Assessment of predictive probability models for effective mechanical design feature reuse
Assessment of predictive probability models for effective mechanical design feature reuse
复制标题
DOI：
10.1017/s0890060422000014
复制
发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
G. Vasantha;David Purves;J. Quigley;J. Corney;A. Sherlock;Geevin Randika
中科院分区：
文献类型：
--
作者：
G. Vasantha;David Purves;J. Quigley;J. Corney;A. Sherlock;Geevin Randika
摘要本研究设想了一个自动化系统，以通知工程师，当有机会在新产品的开发过程中使用现有的功能或配置。这样的系统可以被称为“预测CAD系统”，因为它将能够建议遵循现有产品中建立的模式的特征选择。预测性CAD文献主要集中在使用3D实体模型预测组件的组件。与此相反，本研究工作的重点是基于特征的预测CAD系统，使用B-rep模型。本文研究了预测模型的性能，可以通过评估三种不同的方法来支持推理：顺序，机器学习或概率方法，使用N-Gram，神经网络（NN）和贝叶斯网络（BN）作为这些方法的代表，从而创建这样一个智能CAD系统。在定义了预测设计系统的功能属性后，提出了一种通用的开发方法。该方法是用来进行系统的评估的相对性能的三种方法，每种方法用于预测的直径值的下一个孔和凸台的功能类型被添加在设计的液压阀体。评估预测性能提供了五个建议（$k = 5$）的孔或凸台的功能作为一个新的设计开发，召回@k从大约30%增加到50%，精度@k从大约50%到70%，作为一个到三个功能被添加。结果表明，BN和NN模型的性能优于使用N-Gram。这一贡献的实际影响进行评估，使用原型（实施作为一个商业CAD系统的扩展）的工程师的意见定义了正在进行的研究在这一领域的议程。
Abstract This research envisages an automated system to inform engineers when opportunities occur to use existing features or configurations during the development of new products. Such a system could be termed a "predictive CAD system" because it would be able to suggest feature choices that follow patterns established in existing products. The predictive CAD literature largely focuses on predicting components for assemblies using 3D solid models. In contrast, this research work focuses on feature-based predictive CAD system using B-rep models. This paper investigates the performance of predictive models that could enable the creation of such an intelligent CAD system by assessing three different methods to support inference: sequential, machine learning, or probabilistic methods using N-Grams, Neural Networks (NNs), and Bayesian Networks (BNs) as representative of these methods. After defining the functional properties that characterize a predictive design system, a generic development methodology is presented. The methodology is used to carry out a systematic assessment of the relative performance of three methods each used to predict the diameter value of the next hole and boss feature type being added during the design of a hydraulic valve body. Evaluating predictive performance providing five recommendations ($k = 5$) for hole or boss features as a new design was developed, recall@k increased from around 30% to 50% and precision@k from around 50% to 70% as one to three features were added. The results indicate that the BN and NN models perform better than those using N-Grams. The practical impact of this contribution is assessed using a prototype (implemented as an extension to a commercial CAD system) by engineers whose comments defined an agenda for ongoing research in this area.