The design freedom enabled by additive manufacturing, combined with the unique properties of generative lattice structures, has gained increasing attention in thermal system technologies, especially for the development and fabrication of heat exchangers. In this study, a hybrid heat exchanger was developed by integrating a Diamond unit cell, known for its high thermal performance, with an IWP unit cell, which offers low flow resistance. The goal is to reduce flow resistance while maintaining heat transfer efficiency. A physical model was simulated using ANSYS software to evaluate the heat exchanger’s performance. The results indicate that integrating IWP cells into a Diamond unit cell structure effectively reduces flow resistance, though with a slight compromise in heat transfer performance. This balance provides a viable design strategy for heat exchangers in applications requiring precise flow resistance control.

This paper introduces a novel information sharing mechanism, the Improved Information Sharing Mechanism (I2SM), an adaptive real-time framework designed to enhance the performance of metaheuristic algorithms. I2SM dynamically collects and evaluates critical metrics, such as improvement rates and stagnation levels, through parallel processing, enabling real-time actions such as hybridization and parameter tuning. The mechanism’s adaptive nature ensures efficient handling of diverse optimization challenges by dynamically balancing exploration and exploitation, with a reasonable tradeoff in execution time.To assess the performance of the proposed I2SM mechanism, we selected the Particle Swarm Optimization (PSO) algorithm as a representative test framework. Empirical results from various benchmark functions demonstrate that PSO integrated with I2SM achieves superior performance, outperforming standard PSO in 90% of the cases. Although I2SM-PSO incurs a slightly higher execution time compared to standard PSO, significant improvements in solution quality validate its efficiency. However, this increase in execution time highlights a limitation that should be addressed in future research to optimize computational efficiency while maintaining performance gains.

This paper presents a comprehensive approach for reliability assessment and optimization of ball joints by combining finite element analysis (FEA) method with machine learning algorithms, in particular by exploiting the power of the XGBoost machine learning algorithm. FEA simulations were carried out using ANSYS to generate a dataset encompassing various solder joint geometrical parameters configurations. Key input parameters, including geometric dimensions, were identified by sensitivity analysis and used as features for training the XGBoost model. The trained model demonstrated solid performance. Feature importance analysis revealed critical factors influencing solder joint reliability and provided insights for optimization strategies. This research provides practical recommendations for optimizing solder joint and material design to improve reliability and performance in electronic packaging applications.

Additive manufacturing processes and generative lattice structure are two increasingly popular methods in thermal management applications, particularly in the design and production of heat sinks for electronic devices. In this paper additive manufacturing was used to create heat sinks with lattice structures, specifically triple periodic minimum surfaces (TPMS). Where a conventional pin-fin heat conductor was compared with a heat sink using TPMS lattices, including Gyroid, IWP, and Neovius structures, by evaluating thermal performance. A model was physically simulated to study the thermal performance of the dispersants on the ANSYS software. The results show that the heat sink incorporating TPMS lattices exhibits superior thermal performance than the traditional pin heat sink. The research demonstrates the potential of additive manufacturing to create complex geometries that can improve the thermal dissipation of electronic devices.