Open Access

ARTICLE

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Jinghua Li^1,2, Wenhao Yin², Boxin Yang^1,*, Qinghua Zhou^1,*

1 College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, China
2 College of Shipbuilding Engineering, Harbin Engineering University, Harbin, 150001, China

* Corresponding Authors: Boxin Yang. Email: ; Qinghua Zhou. Email:

(This article belongs to the Special Issue: Computer Modeling in Ocean Engineering Structure and Mechanical Equipment)

Computer Modeling in Engineering & Sciences 2023, 134(1), 699-730. https://doi.org/10.32604/cmes.2022.020811

Received 14 December 2021; Accepted 10 February 2022; Issue published 24 August 2022

Abstract

Quality traceability plays an essential role in assembling and welding offshore platform blocks. The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry. Currently, quality management remains in the era of primary information, and there is a lack of effective tracking and recording of welding quality data. When welding defects are encountered, it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data. In this paper, a composite welding quality traceability model for offshore platform block construction process is proposed, it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm. By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm, the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems. Furthermore, the model and the quality traceability algorithm are checked by cases in actual working conditions. Verification analyses suggest that the proposed early-warning model for welding quality and the algorithm for optimizing backtracking requests are effective and can be applied to the actual construction process.

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