Modeling and parameters identification of the connection interface of bolted joints based on an improved micro-slip model

Li, Chaofeng; Jiang, Yulin; Qiao, Ruihuan; Miao, Xueyang

doi:10.1016/j.ymssp.2020.107514

Cited by 45 publications

(7 citation statements)

References 26 publications

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“…Finally, as the external load is further increased, the bolt will be extruded with the bolt-hole wall, and the bolt-hole wall will experience a complex nonlinear deformation process, which ranges from elastic deformation to plastic deformation and eventual destruction. In order to investigate the influence of joint slippage, various numerical models are proposed such as the numerical model [26] , parametric model [27] , and micro-slip model [28] . In this paper, the four parameters joint slippage model is selected, which divide joint slippage processes into clearance filled slip and extrusion deformation slip [29] (see Fig.…”

Section: Parametric Uncertainty Model Of Bolted Connectionsmentioning

confidence: 99%

Inversion Method of Uncertain Parameters for Truss Structures Based on Graph Neural Networks

2023

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Uncertainty exists widely in practical engineering. It is an important challenge in engineering structural analysis. In truss structures, the uncertainties of axial stiffness of bolted joints will significantly affect the mechanical behavior of the structure as the axial load is dominated by the member internal forces. Structural response analysis based on determined structural parameters is a common forward problem that can be solved by modeling analysis methods. However, the uncertainties parameter of axial stiffness of bolted joint cannot be determined during the design and analysis of truss structure in the direct nonlinear analysis method. Structural parameter identification based on structural response is a typical inverse problem in engineering, which is difficult to solve using traditional analysis tools. In this paper, an inverse model based on Graph Neural Network (GNN) is proposed. The feature encoding method for transforming truss structures into graph representations of GNN is defined. A parameterized acquisition method for large-scale datasets is presented, and an innovative inversion model based on GNN for the inversion of uncertain parameters of truss structures is proposed. The proposed method is shown to perform well with an inversion accuracy, and accurate results can be obtained with limited data sets. The inversion method has strong data mining capability and model interpretability, making it a promising direction for exploring engineering structural analysis.

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Section: Parametric Uncertainty Model Of Bolted Connectionsmentioning

confidence: 99%

Inversion Method of Uncertain Parameters for Truss Structures Based on Graph Neural Networks

2023

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“…Threaded fasteners are widely used in mechanical systems because of their advantages of convenient disassembly and reliable connections. 1 A sufficient preload is important to ensure the connection and fastening performance and improve product reliability. However, threaded fasteners are prone to loosening 2,3 due to exposure to harsh working environments, especially vibration environments.…”

Section: Introductionmentioning

confidence: 99%

CDMTNet: a novel transfer learning model for the loosening detection of mechanical structures with threaded fasteners

Huang

Liu

Gong

et al. 2023

Structural Health Monitoring

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In mechanical systems, various threaded fasteners are widely used to connect separated components. Loosening occurs frequently due to exposure to harsh working environments, especially vibration environments. Loosening detection can eliminate hidden dangers in advance, and its importance cannot be overemphasized. Ultrasonic detection methods based on machine learning have become popular. However, a proposed machine learning model in a published paper is only suitable for a specific mechanical structure with threaded fasteners; the generality of this type of model is poor. In addition, existing models are well-trained based on abundant labeled data. The labeled data are commonly sparse in practical engineering applications, and it is labor-consuming to obtain these data. This paper presents the creation of a generalized detection model suitable for different threaded connection structures based on transfer learning and the exploitation of a small amount of labeled data for model training and accurate loosening detection. A transfer learning network named the cross-domain matching-mix transfer network (CDMTNet) suitable for different mechanical structures was proposed to transfer the knowledge in a source structure to a target structure. The CDMTNet consists of a feature extraction module, a feature disentangling module, and a classification module. The matching-mix method for data augmentation was proposed to finely train the CDMTNet by exploiting only a small amount of labeled data in the target structure. Two different mechanical structures with threaded fasteners (i.e., a lap joint and a globe-cone joint) were tested in the experiment. Their detection accuracies were both greater than 0.8. The results indicated that our method had wide application potential in engineering.

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“…Considerable research has focused on accommodating the complexities in contacts and joints with the goal of improved models and reliability. For example, the nonlinear softening stiffness of contacts associated with microslip has been quantified experimentally by Gimpl et al [10], and models with improved capability are being developed [11,12]. Mathis et al [13] provide a recent review on damping models for contacts which underscores the significance microslip has on nonlinear damping.…”

Section: Introductionmentioning

confidence: 99%

Edge Changes in Contacts and Joints to Reduce High Localized Shear Traction, Microslip, and Fretting

Hess

2023

Lubricants

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Contacts and joints in structures, mechanisms, and dynamic systems often exhibit high localized interface shear at their edges, leading to edge microslip and fretting wear and fatigue. This introduces complexity, nonlinearity, and multiscale friction phenomena. This paper presents a novel approach to address this issue by introducing geometrical changes near contact edges. Two-dimensional contact models are developed and analyzed using asymptotic, closed-form, and numerical methods to study the effect of edge changes on pressure and shear traction. The results show that geometric changes near contact edges can effectively reduce contact edge shear, thereby inhibiting edge microslip and the resulting fretting wear and fatigue in contacts that occur under dynamic conditions. This approach has implications for reduced complexity in contacts and joints for improved capability in modeling, analysis, and measurement characterization.

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Modeling and parameters identification of the connection interface of bolted joints based on an improved micro-slip model

Cited by 45 publications

References 26 publications

Inversion Method of Uncertain Parameters for Truss Structures Based on Graph Neural Networks

Inversion Method of Uncertain Parameters for Truss Structures Based on Graph Neural Networks

CDMTNet: a novel transfer learning model for the loosening detection of mechanical structures with threaded fasteners

Edge Changes in Contacts and Joints to Reduce High Localized Shear Traction, Microslip, and Fretting

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