum Casting[J].Journal of Mechanical Engineering,2013,49(23):28-35.
[13] 张海光,胡庆夕,刘媛媛.差压式真空注型过程仿真及工艺参数优化[J].制造业自动化,2012,34(2):51-52.ZHANG Haiguang,HU Qingxi,LIU Yuanyuan.Simulation and Process Parameter Optimization of Differentiai Pressure Vacuum Casting[J].Manufacturing Automation,2012,34(2):51-52.
[14] 张海光,张壮雅,万福平,等.基于熵权与响应面模型的DPVC工艺多目标优化研究[J].计算机集成制造系统,2014,20(8):1887-1895.ZHANG Haiguang,ZHANG Zhuangya,Wan Fu-ping,et al.Multi-objective Optimization for DPVC Process on Entropy-weight and RSM[J].Computer Integrated Manufacturing Systems,2014,20(8):1887-1895.
[15] 刘佳莉.基于案例推理的机械产品设计决策支持方法研究[D].哈尔滨:哈尔滨工业大学,2009:35-55.LIU Jiali.Study on Decision Support Technique for Mechanical Product Design Based on Case-based Reasoning[D].Harbin:Harbin Institute of Technology,2009:35-55.
[16] 黄鑫,赵捍东.基于广义回归神经网络的弹丸落点预报方法[J].测试科学与仪器,2016,7(1):7-12.HUANG Xin,ZHAO Handong.Projectile Impact Point Prediction Method Based on GRNN[J].Journal of Measurement Science and Instrumentation,2016,7(1):7-12.
[17] 贺徽,周建中,谭建华,等.基于Mamdani模糊PID的同步发电机励磁控制[J].华中科技大学学报(自然科学版),2010,38(2):34-37.HE Hui,ZHOU Jianzhong,TAN Jianhua,et al.Research and Application of a Fuzzy PID Controller for Excitation Control Systems in Synchronous Generators Using Mamdani Model[J].Journal of Huazhong University of Science and Technology(Natural Science Edition),2010,38(2):34-37.
[2] 欧志华.硅胶快速模具真空注型过程CAE分析[D].天津:天津大学,2008.OU Zhihua.CAE Analysis of the Processing of Vacuum Casting for Rapid Tooling[D].Tianjin:Tianjin University,2008.
[3] 周济.智能制造——“中国制造2025”的主攻方向[J].中国机械工程,2015,26(17):2273-2284.ZHOU Ji.Intelligent Manufacturing—Main Direction of “Made in China 2025”[J].China Mechanical Engineering,2015,26(17):2273-2284.
[4] 罗晨,王欣,苏春,等.基于案例推理的夹具设计案例表示与检索[J].机械工程学报,2015,51(7):136-143.LUO Chen,WANG Xin,SU Chun,et al.Case Representation and Retrieval under a Case-based Reasoning System for Fixture Design[J].Journal of Mechanical Engineering,2015,51(7):136-143.
[5] 刘伟,邓朝晖,万林林,等.基于正交试验-遗传神经网络的陶瓷球面精密磨削参数优化[J].中国机械工程,2014,25(4):451-455.LIU Wei,DENG Chaohui,WAN Linlin,et al.Parameter Optimization on Precision Grinding of Ceramic Sphere Using Orthogonal Experiment and Genetic Neural Network[J].China Mechanical Engineering,2014,25(4):451-455.
[6] 曾莎莎,彭卫平,雷金.基于混合算法的薄壁件铣削加工工艺参数优化[J].中国机械工程,2017,28(7):842-845.ZENG Shasha,PENG Weiping,LEI Jin.Optimization of Milling Process Parameters Based on Hybrid Algorithm for Thin-walled Workpieces[J].China Mechanical Engineering,2017,28(7):842-845.
[7] CHEN W C,FU G L,TAI P H,et al.An Integrated Parameter Optimization system for MIMO Plastic Injection Molding Using Soft Computing[J].International Journal of Advanced Manufacturing Technology,2014,73 (9/12):1465-1474.
[8] CHEN W C,KURNIAWAN D.Process Parameters Optimization for Multiple Quality Characteristics in Plastic Injection Molding Using Taguchi Method,BPNN,GA,and Hybrid PSO-GA[J].International Journal of Precision Engineering and Manufacturing,2014,15(8):1583-1593.
[9] ZHAO J,CHENG G.An Innovative Surrogate-based Searching Method for Reducing Warpage and Cycle Time in Injection Molding[J].Advances in Polymer Technology,2016,35(3):288-297.
[10] 时培明,梁凯,赵娜,等.基于深度学习特征提取和粒子群支持向量机状态识别的齿轮智能故障诊断[J].中国机械工程,2017,28(9):1056-1061.SHI Peiming,LIANG Kai,ZHAO Na,et al.Intelligent Fault Diagnosis for Gears Based on Deep Learning Feature Extraction and Particle Swarm Optimization SVM State Identification[J].China Mechanical Engineering,2017,28(9):1056-1061.
[11] 张海光,宋晨霞,胡庆夕.调压式真空注型充型速度智能调控方法研究[J].机械工程学报,2015,51(21):165-173.ZHANG Haiguang,SONG Chenxia,HU Qingxi.Study on the Intelligent Control Method of Filling Velocity Oriented to Adjustable Pressure Vacuum Casting[J].Journal of Mechanical Engineering,2015,51(21):165-173.
[12] 张壮雅,张海光,万福平,等.面向真空注型技术的双组分材料混合脱泡方法[J].机械工程学报,2013,49(23):28-35.ZHANG Zhuangya,ZHANG Haiguang,WAN Fuping,et al.Methodology of Two-components Material Agitating Deaeration for Vacuum Casting[J].Journal of Mechanical Engineering,2013,49(23):28-35.
[13] 张海光,胡庆夕,刘媛媛.差压式真空注型过程仿真及工艺参数优化[J].制造业自动化,2012,34(2):51-52.ZHANG Haiguang,HU Qingxi,LIU Yuanyuan.Simulation and Process Parameter Optimization of Differentiai Pressure Vacuum Casting[J].Manufacturing Automation,2012,34(2):51-52.
[14] 张海光,张壮雅,万福平,等.基于熵权与响应面模型的DPVC工艺多目标优化研究[J].计算机集成制造系统,2014,20(8):1887-1895.ZHANG Haiguang,ZHANG Zhuangya,Wan Fu-ping,et al.Multi-objective Optimization for DPVC Process on Entropy-weight and RSM[J].Computer Integrated Manufacturing Systems,2014,20(8):1887-1895.
[15] 刘佳莉.基于案例推理的机械产品设计决策支持方法研究[D].哈尔滨:哈尔滨工业大学,2009:35-55.LIU Jiali.Study on Decision Support Technique for Mechanical Product Design Based on Case-based Reasoning[D].Harbin:Harbin Institute of Technology,2009:35-55.
[16] 黄鑫,赵捍东.基于广义回归神经网络的弹丸落点预报方法[J].测试科学与仪器,2016,7(1):7-12.HUANG Xin,ZHAO Handong.Projectile Impact Point Prediction Method Based on GRNN[J].Journal of Measurement Science and Instrumentation,2016,7(1):7-12.
[17] 贺徽,周建中,谭建华,等.基于Mamdani模糊PID的同步发电机励磁控制[J].华中科技大学学报(自然科学版),2010,38(2):34-37.HE Hui,ZHOU Jianzhong,TAN Jianhua,et al.Research and Application of a Fuzzy PID Controller for Excitation Control Systems in Synchronous Generators Using Mamdani Model[J].Journal of Huazhong University of Science and Technology(Natural Science Edition),2010,38(2):34-37.