Effect of Machining Parameters and Optimization of Temperature Rise in Turning Operation of Aluminium-6061 Using RSM and Artificial Neural Network

被引：13

作者：

Gopal, Mahesh ^{[1
]}

机构：

[1] Wollega Univ, Coll Engn & Technol, Dept Mech Engn, POB 395, Nekemte, Ethiopia

来源：

PERIODICA POLYTECHNICA-MECHANICAL ENGINEERING | 2021年 / 65卷 / 02期

关键词：

aluminium-6061; machining parameter; Artificial Neural Network (ANN); Response Surface Methodology (RSM); temperature rise; TOOL NOSE RADIUS; SURFACE-ROUGHNESS; CUTTING TEMPERATURE; MINIMUM QUANTITY; LUBRICATION-MQL; INCONEL; 718; PREDICTION; FORCES; FINISH; HEAT;

D O I：

10.3311/PPme.16625

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

The aim of this study is to determine the effect of the machining parameters and tool geometry. The turning operation is carried out as per the Design of Experiments (DoE) of Response Surface Methodology (RSM) to predict the temperature rise of aluminium-6061 as a cutting material and Al2O3 coated carbide tool is used as a cutting tool for turning operation. The ANOVA analysis is used to measure the performance quality and mathematical model is developed. The values of probability >(F) is less than 0.05 indicates, the model conditions are significant. The cutting speed is the most influencing parameters compared to other parameters. For the optimum machining parameters leading to temperature rise, the Artificial Neural Network (ANN) model is trained and tested using MAT Lab software. The ANN recommends best minimum predicted value of temperature rise. The confirmatory analysis results, the predicted values were found to be in commendable agreement with the experimental values.

引用

页码：141 / 150

页数：10

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