Gurbuz, HuseyinGonulacar, Yunus Emre2024-12-242024-12-2420211302-09002147-9429https://doi.org/10.2339/politeknik.833833https://search.trdizin.gov.tr/tr/yayin/detay/455211https://hdl.handle.net/20.500.12604/7657In this study, the effect of cutting speed, feed rate and MQL flow rate on main cutting forces (Fc) and average surface roughness (Ra) in the turning process of AISI 4140 steel was investigated both experimentally and statistically. Accordingly, signal/noise (S/N) ratios and artificial neural networks (ANN) were used to evaluate the experimental results. As cutting parameters in machining experiments, three different cutting speeds (75, 100, 125 m/min), three different feed rates (0.16 - 0.25 - 0.5 mm/rev), three different MQL flow rates (0.35 - 0.8 - 1.7 ml/min) and a constant depth of cut (2.5 mm) were selected. In machining experiments, it was determined that the increase in MQL flow rate is more effective on Fc than Ra. It was also seen that both Fc and Ra increased with the feed, and generally decreased with the cutting speed in all MQL flow rate applications. R-2 values obtained through S/N ratios and ANN for Fc and Ra were found to be R-S/ N(Fc)(2)= 0.9996, R-S/ N(Ra)(2)= 0.9984, R-YSA( Fc)(2)= 0.9990 and R-YSA(Ra)(2)= 0.9884. According to S/N ratios, it was determined that the most effective control factors on Fc and Ra are feed rate, cutting speed and MQL flow rate, respectively. Depending on the regression values obtained, it was determined that S/N ratios and ANN are valid in predicting experimental data at high confidence intervals.trinfo:eu-repo/semantics/openAccessANNS/N ratiominimum quantity lubrication (MQL)main cutting forcesurface roughnessArticle24310931107N/AWOS:00074044070000745521110.2339/politeknik.833833