
Finite Element Analysis (FEA), especially through the ANSYS simulation environment, has become a central pillar in modern manufacturing research due to its ability to predict thermal, mechanical, and multiphysics responses with high precision. ANSYS offers integrated solvers that capture complex interactions among heat transfer, melt pool evolution, residual stress formation, structural deformation, and cooling behavior in both additive and traditional manufacturing processes. Its broad applicability across Selective Laser Melting (SLM), Laser Powder Bed Fusion (LPBF), Fused Deposition Modeling (FDM), Wire Arc Additive Manufacturing (WAAM), casting, molding, and forming has enabled advances in modeling process-induced defects, optimizing parameters, and reducing reliance on trial-and-error experimentation. This review consolidates the evolution, methods, and applications of ANSYS-based simulations with emphasis on thermal–mechanical coupling, residual stress prediction, melt pool behavior, structural performance, and CFD-driven cooling analysis. It highlights the growing integration of ANSYS with machine learning, optimization algorithms, and digital twins for predictive, real-time, and adaptive manufacturing. Despite significant progress, gaps remain in unified simulation standards, material model accuracy, high-fidelity multiphysics coupling, anisotropy modeling, and computational efficiency for fine-mesh AM simulations. The review concludes by outlining future opportunities in AI-accelerated simulation, cloud-based solvers, and real-time digital twin ecosystems aimed at enabling fully autonomous and self-optimizing manufacturing environments.
ANSYS simulation, finite element analysis, additive manufacturing, thermal–mechanical modeling, residual stress prediction, digital twins, process optimization.
ANSYS simulation, finite element analysis, additive manufacturing, thermal–mechanical modeling, residual stress prediction, digital twins, process optimization.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
