Evaluating data-driven algorithms for predicting mechanical properties with small datasets:A case study on gear steel hardenability

Data-driven algorithms for predicting mechanical properties with small datasets are evaluated in a case study on gear steel hardenability.The limitations of current data-driven algorithms and empirical models are identified.Challenges in analysing small datasets are discussed,and solution is proposed to handle small datasets with multiple variables.Gaussian methods in combination with novel predictive algorithms are utilized to overcome the challenges in analysing gear steel hardenability data and to gain insight into alloying elements interaction and structure homogeneity.The gained fundamental knowledge integrated with machine learning is shown to be superior to the empirical equations in predicting hardenability.Metallurgical-property relationships between chemistry,sample size,and hardness are predicted via two optimized machine learning algorithms:neural networks(NNs)and extreme gradient boosting(XGboost).A comparison is drawn between all algorithms,evaluating their performance based on small data sets.The results reveal that XGboost has the highest potential for predicting hardenability using small datasets with class imbalance and large inhomogeneity issues.

Toward learning steelmaking-A review on machine learning for basic oxygen furnace process

Basic oxygen furnace(BOF)steelmaking is the most widely used process in global steel production today,accounting for around 70%of the industry's output.Due to the physical,mechanical,and chemical complexities involved in the process,conventional monitoring and control methods are often pushed to their limits.The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process.Over the past decade,Machine Learning(ML)techniques have garnered substantial attention,offering a promising pathway to enhance efficiency and suitability of steel production.This paper presents the first comprehensive review of ML applications in the BOF steelmaking process.We provide an introduction to both fields:an overview of the BOF steelmaking process and ML.We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories,supporting the identification of common use cases and approaches.This analysis concludes with the elaboration of challenges,potential solutions,and a future outlook for further research directions.