当前位置: X-MOL 学术Mater. Charact. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Zoning additive manufacturing process histories using unsupervised machine learning
Materials Characterization ( IF 4.7 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.matchar.2020.110123
Sean P. Donegan , Edwin J. Schwalbach , Michael A. Groeber

Abstract Metal additive manufacturing (AM) is currently a highly active area of research within the materials processing and manufacturing community, owing to the promises of lower lead time, increased design flexibility, and location-specific process control. These benefits are balanced by a typically complex processing space, resulting in difficulties when attempting to generalize process-structure relationships across different component geometries. We develop a procedure for reducing the overall complexity of a representation of the AM processing space using techniques from time series analysis and dimensionality reduction. This procedure is highly generic and applicable to a variety of time sequenced signals. We then utilize this reduced feature space as input to a cluster analysis, producing zoned maps of process history. We apply the approach to several canonical geometries, describe its overall utility, and discuss the implications of the resulting zonings in terms of the underlying digital process.

中文翻译:

使用无监督机器学习对增材制造过程历史进行分区

摘要金属增材制造 (AM) 目前是材料加工和制造领域中一个非常活跃的研究领域,因为它有望缩短交货时间、提高设计灵活性和特定位置的过程控制。这些优势与通常复杂的处理空间相平衡,导致在尝试概括不同组件几何形状的过程-结构关系时遇到困难。我们开发了一种使用时间序列分析和降维技术来降低 AM 处理空间表示的整体复杂性的程序。这个过程是高度通用的,适用于各种时序信号。然后我们利用这个减少的特征空间作为聚类分析的输入,生成过程历史的分区图。
更新日期:2020-03-01
down
wechat
bug