Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization

Yadi Zhao, Lei Yan, Jian Wu () and Ximing Song
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Yadi Zhao: School of Technology, Beijing Forestry University, Beijing 100083, China
Lei Yan: School of Technology, Beijing Forestry University, Beijing 100083, China
Jian Wu: School of Technology, Beijing Forestry University, Beijing 100083, China
Ximing Song: School of Technology, Beijing Forestry University, Beijing 100083, China

Future Internet, 2023, vol. 16, issue 1, 1-14

Abstract: To address the low level of intelligence and low utilization of logs in current rotary cutting equipment, this paper proposes a digital twin-based system for optimizing the rotary cutting of logs using a five-dimensional model of digital twins. The system features a log perception platform to capture three-dimensional point cloud data, outlining the logs’ contours. Utilizing the Delaunay3D algorithm, this model performs a three-dimensional reconstruction of the log point cloud, constructing a precise digital twin. Feature information is extracted from the point cloud using the least squares method. Processing parameters, determined through the kinematic model, are verified in rotary cutting simulations via Bool operations. The system’s efficacy has been substantiated through experimental validation, demonstrating its capability to output specific processing schemes for irregular logs and to verify these through simulation. This approach notably improves log recovery rates, decreasing volume error from 12.8% to 2.7% and recovery rate error from 23.5% to 5.7% The results validate the efficacy of the proposed digital twin system in optimizing the rotary cutting process, demonstrating its capability not only to enhance the utilization rate of log resources but also to improve the economic efficiency of the factory, thereby facilitating industrial development.

Keywords: digital twin; log centering; control optimization; log rotary cutting simulation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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