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Updated: Jul 11, 2025

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Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture
Published on: May 13, 2018
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在削中有效预测稳定性边界,考虑到工具特性和工件材料的变化
Huijuan Sun1, Huiling Ding2, Congying Deng2
1School of Mechanical Engineering and Automation, Chongqing Industry Polytechnic College, Chongqing 401120, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究引入了一个转移学习框架来预测削稳定性,减少了对广泛实验的需求. 该方法有效地预测了新工具-工件组合的无聊加工参数.
科学领域:
- 机械工程 机械工程
- 制造过程 制造过程 制造过程
- 机床工具动力学 机床工具动力学
背景情况:
- 准确的削稳定性预测对于无聊的加工至关重要.
- 制系统的动态特性,包括工具和工件属性,显著影响稳定性.
- 更新这些动态需要为每一个新的配置进行耗时的实验或模拟.
研究的目的:
- 开发一个有效的转移学习框架,用于预测削稳定性.
- 为了减少新工具工件组件所需的实验和模拟的数量.
- 为了能够更快,更准确地确定无聊的加工参数.
主要方法:
- 确定了一个源工具的动态 (频率响应函数) 和切割力系数.
- 一个多层感知子 (MLP) 使用理论稳定性分析数据进行了预训练.
- 预先训练的MLP是通过新工具工件组件的有限实验数据进行微调的.
主要成果:
- 转移学习方法显著减少了准确预测磨稳定性的必要训练样本.
- 拟议的方法实现了可接受的预测准确性,用于新的工具工件配置,较少的实验.
- 实验验证证了转移学习框架的效率和有效性.
结论:
- 转移学习为预测各种工具工件系统的磨削稳定性的挑战提供了有效的解决方案.
- 该框架尽量减少实验力度,同时保持高预测准确度.
- 这种方法加快了加工参数的优化,提高了制造效率.
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