Spark Mapping Analysis for Large Samples methodology for quantitative full-scale element imaging of bearing steel bloom with varying reduction processes

Background

The characterization of chemical compositions in full-scale large size metal materials with varying processes is helpful for the rapid selection of the optimal processes in the improvement and development of new materials. The process of soft and heavy reduction processes at the end of solidification has proven to be an effective method for enhancing the internal quality of bearing steel. However, traditional analytical methods fail to accurately determine the composition distribution of full-scale large-size metal samples due to limitations in spatial resolution or time-consuming.

Results

The Spark Mapping Analysis for Large Samples (SMALS) technique has been used for element imaging of full-scale GCr15 bearing steel bloom section with varying reduction processes. The experimental conditions, including testing energy, the distance between the sample and the electrode, were systematically optimized to improve the repeatability and accuracy of the SMALS methodology. Six tailor-made Fe-based reference materials with concentration gradient were mapped using SMALS to establish calibration curve, and the results were validated by conducting spark atomic emission spectroscopy (Spark-AES). Additionally, the detection limits for elements such as C, Si, Mn, Cr, Ni, Mo, Cu, V, Ti, Nb, and Al were determined using SMALS. The quantitative element imaging of the cross and longitudinal sections of bearing steel bloom demonstrated the presence of frame-type and V-type segregation and indicated that the short-range reduction process could better alleviate V-type segregation compared to the long-range reduction process.

Significance

This study provides a successful application case of production process optimization by comparing the composition distribution of sample sections with varying reduction processes. Furthermore, the SMALS technology not only identified segregation patterns but also detected surface defects in samples. This versatile elemental imaging technique provides an efficient approach for composition analysis and process evaluation of full-scale samples, contributing significantly to the research and development of new materials.