REAL-TIME STRUCTURAL ANALYSIS BASED ON MACHINE LEARNING FOR CUSTOM PRODUCT DESIGN: A CASE STUDY OF ORTHOPEDIC FIXATOR PRODUCT
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Aji Digdoyo(1)
Departement Mechanical Engineering, Jayabaya University
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Adhitio Satyo Bayangkari Karno(2)
Department of Information System, Faculty of Engineering, Gunadarma University
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Widi Hastomo(3*)
ITB Ahmad Dahlan Jakarta
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Agita Tunjungsari(4)
Department of Psychology; Faculty of Psychology Science, Gunadarma University
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Nada Kamilia(5)
Department of Information System, STMIK Jakarta
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Indra Sari Kusuma Wardhana(6)
Department of Information System, STMIK Jakarta
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Nia Yuningsih(7)
Department of Information System, Faculty of Engineering, Gunadarma University
(*) Corresponding Author
Abstract
Mass customization is related to increasing the balance between the needs of companies that are focused on customers on conditions of production flexibility and efficiency. Product adjustment according to customer needs can increase the company's competitiveness. However, special production processes and adjustments are time consuming and cost inefficient. Parametric product modeling is a fairly popular technique for dealing with this problem. However, it still has challenges related to the high cost of software and a workforce that has special expertise in the field of quality control. In addition, product-specific designs cannot be tested quickly, resulting in a long production time. This study proposes a machine learning (ML) method that aims to obtain a fast time structure to analyze the production of orthopedic fixators. This research process requires a collection of training data with product attributes, physical characteristics, quality, selected ML techniques, and determination of the appropriate set of hyperparameters. Optimization results were obtained using the gradient boosting method with a value of . With these results, the orthopedic fixation device can be used in the case study of developing this machine learning model.
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Copyright (c) 2023 Aji Digdoyo, Adhitio Satyo Bayangkari Karno, Widi Hastomo, Agita Tunjungsari, Nada Kamilia, Indra Sari Kusuma Wardhana, Nia Yuningsih
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