通过人工智能优化Hardox钢的可加工性,Hardox钢经历了不同的加工过程
1Malatya Organized Industrial Zone (OIZ) Vocational High School, Inonu University, Malatya, Turkey. mehmet.altug@inonu.edu.tr.
Scientific reports
|August 29, 2023
概括
这项研究研究了通过热处理,冷和接加工的Hardox 400钢,分析了微结构变化和WEDM性能. 人工智能模型准确地预测了表面粗度,隙,材料去除率和电线磨损率,显示了优化方面的承诺.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造过程 制造过程 制造过程
- 在工程领域的人工智能.
背景情况:
- 哈德克斯400钢是一种高强度材料,用于苛刻的应用.
- 了解各种加工方法 (热处理,冷,接) 对其性能的影响至关重要.
- 线电放电加工 (WEDM) 是塑造这些材料的关键工艺.
研究的目的:
- 研究热处理,冷,等离子体接和mig-mag接对Hardox 400钢的微结构,微硬度和导电性的影响.
- 通过Box-Behnken实验设计,分析WEDM在这些加工样本上的性能.
- 使用深度学习 (DL) 和极端学习机器 (ELM) 开发和比较WEDM输出 (Ra,Kerf,MRR,WWR) 的预测模型.
主要方法:
- 通过热处理,冷,等离子体接和mig-mag接来处理Hardox 400钢样.
- 基于Box-Behnken实验设计的加工样本的WEDM.
- 使用Minitab 21对WEDM参数 (Ra,Kerf,MRR,WWR) 的分析.
- 使用Python 3.9.9中的深度学习 (DL) 和极端学习机器 (ELM) 开发预测模型.
- 与线性回归模型的比较.
主要成果:
- 热处理的样本产生了1.92μm的最佳表面粗度 (Ra) 和最低的隙 (200μm).
- Mig-mag接样本实现了200g/min的最高材料去除率 (MRR).
- 热处理的样本也显示了电线磨损率 (WWR) 最低的0.098g.
- 在预测WEDM输出方面,DL和ELM模型表现出高准确度 (回归的r2值高达0.9563,DL/ELM的0.9444).
- 人工智能模型为Ra,Kerf,MRR和WWR提供了成功的估计结果.
结论:
- 不同的加工方法显著影响Hardox 400钢的WEDM性能.
- 基于人工智能的优化方法 (DL和ELM) 对于预测和优化WEDM参数是有效的.
- 该研究为未来的研究提供了理想的DL和ELM模型,用于优化Hardox 400钢的WEDM工艺.
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