Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.

The additive manufacturing technique of material extrusion has challenge of excessive process defects and not achieving the desired mechanical properties. The industry is trying to develop certification to better control variations in mechanical attributes. The current study is a progress towards understanding the evolution of processing defects and the correlation of mechanical behavior with the process parameters. Modeling of the 3D printing process parameters such as layer thickness, printing speed, and printing temperature is carried out through L27 orthogonal array using Taguchi approach. In addition, CRITIC embedded WASPAS is adopted to optimize the parts' mechanical attributes and overcome the defects. Flexural and tensile poly-lactic acid specimens are printed according to ASTM standards D790 and D638, respectively, and thoroughly analyzed based on the surface morphological analysis to characterize defects. The parametric significance analysis is carried out to explore process science where the layer thickness, print speed, and temperature significantly control the quality and strength of the parts. Mathematical optimization results based on composite desirability show that layer thickness of 0.1 mm, printing speed of 60 mm/s, and printing temperature of 200 °C produce significantly desirable results. The validation experiments yielded the maximum flexural strength of 78.52 MPa, the maximum ultimate tensile strength of 45.52 MPa, and maximum impact strength of 6.21 kJ/m². It is established that multiple fused layers restricted the propagation of cracks with minimum thickness due to enhanced diffusion between the layers.

This study presents the impact of molybdenum (Mo) inclusion on microstructure, mechanical, and machinability behavior of steels manufactured with powder metallurgy (PM) approach. PM steel samples with different molybdenum ratios were pressed at 750-MPa pressing pressure and sintered in the atmosphere-controlled tube furnace at 1400°C for 1 h. While particle size and distribution of phases of PM steels with different molybdenum ratios were determined by optical microscope, mechanical properties were determined by applying tensile test. The results were observed that 3% Mo weight-added steel displayed the maximum yield and tensile strength. In addition, the machinability properties of 3% Mo-added steel, which has the highest yield and tensile strength, were investigated. In this work, we researched the thrust force and surface roughness as machinability output, and drilling parameters on the output were determined by utilizing analysis of variance. Finally, SEM images were taken from the inner surfaces of the machined holes and the fractured surfaces from tensile test, and information about the machinability of this alloy produced with PM was presented. As a result, an increase in yield and tensile strength and a reduction in strain were identified with the increase in Mo content. Moreover, the coated cutting tools are better on the machining output than the uncoated cutting tools in terms of green environment. The most important factors on the Fz and Ra are the coating condition and the feed rate with 56.53% and 43.62% PCR, respectively.