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首页> 外文期刊>International Journal of Production Research >Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression
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Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression

机译：使用响应面方法，人工神经网络和支持向量回归对车削操作进行预测建模

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This paper focuses on developing empirical models for predicting surface roughness, tool wear and power required in turning operations. These response parameters are mainly dependent upon cutting velocity, feed and cutting time. Three competing data mining techniques, response surface methodology (RSM), artificial neural networks (ANN) and support vector regression (SVR), are applied in developing the empirical models. The data of 27 experiments have been used to generate, compare and evaluate the proposed models of tool wear, power required and surface roughness for the selected tool/material combination. Testing results demonstrate that the models developed in this research are suitable for predicting the response parameters with a satisfactory goodness of fit. It has been found that ANN and SVR models are much better than regression and RSM models for predicting the three response parameters. Finally, some future research directions are outlined.
机译：本文着重于开发经验模型，以预测车削操作中的表面粗糙度，刀具磨损和功率。这些响应参数主要取决于切削速度，进给和切削时间。三种竞争数据挖掘技术，响应面方法（RSM），人工神经网络（ANN）和支持向量回归（SVR），被用于开发经验模型。 27个实验的数据已用于生成，比较和评估所选工具/材料组合的工具磨损，所需功率和表面粗糙度的建议模型。测试结果表明，本研究开发的模型适合于以令人满意的拟合优度预测响应参数。已经发现，ANN和SVR模型在预测三个响应参数方面比回归模型和RSM模型好得多。最后，概述了一些未来的研究方向。

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