The object of this research is to investigate the effect of cutting parameters such as cutting speed (Vc), feed rate (f) and depth of cut (ap) on machining parameters including cutting temperature (TC) and tool flank wear (VB) during dry turning of AISI 316L using coated carbide tool. The experiments were conducted according to Taguchi L27 orthogonal array, RSM and ANN have been used. Results revealed that (ap) found to be the dominant factor for TC. VB mainly influenced by Vc, f and ap, respectively. The prediction results obtained by ANN and RSM models showed a good agreement with experimental data. However, ANN models proved their capability to provide more accurate results compared to RSM models. According to the optimization analysis, Desirability function showed good accuracy in optimization.

No abstract

In recent years, reducing the lubricant quantity used for the machining processes have gained much attention in order to limit the excessive use of conventional lubrication, for different considerations such as economic, ecological, and physical aspects. The minimum quantity lubrication (MQL) process is considered as economically, environmentally friendly and to be effective in overcoming this problem. Accordingly, this paper aims to analyze and evaluate the hard turning efficiency of AISI 316L Stainless steel with respect to surface roughness (Ra), and cutting temperature (Tc), according to combinations of cutting speed (Vc), feed rate (f), and cutting depth (ap) using coated carbide insert when turning of AISI 316L under dry, and MQL machining. It could be possible to investigate the efficiency of MQL technique for an environment-friendly ecological machining. The ANOVA analysis has been performed to determine the effect of cutting conditions on studied outputs. The results revealed that the cutting speed had the most effective influence on Ra followed by feed rate and lubrication mode, with contribution ratios of 58.39%, 19.92% and 11.91%, respectively. While the lubrication mode had the most influence on TC, with a contribution ratio of 88.98%.

Stainless steels have gained much attention to be one of the most widely used metallic due to their high mechanical properties, corrosion resistance in moderately corrosive environments and their ability to use in biomedical devices, food industry and implants in human body. However, owing to their low thermal conductivity and high ductility, these materials are classed as materials difficult to machine. Therefore, the object of this experimental study is to investigate the effect of cutting parameters such as cutting speed (Vc), feed rate (f) and depth of cut (ap) on the machining outputs including surface roughness (Ra), cutting temperature (TC) and tool flank wear (VB) during dry turning of AISI 316L using coated carbide insert TP2501. The experiments were conducted according to Taguchi L27 orthogonal array parameter design, response surface methodology (RSM) and artificial neural network (ANN) have been used. Statistical analysis revealed that the feed rate affected for surface roughness, depth of cut the dominant factor impacted for cutting temperature, and tool flank wear mainly influenced by Vc, f and ap, respectively. The prediction results obtained by ANN and RSM models showed a good agreement with experimental data. However, ANN models proved the capability to provide more accurate results compared to RSM models. According to optimization analysis based on desirability function (DF) and non-dominated sorting genetic algorithm (NSGA II), DF results were determined to acquire high machined part quality.