REAL-TIME STRUCTURAL ANALYSIS BASED ON MACHINE LEARNING FOR CUSTOM PRODUCT DESIGN: A CASE STUDY OF ORTHOPEDIC FIXATOR PRODUCT

Mass customization is related to increasing the balance between needs of companies that are focused on customers conditions production flexibility and efficiency. Product adjustment according customer can increase company's competitiveness. However, special processes adjustments time consuming cost inefficient. Parametric product modeling a fairly popular technique for dealing with this problem. it still has challenges high software workforce expertise in field quality control. In addition, product-specific designs cannot be tested quickly, resulting long time. This study proposes machine learning (ML) method aims obtain fast structure analyze orthopedic fixators. research process requires collection training data attributes, physical characteristics, quality, selected ML techniques, determination appropriate set hyperparameters. Optimization results were obtained using gradient boosting value . With these results, fixation device used case developing model.