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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Integrated Machine Learning Framework for Quality Prediction of Hot Forging
Author(s) Jayasinghe Arachchige Sarini Jayasinghe, Elen Lloyd Williams, Laurie Da Silva, Kyle Marshall, Jamie McGourlay
On-Site Speaker (Planned) Jayasinghe Arachchige Sarini Jayasinghe
Abstract Scope Forging is critical in the aerospace and automotive industries for producing high-strength components, where process variability significantly influences quality and cost. This study proposes a machine learning framework (composed of two models) to predict part quality during forging. The first model used simulation data along with key process variables—forging temperature, die temperature, and stroke speed to estimate locational strain variations where multiple algorithms were compared with a Gaussian model achieving 96.3% accuracy when validated with experimental data. Further analysis of process data revealed additional influential KPVs introducing subtle geometric deviations (±1.5mm), prompting a second model for predicting part geometry conformity, a critical dimensional quality metric. This enhanced model integrated factors such as glass coating thickness, initial part geometry, and transfer time (furnace to press), which were validated through correlation mapping. The integrated approach highlights the potential use of ML for proactive quality control and process optimisation during forging operations.
Proceedings Inclusion? Undecided

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