نوع مقاله : مقاله پژوهشی
موضوعات
عنوان مقاله English
نویسنده English
Recent advancements in additive manufacturing (AM), artificial intelligence (AI), and machine learning (ML) have significantly influenced engineering practices, particularly in the design and fabrication of complex components. As the demand grows for predictive capabilities in assessing the mechanical properties of parts designed and produced via layer-by-layer 3D printing, machine learning algorithms offer promising solutions. This study examines the application of classification models to predict the tensile strength and Young’s modulus of components manufactured via fused deposition modeling (FDM) under various process parameters. Tensile test data were systematically analyzed to train decision tree, support vector machine (SVM), and k-nearest neighbor (KNN) models. Among these, the decision tree algorithm exhibited the highest predictive accuracy (90.9%), with an AUC of 0.92 and an F1 score of 0.88. These findings underscore the value of data-driven methodologies in enhancing engineering design processes and establishing reliable experimental databases, contributing to the evolution of intelligent digital manufacturing systems.
کلیدواژهها English
