计算机科学
方向(向量空间)
聚类分析
计算
集合(抽象数据类型)
特征(语言学)
计算机辅助设计
人工智能
工程制图
工业工程
机器学习
数据挖掘
算法
工程类
数学
哲学
几何学
语言学
程序设计语言
作者
Yicha Zhang,Ramy Harik,Georges Fadel,Alain Bernard
出处
期刊:Rapid Prototyping Journal
[Emerald (MCB UP)]
日期:2018-12-14
卷期号:25 (1): 187-207
被引量:49
标识
DOI:10.1108/rpj-04-2018-0102
摘要
Purpose For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining features and costly computation. To deal with these issues, this paper aims to introduce a new statistical method to develop fast automatic decision support tools for additive manufacturing build orientation determination. Design/methodology/approach The proposed method applies a non-supervised machine learning method, K-Means Clustering with Davies–Bouldin Criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. To evaluate alternative build orientations at a generic level, a statistical approach is defined. Findings A group of illustrative examples and comparative case studies are presented in the paper for method validation. The proposed method can help production engineers solve decision problems related to identifying an optimal build orientation for complex and freeform CAD models, especially models from the medical and aerospace application domains with much efficiency. Originality/value The proposed method avoids the limitations of traditional feature-based methods and pure computation-based methods. It provides engineers a new efficient decision-making tool to rapidly determine the optimal build orientation for complex and freeform CAD models.
科研通智能强力驱动
Strongly Powered by AbleSci AI