Robotics & Machine Learning Daily News2024,Issue(Dec.6) :102-103.

Research on Machine Learning Described by Researchers at Singapore University of Technology and Design (Feature-Driven Density Prediction of Maraging Steel Addi tively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learni ng)

新加坡技术与设计大学研究人员描述的机器学习研究(使用高温计传感器和监督机器学习对马氏体时效钢附加制造样品的特征驱动密度预测)

Robotics & Machine Learning Daily News2024,Issue(Dec.6) :102-103.

Research on Machine Learning Described by Researchers at Singapore University of Technology and Design (Feature-Driven Density Prediction of Maraging Steel Addi tively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learni ng)

新加坡技术与设计大学研究人员描述的机器学习研究(使用高温计传感器和监督机器学习对马氏体时效钢附加制造样品的特征驱动密度预测)

扫码查看

摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-关于人工智能的详细数据已经呈现。据新闻报道NewsRx编辑从新加坡坦皮恩斯报道,研究称,“激光粉末床聚变”(LPBF)是一种可旋转的电离添加剂,制造(AM),使粉末颗粒熔化,产生i新产品,但它在监测和预测产品质量方面面临重大挑战打印样品。传统的密度测量方法,如阿基米德技术,已被用来确定在不同机器设置下生产的马氏体时效钢样品的密度(MACH-S)工艺参数,包括激光功率和扫描速度。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Data detailed on artificial intelligen ce have been presented. According to newsreporting out of Tampines, Singapore, by NewsRx editors, research stated, “Laser Powder Bed Fusion(LPBF) is a revolut ionizing additive manufacturing (AM) that melts the powder particles to create innovative products, but it faces substantial challenges in monitoring and predic ting the quality of theprinted samples. Traditional density measurement methods , like the Archimedes technique, have been employed to determine the density of maraging steel samples produced under varying machine settings(MACH-S) process parameters, including laser power and scan speed.”

Key words

Singapore University of Technology and D esign/Tampines/Singapore/Asia/Cyborgs/Emerging Technologies/Machine Learni ng

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
段落导航相关论文