Expanding the frontiers of additive manufacturing: Higher microstructure identification through probability modeling

• In aerospace, healthcare, and automotive, AM is revolutionary. • Definistic models ignore AM processes' stochasticity, making microstructure variability prediction and management problematic. • This study improves AM microstructure predictions with probabilistic modeling and machine learning. • A probabilistic framework was applied to represent metal, polymer, ceramic, and composite microstructural fluctuations. • GPR, FEA, and statistical modeling optimized process parameters and material consistency. Experimental validation using AM process parameters, material properties, and microstructural values. • Metal and polymer microstructural variability were 40 % lower with the probabilistic technique, improving fatigue resistance and tensile strength. • For ceramics, which require precise cooling rate and deposition velocity control, the technique outperformed deterministic models. • ML-assisted probabilistic modeling increased adaptability and real-time process monitoring. • Structural modeling increases the reliability of AM microstructure prediction in this study to address process variability. • These discoveries improve aerospace and biomedical engineering AM methods' scalability and performance. Probabilistic models and machine learning methods create a step forward in making predictions for additive manufacturing (AM) microstructure. In this probabilistic framework, it became possible to express modifications in the properties of metal, polymer, ceramic, and composite microstructures. Process parameters and material consistency reached maximum levels through the use of statistical modeling along with finite element analysis (FEA) and Gaussian process regression (GPR). Experimental validation through AM process parameters, microstructural values, and material characteristics led to 40 % fewer metal and polymer microstructure variations with simultaneous strength increases. The computational system demonstrated its resistance to process modifications through a validated sensitivity analysis. Additionally covered were scalability issues, computing needs, and possible real-time adaption. These results help AM approaches in aerospace and biomedical engineering to be scalable and performable.