The minimum quantity lubrication (MQL) approach is used for improving tool life at a low cost, and it is environmentally friendly. When compared to traditional flood cooling technology, the flow rate in MQL is thought to be 10,000 times lower. The workpiece’s surface smoothness is enhanced by continuous chip formation during turning, but because the tool is always in touch with the chip, a crater wear zone is formed on the rake face due to high friction and thermal stress. While adding nanoparticles to MQL enhances cutting performance, a high concentration of these nanoparticles causes burr adhesion and decreased chip evacuation capability due to the agglomeration of nanoparticles, which affects the surface finish of the workpiece. A novel “coconut-oil-based SiC–MWCNT nano-cutting fluid for a CBN insert cutting tool” is proposed in this approach to overcome these issues. Silicon carbide (SiC) and multi-walled carbon nanotubes (MWCNTs) are added to coconut oil with an appropriate volume fraction for better lubrication. The thermal properties of the proposed nano-cutting fluid are compared with those of some existing nano MQL cutting fluids, and it was found that the MQL cutting fluid under consideration exhibits an elevated thermal conductivity and convective heat transfer coefficient that efficiently reduce tool temperature and improve tool life. The comparative study between the Finite Element Simulation using computational fluid dynamics (CFD) predicted variation in tool temperature and the corresponding experimental values revealed a remarkable alignment with a marginal error ranging from 1.27% to 3.44%.

Nowadays, the minimum quantity lubrication method has become the eye apple of many manufacturing industries. MQL is a cooling procedure in which a very small volume of high quality refrigerant / lubricant is sprayed directly onto the work piece-tool interface. MQL machining has gained popularity over other cooling techniques due to its ability to produce superior surface quality, lower machining temperature, lower tool wear and lower coolant / lubricant consumption. Several researchers are continuing their work on new methods to strengthen the properties of coolants / lubricants. In this paper, efforts are being made to present important work by different researchers on the role of different coolant / lubricant in enhancing MQL environmental capability. A detailed overview of the functions of various processing fluids is presented in this text. such as vegetable oils, mineral oils, synthetic oils and nano-fluid-based machining fluids, in improving the machinability of MQL in spinning, milling and grinding operations. This paper also addresses the role of machining fluids in the enhancement of machining aspects such as surface strength, machining temp erature, tool wear and cutting insert life.

Nickel-based superalloys like Inconel 718 exhibit large machining challenges attributable to their poor thermal conductivity and pronounced work hardening behaviors. They normally contribute to quick tool wearing away and poor surface quality. In order to get around this problem, a hybrid deep learning system that fused a CNN and an LSTM was devised and successfully optimized through the JOA. The model was incorporated in MATLAB/Simulink, making it possible to predict surface roughness and flank wear in real-time while hard turning using a CBN tool under MQL conditions. The experimental data used for model training and validation were derived from 27 full-factorial machining trials that covered a range of cutting speeds, feed rates, and depths of cut. The data was preprocessed through normalization and outlier removal using the IQR and Z-score methods. The CNN–LSTM model that was optimized by JOA displayed remarkable prediction power with R = 0.9991, RMSE = 0.0095, and MAPE = 2.21%, thus being far superior to the conventional models, like SVM, ANN, and ANFIS, etc. The findings indicate the model’s ability to precisely understand complex nonlinear interactions between the machining parameters and the corresponding responses, hence, the model’s strong generalization across different cutting conditions. The inclusion of the MATLAB/Simulink environment extends the model’s real-time deployment potential, digital-twin compatibility, and scalability, providing a low-cost and sensor-free solution that is perfect for smart and sustainable manufacturing. This study offers a scientifically interpretable and industrially deployable method for predictive modeling in the machining of hard-to-cut materials.