Physics-informed Machine Learning for Predicting Heat Treatment Evolution and Mechanical Properties in Wrought Aluminum Alloys

This dataset was collected from multiple sources, including published literature and industrial experimental data of wrought aluminum alloys, with a total of 524 valid samples. Yield strength (YS), ultimate tensile strength (UTS) and elongation (El) were set as the prediction targets, while alloy chemical compositions and heat treatment parameters were adopted as input features.

Data in rows 0–430 (total 429 samples) are sourced from Hu et al. (2024, MSEA, https://doi.org/10.1016/j.msea.2024.147381). The remaining data were collected from published literature and self-conducted experimental measurements.