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In the manufacturing industry, cutting tool failure is a serious event which causes damage to the cutting tool and reduces the quality of the product, which increases the cost of production. A reliable, intelligent, tool wear monitoring system is required in the metal cutting manufacturing process to mitigate these negative effects. This study presents a model-based approach for tool wear monitoring based on an adaptive neuro-fuzzy inference system (ANFIS) for a cold-finished steel bar 1215 turning process. A three-input cutting force (Fx, Fy and Fz) and single-output (tool flank wear) model was designed and implemented using the ANFIS approach. The forces were measured using a piezoelectric dynamometer and data acquisition system. Flank wear was also monitored using a tool maker's microscope. The model prediction results show that it is accurate enough to perform online monitoring of the turning process and can detect wear while operating. [ABSTRACT FROM AUTHOR]

This article presents the influence of machining conditions on typical process performance indicators, namely cutting force, specific cutting energy, cutting temperature, tool wear, and fine dust emission during dry milling of CFRPs. The main goal is to determine the machining process window for obtaining quality parts with acceptable tool performance and limited dust emission. For achieving this, the cutting temperature was examined using analytical and empirical models, and systematic cutting experiments were conducted to assess the reliability of the theoretical predictions. A full factorial design was used for the experimental design. The experiments were conducted on a CNC milling machine with cutting speeds of 10,000, 15,000, and 20,000 rpm and feed rates of 2, 4, and 6 µm/tooth. Based on the results, it was ascertained that spindle speed significantly affects the cutting temperature and fine particle emission while cutting force, specific cutting energy, and tool wear are influenced by the feed rate. The optimal conditions for cutting force and tool wear were observed at a cutting speed of 10,000 rpm. The cutting temperature did not exceed the glass transition temperature for the cutting speeds tested and feed rates used. The fine particles emitted ranged from 0.5 to 10 µm aerodynamic diameters with a maximum concentration of 2776.6 particles for those of 0.5 µm diameters. Finally, results of the experimental optimization are presented, and the model is validated. The results obtained may be used to better understand specific phenomena associated with the milling of CFRPs and provide the means to select effective milling parameters to improve the technology and economics of the process. [ABSTRACT FROM AUTHOR]