Purpose The present study investigated the effects of different input parameters such as cooling/lubrication conditions, cutting speed and feed rate on power consumption, surface roughness, cutting temperature, vibration and wear mechanisms during the milling of AISI 316 Ti alloy. Furthermore, the study aims to determine the estimation method that gives optimum results by estimating the output parameters using response surface methodology (RSM), artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) methods. Besides these, it is aimed to obtain efficient results for the industry in the processing of AISI 316 Ti stainless steel. In addition, it is aimed to save time and cost for the manufacturing industry by determining the optimum cutting parameters and output parameters using modeling methods such as RSM, ANN and ANFIS. Design/methodology/approach AISI 316 Ti alloy was obtained commercially. Power consumption during machining under different cooling/lubrication conditions, cutting speeds, and feed rates was performed using a power analyzer connected to the machine’s electrical input. The average surface roughness values were determined by calculating the average of the measurements taken from different points perpendicular to the machining direction. An infrared camera was used to measure the temperature in the cutting zone during machining tests. Vibrations occurring during machining were measured using a digital vibration measurement system. RSM, ANN and ANFIS models were used to estimate the output parameters. Findings The minimum quantity lubrication (MQL) machining condition resulted in reduced power consumption, surface roughness and vibration values to decrease, while air machining condition led to a decrease in cutting temperature. It was observed that low cutting speed and feed rate caused low power consumption, cutting temperature and vibration. It was observed that increasing the cutting speed and decreasing the feed rate had a positive effect on the surface roughness. The study revealed that the reliability coefficient and predictive capability of the ANN model were higher than those of the RSM and ANFIS models. Social implications The study emphasizes the importance of predicting power consumption, surface roughness, cutting temperature and vibration during the machining of AISI 316 Ti alloy with a specific set of machine parameters, thereby reducing the cost and time associated with experimental trials. This advancement is expected to enhance the efficiency, cost-effectiveness, and product quality for manufacturing companies working with AISI 316 Ti alloy. Moreover, the findings reveal that the Artificial Neural Network (ANN) model demonstrates superior accuracy among the prediction models evaluated, indicating its potential for practical application in industrial settings. These improvements contribute not only to economic benefits but also support sustainable manufacturing practices by minimizing resource waste. Originality/value This study focuses on the effects of different cooling/lubrication conditions and cutting parameters on power consumption, surface roughness, vibration and wear mechanisms during the milling of AISI 316 Ti alloy. It is also a comprehensive investigation in which optimal machining parameters are determined by estimating output parameters using RSM, ANN and ANFIS models.
Karagöz et al. (Wed,) studied this question.
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