Robotics & Machine Learning Daily News2024,Issue(Nov.21) :39-39.

Data from Singapore University of Technology and Design Provide New Insights int o Machine Learning (Architected Design and Fabrication of Soft Mechanical Metama terials)

新加坡技术与设计大学提供的数据机器学习(架构设计和制造)的新见解软机械金属材料

Robotics & Machine Learning Daily News2024,Issue(Nov.21) :39-39.

Data from Singapore University of Technology and Design Provide New Insights int o Machine Learning (Architected Design and Fabrication of Soft Mechanical Metama terials)

新加坡技术与设计大学提供的数据机器学习(架构设计和制造)的新见解软机械金属材料

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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-调查人员讨论机器学习的新发现。根据新闻报道这项研究源于新加坡,新加坡e,由NewsRx记者撰写,研究称:“这项研究提出了一个三维打印柔性机械超材料的系统设计方法以及使用最佳刀轨和打印参数。一层一层地使用的平面印刷工具印刷,如融合沉积模型(FDM)和普遍用于大多数切片算法,严重影响了限制超材料所需复杂拓扑的实现。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingoriginating in Singapore, Singapor e, by NewsRx journalists, research stated, “This study proposes asystematic des ign approach to 3D print soft mechanical metamaterials by tuning material flow b ehaviorand using optimal toolpaths and print parameters. Planar printing tool p aths, utilized in layer-by-layerprinting such as fused deposition modeling (FDM ) and prevalently used in most slicing algorithms, severelylimit the realizatio n of complex topologies required in metamaterials.”

Key words

Singapore/Singapore/Asia/Machine Lear ning/Singapore University of Technology and Design

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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