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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Marine Science and Engineering".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 19627

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Prof. Dr. Jang Hyun Lee

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Guest Editor

Department of Naval Architecture and Ocean Engineering, INHA University, Incheon 22212, Republic of Korea
Interests: machine learning and data analysis; sloshing of LNG and LH₂; naval ship survivability; computational welding mechanics; ship production and design
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Soon Sup Lee

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Guest Editor

Department of Naval Architecture and Ocean Engineering, Gyeongsang National University, 2, Gyeongsang Nam-Do 53064, Korea
Interests: design for ship safety; risk assessment and management; CAD/CAM/CIM; simulation-based design; data analysis; naval ship survivability; condition-based maintenance

Special Issue Information

Dear Colleagues,

This Special Issue focuses on smart shipbuilding technology. The production technology of ships and marine structures has developed over the past 30 years by applying new construction methods and applying CAD/CAM/CAE/ERP technologies. In recent years, smart manufacturing technology based on artificial intelligence and big data analysis has grown tremendously in all industries. The development of smart manufacturing technology requires a new transformation of traditional shipbuilding and offshore production technology based on traditional production methods. Indeed, smart manufacturing technology will present opportunities for the growth of the shipbuilding industry. However, this trend introduces new challenges in terms of smart manufacturing technology and system integration suitable for the shipbuilding and offshore industries. Discussions are needed on the system configuration for proper use of smart manufacturing technology and practical application methods for ship production.

In this context, this Special Issue aims to become an open platform to share knowledge about progress and challenges about smart shipbuilding and smart shipyards. It particularly seeks creative contributions regarding new ideas, recent developments, or mature studies addressing both theoretical and aspects.

Prof. Dr. Jang Hyun Lee
Prof. Dr. Soon Sup Lee
Guest Editors

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Keywords

machine learning and big data analysis applied to shipbuilding
digital twin
anomaly detection
smart process planning and simulation

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Published Papers (6 papers)

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Research

15 pages, 3407 KiB

Open AccessArticle

Using Digital Twin in a Shipbuilding Project

by Zoran Kunkera, Tihomir Opetuk, Neven Hadžić and Nataša Tošanović

Appl. Sci. 2022, 12(24), 12721; https://doi.org/10.3390/app122412721 - 12 Dec 2022

Cited by 19 | Viewed by 4825

Abstract

Three-dimensional modelling software tools enable the creation of a digital replica of the product—“Digital Twin”—a representative of “Virtual Reality” as one of the prominent trends of Industry 4.0. The development of the Digital Twin can start simultaneously with the development of the product, primarily for the purpose of selecting optimal technical and technological solutions prior to and during physical construction, and, ultimately, with the intention of managing the entire product life cycle. The Digital Twin, as one of the key technological achievements in the implementation of the business system transformation from traditional to smart, should also be recognized as the cornerstone of the “Shipyard 4.0” model, i.e., its “Cyber-Physical Space.” This paper is based on statistical and empirical data of the observed shipyard with the aim to represent the significance of the Digital Twin ship in preserving and improving the competitiveness of the shipbuilding industry. Namely, with the emphasis this article places on the contribution of “advanced outfitting” in achieving savings in the shipbuilding process as well as its role in attaining high standards of environmental protection and workplace safety, the importance of its further improvement is an obvious conclusion—with Digital Twin being one of the recognized tools for this purpose. Full article

(This article belongs to the Special Issue Smart Shipbuilding and Marine Production Technologies)

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24 pages, 14710 KiB

Open AccessArticle

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

by Jun-Hyeon Kim and Jong-Ho Nam

Appl. Sci. 2021, 11(21), 10463; https://doi.org/10.3390/app112110463 - 7 Nov 2021

Cited by 2 | Viewed by 2815

Abstract

The proportion of welding work in total man-hours required for shipbuilding processes has been perceived to be significant, and welding man-hours are greatly affected by working posture. Continuous research has been conducted to identify the posture in welding by utilizing the relationship between man-hours and working posture. However, the results that reflect the effect of the welding posture on man-hours are not available. Although studies on posture recognition based on depth image analysis are being positively reviewed, welding operation has difficulties in image interpretation because an external obstacle caused by arcs exists. Therefore, any obstacle element must be removed in advance. This study proposes a method to acquire work postures using a low-cost RGB-D camera and recognize the welding position through image analysis. It removes obstacles that appear as depth holes in the depth image and restores the removed part to the desired state. The welder’s body joints are extracted, and a convolution neural network is used to determine the corresponding welding position. The restored image showed significantly improved recognition accuracy. The proposed method acquires, analyzes, and automates the recognition of welding positions in real-time. It can be applied to all areas where image interpretation is difficult due to obstacles. Full article

(This article belongs to the Special Issue Smart Shipbuilding and Marine Production Technologies)

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13 pages, 4999 KiB

Open AccessArticle

Ultrasonic Attenuation Characteristics of Glass-Fiber-Reinforced Polymer Hull Structure

by Zhiqiang Han, Sookhyun Jeong, Jae-Won Jang, Jong Hun Woo and Daekyun Oh

Appl. Sci. 2021, 11(14), 6614; https://doi.org/10.3390/app11146614 - 19 Jul 2021

Cited by 12 | Viewed by 3339

Abstract

Glass fiber-reinforced polymer (GFRP) ship structures have hull plate thicknesses of 10 mm or more and are fabricated using a higher proportion of resin matrix systems than E-glass fiber reinforcements. Therefore, GFRP is classified as a highly attenuative material, and this characteristic is a major cause of large errors in ultrasonic nondestructive testing for quality inspections. In this study, considering the aforementioned design and fabrication characteristics of GFRP ship structures, hull plate prototypes with various glass fiber weight fractions, glass contents (Gc), and laminate thicknesses were fabricated. Then, a pulse-echo ultrasonic test was performed with the fabricated prototypes, and the attenuation characteristics of the GFRP hull plates were investigated by conducting statistical analyses. These results demonstrated that with a variation of 30–50% in the Gc used for GFRP structure design, the plate thickness variation had a greater impact than the Gc variation on the attenuation characteristics. The increase in Gc naturally increased the scattering of ultrasonic waves but did not significantly affect the attenuation coefficient. The effects of the inner voids on the ultrasonic waves were also investigated, and the results confirmed that the laminates in this Gc region did not significantly affect attenuation. Full article

(This article belongs to the Special Issue Smart Shipbuilding and Marine Production Technologies)

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16 pages, 2314 KiB

Open AccessArticle

Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time

by Haoyu Zhu and Jong Hun Woo

Appl. Sci. 2021, 11(14), 6369; https://doi.org/10.3390/app11146369 - 9 Jul 2021

Cited by 8 | Viewed by 2382

Abstract

In the shipbuilding industry, each production process has a respective lead time; that is, the duration between start and finish times. Lead time is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it only references past records. This paper proposes a new self-organizing hierarchical particle swarm algorithm (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC)-support vector machine (SVM) regression model to increase the accuracy of lead-time prediction by combining the advanced PSO and SVM models. Moreover, this paper compares the prediction results of each SVM-based model with those of other conventional machine-learning algorithms. The results demonstrate that the proposed NHPSO-JTVAC-SVM model can achieve further meaningful enhancements in terms of prediction accuracy. The prediction performance of the NHPSO-JTVAC-SVM model is also better than that of the other SVM-based models or other machine learning algorithms. Overall, the NHPSO–JTVAC-SVM model is feasible for predicting the lead time in shipbuilding. Full article

(This article belongs to the Special Issue Smart Shipbuilding and Marine Production Technologies)

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15 pages, 5921 KiB

Open AccessFeature PaperArticle

Application of PCA and Classification for Fault Diagnosis of MAB Installed in Petrochemical Plant Process Facilities

by Se-Yun Hwang, Kwang-Sik Kim, Hyung-Jin Kim, Hong-Bae Jun and Jang-Hyun Lee

Appl. Sci. 2021, 11(9), 3780; https://doi.org/10.3390/app11093780 - 22 Apr 2021

Cited by 4 | Viewed by 2366

Abstract

In large systems, such as power plants or petrochemical plants, various equipment (e.g., compressors, pumps, turbines, etc.) are typically deployed. Each piece of equipment operates under generally harsh operating conditions, depending on its purpose, and operates with a probability of failure. Therefore, several sensors are attached to monitor the status of each piece of equipment to observe its conditions; however, there are many limitations in monitoring equipment using thresholds such as maximum and minimum values of data. Therefore, this study introduces a technology that can diagnose fault conditions by analyzing several sensor data obtained from plant operation information systems. The equipment for the case study was a main air blower (MAB), an important cooling equipment in the plant process. The driving sensor data were analyzed for approximately three years, measured at the plant. The fault history of the actual process was also analyzed. Due to the large number of sensors installed in the MAB system, a dimension reduction method was applied with the principal component analysis (PCA) method when analyzing collected sensor data. For application to PCA, the collected sensor data were analyzed according to the statistical analysis method and data features were extracted. Then, the features were labeled and classified according to normal and fault operating conditions. The analyzed features were converted with a diagnosis model, by dimensional reduction, applying the PCA method and a classification algorithm. Finally, to validate the diagnosis model, the actual failure signal that occurred in the plant was applied to the suggested method. As a result, the results from diagnosing signs of failure were confirmed even before the failure occurred. This paper explains the case study of fault diagnosis for MAB equipment with the suggested method and its results. Full article

(This article belongs to the Special Issue Smart Shipbuilding and Marine Production Technologies)

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14 pages, 4409 KiB

Open AccessArticle

Thermal Strain-Based Simplified Prediction of Thermal Deformation Caused by Flame Bending

by Se-Yun Hwang, Kyoung-Geun Park, Jeeyeon Heo and Jang-Hyun Lee

Appl. Sci. 2021, 11(5), 2011; https://doi.org/10.3390/app11052011 - 25 Feb 2021

Cited by 2 | Viewed by 2276

Abstract

This paper describes a quick and accurate method for predicting thermal deformation due to flame bending of the curved plate located before and after the hull. Flame bending is a common method to deform the curved plate used in shipyards. Three-dimensional thermo-elasto-plastic analysis is known as the most accurate method for predicting deformed shape in the automation of frame bending. However, the three-dimensional analysis takes a lot of computational time. The quick prediction method, strain as direct boundary (SDB), was introduced, which is a simplified prediction method based on thermal strain. This simplified method implements an equivalent load as a temperature difference that can simulate thermal deformation by flame. In the case of multiple heating lines by the flame bending, the residual strain generated by the first heating line affects the other lines. To consider the effect of residual strain, the plastic material properties are also considered. Then, the distance ratio from the center line is used to generate the same temperature field in grid mesh. The results of the prediction were evaluated for the heat affected zone (HAZ) of the specimen obtained through the flame bending experiment. Therefore, this paper introduced detail procedure of the proposed SDB method and the experimental results for the practical application. Full article

(This article belongs to the Special Issue Smart Shipbuilding and Marine Production Technologies)

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