Grinding is a manufacturing process which significantly contributes in producing high precision and durable components required in numerous applications such as aerospace, defence and automobiles. This review article is focused to uncover history, witness the present and predict the future of the grinding process. While going through the literature, it has been observed that minimal work has been done in explaining the history, present status and future scopes of the grinding process. In this era of information and environmental awareness, sustainability aspects have become a primary concern of almost every research field. In the grinding process too, the research work includes ecological elements such as reducing the consumption of cutting fluids through minimum quantity lubrication, utilizing cryogenics, hybrid lubrication and cooling techniques that are still required to be explored critically. Further, some significant findings of the prevailing research in grinding include modification in grinding wheel surface, merging different grinding principles such as usages of the textured grinding wheel, ultrasonic grinding, 3D printing of grinding wheel and artificial intelligence in grinding are also presented. Another unascertained problem is the management of grinding swarf, which is being attended to by recycling it to fabricate composites which is expected to be another prominent domain of research. Further, the advancements taking place exhibit the potential of the grinding process, suggesting that its future is bright and ever-growing.

The reduction of weight elements is considered as a major objective of several manufacturing companies. This objective will help in growing application sections of the used fiber composites for important structural elements. Modern fiber metal laminate (FML) having lightweight properties is established to be used instead of other substances in different applications including those related to the aerospace industrial sector. Fiber metal laminate is being deemed as an alternative significant substance that is being extensively explored due to its operation, unlike other current materials. There are different profitable FML such as GLARE (glass-reinforced aluminum laminate), established on elevated intensity ARALL glass fibers (aramid-reinforced aluminum laminate), built on fibers of aramid, in addition to CARALL (carbon-reinforced aluminum laminate), centered on fibers of carbon. This paper analyzes important information that contributes to the mechanical characteristics of FMLs under tensile, flexure, impact, etc. conditions.

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.