Spectral Data Processing for Steel Industry
For steel industry, knowing and understanding characteristics of a steel strip surface at every steps of the production process is a key element to control final product quality. Today as the quality requirements increase this task gets more and more important. The surface of new steel grades with complex chemical compositions has behaviors especially hard to master. For those grades in particular, surface control is critical and difficult.
One of the promising technics to assess the problem of surface quality control is spectra analysis. Over the last few years, ArcelorMittal, world’s leading integrated steel and mining company,
has led several projects to investigate the possibility of using devices to measure light spectrum of their product at different stage of the production.
The large amount of data generated by these devices makes it absolutely necessary to develop efficient data treatment pipelines to get meaningful information out of the recorded spectra. In this thesis, we developed mathematical models and statistical tools to treat signal measured with spectrometers in the framework of different research projects.
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