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.

聚醚醚酮（PEEK）及其复合材料在工业中通常使用。具有PEEK的材料由于具有优越的性能（例如高温工作，更好的耐化学性，轻质，良好的能量吸收和高强度）而广泛用于航空，汽车，机械，医疗，机器人和生物力学应用。为了增强未增强PEEK的摩擦学和机械性能，将短纤维添加到基体中。在这项研究中，使用人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）来预测碳纤维含量为30 v / v％和碳纤维含量为30 v / v％的PEEK在非增强和增强PEEK加工过程中的切削力玻璃纤维加工。切削速度，进给速度，材料类型和切削工具被定义为输入参数，切削力定义为系统输出。使用ANN和ANFIS模型预测的实验结果和测试结果根据确定系数进行比较（R ²）和平均绝对百分比误差。测试结果表明，ANFIS和ANN模型提供了良好的预测精度，并且方便了PEEK车削操作中切削力的预测。