Response surface and neural network based predictive models of cutting temperature in hard turning
نویسندگان
چکیده
منابع مشابه
Response surface and neural network based predictive models of cutting temperature in hard turning
The present study aimed to develop the predictive models of average tool-workpiece interface temperature in hard turning of AISI 1060 steels by coated carbide insert. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to predict the temperature in respect of cutting speed, feed rate and material hardness. The number and orientation of the experimental trial...
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The tool wear is an unavoidable phenomenon when using coated carbide tools during hard turning of hardened steels. This work focuses on the prediction of tool wear using regression analysis and artificial neural network (ANN).The work piece taken into consideration is AISI4140 steel hardened to 47 HRC. The models are developed from the results of experiments, which are carried out based on De...
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In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface rough...
متن کاملEstimation of cutting forces and surface roughness for hard turning using neural networks
Metal cutting mechanics is quite complicated and it is very difficult to develop a comprehensive model which involves all cutting parameters affecting machining variables. In this study, machining variables such as cutting forces and surface roughness are measured during turning at different cutting parameters such as approaching angle, speed, feed and depth of cut. The data obtained by experim...
متن کاملCutting Disturbances in Hard Turning Process
Assumption of cutting disturbances in hard turning process (caused by variations in depth of cutting, high passive force Fp or small tool nose radius) could be confirmed with several indicators. These indicators can be monitored during turning process(forces, vibrations, sound, etc.) or after process has been finished (roughness, temperature, wear, etc). Variation of depth of cutting as well as...
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ژورنال
عنوان ژورنال: Journal of Advanced Research
سال: 2016
ISSN: 2090-1232
DOI: 10.1016/j.jare.2016.05.004