Analysis, modelling, and optimization of force in ultra-precision hard turning of cold work hardened steel using the CBN tool

Abstract

The machinability of high-performance materials such as superalloys, composites, and hardened steel has been a big challenge due to their mechanical, physical, and chemical properties, which give them inherent complex machining characteristics. Additionally, majority of machinability tests conducted on these materials have been carried out on conventional and less precise lathes based on Taguchi, composite, and other designs of experiments that do not exploit all the possible combinations of cutting parameters. This work reports an investigation on ultra-precision hard turning (UHT) of cold work hardened AISI D2 steel of HRC 62, based on the full factorial design of experiment, carried out on an ultra-precision lathe. A theoretical analysis of the force components generated is reported. Modelling of the process, based on the resultant force, is also reported through a machine learning model. The model was developed from the experimental data and statistically evaluated with validation data. Its average MAPE values of 1.47%, 4.81%, and 10.66% for training, testing, and validation, respectively, attest to its robustness. The excellent coefficient of determination values, R2, also justify the model’s robustness. Multi-objective optimization was also conducted to optimize material removal rate (MRR), resultant force, and vibration simultaneously. For sustainable and efficient UHT, optimal cutting velocity (158.8 m/min), feed (0.125 mm/rev), and depth of cut (0.074 mm) were proposed to generate optimal resultant force (224.8 N), MRR (2603.6 mm3/min), and vibration (0.03 m/s^2) simultaneously. These results can be beneficial in planning UHT processes for high-performance materials.