Special Issue "Smart Shipbuilding and Marine Production Technologies"

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

Deadline for manuscript submissions: 10 November 2021.

Special Issue Editors

Prof. Dr. Jang Hyun Lee
E-Mail Website
Guest Editor
Department of Naval Architecture and Ocean Engineering, INHA University, Incheon 22212, Korea
Interests: machine learning and data analysis; sloshing of LNG and LH2; naval ship survivability; computational welding mechanics; ship production and design
Prof. Dr. Soon Sup Lee
E-Mail Website
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

Manuscript Submission Information

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Keywords

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

Published Papers (4 papers)

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Research

Article
Ultrasonic Attenuation Characteristics of Glass-Fiber-Reinforced Polymer Hull Structure
Appl. Sci. 2021, 11(14), 6614; https://doi.org/10.3390/app11146614 - 19 Jul 2021
Cited by 1 | Viewed by 359
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 [...] Read more.
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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Article
Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
Appl. Sci. 2021, 11(14), 6369; https://doi.org/10.3390/app11146369 - 09 Jul 2021
Viewed by 494
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 [...] Read more.
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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Article
Application of PCA and Classification for Fault Diagnosis of MAB Installed in Petrochemical Plant Process Facilities
Appl. Sci. 2021, 11(9), 3780; https://doi.org/10.3390/app11093780 - 22 Apr 2021
Viewed by 350
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 [...] Read more.
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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Article
Thermal Strain-Based Simplified Prediction of Thermal Deformation Caused by Flame Bending
Appl. Sci. 2021, 11(5), 2011; https://doi.org/10.3390/app11052011 - 25 Feb 2021
Viewed by 382
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 [...] Read more.
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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