Abstract
In recent years, minimum quantity lubrication machining has played a critical role in extending tool life, improving surface finish, and reducing tool wear during the machining of hardened steel. The reported investigation involved the utility of Hardox 500 steel for the machining process while the cubic boron nitride coated tool performed the function of removing the material. The investigation was carried out by adopting two different techniques to predict the responses, viz., the response surface methodology and the artificial neural network. These techniques have been employed to predict the possible responses in investigating machining characteristics. Also, due consideration has been made concerning the reduction of tool-tip temperature and surface roughness. The entire investigation was carried out using three different environments. They are dry machining, wet, and minimum quantity lubrication. A scanning electron microscope was used for examining the morphology of the worn tool surface.
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