Disassembly 5.0 is crucial for the remanufacturing system, as it has an important role in facilitating adoption of sustainable manufacturing system. In this context, a heuristic algorithm is utilized to determine the tasks allocations for the returned product disassembly with human-robot collaboration taking into consideration the concept of a cyber-physical production system. The heuristic algorithm, modified COMSOAL (Computer Method of Sequencing Operations for Assembly Lines) heuristic, is realized on the random sequence generation. To maintain the worker safety, the direct contact between the worker and robot is prohibited, where sensors are utilized in both human and robot sides. The obtained results explain the efficiency of the heuristic algorithm in reducing the completion of the disassembly workstation, which plays a vital role in increasing the production rate; thus, the profit will increase.

With the growing global energy and environmental problems, electric vehicles that are both environmentally friendly and cost effective have seen rapid growth. An electrified vehicle’s effective thermal management must include all of the vehicle’s systems. However, optimizing the thermal behavior of each component is insufficient. A lithium-ion battery’s operating temperature has a significant impact on its performance. When working at low temperatures, the internal resistance of lithium-ion batteries increases, the available energy and power of the system decreases, and lithium precipitation caused by low-temperature charging may cause safety issues; high-temperature operation and temperature inconsistency between battery cells will cause accelerated ageing of the battery, which may result in safety issues such as thermal runaway. As a result, to keep the temperature of the battery module under control, electric cars require a strong thermal management system. Thermal management must be handled at the system level to maximize the vehicle’s overall performance. The integrated thermal management system for electric vehicles has the potential to significantly increase vehicle energy efficiency. However, it’s construction is complicated due to the thermal requirements of the battery and cabin. In this research, a multi-channel liquid cooling system with a serpentine wavy structure was used to conduct an experimental thermal investigation of a lithium-ion battery pack. The suggested cooling system’s capabilities was examined under various charge/discharge scenarios using varied coolant flow rates and pumping power. The findings revealed that the suggested cooling system was successful across a variety of charging and discharging settings, with most of the tested scenarios achieving a maximum temperature difference.

This research describes a novel technique for anticipating unstable heat transfer in porous media. Convolutional neural networks (CNNs) are used with finite volume method (FVM) and long short-term memory (LSTM) networks to accomplish this. Heat transport networks are difficult to characterise using traditional numerical methodologies owing to their nonlinearity and complexity. The proposed solution combines FVM’s precise physical modelling with CNN’s and LSTM’s superior pattern identification and temporal analysis. This collaboration supports the suggested strategy. Heat transport dynamics simulations in porous materials are more accurate, efficient, and adaptable when employing this hybrid framework. The experimental setup focused on porous material properties and gathered and processed a large amount of data. The building’s three-dimensional shape, heat transfer, and time were investigated. Temporal fluctuations were also used. Multiple indicators are used to evaluate the overall performance of the model. These criteria include convergence speed, F1 score, accuracy, precision, recall, and computational cost. In the most notable numerical results, the proposed strategy surpasses both the Finite Element and the Lattice Boltzmann methods. The presented method enabled fast convergence and reduced processing costs. These results were: accuracy (0.92), precision (0.93), recall (0.91), and F1 score (0.92). The proposed method is generalizable and adaptable, and it can address a variety of heat transport simulation problems in porous media. Unlike CNNs, which can identify significant spatial patterns, LSTM cells can only see temporal dynamics. These two components are required to show heat transfer, which is a continually changing phenomenon. Modern technology enables more complex simulations. Processing expenses are lowered, and estimations are more accurate. These two discoveries were obtained through the inquiry and methodologies. Finally, the CNN-FVM-LSTM technique simulates heat transport using complicated computer models. Predicting unusually high temperatures in porous materials may improve the model’s accuracy, computational efficiency, and flexibility.

The growing need for renewable energy and zero carbon dioxide emissions has fueled the development of thermoelectric generators with improved power generating capability. Along with the endeavor to develop thermoelectric materials with greater figures of merit, the geometrical and structural optimization of thermoelectric generators is equally critical for maximum power output and efficiency. Green energy strategies that are constantly updated are a viable option for addressing the global energy issue while also protecting the environment. There have been significant focuses on the development of thermoelectric modules for a range of solar, automotive, military, and aerospace applications in recent years due to various advantages including as low vibration, great reliability and durability, and the absence of moving components. In order to enhance the system performance of the thermoelectric generator, an artificial neural network (ANN) based algorithm is proposed. Furthermore, to achieve high efficiency and system stability, a buck converter is designed and deployed. Simulation and experimental findings demonstrate that the suggested method is viable and available, and that it is almost similar to the real value in the steady state with the least power losses, making it ideal for vehicle exhaust thermoelectric generator applications. Furthermore, the proposed hybrid algorithm has a high reference value for the development of a dependable and efficient car exhaust thermoelectric generating system.