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@article{189225,
        author = {Divyansh Chandravanshi and Yogesh Verma},
        title = {A Review on ANSYS-Based Thermal Simulation Techniques for Metal Casting Optimization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {5201-5207},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189225},
        abstract = {Metal casting remains a critical manufacturing process for producing complex and high-performance metallic components used across automotive, aerospace, power generation, and industrial machinery sectors. However, achieving defect-free castings continues to be challenging due to the influence of thermal gradients, solidification behaviour, gating and riser design, cooling strategies, and mold material selection. Recent advancements in computational simulation, particularly using ANSYS, have enabled highly accurate prediction and analysis of temperature distribution, heat transfer patterns, solidification time, porosity formation, residual stresses, and microstructural development. This review synthesizes findings from multiple studies conducted between 2016 and 2024, highlighting the role of ANSYS-based thermal simulation in optimizing casting processes. The literature demonstrates that simulation-assisted design significantly reduces shrinkage defects, improves directional solidification, increases yield, enhances dimensional accuracy, and minimizes costly trial-and-error experimentation. Key contributions across studies include adaptive meshing for increased precision, integration of artificial intelligence for defect prediction, incorporation of digital twin frameworks for real-time monitoring, and advanced modeling of complex alloys such as aluminum, copper, and magnesium. The review concludes that computational thermal analysis is essential for modern foundry development and Industry 4.0-based smart manufacturing, and identifies future opportunities involving hybrid AM-casting systems, machine learning-enhanced prediction models, and improved material property databases.},
        keywords = {Thermal analysis, Metal casting, Solidification modeling, Heat transfer simulation, Finite element analysis (FEA)},
        month = {December},
        }