Adaptive control by multi-objective optimisation for drilling process with fuzzy inference system and neural predictive controller

摘要

Tool wear and the surface roughness of the workpiece are vital parameters in machining components that affect cost, quality and productivity. This paper describes an adaptive control strategy for a drilling process to optimise the machine parameters and minimise the tool wear and surface roughness. An adaptive neuro fuzzy inference system (ANFIS) is used for modelling the tool wear and surface roughness. Speed, feed and acceleration signals obtained during drilling are used as inputs to the ANFIS model. Based on the model output, multi-objective desirability optimisation is applied to obtain the optimal feed and speed. Individual desirability functions of tool wear and surface roughness are combined to form the composite desirability. The fuzzy inference system serves to provide the optimal speed and feed based on the composite desirability. A neural predictive controller is used to control the simulated computer numerical control (CNC) servo drive systems. The ANFIS model of tool wear and surface roughness was found to predict values with an error of 6% for tool wear and 4.1 % for surface roughness. The proposed adaptive control output shows a deviation of ±2 mm/min for the feed drive and of ±7 r/min for the speed drive.