Prediction and Optimization of Surface Roughness and Cutting Forces in Turning Process Using ANN, SHAP Analysis, and Hybrid MCDM Method  
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1 Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovo Setalste 9, 71000 Sarajevo, Bosnia and Herzegovina; [EMAIL_ADDRESS] (M.P. ); [EMAIL_ADDRESS] (K.M.) [PERSON]  
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[PERSON]  
2 Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovo Setalste 9, 71000 Sarajevo, Bosnia and Herzegovina; [EMAIL_ADDRESS] (M.P. ); [EMAIL_ADDRESS] (K.M.) [PERSON]  
1 Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovo Setalste 9, 71000 Sarajevo, Bosnia and Herzegovina; [EMAIL_ADDRESS] (M.P. ); [EMAIL_ADDRESS] (K.M.) 2  
[PERSON]  
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[PERSON]  
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Footnote 2: email: {[EMAIL_ADDRESS], [EMAIL_ADDRESS], kenan.muhamedagic@mef. feed rate, and depth of cut during the machining operation. Many studies have shown that the feed rate and cutting depth play a key role in reducing surface roughness and cutting forces. [PERSON] et al. (2018) investigated the effect of work material hardness and cutting parameters during the turning of hardened AISI 4340 on the cutting forces, surface roughness, and tool life. Cutting forces are mostly affected by the depth of cut (nearly 60-70% contribution), followed by feed (nearly 25-30% contribution). Surface roughness is significantly influenced at a higher feed and depth of cut. [PERSON] et al. (2018) have also shown that surface roughness primarily depends on the feed rate. Lower roughness is achieved with smaller feed rate values. In their study, [PERSON] et al. (2018) examined the impact of various machining parameters (cutting speed, depth of cut, and feed rate) on key output performance factors (surface roughness, cutting force, cutting power, and material removal rate) during the dry hard turning of martensitic stainless steel (AISI 420) with a hardness of 59 HRC. The findings showed that surface roughness is most significantly affected by the feed rate, while the depth of cut has the greatest impact on the cutting force, cutting power, and material removal rate. Similar conclusions have been reached in many other studies ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019). [PERSON] et al. (2018) conducted a study on the hard turning of 20 MnCr5 steel, investigating the optimal conditions for achieving low surface roughness. A ceramic-based tool was used in the machining process. Their findings suggest that feed rate is the primary factor influencing surface roughness. Similarly, [PERSON] et al. (2018) performed experiments on the hard turning of high-alloy steel using a ceramic tool, following a 3 \(\times\) 3 full factorial design. Their results indicate that, in terms of surface roughness, feed rate was the most significant factor. Modern optimization and modeling techniques, including the use of neural networks, have become essential tools for improving a machining process. In hard turning, parameters such as cutting speed, feed rate, and depth of cut significantly influence surface quality, cutting forces, tool wear, tool vibrations, and the overall efficiency of the process ([PERSON] et al., 2018; [PERSON] et al., 2018). Optimizing these parameters is challenging due to the complex, nonlinear relationships between them and the process outcomes. One key approach for finding the best combination of machining parameters is to use optimization algorithms such as genetic algorithms (GAs) (Kal  
and ANOVA. The findings revealed that the type of insert and the feed rate were the most significant factors affecting surface roughness. [PERSON] et al. (2019) used RSM to develop statistical models that analyze the impact of machining parameters on various output responses (\(F_{x}\), \(F_{y}\), \(F_{z}\), \(R_{a}\), and tool life) during the hard machining of AISI D3 steel with CBN tools. The study concluded that the depth of cut significantly influenced the cutting forces, while the feed rate had a major effect on surface finish.