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基于视觉、增强现实 (AR) 标记和机器学习的船舶分段装配过程中进度控制的实时物理进度测量方法

A Real-Time Physical Progress Measurement Method for Schedule Performance Control Using Vision, an AR Marker and Machine Learning in a Ship Block Assembly Process.

机构信息

Department of Industrial and Management Engineering, Korea University, Seoul KS013, Korea.

Korea Shipbuilding and Offshore Engineering, Seoul KS013, Korea.

出版信息

Sensors (Basel). 2020 Sep 20;20(18):5386. doi: 10.3390/s20185386.

DOI:10.3390/s20185386
PMID:32962270
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7571170/
Abstract

Progress control is a key technology for successfully carrying out a project by predicting possible problems, particularly production delays, and establishing measures to avoid them (decision-making). However, shipyard progress management is still dependent on the empirical judgment of the manager, and this has led to delays in delivery, which raises ship production costs. Therefore, this paper proposes a methodology for shipyard ship block assembly plants that enables objective process progress measurement based on real-time work performance data, rather than the empirical judgment of a site manager. In particular, an IoT-based physical progress measurement method that can automatically measure work performance without human intervention is presented for the mounting and welding activities of ship block assembly work. Both an augmented reality (AR) marker-based image analysis system and a welding machine time-series data-based machine learning model are presented for measuring the performances of the mounting and welding activities. In addition, the physical progress measurement method proposed in this study was applied to the ship block assembly plant of shipyard H to verify its validity.

摘要

进度控制是成功开展项目的关键技术，通过预测可能出现的问题，特别是生产延误，并制定措施来避免这些问题（决策）。然而，船厂的进度管理仍然依赖于经理的经验判断，这导致了交货延误，从而提高了船舶的生产成本。因此，本文提出了一种船厂船块装配厂的方法，该方法能够基于实时工作绩效数据进行客观的过程进度测量，而不是依赖现场经理的经验判断。特别是，提出了一种基于物联网的物理进度测量方法，可以在无人为干预的情况下自动测量工作绩效，用于船体分段装配工作的装配和焊接活动。为了测量装配和焊接活动的性能，分别提出了基于增强现实（AR）标记的图像分析系统和基于焊接机器时间序列数据的机器学习模型。此外，还将本研究提出的物理进度测量方法应用于船厂 H 的船体分段装配厂，以验证其有效性。

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A Fog Computing and Cloudlet Based Augmented Reality System for the Industry 4.0 Shipyard.基于雾计算和云终端的工业 4.0 船厂增强现实系统。
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