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激光粉末床熔融中小孔生成过程中机器学习辅助的实时检测
作者:小柯机器人 发布时间:2023/1/9 13:40:14

美国弗吉尼亚大学Tao Sun小组报道了在激光粉末床熔融成形过程中,机器学习辅助的小孔生成的实时检测。该项研究成果发表在2023年1月6日出版的《科学》上。

利用高速同步X射线成像和热成像技术,结合多物理场模拟,研究人员发现了Ti-6Al-4V激光粉末床融合中的两种类型的锁孔振荡。利用机器学习,课题组开发了一种检测随机锁孔孔隙生成事件的方法,该方法具有亚毫秒级的时间分辨率和近乎完美的预测率。operando X射线成像所实现的高度准确的数据标记,展示了一种在商业系统中简单而实用的方法。

据介绍,多孔性缺陷是目前阻碍激光金属增材制造技术广泛采用的主要因素。当输入过多的激光能量形成不稳定的蒸汽凹陷区(锁孔)时,就会出现常见的孔隙。

附:英文原文

Title: Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion

Author: Zhongshu Ren, Lin Gao, Samuel J. Clark, Kamel Fezzaa, Pavel Shevchenko, Ann Choi, Wes Everhart, Anthony D. Rollett, Lianyi Chen, Tao Sun

Issue&Volume: 2023-01-06

Abstract: Porosity defects are currently a major factor that hinders the widespread adoption of laser-based metal additive manufacturing technologies. One common porosity occurs when an unstable vapor depression zone (keyhole) forms because of excess laser energy input. With simultaneous high-speed synchrotron x-ray imaging and thermal imaging, coupled with multiphysics simulations, we discovered two types of keyhole oscillation in laser powder bed fusion of Ti-6Al-4V. Amplifying this understanding with machine learning, we developed an approach for detecting the stochastic keyhole porosity generation events with submillisecond temporal resolution and near-perfect prediction rate. The highly accurate data labeling enabled by operando x-ray imaging allowed us to demonstrate a facile and practical way to adopt our approach in commercial systems.

DOI: add4667

Source: https://www.science.org/doi/10.1126/science.add4667

 

期刊信息
Science:《科学》,创刊于1880年。隶属于美国科学促进会,最新IF:63.714