Robotics & Machine Learning Daily News2024,Issue(Jun.18) :51-52.

Studies from University of Stuttgart Add New Findings in the Area of Machine Lea rning (Towards Efficient Powder Quality Control In Additive Manufacturing Via an In Situ Capable Device and Methodology Leveraging Multispectral Machine Learnin g)

斯图加特大学的研究增加了机器学习领域的新发现(通过利用多光谱机器学习的现场设备和方法实现添加剂生产中的有效粉末质量控制)

Robotics & Machine Learning Daily News2024,Issue(Jun.18) :51-52.

Studies from University of Stuttgart Add New Findings in the Area of Machine Lea rning (Towards Efficient Powder Quality Control In Additive Manufacturing Via an In Situ Capable Device and Methodology Leveraging Multispectral Machine Learnin g)

斯图加特大学的研究增加了机器学习领域的新发现(通过利用多光谱机器学习的现场设备和方法实现添加剂生产中的有效粉末质量控制)

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摘要

由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-关于机器学习的最新研究结果已经发表。根据NewsRx编辑在德国斯图加特的新闻报道,research称:“添加制造(AM)工艺能够制造不能用常规制造方法制造的高度复杂的零件。恒定和特定的材料性能对于这些高度优化的零件至关重要。”这项研究的财政支持来自德国联邦宗教事务和能源部斯图加特大学的技术转让项目基金。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting out of Stuttgart, Germany, by NewsRx editors, research stated, "Additive manufacturing (AM) processes enabl es the fabrication of highly complex parts that cannot be manufactured using con ventional manufacturing methods. Constant and specified material properties are of crucial importance for these highly optimized components." Financial support for this research came from German Federal Ministry for Econom ic Affairs and Energy - University of Stuttgart in a technology transfer project fund.

Key words

Stuttgart/Germany/Europe/Cyborgs/Eme rging Technologies/Machine Learning/University of Stuttgart

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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