Exploring research trends in use of finite element analysis for optimization of stress concentration factor in bars with fillets

This study delivers a critical bibliometric and technical synthesis of finite element analysis (FEA)-based optimization of stress concentration factors (SCFs) in stepped flat tension bars (2005–2025). Analysis of 747 publications reveals a clear evolution from early validation studies to optimization-driven, application-ready frameworks. Comparisons of the most-cited works show methodological advances from mesh-converged parametric FEA and experimental benchmarking to evolutionary algorithms, surrogate modeling, and recent AI/ML-enhanced pipelines, though reproducibility and experimental validation remain limited. Keyword clusters align strongly with aerospace, automotive, and energy applications, while biomedical and microscale domains remain underexplored. Emerging research (2023–2025) highlights transformative directions: AI-driven surrogates for rapid SCF prediction, HPC-enabled digital twins for real-time monitoring, and additive manufacturing-specific SCF behaviors. These advances shift the field toward dynamic, data-informed frameworks that integrate computation, optimization, and sensing. By coupling bibliometric mapping with technical interpretation, this study identifies not only the past trajectories but also future opportunities, AI-informed predictive design, multiscale SCF modeling, and optimization for advanced materials, positioning bibliometrics as a strategic tool to guide next-generation structural design. References to ‘biomedical’ and ‘microscale’ indicate emergent mentions in the bibliometric maps (keywords and small clusters) rather than large, focused subfields within the Dimensions.ai export. These areas appeared only in a minority of records and are identified here as promising, under-explored directions requiring targeted systematic reviews and experimental benchmarking.