Intelligent Data Analysis for Materials Obtained Using Selective Laser Melting Technology

In this study, we present a software solution (toolkit) for intelligent data analysis obtained using the selective laser melting (SLM) technology. We have developed a program that uses Data Science approaches and machine learning (ML) algorithms for analyzing and predicting the mechanical properties of materials obtained using the SLM method. The program was trained on a large dataset of SLM materials and was able to achieve an accuracy of 98.9% in terms of the average particle size, using a combination of crystal plasticity and finite element methods (CPFEM) for the Ti-6Al-4V alloy. It allows predicting mechanical properties, such as yield strength, ductility, and toughness, for the structures of Ti-6Al-4V and AlSi10Mg alloys. The study proposes an approach to intelligent data analysis of properties and characteristics of various materials obtained using the SLM technology, based on a formed multidimensional digital model of processes using the developed software solution. The developed set of technologies for intelligent data analysis aimed at optimizing the SLM process demonstrates the potential of machine learning algorithms for improving understanding and optimization of materials obtained through additive manufacturing technologies. Overall, our research emphasizes the importance of developing intelligent solutions for data analysis in materials science and engineering, especially for additive manufacturing technologies such as SLM. By using the developed toolkit that applies machine learning algorithms, specialists can minimize technological production and implementation costs up to 1.2 times, by optimizing the processes of designing and developing materials for various applications, from aerospace industry to biomedical engineering.