---
title: Explainable ML for Aluminum Alloys Properties Prediction
url: https://www.ml-quant.com/papers/ssrn/5082633/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5082633
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5082633
featured: 2025-01-08
citations: unknown
topic: Econometrics & Forecasting
---


# Explainable ML for Aluminum Alloys Properties Prediction

The study introduces a machine learning-based predictive framework for forecasting tensile properties of aluminum alloys, providing a cost-effective method to optimize alloy design.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5082633
- Identifier: SSRN 5082633
- Released: 2025-01-04
- First featured: Quant Letter No. 81 (2025-01-08): https://www.ml-quant.com/issues/2025-01-08/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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