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相关概念视频

Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

91
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
91

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相关实验视频

Updated: Jun 17, 2025

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography
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机器学习用于功能分级材料的增材制造.

Mohammad Karimzadeh1, Deekshith Basvoju2, Aleksandar Vakanski2

  • 1Department of Computer Science, University of Idaho, Moscow, ID 83844, USA.

Materials (Basel, Switzerland)
|August 10, 2024
PubMed
概括
此摘要是机器生成的。

本综述探讨了机器学习 (ML) 如何优化功能级材料 (FGM) 的增材制造 (AM). ML解决了FGM制造方面的挑战,提高了各行各业的组件性能.

关键词:
添加剂制造 添加剂制造 添加剂制造定向能量沉积是指向能量沉积的方法.具有功能分级的材料.机器学习是机器学习.

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 制造业 制造技术 制造技术
  • 人工智能的人工智能

背景情况:

  • 增材制造 (AM) 可从3D数据直接制造复杂的零件.
  • 功能分级材料 (FGM) 通过在材料之间进行过渡来提供量身定制的性能.
  • 在各种行业中,FGM具有显著的潜力来提高组件性能.

研究的目的:

  • 在AM中全面审查有关机器学习 (ML) 实施的文献.
  • 专注于基于ML的方法来优化FGM制造过程.
  • 探索ML在解决FGM生产中的挑战中的作用.

主要方法:

  • 对已发表的关于女性生殖器切割在女性生殖器切割中ML的文献进行了广泛的调查.
  • 对FGM的参数优化中ML应用的分析.
  • 在AM中检测缺陷和实时监控的ML技术的审查.

主要成果:

  • ML技术越来越多地应用于克服FGM制造中固有的挑战.
  • 机器学习有助于优化过程参数,改善缺陷检测,并使实时监控成为可能.
  • 机器学习的整合增强了AM生产先进FGM的潜力.

结论:

  • 机器学习提供了强大的工具,可以通过增材制造来推进功能级材料的制造.
  • 需要进一步的研究来应对挑战,并释放基于ML的FGM的AM的全部潜力.
  • 基于ML的优化对于实现FGM在工业应用中的好处至关重要.