Supervised artificial intelligence classification of clinker processing conditions by optical microscopy

Optical microscopy is known to be a powerful technique for the characterization of industrial clinkers, allowing for obtaining valuable information on many aspects of the production process. The time required for the preparation of samples and collection, and interpretation of pictures strongly limits its application on a large scale. Notably, the analysis of samples requires skilled operators and is often influenced by a significant degree of subjectivity. Consequently, the automation of this task is of great interest to the field. For this reason, in this study, we explore the possibility of adopting supervised artificial intelligence (AI) methodologies on clinker micrographs. This study aims to assess whether an AI model can be employed to support human operators in the interpretation of experimental results. Specifically, this paper focuses on the model's ability to autonomously determine the parameters of the experiment. The results demonstrate a promising capability of AI models to perform this task.