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

Taihun Choi, Yoonho Seo

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number5386
Pages (from-to)1-25
Number of pages25
JournalSensors (Switzerland)
Volume20
Issue number18
DOIs
Publication statusPublished - 2020 Sep 2

Keywords

  • AR marker
  • Industry 4.0
  • Internet of Things (IoT)
  • Machine learning
  • Performance measurement
  • Process progress management
  • Smart shipyard

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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