Fuzzy rule based predictive model for cutting force in turning of reinforced PEEK composite

Carbon fiber reinforced plastics have gained large interest among the community of composites manufactures and consumers due to their excellent adaptability to various industrial applications. In particular, there exists a demand for optimizing machining conditions of mechanical parts made from poly ether ether ketone reinforced with 30% of carbon fiber when using TiN coated cutting tools. In this work, predictive models that describe the relationship between the independent machining variables: cutting speed, feed rate and depth of cut, and the criteria of machinability: cutting force, cutting power and specific cutting pressure were derived. This was achieved by using either classical response surface regression technique or by implementing fuzzy logic models which are based on the compositional rule of inference that establish a parametric relation between a given response and the independent input variables. Effectiveness of these models has been proved by analyzing their coefficients of correlation and by comparing predictions they give with experimental results.

► Machining force, cutting power and specific pressure are measured for PEEK CF30.
► Predicting the above responses simultaneously is very difficult and is analysed.
► Experimental investigation is carried out using Taguchi’s method.
► A new cutting tool has been used during machining experiments.
► Fuzzy logic is applied to build predictive models for the multiple quality criteria.