Artificial neural network modeling for prediction of cutting forces in turning unreinforced and reinforced polyamide

Abstract

Cutting force measurement in manufacturing is very important to optimize the machining process. The parameters, such as the type of material, feed rate, cutting speed, and cutting tool, affect the cutting forces in the turning operation. In this study, an artificial neural network (ANN) model is used to predict the cutting forces during the turning operation of unreinforced and reinforced polyamide (PA) with 30 v/v% carbon fibers using the cutting tools K15 and polycrystalline diamond (PCD). The cutting speed (50–200 m/min), feed rate (0.05–0.2 mm/rev), type of material, and cutting tools (K15 and PCD) are defined as input parameters of the system. The predicted values obtained from ANN model and experimental results are compared in terms of the coefficient of determination ( R2) and the mean absolute percentage error. The results of the model are in good agreement with the experimental data and show the effectiveness of the ANN method in predicting cutting forces in the turning operation of PA.

Modeling and optimization in turning of PA66-GF30% and PA66 using multi-criteria decision-making (PSI, MABAC, and MAIRCA) methods: a comparative study

“…The effects of process parameters (Vc and f) on machinability characteristics, namely (Ra, Fz and Ks) have been studied. G. Özden et al [13] proposed an approach using (ANN) to predict the cutting force components when turning polyamides with and without reinforcement using two cutting tools (K15 and PCD). Based on the deviation indicators (R 2 and MAPE), they concluded that the predicted results are very close to experimental results.…”

Multiresponse optimization of drillability factors and mechanical properties of chitosan-reinforced polypropylene composite

“…At this point, precision machining methods such as turning and milling play an important role for improving different forms of polymeric parts. 21–24 Also, many parts and components are combined with riveting and bolting techniques for a composite-based complex manufacturing. These techniques are one of the main joining methods, and therefore drilling holes becomes mandatory to facilitate assembly in polymer or polymer-based composite parts.…”

Neural network modeling and combined compromise solution (CoCoSo) method for optimization of drilling performances in polymer nanocomposites

“…The artificial neural network (ANN) is observed as a useful modeling tool for varying parameters of the machining process. 30–32 It is a statistical pattern that imitates the natural behavior of neurons. Gaitonde et al 33 discussed an ANN-based multilayer model of feedforward to investigate the cutting parameters of polyamide reinforced by glass fibers (PA66 GF30).…”

“…The outcomes of this model reduced the cutting force by controlling speed and feed rate during machining. 31 It can provide several process properties with the most complicated analytical relationship about process parameters at the same time. Jenarthanan et al 34 established the ANN model by considering the varying parameters such as feed rate, the orientation of fiber, speed, and helix angle for prediction of engendered cutting force in milling of glass fiber reinforced polymer(GFRP).…”