An efficient tool condition monitoring system was designed for keyway milling of 7075-T6 hybrid aluminium alloy composite with resultant machining force and sound acquired while the milling process. During the milling process, sound pressure and machining force were measured using a microphone and milling tool dynamometer with NI USB 6221 DAQ card and monitored using LabVIEW. The resultant cutting force for fresh and dull tool varies up to 1 kN and 1.8 kN respectively. The sound pressure for fresh, working and dull tool varies up to 1.9 Pa, 2 Pa and 2.5 Pa respectively. The tool condition was estimated from the Artificial Intelligence techniques based on the acquired signals. The acquired signals were given as an input signal to the expert system. The predictor output varies from 0 to 3 to indicate the progression of flank wear and it was utilised to evaluate the tool condition. When the output exceeds the value of 3, it indicates that the tool has to be replaced for the machining process. The Mean Squared Error (MSE) for a feedforward backpropagation neural network and ANFIS model were 2.06517e-9 mm and 0.487505e-3 mm respectively. The neural network had the regression coefficient of 0.99 which shows the accuracy of the model.

Aluminium alloy components are used in lightweight engineering applications and lack mechanical properties, wear resistance, and corrosion resistance. When hard reinforcement particulates are dispersed in the aluminium matrix alloy, aluminium matrix composites (AMCs) are formed. This new material exhibits enhanced properties such as better specific stiffness, specific strength, corrosion resistance, elastic modulus, wear resistance, and lightweight. AMCs have many favorable properties compared to their alloy and extensive applications. Especially, in the space industry, where weight reduction and an increase in modulus are prevalent, the strength-to-weight ratio is more important. This study focuses on the tribological, mechanical, and corrosion properties of Al 7075 metal matrix composites (MMCs) reinforced with one or more particulates such as aluminium oxide (Al₂O₃), boron carbide (B₄C), titanium carbide (TiC), silicon carbide (SiC), titanium dioxide (TiO₂), and titanium diboride (TiB₂). In AMCs, the reinforcements are incorporated by either the solid or liquid metallurgy route. The study addresses the findings and shortcomings in the preparation of MMCs with reinforcements and their improvement in corrosion resistance, wear resistance, mechanical properties, and lower coefficient of thermal expansion than the Al 7075 base alloy. A comprehensive survey of reinforcement particulates in MMCs and their influences are outlined from the literature, encompassing recent research trends to enhance the structural properties.

In this work, the flank wear of the cutting tool is predicted using artificial neural network based on the responses of cutting force and surface roughness. EN8 steel is chosen as a work piece material and turning test is conducted with various levels of speed, feed and depth of cut. Cutting force and surface roughness are measured for both the fresh and dull tool under dry cutting conditions. The tool insert used is CNMG 120408 grade, TiN coated cemented carbide tool. The experiments are conducted based on the response surface methodology face central composite design of experiments. The feed rate (14.52%), depth of cut (27.72%) and the interaction of feed rate and depth of cut (50.39%) influence the cutting force. The feed rate (21.33%) and the interaction of cutting speed and depth of cut (26.67%) influence the flank wear. The feed rate (61.63%) has the significant influence on surface roughness. The feed forward back propagation neural network of 5-n-1 architecture is trained using the algorithms like Levenberg Marquardt, BFGS quasi-Newton, and Gradient Descent with Momentum and Gradient descent with adaptive learning rate. The network performance has been assessed based on their mean square error and computation time. From this analysis, the BFGS quasi-Newton back propagation algorithm produced the least mean squared error value with minimum computation time.

Tool condition monitoring (TCM) systems have evolved into an essential requirement for contemporary manufacturing sectors of Industry 4.0. These systems employ sensors and diverse monitoring techniques to swiftly identify and diagnose tool wear, defects, and malfunctions of computer numerical control (CNC) machines. Their pivotal role lies in augmenting tool lifespan, minimizing machine downtime, and elevating productivity, thereby contributing to industry growth. However, the efficacy of CNC machine TCM hinges upon multiple factors, encompassing system type, data precision, reliability, and adeptness in data analysis. Globally, extensive research is underway to enhance real-time TCM system efficiency. This review focuses on the significance and attributes of proficient real-time TCM systems of CNC turning centers. It underscores TCM’s paramount role in manufacturing and outlines the challenges linked to TCM data processing and analysis. Moreover, the review elucidates various TCM system variants, including cutting force, acoustic emission, vibration, and temperature monitoring systems. Furthermore, the integration of industrial Internet of things (IIoT) and machine learning (ML) into CNC machine TCM systems are also explored. This article concludes by underscoring the ongoing necessity for research and development in TCM technology to empower modern intelligent industries to operate at peak efficiency.

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.