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用计算机数控机器系统的切削力信号来识别能源效率和预测表面粗度.
Chunhua Feng1, Meng Li2, Haohao Guo2
1School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China. science_chf@126.com.
Scientific reports
|August 16, 2024
概括
本研究引入了一种新的方法,用于实时识别机械加工中的能量效率,使用切削力信号. 该方法准确地预测了表面粗度,有助于可持续制造.
科学领域:
- 制造业 工程 制造工程
- 材料科学 材料科学 材料科学
- 信号处理 信号处理
背景情况:
- 在机械加工中实时跟踪能源效率是具有挑战性的,因为有多种影响因素.
- 传统的电源信号不足以进行全面的能源效率评估.
- 精确的监测对于节能制造和提高生产率至关重要.
研究的目的:
- 开发一种可靠的方法来识别加工过程中的能效.
- 建立一个可靠的表面粗度预测模型.
- 调查切割参数对能源效率和表面质量的影响.
主要方法:
- 使用完整集体实证模式分解与自适应噪声 (CEEMDAN) 来将切断力信号分解为内在模式函数 (IMF).
- 使用PCA-Fast独立组件分析 (ICA) 来基于IMF比例的能源效率表征.
- 开发了一种支持向量回归 (SVR) 模型,用于使用特定切割能耗 (SCEC) 预测表面粗度.
- 进行了直角测试,变化了线速度,料速度,切割深度和宽度.
主要成果:
- 成功地将能源效率分为高,中,低等级.
- 提取了切断力信号的独特时间频率特征,对应于不同的能源效率水平.
- 在表面粗度预测中达到0.058的平均绝对误差.
- 证明了切割参数对切割力,SCEC和表面粗度的显著影响.
结论:
- 拟议的方法有效地识别了使用切削力信号的加工能效.
- 开发的模型准确地预测了表面粗度,满足了行业的要求.
- 这种方法通过加强能源监测和质量预测来支持可持续制造.
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