Prediction of Mechanical Properties of Squeeze-cast Aluminum Alloys Based on Machine Learning [基于机器学习的挤压铸造铝合金力学性能预测]

作者：Hao Y.; Zhao H.; Lin J.

来源：Tezhong Zhuzao Ji Youse Hejin/Special Casting and Nonferrous Alloys, 2019, 39(8): 859-862.

DOI：10.15980/j.tzzz.2019.08.012

• Aluminum Alloy
• Factorization Machine
• Gradient Descent Algorithm
• Machine Learning
• Squeeze Casting

A polynomial regression model with factorization machine (FM) was applied for predicting the mechanical properties of squeeze-cast aluminum alloys. The data of mechanical properties with different element contents from reported researches was used as training samples based on a gradient descent algorithm in the machine learning model. Then the element contents were input to predict the mechanical properties of aluminum alloys, which was also validated with the experiment data. The validation reveals that the model resulted from machine learning can well predict tensile strength, yield strength, hardness and elongation of alloy with different Al content.

• 单位
  华南理工大学