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Development of a surface roughness predictive model for STEP-compliant machining optimisation

Inappropriate machining parameters often cause tool failures, poor surface quality and even machine breakdowns. It can be overcome by optimising some machining parameters, e.g., feed rate. When quality is of a priority as in the case of finishing operations, quality-critical optimisation is often considered. This paper discusses a surface roughness predictive model that has been developed to study and obtain optimised machining parameters. This predictive model considers the interconnected machining parameters, e.g., feed rate, depth of cut and spindle speed, as well as predictors of the surface roughness, e.g., cutting force, torque and chatter vibrations signals. The correlation of the surface roughness is evaluated by signal-to-noise ratios and compared with the behaviour of the amplitude along the time axis of short time Fourier transform at the chatter frequency. A first-order and second-order models have been established between criterion and predictor variables.