Potentials and challenges of deep-learningassisted porosity prediction based on thermographic in situ monitoring in laser powder bed fusion

IF 0.8 4区工程技术 Q4 INSTRUMENTS & INSTRUMENTATION

Tm-Technisches Messen Pub Date : 2023-08-18 DOI:10.1515/teme-2023-0062

Simon Oster, N. Scheuschner, Keerthana Chand, S. Altenburg, G. Gerlach

引用次数: 0

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基于热成像原位监测的深度学习辅助孔隙度预测的潜力和挑战

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Tm-Technisches Messen 工程技术-仪器仪表

CiteScore

1.70

自引率

20.00%

发文量

105

审稿时长

6-12 weeks

期刊介绍： The journal promotes dialogue between the developers of application-oriented sensors, measurement systems, and measurement methods and the manufacturers and measurement technologists who use them. Topics The manufacture and characteristics of new sensors for measurement technology in the industrial sector New measurement methods Hardware and software based processing and analysis of measurement signals to obtain measurement values The outcomes of employing new measurement systems and methods.