Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

Abstract

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination ( R2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.

Design and Implementation of Fuzzy Logic for Obstacle Avoidance in Differential Drive Mobile Robot

“…This allows robots to deal with diverse situations, such as passing through narrow passageways, interacting with users, or moving in unpredictable environments. It can also be used in mobile robots that must make decisions about speed and direction to reach a destination without exact knowledge of road conditions [74].…”

Process characteristics, damage mechanisms and challenges in machining of fibre reinforced thermoplastic polymer (FRTP) composites: A review

“…A wide range of advanced prediction methods (e.g. ANN [102][103][104], Fuzzy logic based model [105,106] ) and optimization techniques (e.g. Particle swarm optimization (PSO) [107], grey relational analysis and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) [108] ) have been reported for optimization of cutting force [103][104][105]107,[109][110][111], surface roughness [103,106,[110][111][112][113][114], cutting power consumption [107,111,112] and specific cutting pressure [107,108]).…”

Cutting force, Vibration, and Temperature in Drilling on a Thermoplastic Material of PEEK

“…In addition to the better mechanical and/or thermal properties, their prices are competitive, very suitable for structural parts in different fields such as automobile, medical appliances, electronics, aerospace, construction machinery, and biomechanical applications. 1,4…”

“…In general, the cutting force is the most sensitive indicator of machining performance including the tool condition and chip formation. 10 Özden et al 4 indicated that machinability of PEEK and its composites can be improved based on the cutting force induced. An appropriate cutting force during machining results in less tool wear and better dimensional accuracy.…”

“…However, few studies on the machining of PEEK thermoplastic and its composites are reported recently. 4,7,23…”