In this study, a comparison of measured cutting parameters is discussed while machining AISI 52100 low-alloy hardened steel under two different sustainable cutting environments, those in which a dry and minimum quantity lubrication (MQL) medium are used. A two-level full factorial design method has been utilized to specify the effect of different experimental inputs on the turning trials. Experiments were carried out to investigate the effects of three basic defining parameters of turning operation which are namely cutting speed, cutting depth, feed rate effects and also the effects of the cutting environment. The trials were repeated for the combination of different cutting input parameters. The scanning electron microscopy imaging method was used to characterize the tool wear phenomenon. The macro-morphology of chips was analyzed to define the influence of cutting conditions. The optimum cutting condition for high-strength AISI 52100 bearing steel was obtained using the MQL medium. The results were evaluated with graphical representations and they indicated the superiority of the pulverized oil particles on tribological performance of the cutting process with application of the MQL system.

This comprehensive review explores recent developments in Proton Exchange Membrane Fuel Cells (PEMFCs) and evaluates their alignment with the ambitious targets established by the U.S. Department of Energy (DOE). Notable advancements have been made in developing catalysts, membrane technology advancements, gas diffusion layers (GDLs), and enhancements in bipolar plates. Notable findings include using carbon nanotubes and graphene oxide in membranes, leading to substantial performance enhancements. Innovative coatings and materials for bipolar plates have demonstrated improved corrosion resistance and reduced interfacial contact resistance, approaching DOE targets. Nevertheless, the persistent trade-off between durability and cost remains a formidable challenge. Extending fuel cell lifetimes to DOE standards often necessitates higher catalyst loadings, conflicting with cost reduction objectives. Despite substantial advancements, the ultimate DOE goals of USD 30/kW for fuel cell electric vehicles (FCEVs) and USD 600,000 for fuel cell electric buses (FCEBs) remain elusive. This review underscores the necessity for continuous research and innovation, emphasizing the importance of collaborative efforts among academia, industry, and government agencies to overcome the remaining technical barriers.

Tool condition affects the tolerances and the energy consumption and hence needs to be monitored. Artificial intelligence based data-driven techniques for tool condition determination are proposed. Unfortunately, the data-driven techniques are data-hungry. This paper proposes a methodology for classification based on unsupervised learning using limited unlabeled training data. The work presents a multi-class classification problem for the tool condition monitoring. The principal component analysis (PCA) is employed for dimensionality reduction and the principal components (PCs) are used as input for classification using k-means clustering. New collected data is then projected on the PC space, and classified using the clusters from the training. The methodology has been applied for classification of tool faults in 6 classes. The use of limited input parameters from the user makes the method ideal for monitoring a large number of machines with minimal intervention. Furthermore, due to the small amount of data needed for the training, the method has the potential to be transferable.

Road traffic injuries and deaths are a growing public health concern worldwide, majorly in developing countries. Brake failure constitutes to be one of the primary reasons for accidents. The majority of brake failures are caused due to overheating of the brakes, while wear of lining is another big share-holder. Early detection of such causes can prevent these accidents. This study puts forth a model that can be used for onboard monitoring of drum/disc temperature & lining/pad thickness by taking velocity & road inclination in real-time as inputs. Many quantities are interdependent and vary with respect to time/temperature. Therefore, an incremental approach is used. The model is implemented in the Simulink software. Many standard profiles are also fed to compare results for different terrains and driving conditions. The drivers can also be classified based on their driving behavior. The thermal model can give us an early warning about the brake overheating. This model can be used to study the energy distribution while braking. Researchers and designers can also use this model to study & optimize the brake system.