Utilizing 3D models for the Prediction of Work Man-Hour in Complex Industrial Products using Machine Learning

Volume 9, Issue 6, Page No 01-11, 2024

Author’s Name: Ahmet Emin Ünal ^1,2, Halit Boyar ¹, Burcu Kuleli Pak ¹, Vehbi Çağrı Güngör ³

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¹R&D Department, Adesso Turkey, Istanbul, 34398, Turkey
²Dept. of Comp. Engineering, Istanbul Technical University, Istanbul, 34485, Turkiye
³Dept. of Comp. Engineering, Abdullah Gül University, Kayseri, 38080, Turkiye

^a)whom correspondence should be addressed. E-mail: ahmet.unal@adesso.com.tr

Adv. Sci. Technol. Eng. Syst. J. 9(6), 01-11 (2024); DOI: 10.25046/aj090601

Keywords: Complex Industrial Products, Metal Sheet Stamping, Work Man-hour Prediction, Machine Learning, Gradient Boosting

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Abstract

The integration of machine learning techniques in industrial production has the potential to revolutionize traditional manufacturing processes. In this study, we examine the efficacy of gradient-boosting machine learning models, specifically focusing on feature engineering techniques, applied to a novel dataset with 3D product models pertaining to work moan-hours in metal sheet stamping projects, framed as a regression task. The results indicate that LightGBM and XGBoost surpass other models, and their effectiveness is further enhanced by employing feature selection and synthetic data generation methods. The optimized LightGBM model exhibited superior performance, achieving a MAPE score of 10.78%, which highlights the effectiveness of gradient boosting mechanisms in handling heterogeneous data sets typical in custom manufacturing. Additionally, we introduce a methodology that enables domain experts to observe and critique the results through explainable AI visualizations.

Received: 20 September 2024 Revised: 04 November 2024 Accepted: 05 November 2024 Online: 18 November 2024

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Utilizing 3D models for the Prediction of Work Man-Hour in Complex Industrial Products using Machine Learning