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Traditional cooling systems lack the ability to ensure efficient thermal energy transfer. The evolution of these systems has made it possible to improve new devices. This research demonstrates the effect of phase change materials (PCMs) on optimal thermal management using a heat sink. This type of material has the ability to absorb the thermal energy dissipated by electronic components. The material used is n-Eicosane, chosen for its physical and chemical properties. The finite element method is used to model this phenomenon, and the simulation results are obtained using ANSYS software. This article presents the results of a comparison between a heat sink without phase change materials (PCMs) and a heat sink with PCMs, in order to quantify the effect of the presence of PCMs on the thermal management of electronic components. N-Eicosane, whose melting point is 36.5°C. These heat sinks generally require the use of techniques to improve heat transfer, due to the low thermal conductivity of phase change materials (PCMs). An Aluminum plate fin array is used in this study to increase heat transfer. Therefore, the material used in this simulation decreases the overheating effect of the heat sink (maximum temperature without PCM: 69 °C - maximum temperature with PCM: 61 °C).
Additive Manufacturing (AM), also known as 3D printing, is revolutionizing the industrial sector by enabling the production of complex and customized components. The integration of Artificial Intelligence (AI) into AM processes holds significant promise for enhancing design optimization, process efficiency, and quality control. This article explores the convergence of AI and AM, focusing on innovation, trends, and applications such as topology optimization, performance prediction, real-time monitoring, and automated defect detection. We discuss the challenges associated with integrating AI into AM, including data availability, computational requirements, and the need for multidisciplinary expertise. This review aims to provide valuable insights for researchers and industry professionals interested in leveraging AI to advance additive manufacturing technologies.
2025
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Optimization and Reliability