To implement energy savings in multistage centrifugal pumps, a return channel is utilized to replace the origin inter-stage flow channel structure, and then a single-objective optimization work containing high-precision numerical simulation, design variable dimensionality reduction, and machine learning is conducted to obtain the optimal geometric parameters. The variable dimensionality reduction process is based on the Spearman correlation analysis method. The influence of 15 design variables of the impeller and return channel is investigated, and seven of them with high-impact factors are selected as the final optimization variables. Thereafter, a genetic algorithm-backpropagation neural network (GA-BPNN) model is used to create a surrogate model with a high-fitting performance by employing a GA to optimize the initial thresholds and weights of a BPNN. Finally, a multi-island genetic algorithm (MIGA) is employed to maximize hydraulic efficiency under the nominal condition. The findings demonstrate that the optimized model’s efficiency is increased by 4.29% at 1.0Qd, and the deterioration of the pump performance under overload conditions is effectively eliminated (the maximum efficiency increase is 14.72% at 1.3Qd). Furthermore, the internal flow analysis indicates that the optimization scheme can improve the turbulence kinetic energy distribution and reduce unstable flow structures in the multistage centrifugal pump.
{"title":"Structural optimization of multistage centrifugal pump via computational fluid dynamics and machine learning method","authors":"Jiantao Zhao, J. Pei, J. Yuan, Wenjie Wang","doi":"10.1093/jcde/qwad045","DOIUrl":"https://doi.org/10.1093/jcde/qwad045","url":null,"abstract":"\u0000 To implement energy savings in multistage centrifugal pumps, a return channel is utilized to replace the origin inter-stage flow channel structure, and then a single-objective optimization work containing high-precision numerical simulation, design variable dimensionality reduction, and machine learning is conducted to obtain the optimal geometric parameters. The variable dimensionality reduction process is based on the Spearman correlation analysis method. The influence of 15 design variables of the impeller and return channel is investigated, and seven of them with high-impact factors are selected as the final optimization variables. Thereafter, a genetic algorithm-backpropagation neural network (GA-BPNN) model is used to create a surrogate model with a high-fitting performance by employing a GA to optimize the initial thresholds and weights of a BPNN. Finally, a multi-island genetic algorithm (MIGA) is employed to maximize hydraulic efficiency under the nominal condition. The findings demonstrate that the optimized model’s efficiency is increased by 4.29% at 1.0Qd, and the deterioration of the pump performance under overload conditions is effectively eliminated (the maximum efficiency increase is 14.72% at 1.3Qd). Furthermore, the internal flow analysis indicates that the optimization scheme can improve the turbulence kinetic energy distribution and reduce unstable flow structures in the multistage centrifugal pump.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"26 1","pages":"1204-1218"},"PeriodicalIF":4.9,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88356683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hong-Kyun Noh, Jae Hyuk Lim, Seungchul Lee, Taejoo Kim, Deog-Kwan Kim
This study proposes an image composition technique based on convolutional neural networks (CNNs) to construct a surrogate model for predicting fan plots of three-dimensional (3D) composite blades, which represent natural frequency lists at different rotational speeds. The proposed method composes critical 2D cross-section images to improve the accuracy of the model. Numerical examples with various compositions of cross-section images are presented to demonstrate the efficacy of the CNN model. Additionally, gradient-weighted class activation mapping analysis is used to reveal the relationship between the internal structure of the blade and the fan plots. The study shows that using multiple images in the image composition technique improves the accuracy of the model compared to using single or fewer images. Overall, the proposed method provides a promising approach for predicting fan plots of 3D composite blades using CNN models.
{"title":"Surrogate modeling of the fan plot of a rotor system considering composite blades using convolutional neural networks with image composition","authors":"Hong-Kyun Noh, Jae Hyuk Lim, Seungchul Lee, Taejoo Kim, Deog-Kwan Kim","doi":"10.1093/jcde/qwad049","DOIUrl":"https://doi.org/10.1093/jcde/qwad049","url":null,"abstract":"\u0000 This study proposes an image composition technique based on convolutional neural networks (CNNs) to construct a surrogate model for predicting fan plots of three-dimensional (3D) composite blades, which represent natural frequency lists at different rotational speeds. The proposed method composes critical 2D cross-section images to improve the accuracy of the model. Numerical examples with various compositions of cross-section images are presented to demonstrate the efficacy of the CNN model. Additionally, gradient-weighted class activation mapping analysis is used to reveal the relationship between the internal structure of the blade and the fan plots. The study shows that using multiple images in the image composition technique improves the accuracy of the model compared to using single or fewer images. Overall, the proposed method provides a promising approach for predicting fan plots of 3D composite blades using CNN models.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"133 1","pages":"1250-1266"},"PeriodicalIF":4.9,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86202587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The lightweight design of the hood is crucial for the structural optimization of an entire vehicle. However, traditional high-fidelity-based lightweight methods are time-consuming due to the complex structures of the hood, and the lightweight results heavily rely on engineering experiences. To this end, an improved adaptive marine predator algorithm (AMPA) is proposed to solve this problem. Compared to the original marine predator algorithm (MPA), the proposed AMPA adapts to optimization problems through three enhancements, including chaotic theory-based initialization, a mixed search strategy, and dynamic partitioning of iteration phases. Experimental comparisons of AMPA, MPA, and eight state-of-the-art algorithms are conducted on IEEE CEC2017 benchmark functions. AMPA outperforms the others in both 30- and 50-dimensional experiments. Friedman and Wilcoxon’s sign-rank tests further confirm AMPA’s superiority and statistical significance. An implicit parametric model of the hood is generated, and the critical design variables are determined through global sensitivity analysis to realize hood lightweight. The stacking method is employed to construct a surrogate meta-model of the hood to accelerate the optimization efficiency of the vehicle hood. Utilizing the meta-model and the proposed AMPA, the hood mass is reduced by 7.43% while all six static and dynamic stiffness metrics are enhanced. The effectiveness of the proposed optimization method is validated through finite element analysis.
{"title":"An adaptive marine predator algorithm based optimization method for hood lightweight design","authors":"Chenglin Zhang, Zhicheng He, Qiqi Li, Yong Chen, Shaowei Chen, X. Nie","doi":"10.1093/jcde/qwad047","DOIUrl":"https://doi.org/10.1093/jcde/qwad047","url":null,"abstract":"The lightweight design of the hood is crucial for the structural optimization of an entire vehicle. However, traditional high-fidelity-based lightweight methods are time-consuming due to the complex structures of the hood, and the lightweight results heavily rely on engineering experiences. To this end, an improved adaptive marine predator algorithm (AMPA) is proposed to solve this problem. Compared to the original marine predator algorithm (MPA), the proposed AMPA adapts to optimization problems through three enhancements, including chaotic theory-based initialization, a mixed search strategy, and dynamic partitioning of iteration phases. Experimental comparisons of AMPA, MPA, and eight state-of-the-art algorithms are conducted on IEEE CEC2017 benchmark functions. AMPA outperforms the others in both 30- and 50-dimensional experiments. Friedman and Wilcoxon’s sign-rank tests further confirm AMPA’s superiority and statistical significance. An implicit parametric model of the hood is generated, and the critical design variables are determined through global sensitivity analysis to realize hood lightweight. The stacking method is employed to construct a surrogate meta-model of the hood to accelerate the optimization efficiency of the vehicle hood. Utilizing the meta-model and the proposed AMPA, the hood mass is reduced by 7.43% while all six static and dynamic stiffness metrics are enhanced. The effectiveness of the proposed optimization method is validated through finite element analysis.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"6 1","pages":"1219-1249"},"PeriodicalIF":4.9,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87167189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Complex nonlinear distributed parameter systems (DPSs) exist widely in advanced industrial thermal processes. The modeling of such highly nonlinear systems is a challenge for traditional time/space-separation-based methods since they employ linear methods for the model reduction and spatiotemporal reconstruction, which may lead to an inefficient application of the nonlinear spatial structure features represented by the spatial basis functions. To overcome this problem, a novel spatiotemporal modeling framework composed of nonlinear temporal domain transformation and nonlinear spatiotemporal domain reconstruction is proposed in this paper. Firstly, local nonlinear dimension reduction based on the locally linear embedding technique is utilized to perform nonlinear temporal domain transformation of the spatiotemporal output of nonlinear DPSs. In this step, the original spatiotemporal data can be directly transformed into low-order time coefficients. Then, the extreme learning machine (ELM) method is utilized to establish a temporal model. Finally, through the spatiotemporal domain reconstruction based on the kernel-based ELM method, the prediction of the temporal dynamics obtained from the temporal model can be reconstructed back to the spatiotemporal output. The effectiveness and performance of the proposed method are demonstrated in experiments on the thermal processes of a snap curing oven and a lithium-ion battery.
{"title":"A temporal-spatiotemporal domain transformation-based modeling method for nonlinear distributed parameter systems","authors":"Xi Jin, Daibiao Wu, Haidong Yang, Chengjiu Zhu, Wenjing Shen, Kangkang Xu","doi":"10.1093/jcde/qwad052","DOIUrl":"https://doi.org/10.1093/jcde/qwad052","url":null,"abstract":"\u0000 Complex nonlinear distributed parameter systems (DPSs) exist widely in advanced industrial thermal processes. The modeling of such highly nonlinear systems is a challenge for traditional time/space-separation-based methods since they employ linear methods for the model reduction and spatiotemporal reconstruction, which may lead to an inefficient application of the nonlinear spatial structure features represented by the spatial basis functions. To overcome this problem, a novel spatiotemporal modeling framework composed of nonlinear temporal domain transformation and nonlinear spatiotemporal domain reconstruction is proposed in this paper. Firstly, local nonlinear dimension reduction based on the locally linear embedding technique is utilized to perform nonlinear temporal domain transformation of the spatiotemporal output of nonlinear DPSs. In this step, the original spatiotemporal data can be directly transformed into low-order time coefficients. Then, the extreme learning machine (ELM) method is utilized to establish a temporal model. Finally, through the spatiotemporal domain reconstruction based on the kernel-based ELM method, the prediction of the temporal dynamics obtained from the temporal model can be reconstructed back to the spatiotemporal output. The effectiveness and performance of the proposed method are demonstrated in experiments on the thermal processes of a snap curing oven and a lithium-ion battery.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"31 1","pages":"1267-1279"},"PeriodicalIF":4.9,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85521105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research proposes an integrated voyage optimization algorithm that combines quadtree graph generation, visibility graph simplification, Dijkstra’s algorithm, and a 3D dynamic programming (3DDP) method. This approach enables the determination of a minimum distance initial reference route and the creation of a 2D navigational graph for efficient route optimization. We effectively store and process complex terrain information by transforming the GEBCO uniform grid into a quadtree structure. By utilizing a nearest neighbour search algorithm, edges are connected between adjacent ocean nodes, facilitating the generation of a quadtree graph. Applying Dijkstra’s algorithm to the quadtree graph, we derive the shortest initial route and construct a visibility graph based on the waypoints. This results in a simplified reference route with reduced search distance, allowing for more efficient navigation. For each waypoint along the reference route, a boundary is defined angled at 90 degrees to the left and right, based on the waypoint’s reference bearing. A line segment formed by the waypoint and both boundaries is defined as a navigational stage. A navigational graph is defined by connecting adjacent stages. Employing a 3DDP method on the navigational graph, and incorporating weather forecasting data, including wind, wave, and currents, we search for a route that minimizes fuel oil consumption with estimated time of arrival restrictions. Our approach is tested on several shipping routes, demonstrating a fuel consumption reduction compared to other voyage optimization routes. This integrated algorithm offers a potential solution for tackling complex voyage optimization problems in marine environments while considering various weather factors.
{"title":"Voyage optimization using dynamic programming with initial quadtree based route","authors":"Gwang-Hyeok Choi, Wonhee Lee, Tae-wan Kim","doi":"10.1093/jcde/qwad055","DOIUrl":"https://doi.org/10.1093/jcde/qwad055","url":null,"abstract":"\u0000 This research proposes an integrated voyage optimization algorithm that combines quadtree graph generation, visibility graph simplification, Dijkstra’s algorithm, and a 3D dynamic programming (3DDP) method. This approach enables the determination of a minimum distance initial reference route and the creation of a 2D navigational graph for efficient route optimization. We effectively store and process complex terrain information by transforming the GEBCO uniform grid into a quadtree structure. By utilizing a nearest neighbour search algorithm, edges are connected between adjacent ocean nodes, facilitating the generation of a quadtree graph. Applying Dijkstra’s algorithm to the quadtree graph, we derive the shortest initial route and construct a visibility graph based on the waypoints. This results in a simplified reference route with reduced search distance, allowing for more efficient navigation. For each waypoint along the reference route, a boundary is defined angled at 90 degrees to the left and right, based on the waypoint’s reference bearing. A line segment formed by the waypoint and both boundaries is defined as a navigational stage. A navigational graph is defined by connecting adjacent stages. Employing a 3DDP method on the navigational graph, and incorporating weather forecasting data, including wind, wave, and currents, we search for a route that minimizes fuel oil consumption with estimated time of arrival restrictions. Our approach is tested on several shipping routes, demonstrating a fuel consumption reduction compared to other voyage optimization routes. This integrated algorithm offers a potential solution for tackling complex voyage optimization problems in marine environments while considering various weather factors.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"39 1","pages":"1185-1203"},"PeriodicalIF":4.9,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87866451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, as the service robot market has grown, robots have emerged in various fields such as industry, service, and sports. In the field of sports, robots that can play with humans have been developed. We proposed a novel vision system for measuring the trajectory of a tennis ball and predicting its bound position, which can be utilized in the development of tennis robots. In this paper, we introduce a ball detection algorithm using an artificial neural network and a ball trajectory prediction algorithm using stereo vision. Our approach involved the use of a net vision system and a robot vision system to accurately detect and track the ball as it moves across the court. By combining these two systems, we were able to predict the trajectory and bound position of the tennis ball with high accuracy. As a result, the accuracy of the neural network for ball detection in actual tennis images reaches 81.4%. The ball trajectory prediction error in Gazebo simulation is 29.6 cm in the x-axis, 7.2 cm in the y-axis, and 11.7 cm in the z-axis on average.
最近，随着服务机器人市场的增长，机器人在工业、服务、体育等各个领域都出现了。在体育领域，可以和人类一起玩的机器人已经被开发出来。提出了一种测量网球运动轨迹并预测其边界位置的视觉系统，可用于网球机器人的开发。本文介绍了一种基于人工神经网络的球检测算法和一种基于立体视觉的球轨迹预测算法。我们的方法包括使用网络视觉系统和机器人视觉系统来准确地检测和跟踪球在球场上的移动。通过结合这两个系统，我们能够以较高的精度预测网球的轨迹和束缚位置。结果表明，神经网络在实际网球图像中的球检测准确率达到81.4%。Gazebo仿真的球轨迹预测误差平均为x轴29.6 cm, y轴7.2 cm, z轴11.7 cm。
{"title":"Ball tracking and trajectory prediction system for tennis robots","authors":"Yoseph Yang, David Kim, Dongil Choi","doi":"10.1093/jcde/qwad054","DOIUrl":"https://doi.org/10.1093/jcde/qwad054","url":null,"abstract":"\u0000 Recently, as the service robot market has grown, robots have emerged in various fields such as industry, service, and sports. In the field of sports, robots that can play with humans have been developed. We proposed a novel vision system for measuring the trajectory of a tennis ball and predicting its bound position, which can be utilized in the development of tennis robots. In this paper, we introduce a ball detection algorithm using an artificial neural network and a ball trajectory prediction algorithm using stereo vision. Our approach involved the use of a net vision system and a robot vision system to accurately detect and track the ball as it moves across the court. By combining these two systems, we were able to predict the trajectory and bound position of the tennis ball with high accuracy. As a result, the accuracy of the neural network for ball detection in actual tennis images reaches 81.4%. The ball trajectory prediction error in Gazebo simulation is 29.6 cm in the x-axis, 7.2 cm in the y-axis, and 11.7 cm in the z-axis on average.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"262 1","pages":"1176-1184"},"PeriodicalIF":4.9,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76124968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gaze estimation is a fundamental task in many applications of cognitive sciences, human-computer interaction, and robotics. The purely data-driven appearance-based gaze estimation methods may suffer from a lack of interpretability, which prevents their applicability to pervasive scenarios. In this study, a feature fusion method with multi-level information elements is proposed to improve the comprehensive performance of the appearance-based gaze estimation model. The multi-level feature extraction and expression are carried out from the originally captured images, and a multi-level information element matrix is established. A gaze conduction principle is formulated for reasonably fusing information elements from the established matrix. According to the gaze conduction principle along with the matrix, a multi-level information element fusion (MIEF) model for gaze estimation is proposed. Then, several input modes and network structures of the MIEF model are designed, and a series of grouping experiments are carried out on a small-scale sub-dataset. Furthermore, the optimized input modes and network structures of the MIEF model are selected for training and testing on the whole dataset to verify and compare model performance. Experimental results show that optimizing the feature combination in the input control module and fine-tuning the computational architecture in the feature extraction module can improve the performance of the gaze estimation model, which would enable the reduction of the model by incorporating the critical features and thus improve the performance and accessibility of the method. Compared with the reference baseline, the optimized model based on the proposed feature fusion method of multi-level information elements can achieve efficient training and improve the test accuracy in the verification experiment. The average error is 1.63 cm on phones on the GazeCapture dataset, which achieves comparable accuracy with state-of-the-art methods.
{"title":"Appearance-based gaze estimation with feature fusion of multi-level information elements","authors":"Zhonghe Ren, Fengzhou Fang, Gaofeng Hou, Zihao Li, Rui Niu","doi":"10.1093/jcde/qwad038","DOIUrl":"https://doi.org/10.1093/jcde/qwad038","url":null,"abstract":"\u0000 Gaze estimation is a fundamental task in many applications of cognitive sciences, human-computer interaction, and robotics. The purely data-driven appearance-based gaze estimation methods may suffer from a lack of interpretability, which prevents their applicability to pervasive scenarios. In this study, a feature fusion method with multi-level information elements is proposed to improve the comprehensive performance of the appearance-based gaze estimation model. The multi-level feature extraction and expression are carried out from the originally captured images, and a multi-level information element matrix is established. A gaze conduction principle is formulated for reasonably fusing information elements from the established matrix. According to the gaze conduction principle along with the matrix, a multi-level information element fusion (MIEF) model for gaze estimation is proposed. Then, several input modes and network structures of the MIEF model are designed, and a series of grouping experiments are carried out on a small-scale sub-dataset. Furthermore, the optimized input modes and network structures of the MIEF model are selected for training and testing on the whole dataset to verify and compare model performance. Experimental results show that optimizing the feature combination in the input control module and fine-tuning the computational architecture in the feature extraction module can improve the performance of the gaze estimation model, which would enable the reduction of the model by incorporating the critical features and thus improve the performance and accessibility of the method. Compared with the reference baseline, the optimized model based on the proposed feature fusion method of multi-level information elements can achieve efficient training and improve the test accuracy in the verification experiment. The average error is 1.63 cm on phones on the GazeCapture dataset, which achieves comparable accuracy with state-of-the-art methods.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"361 1","pages":"1080-1109"},"PeriodicalIF":4.9,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74167412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Lee, Sanghoon Lee, Young-chae Kim, Sumin Kim, S. Hong
This paper discusses an approach to augmented virtual reality (AVR) and 360-degree spatial visualization. The approach involves locating stereoscopic 3D virtual objects into a real off-site panorama, supporting spatial remodel design decision-making through realistic comparisons. Previous studies have shown that in the design process, end-user engagement promotes the quality and satisfaction of design solutions. Immersive media such as virtual reality (VR) and augmented reality (AR) have increasingly been used as communication tools for user engagement in design, as they provide intuitive and realistic user experiences, particularly in comparing design plans. However, the dichotomous affordance of current VR and AR devices is limited in satisfying both the sense of realism and immersion that are essential for user engagement. To overcome this shortcoming, we propose an AVR-based design visualization approach that integrates the advantages of both media technologies to provide a high sense of realism and immersion off-site, responding to location and environmental stimuli, such as lighting, material, and other factors. To achieve this goal, we used 360-degree panorama data of the target space as a design visualization background, with content immersion experienced through VR hardware. Additionally, we developed software to demonstrate the actual use of the AVR-based approach, and various visualization-purposed file formats can be exported automatically using this software. The software supports the authoring of 360-degree spatial visualization videos for realistic design comparisons, which can be easily accessed by end-users using a head-mounted display or smartphone, even in real-time. We performed a demonstration of this approach using an actual remodel design project for the university library lobby, and this paper shows the usability and applicability of the AVR-based approach for user engagement.
{"title":"Augmented virtual reality and 360 spatial visualization for supporting user-engaged design","authors":"J. Lee, Sanghoon Lee, Young-chae Kim, Sumin Kim, S. Hong","doi":"10.1093/jcde/qwad035","DOIUrl":"https://doi.org/10.1093/jcde/qwad035","url":null,"abstract":"\u0000 This paper discusses an approach to augmented virtual reality (AVR) and 360-degree spatial visualization. The approach involves locating stereoscopic 3D virtual objects into a real off-site panorama, supporting spatial remodel design decision-making through realistic comparisons. Previous studies have shown that in the design process, end-user engagement promotes the quality and satisfaction of design solutions. Immersive media such as virtual reality (VR) and augmented reality (AR) have increasingly been used as communication tools for user engagement in design, as they provide intuitive and realistic user experiences, particularly in comparing design plans. However, the dichotomous affordance of current VR and AR devices is limited in satisfying both the sense of realism and immersion that are essential for user engagement. To overcome this shortcoming, we propose an AVR-based design visualization approach that integrates the advantages of both media technologies to provide a high sense of realism and immersion off-site, responding to location and environmental stimuli, such as lighting, material, and other factors. To achieve this goal, we used 360-degree panorama data of the target space as a design visualization background, with content immersion experienced through VR hardware. Additionally, we developed software to demonstrate the actual use of the AVR-based approach, and various visualization-purposed file formats can be exported automatically using this software. The software supports the authoring of 360-degree spatial visualization videos for realistic design comparisons, which can be easily accessed by end-users using a head-mounted display or smartphone, even in real-time. We performed a demonstration of this approach using an actual remodel design project for the university library lobby, and this paper shows the usability and applicability of the AVR-based approach for user engagement.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"18 1","pages":"1047-1059"},"PeriodicalIF":4.9,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73686431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the study of origami, various parametric methods have been proposed to design crease patterns under geometric conditions for flat-folding. Each design method contributes to finding a desirable crease pattern, e.g., one with superior engineering properties, by manipulating parameters. On the other hand, to continuously deform other crease patterns, it is necessary to recreate it once with such a parametric method; however, this inverse problem is less studied. This paper is basic research to solve this problem and to allow parametric deformation of flat-foldable crease patterns. Given crease patterns are interpreted as networks consisting of twist-folding patterns that can be generated by an existing parametric method named Twist-based design method. Then, by manipulating the parameters, the crease pattern is deformed. Importantly, any flat-foldable crease pattern having no crease line connecting two points on the boundary can be targeted, and it is locally guaranteed that deformed crease patterns have non-intersecting crease lines and are flat-foldable. The proposed method contributes to increased origami variations by deformations of existing crease patterns.
{"title":"Continuous deformation of flat-foldable crease patterns via interpretation as set of twist-patterns","authors":"Yohei Yamamoto, J. Mitani","doi":"10.1093/jcde/qwad036","DOIUrl":"https://doi.org/10.1093/jcde/qwad036","url":null,"abstract":"In the study of origami, various parametric methods have been proposed to design crease patterns under geometric conditions for flat-folding. Each design method contributes to finding a desirable crease pattern, e.g., one with superior engineering properties, by manipulating parameters. On the other hand, to continuously deform other crease patterns, it is necessary to recreate it once with such a parametric method; however, this inverse problem is less studied. This paper is basic research to solve this problem and to allow parametric deformation of flat-foldable crease patterns. Given crease patterns are interpreted as networks consisting of twist-folding patterns that can be generated by an existing parametric method named Twist-based design method. Then, by manipulating the parameters, the crease pattern is deformed. Importantly, any flat-foldable crease pattern having no crease line connecting two points on the boundary can be targeted, and it is locally guaranteed that deformed crease patterns have non-intersecting crease lines and are flat-foldable. The proposed method contributes to increased origami variations by deformations of existing crease patterns.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"28 1","pages":"979-991"},"PeriodicalIF":4.9,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81511670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Caicheng Wang, Zili Wang, Shuyou Zhang, Xiaojian Liu, Jianrong Tan
With lightweight, high strength, and high performance, metal bent tubes have attracted increasing applications in aeronautics. However, the growing demand for customized tubular parts has led to a significant increase in the cost of conventional tube bending processes, as they can only process tubes of a specific diameter. To this end, this paper proposes a variable diameter die (VDD) scheme which can bend tubes with a specific range of diameters. To investigate the formability of VDD-processed tubes for practical VDD applications, an accurate and reliable prediction method of cross-sectional distortion is imperative. Hence, we pioneer a novel intelligent model based on quantum-behaved particle swarm optimization (QPSO) optimized back-propagation neural network (BPNN) to predict a rational cross-sectional distortion characterization index: average distortion rate (ADR). The adaptive adjustment of coefficients and the Gaussian distributed random vector are introduced to QPSO, which balance the search and enhance the diversity of the population, respectively. For further improvement in optimization performance, the informed initialization strategy is applied to QPSO. The efficiency of the proposed reinforced QPSO (RQPSO) optimized BPNN model is evaluated by comparing the results with those of the BPNN, BPNN with Xavier initialization, several different PSO variants optimized BPNN models and variants of popular machine learning models. The results indicated the superiority of RQPSO over other methods in terms of the coefficient of determination (${R}^2$), variance account for (VAF), root mean square error (MSE), mean absolute error (MAE), and standard deviation of MSE (SDM). Thus, the proposed novel algorithm could be employed as a reliable and accurate technique to predict the cross-sectional distortion of VDD-processed tubes.
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