ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering最新文献_第2页

Uncertainty Quantification with Mixed Data by Hybrid Convolutional Neural Network for Additive Manufacturing 利用混合卷积神经网络对增材制造中的混合数据进行不确定性量化

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-05-06 DOI: 10.1115/1.4065444

Jianhua Yin, Zhen Hu, X. Du

{"title":"Uncertainty Quantification with Mixed Data by Hybrid Convolutional Neural Network for Additive Manufacturing","authors":"Jianhua Yin, Zhen Hu, X. Du","doi":"10.1115/1.4065444","DOIUrl":"https://doi.org/10.1115/1.4065444","url":null,"abstract":"\u0000 Surrogate models have become increasingly essential for replacing simulation models in additive manufacturing (AM) process analysis and design, particularly for assessing the impact of microstructural variations and process imperfections (aleatory uncertainty). However, these surrogate models can introduce predictive errors, introducing epistemic uncertainty. The challenge arises when dealing with image input data, which is inherently high-dimensional, making it challenging to apply existing Uncertainty Quantification (UQ) techniques effectively. To address this challenge, this study develops a new UQ methodology based on an existing concept of combining Convolutional Neural Network (CNN) and Gaussian Process Regression (GPR). This CNN-GP method converts both numerical and image inputs into a unified, larger-sized image dataset, enabling direct dimension reduction with CNN. Subsequently, GPR constructs the surrogate model, not only providing predictions but also quantifying the associated model uncertainty. This approach ensures that the surrogate model considers both input-related aleatory uncertainty and model-related epistemic uncertainty when it is used for prediction, enhancing confidence in image-based AM simulations and informed decision-making. Three examples validate the high accuracy and effectiveness of the proposed method.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141006338","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}

引用次数: 0

Uncertainty Quantification of Process- Property-Structure Linkage for Fused Filament Fabrication Parts 熔融长丝制造部件工艺-属性-结构联系的不确定性量化

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-05-06 DOI: 10.1115/1.4065443

Yongjie Zhang, Seung Ki Moon

{"title":"Uncertainty Quantification of Process- Property-Structure Linkage for Fused Filament Fabrication Parts","authors":"Yongjie Zhang, Seung Ki Moon","doi":"10.1115/1.4065443","DOIUrl":"https://doi.org/10.1115/1.4065443","url":null,"abstract":"\u0000 Due to the nature of additive manufacturing (AM), design and manufacturing are deeply coupled. Toolpaths are defined based on the part geometry, and in turn, these toolpaths can influence the bonding between adjacent toolpaths, especially for Fused Filament Fabrication (FFF) process. In FFF, bonding between adjacent rasters is critical to the FFF part mechanical strength. And, the bonding is driven by factors such as thermal history and a deposition strategy which are dictated by the geometry of a part and process parameters. In this research, a data-driven physics-based methodology is proposed to predict the mechanical properties of FFF parts using Bayesian inference. In the proposed methodology, geometry and variance in process parameters are used to quantify uncertainties in the mechanical properties. Empirical data derived from the mesostructure of specimens are utilised to generate priors of predictors. Hamilton Monte Carlo is then used to sample the posterior distribution. Subsequently, random draw from posterior predictive distribution is performed and the results are validated against empirical date to establish the accuracy of the proposed methodology. The proposed methodology can provide more accurate prediction of the mechanical properties by considering the influence of geometry, process parameters and uncertainty in AM process.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141010309","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}

引用次数: 0

A Hierarchical Fault Diagnosis Model for Planetary Gearbox with Shift-invariant Dictionary and OMPAN 使用移位变量词典和 OMPAN 的行星齿轮箱分层故障诊断模型

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-05-06 DOI: 10.1115/1.4065442

Ronghua Chen, Yingkui Gu, Peng Huang, Junjie Chen, Guangqi Qiu

{"title":"A Hierarchical Fault Diagnosis Model for Planetary Gearbox with Shift-invariant Dictionary and OMPAN","authors":"Ronghua Chen, Yingkui Gu, Peng Huang, Junjie Chen, Guangqi Qiu","doi":"10.1115/1.4065442","DOIUrl":"https://doi.org/10.1115/1.4065442","url":null,"abstract":"\u0000 Planetary gearbox has been widely applied in the mechanical transmission system, and the failure types of planetary gearbox are more and more diversified. The conventional fault diagnosis methods focus on identifying the faults in the fault library, but ignored the faults outside the fault library. However, it is impossible to build a fault library for all failure types. Targeting the problem of identifying the faults outside the fault library, a hierarchical fault diagnosis method for planetary gearbox with shift-invariant dictionary and orthogonal matching pursuit with adaptive noise (OMPAN) is proposed in this paper. By k-means singular value decomposition (K-SVD) dictionary learning method and shift-invariant strategy, a shift-invariant dictionary is constructed so that the normal modulation components of signals can be completed decomposed. OMPAN algorithm is proposed, which uses the white Gaussian noise to improve the solution method of the orthogonal matching pursuit (OMP) algorithm so that it can separate the modulation components in the signal more accurately. The fault feature extraction is developed via shift-invariant dictionary and OMPAN. A hierarchical classifier is proposed with 3 sub-classifiers so that both the faults in the fault library and the faults outside the fault library are identified. The effectiveness of the proposed hierarchical fault diagnosis method is validated by a planetary gearbox experiment. Result show that the proposed shift-invariant dictionary and OMPAN method has achieved a superior performance in highlighting fault features compared with other two sparse decomposition methods. The proposed hierarchical fault diagnosis approach has achieved a good performance both in classification of the faults in the fault library and identification of the faults outside the fault library.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141009118","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}

引用次数: 0

A Mode Sensitivity Enhancement Method for Beam Bridge Using High-density Strain Feedback 利用高密度应变反馈增强梁桥模式灵敏度的方法

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-04-06 DOI: 10.1115/1.4065268

Zheng Zhou, Qianen Xu, Qingfei Gao, Yang Liu

引用次数: 0

Complementing Drawability Assessment of Deep-Drawn Components with Surrogate-Based Global Sensitivity Analysis 用基于替代品的全局敏感性分析补充深冲部件的可拉伸性评估

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-03-22 DOI: 10.1115/1.4065143

Tobias Lehrer, A. Kaps, I. Lepenies, Elena Raponi, Marcus Wagner, Fabian Duddeck

{"title":"Complementing Drawability Assessment of Deep-Drawn Components with Surrogate-Based Global Sensitivity Analysis","authors":"Tobias Lehrer, A. Kaps, I. Lepenies, Elena Raponi, Marcus Wagner, Fabian Duddeck","doi":"10.1115/1.4065143","DOIUrl":"https://doi.org/10.1115/1.4065143","url":null,"abstract":"\u0000 In the early-stage development of sheet metal parts, key design properties of new structures must be specified. As these decisions are made under significant uncertainty regarding drawing configuration changes, they sometimes result in the development of new parts that, at a later design stage, will not be drawable. As a result, there is a need to increase the certainty of experience-driven drawing configuration decisions.\u0000 Complementing this process with a global sensitivity analysis can provide insight into the impact of various changes in drawing configurations on drawability, unveiling cost-effective strategies to ensure the drawability of new parts. However, when quantitative global sensitivity approaches, such as Sobol's method, are utilized, the computational requirements for obtaining Sobol indices can become prohibitive even for small application problems. To circumvent computational limitations, we evaluate the applicability of different surrogate models engaged in computing global design variable sensitivities for the drawability assessment of a deep-drawn component.\u0000 Here, we show in an exemplary application problem, that both a standard kriging model and an ensemble model can provide commendable results at a fraction of the computational cost. Moreover, we compare our surrogate models to existing approaches in the field. Furthermore, we show that the error introduced by the surrogate models is of the same order of magnitude as that from the choice of drawability measure. In consequence, our surrogate models can improve the cost-effective development of a component in the early design phase.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140212396","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}

引用次数: 0

Diminishing Safety Margins of Telescoping-Boom Aerial Lifts 缩减伸缩臂式高空作业升降机的安全系数

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-03-09 DOI: 10.1115/1.4065028

William Singhose

引用次数: 0

A Hybrid Perspective of Vision-Based Methods for Estimating Structural Displacements Based On Mask R-CNN 基于掩模 R-CNN 的结构位移估算视觉方法的混合视角

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-02-27 DOI: 10.1115/1.4064844

Chuanchang Xu, Cass Wai Gwan Lai, Yangchun Wang, Jiale Hou, Zhufeng Shao, Enjian Cai, Xingjian Yang

{"title":"A Hybrid Perspective of Vision-Based Methods for Estimating Structural Displacements Based On Mask R-CNN","authors":"Chuanchang Xu, Cass Wai Gwan Lai, Yangchun Wang, Jiale Hou, Zhufeng Shao, Enjian Cai, Xingjian Yang","doi":"10.1115/1.4064844","DOIUrl":"https://doi.org/10.1115/1.4064844","url":null,"abstract":"\u0000 Vision-based methods have shown great potential in vibration-based structural health monitoring (SHM), which can be classified as target-based and target-free methods. However, target-based methods cannot achieve sub-pixel accuracy, and target-free methods are sensitive to environmental effects. To this end, this paper proposed a hybrid perspective of vision-based methods for estimating structural displacements, based on Mask region-based convolutional neural networks (Mask R-CNN). In proposed methods, Mask R-CNN is used to first locate the target region, and then target-free vision-based methods are used to estimate structural displacements from the located target. The performances of proposed methods were validated in a shaking table test of a cold-formed steel (CFS) wall system. It can seen that Mask R-CNN can significantly improve the accuracy of feature point matching results of the target-free method. The comparisons of estimated structural displacements using proposed methods are conducted and detailed into accuracy, stability, and computation burden, to guide the selection of the proper proposed method for the specific problem in vibration-based SHM. Proposed methods can also achieve even 1/15 pixel level accuracy. Moreover, different image denoising methods in different lighting conditions are compared.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140426637","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}

引用次数: 0

Experimental Investigation of a Rapid Calculation and Damage Diagnosis of the Quasistatic Influence Line of a Self-Anchored Suspension Bridge Based On Deflection Theory 基于挠度理论的自锚式悬索桥准静力影响线快速计算和损伤诊断实验研究

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-02-27 DOI: 10.1115/1.4064845

Q. Gao, Yidian Dong, Tong Wang, Baolong Pang, Shishuo Yang

{"title":"Experimental Investigation of a Rapid Calculation and Damage Diagnosis of the Quasistatic Influence Line of a Self-Anchored Suspension Bridge Based On Deflection Theory","authors":"Q. Gao, Yidian Dong, Tong Wang, Baolong Pang, Shishuo Yang","doi":"10.1115/1.4064845","DOIUrl":"https://doi.org/10.1115/1.4064845","url":null,"abstract":"\u0000 Based on deflection theory (DT), the basic differential equations and deformation compatibility equations of self-anchored suspension bridges (SASBs) are established. The calculation formula for the stiffening girder (SG) deflection of a three-span SASB is derived, and the validity of the formula is confirmed through an example. In addition, a fast calculation method for the theoretical deflection influence line (TDIL) is proposed by using the deflection calculation formula, and a calculation method for the actual deflection influence line (ADIL) is founded on the concept of time and space coordinate transformation. By loading the bridge with a load car and using real-time monitoring of the displacement response of the bridge test section, combined with the proposed intelligent damage evaluation program, the abnormal situation of the damage of the three-span SASB can be quickly and accurately identified. The research results of this paper can provide flexible, economical and effective guidance for detecting the capacity of real bridges.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424769","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}

引用次数: 0

Optimal Transport Theory for Railway Track Safety Applications 铁路轨道安全应用的优化运输理论

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-02-20 DOI: 10.1115/1.4064787

Nii Attoh-Okine

引用次数: 0

Impact of Sources of Damping On the Fragility Estimates of Wind Turbine Towers 阻尼源对风力涡轮机塔架脆性估计值的影响

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Pub Date : 2024-02-19 DOI: 10.1115/1.4064766

Shubham Baishthakur, V. Pakrashi, Subhamoy Bhattacharya, B. Fitzgerald

引用次数: 0