Optimizing Cutting Fluid Use via Machine Learning in Smart Manufacturing for Enhanced Sustainability

High temperatures generated during machining can lead to undesirable outcomes such as surface deterioration, subsurface damage, tool wear, and shortened tool life. Effective heat removal from the cutting zone is therefore essential for maintaining process stability and part quality. Conventional cooling systems typically apply a constant amount of cutting fluid without considering the actual temperature in the cutting zone, which may result in unnecessary coolant use and inefficient temperature control. This study introduces an innovative machining approach that integrates machine learning techniques to estimate the optimal lubrication interval and maintain the desired cutting temperature. The proposed system dynamically adjusts coolant application based on real-time temperature data and machining parameters, preventing excessive or insufficient cooling. Comparative analyses show that the new system reduces coolant consumption by 22.5 mL per minute compared with conventional cooling and by 2.5 mL per minute compared with minimum quantity lubrication (MQL). This improvement corresponds to an annual reduction of approximately 12.3 T of CO2e emissions. The results demonstrate that the developed system enables machining at the optimum temperature, enhancing tool life, surface quality, and energy efficiency while significantly lowering environmental and health impacts associated with cutting fluids. The integration of machine learning also supports automated decision-making in smart manufacturing environments, reducing operator dependency and contributing to sustainable and economically efficient production.