Assessment of the Quality and Mechanical Parameters of Castings Using Machine Learning Methods

• Published in: Materials 2022-04, Vol.15 (8), p.2884
• Main Authors: Jaśkowiec, Krzysztof, Wilk-Kołodziejczyk, Dorota, Bartłomiej, Śnieżyński, Reczek, Witor, Bitka, Adam, Małysza, Marcin, Doroszewski, Maciej, Pirowski, Zenon, Boroń, Łukasz
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation; Cast iron; Casting machines; Castings; Deep learning; Literature reviews; Machine learning; Mechanical properties; Microstructure; Neural networks; Quality assessment; Scientific papers; Tensile strength; Variables
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma15082884

The aim of the work is to investigate the effectiveness of selected classification algorithms and their extensions in assessing microstructure of castings. Experiments were carried out in which the prepared algorithms and machine learning methods were tested in various conditions and configurations, as well as for various input data, which are photos of castings (photos of the microstructure) or information about the material (e.g., type, composition). As shown by the literature review, there are few scientific papers on this subject (i.e., in the use of machine learning to assess the quality of the microstructure and the obtained strength properties of cast iron). The effectiveness of machine learning algorithms in assessing the quality of castings will be tested using the most universal methods. Results obtained by classic machine learning methods and by neural networks will be compared with each other, taking into account aspects such as interpretability of results, ease of model implementation, algorithm simplicity, and learning time.