Saharsa College of Engineering

The emergence of Industry 5.0 marks a new chapter in industrial development. Instead of focusing mainly on automation and efficiency, like Industry 4.0, this new phase brings people back to the center of progress. The goal is not only to use advanced technologies but to pair them with human intelligence, creativity, and values. With this shift, Industry 5.0 encourages a future that supports social well-being, environmental responsibility, and innovation that benefits everyone. These advancements are expected to help address major global issues, including sustainability, climate concerns, and social inequality. The study also points out areas where further research is needed and provides direction for future development, offering valuable guidance to researchers, governments, and industry leaders navigating this new landscape. This chapter explores these ideas in greater depth, focusing on the core principles of human-focused design and how they help guide sustainability within the context of Industry 5.0.

Dissimilar multilayer copper (Cu) and aluminium (Al) joints find widespread applications in electrical and energy storage systems, where both electrical resistance and mechanical stability are required. The solid-state joining method, ultrasonic welding (USW), is highly promising for such material systems, as it allows joining thin layers with good conductivity while simultaneously preventing excessive intermetallic formation and porosity. However, weld quality in multilayer Cu-Al-Cu-Al assemblies is highly dependent on ultrasonic process parameters. The test material for ultrasonic welding was a Cu-Al-Cu-Al four-layer stack (0.2 mm thick). Furthermore, this study investigated the correlation between vibration amplitude, welding pressure, and welding time during joint formation. The process parameters were optimised using response surface methodology-central composite design (RSM-CCD). Lap shear strength and microstructural analyses characterised the weld joints and investigated interfacial morphology, material flow, and bonding behaviour under under-weld, good-weld, and over-weld conditions. Electron backscatter diffraction (EBSD) examined the grain structure across the weld interface. Layer-by-layer micro-hardness measurements determined the hardness variation near the weld interface. Additionally, thermal analysis and electrical resistance measurements evaluated the thermal and electrical behaviour of the welded joints. Those findings show that localised plastic deformation, mixing of interfacial materials, wave-like interface morphology, and the gradual buildup of interface micro-bonds predominantly control bonding in the Cu-Al-Cu-Al system. The results of the microstructural, thermal, and electrical analyses indicate that the current ultrasonic welding conditions can produce stable, low-resistance multilayer dissimilar metal joints.

Powder metallurgy is a versatile manufacturing process known for its ability to produce complex-shaped components with tailored properties, making it invaluable across multiple industries. This study investigates and compares the compaction behaviour, mechanical properties, and microstructure of pure aluminium powder using both conventional strain rate compaction, i.e. radial mechanical pressing (RMP) and high strain rate compaction, i.e. electromagnetic compaction (EMC) methods. The RMP process employs a hydraulic press to compact the powder conventionally. In contrast, in the EMC process, a rapid and intense magnetic field is generated through a solenoid coil, resulting in high-strain-rate compaction. Both process involves radial compaction of powder to generate a cylindrical specimen. After compaction, the samples are sintered, prepared, and tested. The testing includes hardness tests and microstructural analysis by using optical microscopy as well as scanning electron microscopy (SEM) imaging. Results are compared between the RMP and the EMC processes. This research aims to fill a critical gap in the field by enabling a direct comparison of radial compaction of powder at conventional and high strain rates, providing valuable insights into the influence of strain rate on the properties of powder compacted samples.

To achieve a smooth surface finish and minimum tolerances on cylindrical objects, cylindrical grinding is a popular practical method in industry. In this process, material is removed from the workpiece surface as small chips, and after this process, post-processing is not required. A grinding wheel is used in this process. Currently, there are various grinding processes, such as creep-feed grinding, surface grinding, centerless grinding, internal grinding, and cylindrical grinding. This process is used to grind external cylindrical parts such as spindles, connecting rods, cam shafts, engine shafts, axles, propeller shafts, etc. A cylindrical grinding machine is used to achieve a high machining rate and a high surface finish. The effect of the process parameters on the response parameters is the main goal of this work. Here, the most influential parameters are speed, feed rate, and the depth of cut. The response parameters are the metal removal rate and Ra. In the present work, machining is performed on EN24 alloy steel, and the objective is to achieve the specified material removal rate (MRR) and surface finish. The EN24 is a very high-strength steel alloy that is widely used in aircraft and heavy vehicle crankshafts, connecting rods, gearshafts, etc. To find the optimum set of process parameters, a Taguchi design of experiment with an L16 orthogonal array was used. Later, the experiment results are optimized by gray relational analysis (GRA). In the proposed work, the minimum surface roughness and the maximum MRR are obtained. The parameters that greatly influence obtaining the optimum result are the feed rate and the speed of machining.

The development of urban infrastructure for optimal energy use is very important for economic growth of any country which can benefit from recent advances in intelligent technologies. The combined use of the Internet of Things (IoT) and Artificial Intelligence (AI) plays a crucial role in cities to monitor and manage their energy use. These tools allow for real-time observation, control, and adjustment of energy flows across transportation, lighting, water, and building systems through sensors and decision support systems. The result is a more efficient for responsible use of resources that supports long-term sustainability goals. The use of AI and IoT mechanism provides the energy management which helps cities to reduce waste, lower emissions, and make informed operational decisions based on continuous data feedback. This approach not only improves overall energy efficiency but also contributes directly to environmental protection and climate resilience. . .