The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

Minimum quantity lubrication (MQL) is a sustainable method that has been efficiently applied to achieve machinability improvements with various materials in recent years, such as hardened steels, superalloys, soft metals, and composites. This study is the first to focus on the performance evaluation of MQL and dry milling environments with AISI 1040 steel. The tool wear, cutting temperature, and power consumption were considered as the quality responses while cutting speed, feed rate and machining environment are taken as input parameters. The effects of the influential factors are analyzed using analysis of variance (ANOVA) and bar charts. Additionally, Taguchi signal-to-noise (S/N) ratios are utilized in order to determine the optimum parameters for the best quality responses. The results show that the MQL system provides better performance compared to dry milling by reducing the tool wear, cutting temperature, and power consumption. According to the ANOVA results, the cutting environment affects the cutting temperature (37%) and power consumption (94%), while cutting speed has importance effects on the tool wear (74%). A lower cutting speed (100 m/min) and feed rate (0.10 mm/rev) should be selected under MQL conditions to ensure minimum tool wear and power consumption; however, a higher feed rate (0.15 mm/rev) needs to be selected along with a low cutting speed and MQL conditions to ensure better temperatures. A comparative evaluation is carried out on the tool wear, cutting temperature, and power consumption under MQL and dry environments. This investigation is expected to contribute to the current literature, highlighting the superiority of sustainable methods in the milling of industrially important materials.

Machining of AISI 304 austenitic stainless steel is considered to be difficult due to its structural aspects and low thermal conductivity, which leads to increased temperatures during machining. To overcome the challenges of machining AISI 304 stainless steel, several cooling and lubricating techniques have been developed. The main objective of this experimental study is to evaluate the different turning conditions of AISI304 stainless steel under dry and minimum quantity lubrication (MQL) environment conditions. The machining experiments were conducted using a two-level full factorial design method and utilized a TiC-coated cutting tool. The tool-tip temperature, cutting force and surface roughness were analyzed regarding three cutting parameters namely, cutting speed, feed rate and cutting depth. Also, chip macro-morphology was investigated to define the interaction at the tool-chip-workpiece region. The cutting medium affects the surface roughness significantly (more than 100%) for all cutting parameter values. In some environmental cutting conditions, high cutting speed provides 10% lesser surface roughness than low cutting speed parameters. Also, the cutting force decreases by 20% in low feed rate machining conditions. However, the effect of this parameter disappeared for cutting forces in high feed rates and low cutting depth conditions in both MQL and dry environments. Cutting speed was observed as the most influential factor on surface roughness, followed by feed rate. The depth of cut was the main parameter that caused the temperature to increase in the dry machining environment.