Dry machining of alloy steels – A review

Abstract

Dry machining has been used as an alternative method in place of the conventional machining process as the usage of cutting fluid has many side effects and adds more cost to the manufacturing process. Handling and recycling of chips is the main feature of the dry machining process. The heat generated and the tool life are the major concerns in dry machining. There is an alternative method called the minimum quantity lubrication technique where the cutting fluid is sprayed in the form of a mist. Alloy steels are extensively used for the manufacturing of various components in automobile, aircraft, military, and medical industries. It’s crucial to study the dry machining of alloy steels to achieve high-quality finished products at economical costs. In turning operation cutting speed, feed rate, depth of cut and cutting environment are the controllable machining parameters which affect the outputs like surface roughness, tool wear, tool life, MRR, power consumption, etc. In practice when machining is done there are some desired characteristics for the output parameters, that can’t be achieved with some random input parameters combination, the combination of input parameters where desired characteristic of output parameters is achieved is called optimal parameter condition. Optimal conditions for the machining process can be found using the Taguchi technique based on means method or S/N ratio method and ANOVA is used to find the significance of input dimension on outputs. This optimal condition can be found for each individual output parameter or else for all output parameters at once which is called multi-objective optimization. Machine learning model can be developed using experimental data for prediction. The model will be trained based on the training data that has been provided to it. Machine learning models can avoid the waste of time of manual experimentation and directly predict the values.