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A Study on the Man-Hour Prediction in Structural Steel Fabrication
Version 1
: Received: 2 May 2024 / Approved: 4 May 2024 / Online: 6 May 2024 (08:46:34 CEST)
A peer-reviewed article of this Preprint also exists.
Wei, Z.; Li, Z.; Niu, R.; Jin, P.; Yu, Z. A Study on the Man-Hour Prediction in Structural Steel Fabrication. Processes 2024, 12, 1068. Wei, Z.; Li, Z.; Niu, R.; Jin, P.; Yu, Z. A Study on the Man-Hour Prediction in Structural Steel Fabrication. Processes 2024, 12, 1068.
Abstract
Longitudinal cutting is a most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules.However, currently the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low.In this study,we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication.The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques,Pearson correlation coefficient for feature selection,and the Random Forest Regression(RFR) for prediction.Compared with the other three Machine Learning(ML) algorithms, the Random Forest algorithm has the best performance.The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy, so that it is suitable for man-hours prediction.
Keywords
Man-hour prediction; RFR; steel fabrication; ML; predictive system
Subject
Engineering, Mechanical Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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