The present study aims to investigate the effects of cutting parameters (cutting speed, Vc: 60–90–120 m/min; feed rate, f: 0.055–0.085–0.115 mm/rev), cutting tool coatings (CVD: TiN/TiCN/Al₂O₃ and PVD: TiAlN), and machining conditions (dry, air, and MQL) on cutting force (Fc), specific energy consumption (SEC), surface roughness (Ra), cutting temperature (T), and tool wear (Vb) during the milling of Ti6Al4V alloy. As a result, it was observed that all machining tests conducted with the Al₂O₃-coated cutting tool showed improvements of 4.7%, 10.75%, 3.8%, and 6.3% in Fc, SEC, Ra, and T, respectively, compared to the tests performed with the TiAlN-coated cutting tool. Under dry machining conditions, the average Fc, SEC, Ra, and T values were 302.82 N, 4.88 j/mm³, 0.653 µm, and 241.06 °C, respectively. Compared to dry machining conditions, the air and MQL machining conditions demonstrated improvements in the average Fc by 5.15% and 6.3%, SEC by 10.27% and 17.79%, Ra by 6.23% and 11.17%, and T by 8.9% and 19.68%, respectively. The lowest Fc and Ra values for the Al₂O₃-coated cutting tool were measured at 228.33 N and 0.402 µm, respectively, under the MQL machining condition, at a cutting speed of 120 m/min and a feed rate of 0.055 mm/rev. The lowest SEC value (2.694 J/mm³) was also obtained using the Al₂O₃-coated tool under MQL conditions at a cutting speed of 120 m/min and a feed rate of 0.115 mm/rev. Similarly, the lowest cutting temperature (129 °C) was achieved with the Al₂O₃-coated tool under MQL conditions at a cutting speed of 60 m/min and a feed rate of 0.055 mm/rev. The wear performance of the Al₂O₃-coated cutting tool was observed to be superior to that of the TiAlN-coated tool.

In this study, it has been focused on examining the effects of production parameters on quality parameters such as surface roughness and geometric tolerances in the production of AlSi10Mg samples by the additive manufacturing method. The experimental design has been prepared according to the Taguchi L₂₇ orthogonal array. As a result, in the production of samples, increasing laser power (P) contributed positively to surface roughness and diameter change, and increasing scanning distance (SD) negatively contributed to circularity change and concentricity. Further, it has been determined that increasing the scanning speed (SS) negatively affects the concentricity change of the produced samples. The optimum production parameters for surface roughness and diameter variation has been determined as A₁B₁C₃. The optimum production parameters for circularity variation and concentricity have been determined as A₃B₃C₁ and A₃B₁C₁, respectively. According to the ANOVA analysis results, the most effective parameters for surface roughness, diameter change, circularity change and concentricity have been 53.22% P, 62.45% SD, 37.23% SS and 40.41% SD, respectively. Furthermore, as a result of the gray relationship analysis (GRA) performed for the output parameters, the optimum production parameter has been determined as A₂B₁C₃.

The present study focuses on the effects of cooling/lubrication conditions and cutting parameters on energy consumption (EC), carbon emissions (CE), surface roughness (Ra), cutting temperature (T), tool wear (Vb) and vibration (Vib) in sustainable milling of Inconel 718 alloy. Also, it was aimed to estimate the EC, CE, Ra, T and Vib values obtained in milling experiments using three different regression-based machine learning (ML) models. The performances of the models used in ML were compared using R-squared, MSE and MAPE performance criteria. In the experiments conducted by reducing the feed rate and cutting speed and in MQL machining conditions, it was observed that EC and CE values reached minimum values. In MQL machining conditions, it was observed that the lowest Ra values were achieved at high cutting speed and low feed rate. The lowest Vb was measured at low cutting speed and feed rate in air machining conditions. Increasing the cutting speed and decreasing the feed rate in MQL machining conditions had a positive effect on Vib. In the MQL machining condition, at 40 m/min cutting speed and 0.06 mm/rev feed rate, the lowest energy consumption and carbon emission were 0.76 kJ/s and 0.54796 kg-CO₂ respectively. The lowest surface roughness and vibration values were measured as 0.234 μm and 1.91 mm/s respectively, at 80 m/min cutting speed and 0.06 mm/rev feed rate in MQL machining condition. The lowest cutting temperature was measured as 31 °C at a cutting speed of 40 m/min and a feed rate of 0.06 mm/rev under air machining conditions. It was seen that the EC, CE, Ra, T and Vib values arising from the input parameters in the machining of Inconel 718 alloy could be successfully predicted using three different regression-based ML models.

In this study, the drilling of an Al 6082-T6 alloy and the effects of cutting tool coating and cutting parameters on surface roughness, cutting temperature, hole diameter, circularity, and cylindrical variations was investigated. In addition, the prediction accuracy of Taguchi, artificial neural networks (ANNs), and adaptive neuro-fuzzy inference system (ANFIS) methods was compared using both experimental results and Signal/Noise (S/N) ratios derived from the experimental results. The experimental design was prepared according to Taguchi L27 orthogonal indexing. As a result, it was observed that increasing the cutting speed and feed rate increases the cutting temperature hole error, circularity error and cylindricity error. Increasing the cutting speed positively affected the surface roughness, while increasing the feed rate led to an increase in the surface roughness. The lowest surface roughness, cutting temperature, hole diameter error and hole circularity error values were measured for the uncoated cutting tool. The minimum cylindricity variation was measured for drilling with TiAlN-coated cutting tools. The optimum cutting parameters were A1B1C3 (Uncoated, 0.11 mm/rev, 200 m/min) for surface roughness, A1B1C1 (Uncoated, 0.11 mm/rev, 120 m/min) for cutting temperature, hole error, circularity error and cylindricity error. In the estimation of the output parameters with Taguchi, ANNs and ANFIS, it was observed that the estimates made by converting the experimental values into S/N ratios were more accurate than the estimates made with the experimental results. The reliability coefficient and prediction ability of the ANN model were found to be higher than Taguchi and ANFIS models in estimating the output parameters.

Özellikle kalıpçılık sektöründeki firmalar yüksek mukavemet ve kolay işlenebilirlik özelliklerine sahip malzemelere ihtiyaç duymaktadır. Bu malzemeleri talaşlı imalat operasyonları ile istenen kalitede, minimum işleme süresinde ve düşük maliyetli ürünlere ulaşmayı hedeflenmektedir. Bu nedenle, son zamanlarda üretilen Sleipner soğuk iş takım çeliğinin tornalanmasında kesme parametrelerinin kesme kuvveti bileşenleri, esas kesme kuvveti, yüzey pürüzlülüğü ve talaş oluşumu üzerine etkileri deneysel olarak incelenmiştir. Tornalama deneyleri CVD yöntemi ile kaplanmış sementit karbür kesici takım kullanılmıştır. Deneylerde kesme parametreleri olarak beş farklı kesme hızı, üç farklı ilerleme miktarı ve sabit talaş derinliği şeklinde belirlenmiştir. Sonuç olarak kesme kuvveti bileşenlerinden esas kesme kuvvetinin kesme hızından ve ilerleme miktarından pasif kuvvet ve ilerleme kuvvetine göre daha fazla etkilenmiştir. Esas kesme kuvveti için en iyi kesme parametreleri 100 m/dak kesme hızı ve 0,1 mm/dev ilerleme miktarıdır. İşlenmiş yüzeyler arasında en düşük yüzey pürüzlülüğü 150 m/dak kesme hızında ve 0,1 ilerleme miktarında elde edildi. Tornalama deneylerinde düşük kesme hızlarında kullanılan kesici takımlarda kararsız yığıntı talaşın (BUE) oluştuğu görülmüştür. Bununla birlikte, yüksek kesme hızlarında ve ilerleme miktarında kullanılan kesici takımların aşındığı görülmüştür. Tornalama deneyleri sonucunda istenilen sipral tipi talaş oluşumu 150 m/dak kesme hızında ve 0,1 mm/dev ilerleme miktarında görülmüştür.