In this study, we proposed a methodology for predicting tool wear in the turning process using the SVR model. This model maintains stable performance even in small-scale data environments and demonstrates robust characteristics against outliers. We detected changes in machining performance caused by tool wear through an AE sensor and accelerometer. Features were extracted from the acquired sensor signals and utilized in the machine learning model. Prior to training, the extracted features underwent a preliminary screening process based on distance correlation. By optimizing the feature combination using the RFECV algorithm, we achieved a prediction accuracy of R² = 0.95. The analysis revealed that key features influencing the tool wear prediction model included several significant variables. Additionally, we found that evaluating feature importance allowed for more efficient model improvement. Overall, when developing a tool wear prediction model for cutting, it is crucial to utilize various sensor signals, extract features in both the time and frequency domains, and optimize the combination of those features.

Zinc sulfide (ZnS) is a widely used material in far-infrared and near-infrared imaging systems due to its exceptional optical transmittance properties. Through a hot isostatic compression process, during manufacturing, ZnS undergoes crystal structure modifications, resulting in increased transmittance across the visible and infrared spectra. However, ZnS exhibits low fracture toughness and irregular crystal orientations, making it prone to brittle fracture during the conventional cutting processes. Such brittleness often leads to surface defects that scatter light, diminishing optical transmittance. Therefore, understanding the conditions conducive to ductile processing is critical and necessitates a thorough brittle fracture analysis. This study introduces a novel quantitative analysis method to determine the occurrence of ductile processing and brittle fracture in ZnS materials after the turning process. To validate the efficacy of this approach, experimental machining was conducted through diamond turning and magnetorheological fluid polishing processes. Subsequently, a comprehensive quantitative assessment of brittle fracture was performed. Additionally, the relationship between brittle fracture and optical transmittance was explored using the proposed analysis method.

Titanium alloy has been widely used in the aerospace industry because of its high strength and good corrosion resistance. During cutting, the low thermal conductivity and high chemical reactivity of titanium generate a high cutting temperature and accelerates tool wear. To improve cutting tool life, cryogenic machining by using a liquid nitrogen (LN2) jet is suggested. In cryogenic jet cooling, evaporation of LN2 in the tank and transfer tube could cause pressure fluctuation and change the cooling rate. In this work, cooling uniformity is investigated in terms of liquid nitrogen jet pressure in cryogenic jet cooling during titanium alloy turning. Fluctuation of jet spraying pressure causes tool temperature to fluctuate. It is possible to suppress the fluctuation of the jet pressure and improve cooling by using a phase separator. Measuring tool temperature shows that consistent LN2 jet pressure improves cryogenic cooling uniformity.