Machine learning-based performance predictions for steels considering manufacturing process parameters: a review

Steels are widely used as structural materials, making them essential for supporting our lives and industries. However, further improving the comprehensive properties of steel through traditional trial-and-error methods becomes challenging due to the continuous development and numerous processing parameters involved in steel production. To address this challenge, the application of machine learning methods becomes crucial in establishing complex relationships between manufacturing processes and steel performance. This review begins with a general overview of machine learning methods and subsequently introduces various performance predictions in steel materials. The classification of performance prediction was used to assess the current application of machine learning model-assisted design. Several important issues, such as data source and characteristics, intermediate features, algorithm optimization, key feature analysis, and the role of environmental factors, were summarized and analyzed. These insights will be beneficial and enlightening to future research endeavors in this field.