Traditional accuracy check methods for cargo hold in container ships rely solely on manual and visual operations, which are time-consuming and resource-intensive. Addressing the challenge of extracting and analyzing key data, such as cell guides and container pedestals, from large-scale point clouds obtained through three-dimensional (3D) laser scanning in container ship trial runs, this paper proposes an algorithmic framework based on 3D laser scanning. Building upon this framework, an improved coordinate-axis filtering RANSAC algorithm is employed to optimize the extraction of cell guide planes. Additionally, an algorithmic process based on Bhattacharyya distance is utilized to automatically extract container pedestal point clouds. Furthermore, a combination of the genetic algorithm and the ICP algorithm is proposed to achieve the fitting of the container pedestal edge contour through point cloud registration. Experimental results demonstrate the consistency between the extracted cell guide and container pedestal data and the actual results, indicating the high practical value of the proposed methodology. container ship loading test; cargo hold accuracy check; 3D point cloud processing
{"title":"Research on Key Technologies for Container Ship Loading Test Based on 3D Laser Scanning","authors":"Rui Li, Lei Liao, Ji Wang, Shilin Huo, Hexin Wan","doi":"10.5957/jspd.09230025","DOIUrl":"https://doi.org/10.5957/jspd.09230025","url":null,"abstract":"\u0000 \u0000 Traditional accuracy check methods for cargo hold in container ships rely solely on manual and visual operations, which are time-consuming and resource-intensive. Addressing the challenge of extracting and analyzing key data, such as cell guides and container pedestals, from large-scale point clouds obtained through three-dimensional (3D) laser scanning in container ship trial runs, this paper proposes an algorithmic framework based on 3D laser scanning. Building upon this framework, an improved coordinate-axis filtering RANSAC algorithm is employed to optimize the extraction of cell guide planes. Additionally, an algorithmic process based on Bhattacharyya distance is utilized to automatically extract container pedestal point clouds. Furthermore, a combination of the genetic algorithm and the ICP algorithm is proposed to achieve the fitting of the container pedestal edge contour through point cloud registration. Experimental results demonstrate the consistency between the extracted cell guide and container pedestal data and the actual results, indicating the high practical value of the proposed methodology.\u0000 \u0000 \u0000 \u0000 container ship loading test; cargo hold accuracy check; 3D point cloud processing\u0000","PeriodicalId":509716,"journal":{"name":"Journal of Ship Production and Design","volume":"16 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiaoyu Zhang, Hongshuo Zhang, Yan Lin, Yuansong Yang, Haiyang Liu
The paper proposes an innovative methodology of Human Cognitive Reliability- Cognitive Reliability and Error Analysis Method (HCR-CREAM) coupled with A* search and genetic algorithm (GA) to tackle ship cabin equipment layout considering human factor reliability optimization with the goal of minimizing human error probability (HEP) subjected to practical requirements. After establishing the mathematical model of cabin equipment inspection tasks in ship cabin equipment layout problem through HCR-CREAM and equipment geometric simplification, a method of the horizontal movement based on minimum distance is presented to avoid the equipment overlapping, then A* search is used for planning inspection paths and GA with selection, crossover, and mutation operators is applied to solve equipment layout results. A case of equipment layout in a certain ship engine room has been taken to carry out parameter sampling experiments by Latin Hypercube for GA. The results show the solution effect of GA is less affected by its parameter variation. And through the comparison with the initial equipment layout, the indicators influencing the HEP of the optimized result have been improved, thus significantly reducing HEP. ship cabin equipment layout; human factor reliability optimization; HCRCREAM; A* search; genetic algorithm
本文提出了一种创新方法--人的认知可靠性--认知可靠性和误差分析方法（HCR-CREAM），并结合 A* 搜索和遗传算法（GA），在考虑人的因素可靠性优化的前提下解决船舱设备布局问题，目标是使人为误差概率（HEP）最小，并符合实际要求。通过 HCR-CREAM 和设备几何简化建立了船舱设备布局问题中船舱设备检查任务的数学模型，提出了基于最小距离的水平移动方法以避免设备重叠，然后使用 A* 搜索规划检查路径，并应用带有选择、交叉和变异算子的 GA 求解设备布局结果。以某船舶机舱的设备布局为例，通过拉丁超立方对 GA 进行参数采样实验。结果表明，GA 的求解效果受参数变化的影响较小。通过与初始设备布局的对比，优化结果中影响 HEP 的指标得到了改善，从而显著降低了 HEP。 船舶机舱设备布局；人为因素可靠性优化；HCRCREAM；A*搜索；遗传算法
{"title":"A Methodology for Ship Cabin Equipment Layout Considering Human Factor Reliability Optimization","authors":"Qiaoyu Zhang, Hongshuo Zhang, Yan Lin, Yuansong Yang, Haiyang Liu","doi":"10.5957/jspd.11230031","DOIUrl":"https://doi.org/10.5957/jspd.11230031","url":null,"abstract":"\u0000 \u0000 The paper proposes an innovative methodology of Human Cognitive Reliability- Cognitive Reliability and Error Analysis Method (HCR-CREAM) coupled with A* search and genetic algorithm (GA) to tackle ship cabin equipment layout considering human factor reliability optimization with the goal of minimizing human error probability (HEP) subjected to practical requirements. After establishing the mathematical model of cabin equipment inspection tasks in ship cabin equipment layout problem through HCR-CREAM and equipment geometric simplification, a method of the horizontal movement based on minimum distance is presented to avoid the equipment overlapping, then A* search is used for planning inspection paths and GA with selection, crossover, and mutation operators is applied to solve equipment layout results. A case of equipment layout in a certain ship engine room has been taken to carry out parameter sampling experiments by Latin Hypercube for GA. The results show the solution effect of GA is less affected by its parameter variation. And through the comparison with the initial equipment layout, the indicators influencing the HEP of the optimized result have been improved, thus significantly reducing HEP.\u0000 \u0000 \u0000 \u0000 ship cabin equipment layout; human factor reliability optimization; HCRCREAM; A* search; genetic algorithm\u0000","PeriodicalId":509716,"journal":{"name":"Journal of Ship Production and Design","volume":"14 S7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140438737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dong Sheng Zhao, Tang Ren Niu, Ji Wang, Yu Jun Liu
The melt-inert gas-shielded arc additive manufacturing experiment of Inconel 625 nickel-based alloy bulk was carried out. The influence of current mode, current, travel speed, weld bead spacing, deposition path, and arc length on the formation of overlapping weld beads was studied. The incomplete fusion near the bottom weld toe of adjacent weld beads was the main defect. The optimized parameters for bulk additive manufacturing obtained from the experiment were as follows: pulse current of 70 A, travel speed of 200 mm/min, weld bead spacing of 3 mm, dry extension of 10 mm, continuous deposition, and arc length of 1 mm. Due to the thermal influence during the deposition of adjacent weld beads, directional grains were formed inside the weld bead and at the boundary with adjacent weld beads. However, the direction of grain growth was not uniform throughout the entire weld bead, and a consistent texture of the entire weld bead was not formed. The tensile strengths of the specimens in the X, Y, and Z directions were 858.5, 838.7, and 827.2 MPa, respectively, and the fracture elongations were 53.3%, 37.6%, and 42.1%, respectively. The strength in the Z direction, which is the vertical direction, was the lowest. arc additive manufacturing; nickel-based alloy; incomplete fusion; texture
{"title":"Study on the Arc Additive Manufacturing Process and Mechanical Properties of Inconel 625 Nickel-Based Alloy Bulk","authors":"Dong Sheng Zhao, Tang Ren Niu, Ji Wang, Yu Jun Liu","doi":"10.5957/jspd.09230022","DOIUrl":"https://doi.org/10.5957/jspd.09230022","url":null,"abstract":"\u0000 \u0000 The melt-inert gas-shielded arc additive manufacturing experiment of Inconel 625 nickel-based alloy bulk was carried out. The influence of current mode, current, travel speed, weld bead spacing, deposition path, and arc length on the formation of overlapping weld beads was studied. The incomplete fusion near the bottom weld toe of adjacent weld beads was the main defect. The optimized parameters for bulk additive manufacturing obtained from the experiment were as follows: pulse current of 70 A, travel speed of 200 mm/min, weld bead spacing of 3 mm, dry extension of 10 mm, continuous deposition, and arc length of 1 mm. Due to the thermal influence during the deposition of adjacent weld beads, directional grains were formed inside the weld bead and at the boundary with adjacent weld beads. However, the direction of grain growth was not uniform throughout the entire weld bead, and a consistent texture of the entire weld bead was not formed. The tensile strengths of the specimens in the X, Y, and Z directions were 858.5, 838.7, and 827.2 MPa, respectively, and the fracture elongations were 53.3%, 37.6%, and 42.1%, respectively. The strength in the Z direction, which is the vertical direction, was the lowest.\u0000 \u0000 \u0000 \u0000 arc additive manufacturing; nickel-based alloy; incomplete fusion; texture\u0000","PeriodicalId":509716,"journal":{"name":"Journal of Ship Production and Design","volume":"18 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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