OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 Introduction Surface fi nish is critical toqualitybecause it directly aff ects the appearance, functionality, andperformance of machined components. Precision machining is essential, especially in aerospace and medical applications where specifi ed surface fi nish is required to reduce friction, improve wear resistance, or improve corrosion resistance. The infl uence of surface fi nish on tribological parameters such as friction and lubrication is crucial to achieve maximum performance and durability. Increased temperatures during machining have a signifi cant impact on tool wear, material integrity and dimensional accuracy. Temperature control is critical for extending tool life and maintaining the structural integrity of machined parts. Predictive modelling optimizes processes by identifying optimal parameters for cost savings through increasing tool life, reducing scrap rates and increasing effi ciency. The use of cutting fl uid in hard turning is not recommended, since at elevated temperatures when processing materials with a hardness of 48 to 68 HRC, the coolant in the cutting zone begins to boil. This boiling phenomenon promotes thermal deformation, thereby reducing both Ra (surface roughness) and the service life of the cutting tool [1]. In case of machining diff erent materials, its machinability was evaluated using various process parameters such as tool life, material removal rate (MRR), cutting force (Fc), energy consumption, chip morphology and machined surface roughness (Ra). Using high speed machining (HSM) while maintaining surface integrity and maintaining tolerance limits requires optimal coordination of factors such as cutting force (Fc), process and machine parameters. The right combination of these elements is critical to increasing the effi ciency of HSM without compromising the quality of the machined surfaces or exceeding specifi ed tolerance limits. This balance ensures that machining can proceed without compromising accuracy and surface quality, contributing to the overall success of high-speed machining operations [2]. Zhao et al., [3] measured the cutting temperature of Inconel 718 using a two-color infrared thermometer with a ceramic whisker-reinforced tool, and concluded that the large amount of heat generated during machining deteriorates the surface quality of the machined material. Due to the increase in temperature in the cutting zone during machining, the surface quality deteriorated [4]. High tool wear and temperature increase during machining of hardened AISI 4340 steel can be eliminated using bio-cutting fl uid [5]. Postmachining operations are required to improve the surface quality of superalloys, [6]. Kumar et al. [7] compared a RSM model with an ANN model to analyze the turning performance of AISI D2 steel and concluded that that the RSM-based prediction model is more accurate than the ANN model for predicting surface quality and cutting temperature. Gosai and Bhavsar [8] used mathematical models and equations generated by CCD-based RSM to predict cutting temperature. The material removal rate during the turning process was higher compared to other traditional machining processes. Abhang et al. [9] experimentally measured the temperature of the EN 31 alloy during turning with tungsten carbide inserts using the natural thermocouple technique. F has a signifi cant eff ect on the surface roughness: as the f increases, the roughness increases, and as the Vc increases, the roughness decreases [10– 12]. Bhopal et al. [13] used RSM with CCD for turning austenized high-strength cast iron with a carbide tool and found that Vc has a more signifi cant eff ect on surface roughness. Aouici et al. [14] used a CBN tool for turning AISI H11 steel, as well as a mathematical model based on RSM for Ra and Fc, however, when processing materials reinforced with particles, the surface morphology was changed. Longbottom and Lanham [15] conducted a review of temperature measuring devices and found that the measured temperature varied in diff erent places. Korkut et al. [16] compared the ANN model and the RA model and found that the training ANN model with the LM algorithm demonstrated a higher prediction rate and was useful in measuring the cutting temperature when tested by a qualifi ed RA method during machining. Dhar and Kamruzzaman [17] found that an increase in temperature signifi cantly aff ects tool wear and surface roughness, and the use of cryogenic cooling gives good results. Patil and Brahmankar [18] developed a model for surface roughness that takes into account the input parameters, material properties, size of ceramic particles and its volume fraction, and found that the volume fraction and particle size signifi cantly aff ect the output parameters, as well as that the presence of ceramic particles aff ects the surface roughness. Patel and Kiran [19] used a linear regression model to analyze the assessment of the roughness of the surface
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