应用特征识别的神经网络模增强塑料部件

第第页应用特征识别的神经网络模增强塑料部件应用特征识别的神经网络模加强塑料部件

Applicationofneuralnetworksinfeaturerecognitionofmouldreinforcedplasticparts

Marquez,M.1,3,4,5;Gill,R.2,6,7;White,A.2,6

Source:ConcurrentEngineeringResearchandApplications,v7,n2,p115-122,June1999;ISSN:1063293*;Publisher:TechnomicPublCoInc

Authoraffiliations:

1

2

3UniversidadNacionalE*perimentaldelTachira(UNET),SanCristobal,VenezuelaSchoolofEngineeringSystems,Middlese*University,London,UnitedKingdomMiddlese*University,SchoolofEngineeringSystems,BoundsGreenRoad,N112NQ,London,UnitedKingdom

4

5

6

7UniversidadNacionalE*perimentaldelTachira(UNET,Venezuela)Middlese*University,London,UnitedKingdomSchoolofEngineeringSystems,Middlese*UniversityCambridgeUniversityManufacturingGroup

Abstract:

Featurerecognitionisanapplicationdependanttask,whichhasbeenmostlyfocusedinproductionplanningofmachiningprocess.Itplaysafundamentalroleandusuallyisthefirststepindownstreamactivitiesconcerningproductdevelopmentprocesssuchasdesignformanufacturing,designforassemblyandprocessplanning.Thisreportpresentsamethodologytocarryoutrecognitionofdesignformanufacturingfeaturesofreinforcedplasticcomponents.Athree-layerneuralnetworksystemwascreatedandtrainedusingback-propagation-supervisedlearningtorecognisenineofthemostimportantdesignfeaturesrelatedtothismanufacturingprocess.Also,amethodologyforpre-processing3-Dsolidmodelssuchthatgeometricalandtopologicalinformationofthepartcouldbesuitableasnetworkinputispresented.Highperformanceofthenetsystemwasachievedontherecognitionofthetrainedfeaturesasitwasobservedinseveraltestparts.(7refs)

Mainheading:

Patternrecognition

Controlledterms:

Backpropagation-Computeraideddesign-Concurrentengineering-Learningsystems-Mathematicalmodels-Neuralnetworks-Plasticparts-Reinforcedplastics

Uncontrolledterms:

Designformanufacturing-Featurerecognition-Mouldreinforcedplasticparts

ClassificationCode:

723.4ArtificialIntelligence-723.5ComputerApplications-817.1PolymerProducts-913.6ProductDevelopment;ConcurrentEngineering-921.6NumericalMethods

Treatment:

Applications(APP)

应用特征识别的神经网络模加强塑料部件

Database:

Compende*应用特征识别的神经网络模加强塑料部件

马尔克斯,M。1、3、4、5,吉尔。2、6、7,白色。2、6

来源:并行工程的讨论和应用,v7n2,p115-122年,1999年6月,台北:1063293*;出版者:Technomic出版有限公司

社会兼职:

1高校试验举措让德尔(UNET)、圣克里斯托瓦尔委内瑞拉

2工程系统,密德萨斯高校,伦敦,英国

3米德尔塞克斯高校工程学院的系统范围内绿色道路,N112向,伦敦,英国

4高校试验举措让德尔(UNET,委内瑞拉)

5米德尔塞克斯高校,伦敦,英国

6工程系统,密德萨斯高校

7剑桥高校制造集团

文摘:

特征识别是一个应用程序依靠的任务,一贯主要集中在加工过程的生产计划。它起着基本的作用,通常是第一步在下游活动涉及产品开发过程如设计制造、组装和设计流程规划。这份报告提出了一个方法进行识别的设计制造加强塑料组件的特性。创建一个三层的神经网络系统学习和训练运用back-propagation-supervised承认九最重要的设计特点与此相关的制造过程。此外,预处理三维固体模型的方法,几何和拓扑信息的一部分可能是合适的作为网络的输入。高性能网络系统实现了识别的训练有素的特性是观测在几个测试部分。参(7)主标题:模式识别

掌握:反向传播——计算机帮助设计并行工程-学习系统数学模型-神经网络——塑料部件——加强塑料

掌握方面:设计制造-特性识别模具加强塑料部件

人工智能分类代码:723.4-723.5计算机应用817.1-817.1聚合物产品——产品开发,并行工程-921.6数值方法

治疗:应用程序(应用程序)

数据库:核心期刊

应用特征识别的神经网络模加强塑料部件

Applicationofneuralnetworksinfeaturerecognitionofmouldreinforcedplasticparts

Marquez,M.1,3,4,5;Gill,R.2,6,7;White,A.2,6

Source:ConcurrentEngineeringResearchandApplications,v7,n2,p115-122,June1999;ISSN:1063293*;Publisher:TechnomicPublCoInc

Authoraffiliations:

1

2

3UniversidadNacionalE*perimentaldelTachira(UNET),SanCristobal,VenezuelaSchoolofEngineeringSystems,Middlese*University,London,UnitedKingdomMiddlese*University,SchoolofEngineeringSystems,BoundsGreenRoad,N112NQ,London,UnitedKingdom

4

5

6

7UniversidadNacionalE*perimentaldelTachira(UNET,Venezuela)Middlese*University,London,UnitedKingdomSchoolofEngineeringSystems,Middlese*UniversityCambridgeUniversityManufacturingGroup

Abstract:

Featurerecognitionisanapplicationdependanttask,whichhasbeenmostlyfocusedinproductionplanningofmachiningprocess.Itplaysafundamentalroleandusuallyisthefirststepindownstreamactivitiesconcerningproductdevelopmentprocesssuchasdesignformanufacturing,designforassemblyandprocessplanning.Thisreportpresentsamethodologytocarryoutrecognitionofdesignformanufacturingfeaturesofreinforcedplasticcomponents.Athree-layerneuralnetworksystemwascreatedandtrainedusingback-propagation-supervisedlearningtorecognisenineofthemostimportantdesignfeaturesrelatedtothismanufacturingprocess.Also,amethodologyforpre-processing3-Dsolidmodelssuchthatgeometricalandtopologicalinformationofthepartcouldbesuitableasnetworkinputispresented.Highperformanceofthenetsystemwasachievedontherecognitionofthetrainedfeaturesasitwasobservedinseveraltestparts.(7refs)

Mainheading:

Patternrecognition

Controlledterms:

Backpropagation-Computeraideddesign-Concurrentengineering-Learningsystems-Mathematicalmodels-Neuralnetworks-Plasticparts-Reinforcedplastics

Uncontrolledterms:

Designformanufacturing-Featurerecognition