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Residual Stress Prediction by Adaptive Neuro-Fuzzy System in Milling Aluminum Alloy

机译：自适应神经模糊系统在铣削铝合金中的残余应力预测

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As a sort of large-scaled structural components in modern aircraft, aluminum part has been widely used nowadays. Its residual stress measurement and prediction are necessary to reduce machining deformation and keep machining precision. By Adaptive Neuro-Fuzzy Inference System (ANFIS), residual stress prediction model is set up based on different cutting parameters. Due to data sample scarcity, input selection and regression are analyzed comparatively to reduce input data dimension. It shows that cutting speed and feed per tooth have major impacts on residual stress, but they do not have better prediction ability in ANFIS model. The combination of cutting speed and radial depth of cut can predict the residual stress better.

机译：铝制零件作为现代飞机中的一种大型结构部件，如今已被广泛使用。它的残余应力测量和预测对于减少加工变形并保持加工精度是必需的。通过自适应神经模糊推理系统（ANFIS），基于不同的切削参数建立了残余应力预测模型。由于数据样本稀缺，对输入选择和回归进行了比较分析，以减少输入数据的维数。结果表明，切削速度和每齿进给量对残余应力有重要影响，但在ANFIS模型中没有更好的预测能力。切削速度和径向切削深度的组合可以更好地预测残余应力。

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来源
《Manufacturing Automation Technology》|2008年|504-508|共5页
会议地点 Harbin(CN);Harbin(CN)
作者
X.H. Zhang; Q.L. An; M. Chen;
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作者单位

School of mechanical engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;

School of mechanical engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;

School of mechanical engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类机械制造工艺;
关键词
aluminum; residual stress; prediction; ANFIS;

机译：铝;残余应力预测;航空情报服务;

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专利

1. Residual Stress Prediction By Adaptive Neuro-fuzzy System In Milling Aluminum Alloy [J] . X.H. Zhang, Q.L. An, M. Chen Key Engineering Materials . 2009,第期

机译：自适应神经模糊系统在铣削铝合金中的残余应力预测
2. Prediction of welding residual stresses using machine learning: Comparison between neural networks and neuro-fuzzy systems [J] . Mathew J., Griffin J., Alamaniotis M., Applied Soft Computing . 2018,第期

机译：使用机器学习预测焊接残余应力：神经网络与神经模糊系统的比较
3. Modeling of Hardened Die Steel Bohler K340 for Prediction of Surface Roughness in High Speed End Milling using Adaptive Neuro-Fuzzy Inference System [J] . Purushottam S. Desale, Ramchandra S. Jahagirdar International Journal of Applied Engineering Research . 2012,第11期

机译：自适应神经模糊推理系统对淬硬模具Bohler K340进行建模以预测高速端铣削中的表面粗糙度
4. Residual Stress Prediction by Adaptive Neuro-Fuzzy System in Milling Aluminum Alloy [C] . Conference of China University Society on Manufacturing Automation . 2009

机译：铣削铝合金自适应神经模糊系统的残余应力预测
5. Prediction of residual stress and distortion from residual stress in heat treated and machined aluminum parts. [D] . Jones, Robert. 2014

机译：通过热处理和机械加工的铝制零件预测残余应力和残余应力引起的变形。
6. Prediction and optimization of tool wear rate during electric discharge machining of Al/Cu/Ni alloy using adaptive neuro-fuzzy inference system [O] . Mangesh Phate, Aditya Bendale, Shraddha Toney, 2020

机译：应用自适应神经模糊推理系统Al / Cu / Ni合金电气放电加工过程中工具磨损率的预测与优化
7. Experimental Analysis and Prediction Model of Milling-Induced Residual Stress of Aeronautical Aluminum Alloys [O] . Shouhua Yi, Yunxin Wu, Hai Gong, 2021

机译：铣削诱导的航空铝合金残余应力的实验分析与预测模型

Residual Stress Prediction by Adaptive Neuro-Fuzzy System in Milling Aluminum Alloy

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