Journal of Computational Design and Engineering最新文献

{"title":"A C3 continuous double circumscribed corner rounding method for five-axis linear tool path with improved kinematics performance","authors":"Guangwen Yan, Desheng Zhang, Jinting Xu, Yuwen Sun","doi":"10.1093/jcde/qwad066","DOIUrl":"https://doi.org/10.1093/jcde/qwad066","url":null,"abstract":"\u0000 Corner rounding methods have been widely developed to pursue the smooth motions of machine tools. However, most corner rounding methods, which adopt the double inscribed transitions, still remain an inherent issue of retaining large curvatures of transition curves. Even for those double circumscribed transitions-based methods with relatively small curvatures, they also constrain excessively the transition lengths and are limited to a low-order continuity, deteriorating the feedrate and jerk of machine tools. For addressing these problems, a C3 continuous double circumscribed corner rounding (DCCR) method is proposed for five-axis linear tool path. In this method, the C3 continuous double circumscribed B-splines are specially designed to round the corners of tool position and tool orientation, whose transition lengths are analytically determined by jointly constraining the approximation errors, overlaps elimination and parameter synchronization. Moreover, the excessive constrains of transition lengths imposed by traditional methods are alleviated by fully considering the effects of overlaps and parameter synchronization, and the jerk of rotary axes is also limited with a high-order continuity. Compared to the existing double inscribed corner rounding (DICR) and DCCR methods, experiment results demonstrate that our method can improve further the feedrate while limiting the jerk of machine tools.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73939800","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}

{"title":"Split liability assessment in car accident using 3D convolutional neural network","authors":"Sungjae Lee, Yong-Gu Lee","doi":"10.1093/jcde/qwad063","DOIUrl":"https://doi.org/10.1093/jcde/qwad063","url":null,"abstract":"\u0000 In a car accident, negligence is evaluated through a process known as split liability assessment. This assessment involves reconstructing the accident scenario based on information gathered from sources such as dashcam footage. The final determination of negligence is made by simulating the information contained in the video. Therefore, accident cases for split liability assessment should be classified based on information affecting the negligence degree. While deep learning has recently been in the spotlight for video recognition using short video clips, no research has been conducted to extract meaningful information from long videos, which are necessary for split liability assessment. To address this issue, we propose a new task for analyzing long videos by stacking the important information predicted through the 3D CNNs model. We demonstrate the feasibility of our approach by proposing a split liability assessment method using dashcam footage.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89385467","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}

{"title":"Data processing, analysis, and evaluation methods for co-design of coreless filament-wound building systems","authors":"Marta Gil pérez, Pascal Mindermann, C. Zechmeister, David Forster, Yanan Guo, S. Hügle, Fabian Kannenberg, L. Balangé, V. Schwieger, P. Middendorf, M. Bischoff, A. Menges, G. T. Gresser, J. Knippers","doi":"10.1093/jcde/qwad064","DOIUrl":"https://doi.org/10.1093/jcde/qwad064","url":null,"abstract":"\u0000 The linear design workflow for structural systems, involving a multitude of iterative loops and specialists, obstructs disruptive innovations. During design iterations, vast amounts of data in different reference systems, origins, and significance are generated. This data is often not directly comparable or is not collected at all, which implies a great unused potential for advancements in the process. In this paper, a novel workflow to process and analyze the data sets in a unified reference frame is proposed. From this, differently sophisticated iteration loops can be derived. The developed methods are presented within a case study using coreless filament winding as an exemplary fabrication process within an architectural context. This additive manufacturing process, using fiber-reinforced plastics, exhibits great potential for efficient structures when its intrinsic parameter variations can be minimized. The presented method aims to make data sets comparable by identifying the steps each data set needs to undergo (acquisition, pre-processing, mapping, post-processing, analysis, and evaluation). These processes are imperative to provide the means to find domain interrelations, which in the future can provide quantitative results that will help to inform the design process, making it more reliable, and allowing for the reduction of safety factors. The results of the case study demonstrate the data set processes, proving the necessity of these methods for the comprehensive inter-domain data comparison.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80928453","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}

{"title":"HBWO-JS: jellyfish search boosted hybrid beluga whale optimization algorithm for engineering applications","authors":"Xinguang Yuan, Gang Hu, J. Zhong, Guo Wei","doi":"10.1093/jcde/qwad060","DOIUrl":"https://doi.org/10.1093/jcde/qwad060","url":null,"abstract":"\u0000 Beluga whale optimization (BWO) algorithm is a recently proposed population intelligence algorithm. Inspired by the swimming, foraging and whale falling behaviors of beluga whale populations, it shows good competitive performance compared to other state-of-the-art algorithms. However, the original BWO faces the challenges of unbalanced exploration and exploitation, premature stagnation of iterations, and low convergence accuracy in high-dimensional complex applications. Aiming at these challenges, a hybrid beluga whale optimization based on the jellyfish search optimizer (HBWO-JS), which combines the vertical crossover operator and Gaussian variation strategy with a fusion of jellyfish search (JS) optimizer, is developed for solving global optimization in this paper. First, the BWO algorithm is fused with the JS optimizer to improve the problem that BWO tends to fall into the best local solution and low convergence accuracy in the exploitation stage through multi-stage exploration and collaborative exploitation. Then, the introduced vertical cross operator solves the problem of unbalanced exploration and exploitation processes by normalizing the upper and lower bounds of two stochastic dimensions of the search agent, thus further improving the overall optimization capability. In addition, the introduced Gaussian variation strategy forces the agent to explore the minimum neighborhood, extending the entire iterative search process and thus alleviating the problem of premature stagnation of the algorithm. Finally, the superiority of the proposed HBWO-JS is verified in detail by comparing it with basic BWO and eight state-of-the-art algorithms on the CEC2019 and CEC2020 test suites, respectively. Also, the scalability of HBWO-JS is evaluated in three dimensions (10-dim, 30-dim, 50-dim), and the results show the stable performance of the proposed algorithm in terms of dimensional scalability. In addition, three practical engineering designs and two Truss topology optimization problems demonstrate the practicality of HBWO-JS. The optimization results show that HBWO-JS has a strong competitive ability and broad application prospects.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72586451","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}

{"title":"Robust deep learning-based fault detection of planetary gearbox using enhanced health data map under domain shift problem","authors":"Taewan Hwang, J. Ha, B. Youn","doi":"10.1093/jcde/qwad056","DOIUrl":"https://doi.org/10.1093/jcde/qwad056","url":null,"abstract":"\u0000 The conventional deep learning-based fault diagnosis approach faces challenges under the domain shift problem, where the model encounters different working conditions from the ones it was trained on. This challenge is particularly pronounced in the diagnosis of planetary gearboxes due to the complicated vibrations they generate, which can vary significantly based on the system characteristics of the gearbox. To solve this challenge, this paper proposes a robust deep-learning-based fault-detection approach for planetary gearboxes by utilizing an enhanced health data map (enHDMap). Although there is an HDMap method that visually expresses the vibration signal of the planetary gearbox according to the gear meshing position, it is greatly influenced by machine operating conditions. In this study, domain-specific features from the HDMap are further removed, while the fault-related features are enhanced. Autoencoder-based residual analysis and digital image-processing techniques are employed to address the domain-shift problem. The performance of the proposed method was validated under significant domain-shift problem conditions, as demonstrated by studying two gearbox test rigs with different configurations operated under stationary and non-stationary operating conditions. Validation accuracy was measured in all 12 possible domain-shift scenarios. The proposed method achieved robust fault detection accuracy, outperforming prior methods in most cases.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81980144","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}

{"title":"Mixed-reality for quadruped-robotic guidance in SAR tasks","authors":"Christyan Cruz Ulloa, J. Cerro, A. Barrientos","doi":"10.1093/jcde/qwad061","DOIUrl":"https://doi.org/10.1093/jcde/qwad061","url":null,"abstract":"\u0000 In recent years, exploration tasks in disaster environments, victim localization and primary assistance have been the main focuses of Search and Rescue (SAR) Robotics. Developing new technologies in Mixed Reality (M-R) and legged robotics has taken a big step in developing robust field applications in the Robotics field. This article presents MR-RAS (Mixed-Reality for Robotic Assistance), which aims to assist rescuers and protect their integrity when exploring post-disaster areas (against collapse, electrical, and toxic risks) by facilitating the robot’s gesture guidance and allowing them to manage interest visual information of the environment. Thus, ARTU-R (A1 Rescue Tasks UPM Robot) quadruped robot has been equipped with a sensory system (lidar, thermal and RGB-D cameras) to validate this proof of concept. On the other hand, Human-Robot interaction is executed by using the Hololens glasses. This work’s main contribution is the implementation and evaluation of a Mixed-Reality system based on a ROS-Unity solution, capable of managing at a high level the guidance of a complex legged robot through different interest zones (defined by a Neural Network and a vision system) of a post-disaster environment. The robot’s main tasks at each point visited involve detecting victims through thermal, RGB imaging and neural networks and assisting victims with medical equipment. Tests have been carried out in scenarios that recreate the conditions of post-disaster environments (debris, simulation of victims, etc.). An average efficiency improvement of 48% has been obtained when using the immersive interface and a time optimization of 21.4% compared to conventional interfaces. The proposed method has proven to improve rescuers’ immersive experience of controlling a complex robotic system.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81163643","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}

{"title":"Damage visualization and vulnerability assessment of surface ship considering the 3D multihit location of air-explosion threat","authors":"Kwang-Sik Kim, Jang-Hyun Lee, J. Yoon","doi":"10.1093/jcde/qwad051","DOIUrl":"https://doi.org/10.1093/jcde/qwad051","url":null,"abstract":"\u0000 The purpose of this study is to propose a method for rapidly assessing the hit locations and extent of damage to a naval ship attacked by threatening weapons. An integrated method is presented for assessing the vulnerability to the hull and systems caused by the blast or penetrating effects of a weapon at multiple possible impact locations. The proposed method enables the assessment of vulnerability changes for design alternatives during the early design phase of naval vessels. To predict the extent of damage and to visualize the damaged hull and equipment, it is first assumed that a weapon has multiple hit locations. It is shown that a set of possible hit locations was generated by assuming the trajectory for air-explosion (AIREX) weapons by dividing the trajectory into an air-to-ship trajectory and a ship-to-ship trajectory. To account for the non-deterministic nature of weapon hits, a probability distribution approach was used to generate random multiple hit locations for each trajectory while the hit locations of AIREX in all directions were predicted using a multivariate probability distribution that generates three-dimensional random hit points. The extent of damage was then calculated, taking into account the topology of the hull structure and the equipment installed within the hull compartment, along with the damage volume associated with the weapon at each hit point. To identify the damage volume, axis-aligned bounding box (AABB) components were used, which provide a simplified representation of the ship’s geometry, as well as the relative position and dimensions of the hull structure and installed equipment. The damage compartment was defined as the portion of the hull that overlapped the damaged volume. While AABB’s overlap detection algorithm was applied to the damaged hull compartments, the algorithm identified the equipment that overlapped the damaged volume of the hull. Finally, the geometric modeling module, the probabilistic multiple hit location prediction module, and the damage analysis module were developed for damage visualization and vulnerability assessment.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86019287","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}

{"title":"A state-dependent M/M/1 queueing location-allocation model for vaccine distribution using metaheuristic algorithms","authors":"Fatemeh Hirbod, Masoud Eshghali, Mohammad Sheikhasadi, F. Jolai, A. Aghsami","doi":"10.1093/jcde/qwad058","DOIUrl":"https://doi.org/10.1093/jcde/qwad058","url":null,"abstract":"\u0000 Controlling and maintaining public health in the face of diseases necessitates the effective implementation of response strategies, including the distribution of vaccines. By distributing vaccines, vulnerable populations can be targeted, individuals can be protected, and the spread of diseases can be minimized. However, managing vaccine distribution poses challenges that require careful consideration of various factors, including the location of distribution facilities. This paper proposes a novel model that combines location-allocation problems with queueing systems methodologies to optimize the efficiency of vaccine distribution. The proposed model considers factors such as uncertain demand, varying service rates, depending on the system state. Its primary objective is to minimize total costs, which encompass the establishment and adjustment of the service mechanism, travel times, and customer waiting time. To forecast customer demand rates, the model utilizes time series techniques, specifically the seasonal Autoregressive Integrated Moving Average (ARIMA) model. In order to tackle large-scale problems, a total of 16 newly developed Metaheuristic algorithms are employed, and their performance is thoroughly evaluated. This approach facilitates the generation of solutions that are nearly optimal within a reasonable timeframe. The effectiveness of the model is evaluated through a real case study focused on vaccination distribution in Iran. Furthermore, a comprehensive sensitivity analysis is conducted to demonstrate the practical applicability of the proposed model. The study contributes to the advancement of robust decision-making frameworks and provides valuable insights for addressing location-related challenges in health systems.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86102700","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}

{"title":"An Improved Reptile Search Algorithm with Ghost Opposition-based Learning for Global Optimization Problems","authors":"H. Jia, Chenghao Lu, Di Wu, Changsheng Wen, Honghua Rao, L. Abualigah","doi":"10.1093/jcde/qwad048","DOIUrl":"https://doi.org/10.1093/jcde/qwad048","url":null,"abstract":"\u0000 In 2021, a meta-heuristic algorithm, Reptile Search Algorithm (RSA), was proposed. RSA mainly simulates the cooperative predatory behavior of crocodiles. Although RSA has a fast convergence speed, due to the influence of the crocodile predation mechanism, if the algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out of the local optimum, resulting in a poor comprehensive performance. Because of the shortcomings of RSA, introducing the local escape operator can effectively improve crocodiles' ability to explore space and generate new crocodiles to replace poor crocodiles. Benefiting from adding a restart strategy, when the optimal solution of RSA is no longer updated, the algorithm’s ability to jump out of the local optimum is effectively improved by randomly initializing the crocodile. Then joining Ghost opposition-based learning to balance the IRSA’s exploitation and exploration, the Improved RSA with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance of IRSA, we used nine famous optimization algorithms to compare with IRSA in 23 standard benchmark functions and CEC2020 test functions. The experiments show that IRSA has good optimization performance and robustness, and can effectively solve six classical engineering problems, thus proving its effectiveness in solving practical problems.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82183127","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}

{"title":"Hyperparameters optimization of convolutional neural network based on local autonomous competition harmony search algorithm","authors":"Dongmei Liu, H. Ouyang, Steven Li, Chunliang Zhang, Zhi-hui Zhan","doi":"10.1093/jcde/qwad050","DOIUrl":"https://doi.org/10.1093/jcde/qwad050","url":null,"abstract":"\u0000 Because of the good performance of convolutional neural network (CNN), it has been extensively used in many fields, such as image, speech, text, etc. However, it is easily affected by hyperparameters. How to effectively configure hyperparameters at a reasonable time to improve the performance of CNNs has always been a complex problem. To solve this problem, this paper proposes a method to automatically optimize CNN hyperparameters based on the local autonomous competitive harmony search (LACHS) algorithm. To avoid the influence of complicated parameter adjustment of LACHS algorithm on its performance, a parameter dynamic adjustment strategy is adopted, which makes the pitch adjustment probability PAR and step factor BW dynamically adjust according to the actual situation. To strengthen the fine search of neighborhood space and reduce the possibility of falling into local optima for a long time, an autonomous decision-making search strategy based on the optimal state is designed. To help the algorithm jump out of the local fitting situation, this paper proposes a local competition mechanism to make the new sound competes with the worst harmonic progression of local selection. In addition, an evaluation function is proposed, which integrates the training times and recognition accuracy. To achieve the purpose of saving the calculation cost without affecting the search result, it makes the training time for each model depending on the learning rate and batch size. In order to prove the feasibility of LACHS algorithm in configuring CNN superparameters, the classification of the Fashion-MNIST dataset and CIFAR10 dataset is tested. The comparison is made between CNN based on empirical configuration and CNN based on classical algorithms to optimize hyperparameters automatically. The results show that the performance of CNN based on the LACHS algorithm has been improved effectively, so this algorithm has certain advantages in hyperparametric optimization. In addition, this paper applies the LACHS algorithm to expression recognition. Experiments show that the performance of CNN optimized based on the LACHS algorithm is better than that of the same type of artificially designed CNN. Therefore, the method proposed in this paper is feasible in practical application.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89404130","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}