基于反向传播神经网络的注塑模具用零件报价模型

doi:10.16183/j.cnki.jsjtu.2017.10.009

摘要/Abstract

摘要： 针对诸多通用报价方法在注塑模具用零件价格评估过程中存在的效率与精度等问题，提出了基于反向传播（Back Propagation, BP）神经网络的多专家报价模型.利用遗传算法优化BP网络权值和阈值抵御随机参数引起的局部最优解，同时对报价误差的高斯分布规律进行分析以确定理想的拓扑结构.通过多专家模型给出的多个报价，提出了价格评估算法筛选合理报价，避免单一模型报价的随机性.经过验证，该方法有较高的准确性和效率，报价平均误差约为6.90%.此报价模型降低了传统报价的难度，并提高了其稳定性.

关键词: , 注塑模具用零件, 价格评估, 多专家模型, 神经网络

Abstract: General part quotations for injection mould often hold shortcomings such as low efficiency and poor accuracy. Aiming at this issue, a multiexpert quotation framework is proposed based on back propagation (BP) neural network (NN) in this paper. Weights and thresholds of the network are optimized by using generic algorithm which can avoid to fall into a local optimum solution. Meanwhile, an ideal topological structure of NN is decided by studying the Gaussian distribution of errors. A rotational quotation is thereby achieved after synthetical consideration of the evaluating results by synthesizing multiple experts. It is proved that the method greatly eliminates the randomness of a single quotation. Experiments exhibit a preferable performance, in which the average error of quotations is about 6.90%. This scheme reduces the difficulty of traditional quotation and enhances its robustness.

Key words: part of injection mould, price estimation, multiexpert model, neural network

中图分类号:

TP 391

柳伟，杨超. 基于反向传播神经网络的注塑模具用零件报价模型[J]. 上海交通大学学报（自然版）, 2017, 51(10): 1207-1213.

LIU Wei,YANG Chao . A Study on Injection Mould Part Quotation Model
Based on Back Propagation Neural Network[J]. Journal of Shanghai Jiaotong University, 2017, 51(10): 1207-1213.

参考文献

［1］刘航. 模具价格估算［M］. 北京: 机械工业出版社, 2015.
［2］NGEL G C, BELN R M, JOS L L, et al. A review of conventional and knowledge based systems for machining price quotation［J］. Journal of Intelligent Manufacturing, 2011, 22(6): 823841.
［3］罗志清, 王润孝, 骞爱荣. 模具产品制造成本与生产周期估计模型研究［J］. 计算机集成制造系统, 2005, 11(12): 16591662.
LUO Zhiqing, WANG Runxiao, QIAN Airong. Research on estimation model of die manufacturing cost & production cycle［J］. Computer Integrated Manufacturing Systems, 2005, 11(12): 16591662.
［4］刘吉祥, 柳玉起, 章志兵, 等. 基于零件特征的汽车覆盖件模具精细报价方法研究［J］. 模具工业, 2016, 42(2): 16.
LIU Jixiang, LIU Yuqi, ZHANG Zhibing, et al. Research on fine die quotation methods based on the features of automobile panel［J］. Die & Mould Industry, 2016, 42(2): 16.
［5］李亨, 王成勇, 肖福成. 基于规则的小型冲压模具报价系统［J］. 合肥工业大学学报(自然科学版), 2009, 32(1): 3639.
LI Heng, WANG Chengyong, XIAO Fucheng. Rulebased cost estimation system of smallsized stamping dies［J］. Journal of Hefei University of Technology (Science), 2009, 31(1): 3639.
［6］LAN Hongbo, DING Yucheng, HONG Jun, et al. Webbased quotation system for stereolithography parts［J］. Computers in Industry, 59(8): 777785.
［7］BOUAZIZ Z, YOUNES J B, ZGHAL A. Methodology of machining cost evaluation for die and mold manufacturing［J］. Journal of Materials Processing Technology, 2004, 152(2):237245.
［8］WU J D, LIU J C. A forecasting system for car fuel consumption using a radial basis function neural network［J］. Expert Systems with Applications, 2012, 39(2): 18831888.
［9］CHE Z H. Psobased backpropagation artificial neural network for product and mold cost estimation of plastic injection molding［J］. Computers & Industrial Engineering, 2010, 58(4): 625637.
［10］DENG S, YEH T H. Using least squares support vector machines for the airframe structures manufacturing cost estimation［J］. International Journal of Production Economics, 2011, 131(2): 701708.
［11］JRGEN B. Neural networks for cost estimation: Simulations and pilot application［J］. International Journal of Production Research, 2000, 38(6): 12311254.
［12］LIU W, HE Y J. Representation and retrieval of 3D CAD models in parts library［J］. The International Journal of Advanced Manufacturing Technology, 2008, 36(910): 950958.
［13］LI Z, ZHOU X H, LIU W. A geometric reasoning approach to hierarchical representation for Brep model retrieval［J］. ComputerAided Design, 2015, 62(5): 190202.
［14］SIMON O H. Neural networks and learning machines ［M］. New Jersey: Prentice Hall, 2008.
［15］单汨源, 於永和. 大规模定制产品多级神经网络成本估算方法研究［J］. 中国机械工程, 2004, 15(11): 10041007.
SHAN Miyuan, YU Yonghe. A study on multilayer neural networks cost evaluation of mass customization product［J］. China Mechanical Engineering, 2004, 15(11): 10041007.
［16］蔺威, 朱玉明, 刘继红. 基于前馈神经网络的汽车覆盖件模具报价系统［J］. 计算机集成制造系统, 2009, 15(11): 22802287.
LIN Wei, ZHU Yuming, LIU Jihong. Auto panel die quotaion system based on back propagation neural network［J］. Computer Integrated Manufacturing Systems, 2009, 15(11): 22802287.
［17］贾超. 基于神经网络的多模型自适应控制方法研究［D］. 北京: 北京科技大学自动化学院, 2017.

[1]	曾国治, 魏子清, 岳宝, 丁云霄, 郑春元, 翟晓强. 基于CNN-RNN组合模型的办公建筑能耗预测[J]. 上海交通大学学报, 2022, 56(9): 1256-1261.
[2]	吴庶宸, 戚宗锋, 李建勋. 基于深度学习的智能全局灵敏度分析[J]. 上海交通大学学报, 2022, 56(7): 840-849.
[3]	全大英, 陈赟, 唐泽雨, 李世通, 汪晓锋, 金小萍. 基于双通道卷积神经网络的雷达信号识别[J]. 上海交通大学学报, 2022, 56(7): 877-885.
[4]	秦艺超, 黄礼敏, 王骁, 马学文, 段文洋, 郝伟. 基于人工神经网络的自航浮标测波方法可行性[J]. 上海交通大学学报, 2022, 56(4): 498-505.
[5]	赵勇, 苏丹. 基于4种长短时记忆神经网络组合模型的畸形波预报[J]. 上海交通大学学报, 2022, 56(4): 516-522.
[6]	聂瑞, 王红茹. 基于神经网络观测器的无人机编队执行器故障诊断[J]. 空天防御, 2022, 5(2): 32-41.
[7]	王聚团, 戚晓宁, 黄志明. 水下生产管汇测试技术及其改进研究[J]. 海洋工程装备与技术, 2022, 9(2): 43-49.
[8]	袁振钦, 邹科, 孙亚峰, 刘刚, 屈衍, 李居跃. 基于时域分析法的动态电缆疲劳分析[J]. 海洋工程装备与技术, 2022, 9(2): 50-55.
[9]	丁明, 孟帅, 王书恒, 夏玺. 六自由度波浪补偿平台的神经网络自适应反馈线性化控制[J]. 上海交通大学学报, 2022, 56(2): 165-172.
[10]	刘秀丽, 徐小力. 基于特征金字塔卷积循环神经网络的故障诊断方法[J]. 上海交通大学学报, 2022, 56(2): 182-190.
[11]	王卓鑫, 赵海涛, 谢月涵, 任翰韬, 袁明清, 张博明, 陈吉安. 反向传播神经网络联合遗传算法对复合材料模量的预测[J]. 上海交通大学学报, 2022, 56(10): 1341-1348.
[12]	王娟, 杨明旺, 郑茂尧, 刘凌云, 赵立君. 高强钢在大型半潜式平台组块建造中的应用[J]. 海洋工程装备与技术, 2022, 9(1): 27-31.
[13]	陈欣, 赵晓磊, 王立坤, 肖德明, 张腾月. 深水大型吸力锚建造技术研究[J]. 海洋工程装备与技术, 2022, 9(1): 32-36.
[14]	尹彦坤, 易涤非. 半潜式生产平台船体结构关键节点工程临界评估[J]. 海洋工程装备与技术, 2022, 9(1): 52-57.
[15]	罗睿乔. 井下节流技术在南海东部高温气田的应用[J]. 海洋工程装备与技术, 2022, 9(1): 58-66.