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增强塑料部件

Application of neural networks in feature recognition of mould reinforced plastic parts

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

Source: Concurrent Engineering Research and Applications, v 7, n 2, p 115-122, June 1999; ISSN: 1063293X; Publisher: Technomic Publ Co Inc

Author affiliations:

1

2

3 Universidad Nacional Experimental del Tachira (UNET), San Cristobal, Venezuela School of Engineering Systems, Middlesex University, London, United Kingdom Middlesex University, School of Engineering Systems, Bounds Green Road, N11 2NQ, London, United Kingdom

4

5

6

7 Universidad Nacional Experimental del Tachira (UNET, Venezuela) Middlesex University, London, United Kingdom School of Engineering Systems, Middlesex University Cambridge University Manufacturing Group

Abstract:

Feature recognition is an application dependant task, which has been mostly focused in production planning of machining process. It plays a fundamental role and usually is the first step in downstream activities concerning product development process such as design for manufacturing, design for assembly and process planning. This report presents a methodology to carry out recognition of design for manufacturing features of reinforced plastic components. A three-layer neural network system was created and trained using back-propagation-supervised learning to recognise nine of the most important design features related to this manufacturing process. Also, a methodology for pre-processing 3-D solid models such that geometrical and topological information of the part could be suitable as network input is presented. High performance of the net system was achieved on the recognition of the trained features as it was observed in several test parts.(7 refs)

Main heading:

Pattern recognition

Controlled terms:

Backpropagation - Computer aided design - Concurrent engineering - Learning systems - Mathematical models - Neural networks - Plastic parts - Reinforced plastics

Uncontrolled terms:

Design for manufacturing - Feature recognition - Mould reinforced plastic parts

Classification Code:

723.4 Artificial Intelligence - 723.5 Computer Applications - 817.1 Polymer Products - 913.6 Product Development; Concurrent Engineering - 921.6 Numerical Methods

Treatment:

Applications (APP)

应用特征识别的神经网络模增强塑料部件

Database:

Compendex 应用特征识别的神经网络模增强塑料部件

马尔克斯,M。1、3、4、5,吉尔。2、6、7,白色。2、6

来源:并行工程的研究和应用,v 7 n 2,p 115 - 122年,1999年6月,台北:1063293 x;出版者:Technomic出版有限公司

作者社会兼职:

1大学实验举措让德尔(UNET)、圣克里斯托瓦尔委内瑞拉

2工程系统,密德萨斯大学,伦敦,英国

3米德尔塞克斯大学工程学院的系统范围内绿色道路,N11 2向,伦敦,英国

4大学实验举措让德尔(UNET,委内瑞拉)

5米德尔塞克斯大学,伦敦,英国

6工程系统,密德萨斯大学

7剑桥大学制造集团

文摘:

特征识别是一个应用程序依赖的任务,一直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增强塑料组件的特性。创建一个三层的神经网络系统学习和训练使用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察在几个测试部分。参(7) 主标题:模式识别

控制:反向传播——计算机辅助设计并行工程-学习系统数学模型-神经网络——塑料部件——增强塑料

控制方面:设计制造-特性识别模具增强塑料部件

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

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