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相关概念视频

Machines: Problem Solving II01:30

Machines: Problem Solving II

335
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

Machines: Problem Solving I

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
355

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相关实验视频

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通过可解释机器学习获得基于挤出的增材制造过程知识.

Lukas Pelzer1, Tobias Schulze2, Daniel Buschmann2

  • 1Institute for Plastics Processing at RWTH Aachen University, 52074 Aachen, Germany.

Polymers
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概括
此摘要是机器生成的。

本研究使用可解释的机器学习来理解增材制造 (AM) 过程参数. 它识别了最佳设置和参数相互作用,使得更好的零件质量不需要复杂的分析模型.

关键词:
添加剂制造 添加剂制造 添加剂制造重要的特征 重要的特征 重要的特征融合层建模 融合层建模可以解释的机器学习.机器学习 机器学习过程的表征过程的表征.过程知识知识过程知识.过程优化优化过程优化

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相关实验视频

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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) 工艺,特别是基于挤压的方法,涉及许多影响零件性能的参数.
  • 这些参数之间的复杂的相互依赖使分析建模具有挑战性.
  • 机器学习 (ML) 为优化AM参数提供了一个潜在的解决方案,但通常会充当黑子,阻碍知识提取.

研究的目的:

  • 应用可解释机器学习 (IML) 方法来从AM数据中获得过程知识.
  • 克服黑盒ML模型在理解和验证AM过程输出方面的局限性.
  • 客观地确定最佳的过程参数,并在AM中发现参数相互作用.

主要方法:

  • 利用可解释的机器学习技术来分析AM数据集中的特征重要性.
  • 应用化层建模 (FLM) 作为示范方法的案例研究.
  • 解释模型输出以确定过程参数和部件属性之间的关系.

主要成果:

  • 证明了使用IML.IML可以充分描述FLM过程.
  • 客观地确定过程参数的"甜点",以实现所需的部件特性.
  • 发现了各种过程参数之间的显著相互作用.

结论:

  • 可解释机器学习为获得人工智能过程知识提供了一种可行的方法.
  • 客观地确定最佳过程参数和理解参数相互作用是可以实现的.
  • 该研究为进一步研究AM流程优化和控制奠定了基础.