Prediction of contact stress in bolted joints using the Polynomial Chaos-Kriging model
Source abstract
Abstract
Bolted joints are one of the most widely used forms of connection. As the weak link, its contact stress directly influences the mechanical properties of a structure. In this work, Polynomial ChaosKriging (PCK) surrogate model is adopted to predict the contact stress in bolted joints. Firstly, a Finite Element (FE) model of the bolted joint is established using a parametric modeling method. On this basis, the stress datasets for the contact area of the plate are obtained. Subsequently, the generated stress datasets are imported into the PCK surrogate model for training and validation. Maximum axial stress and influence coefficient are used to conduct the characterization analysis of the contact stress. Results show that the PCK surrogate model is suitable to predict these two parameters. In the case that compressive stress does not exist in all contact areas, quantitative analysis of the contact stress can be further carried out by using this model. The main influencing parameters of contact stress are extracted according to the correlation analysis results. The trained PCK surrogate model can be used as an alternative to finite element analysis for the prediction of contact stress. Design of bolted joints can also be achieved by the parametric control using this model.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Probabilistic and Robust Engineering Design · Structural Health Monitoring Techniques · Fatigue and fracture mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (75)
Show source-provided references
1. Analytical lump model for the nonlinear dynamic response of bolted flanges in aero-engine casings10.1016/j.ymssp.2018.05.056 ↗
2. The mechanical degradation mechanism of CFRP/Al double-lap bolted joints (with and without corrosion protections) after seawater ageing10.1016/j.compstruct.2021.114561 ↗
3. Modeling and vibration analysis of bolted joint multi-plate structures with general boundary conditions10.1016/j.engstruct.2023.115813 ↗
4. Seismic behaviour of diaphragm-through bolted-welded joints between CFST column to steel beam10.1016/j.jcsr.2022.107651 ↗
5. Uncertainties and dynamic problems of bolted joints and other fasteners10.1016/j.jsv.2003.11.064 ↗
6. Analysis of No. 50 Taper Joint Stiffness Under Axial and Radial Loading10.1016/S1526-6125(00)70118-6 ↗
7. Evaluation of Leakage Through Graphite-Based Compression Packing Rings
8. A Method to Reduce the Number of Assembly Tightening Passes in Bolted Flange Joints
9. Member Stiffness and Contact Pressure Distribution of Bolted Joints10.1115/1.2919413 ↗
10. Equivalent axial stiffness of various components in bolted joints subjected to axial loading10.1016/j.finel.2006.12.013 ↗
11. An Improved Stiffness Model for Bolted Joints
12. The effect of preload and surface roughness quality on linear joint model parameters10.1016/j.jsv.2019.01.050 ↗
13. A novel nonlinear contact stiffness model of concrete–steel joint based on the fractal contact theory10.1007/s11071-018-4350-7 ↗
14. Tangential Damping Model of Bolted Joint with the Physics-Based Friction Coefficient10.1007/s12541-021-00485-2 ↗
15. A new approach for the determination of the Iwan density function in modeling friction contact10.1016/j.ijmecsci.2020.105671 ↗
16. Modeling tangential friction based on contact pressure distribution for predicting dynamic responses of bolted joint structures10.1007/s11071-020-05765-6 ↗
17. Experimental study on performance characterization of bolted joint under transverse loading10.1016/j.measurement.2021.109608 ↗
18. Degradation and failure analysis of bolted joints under transverse loading10.1016/j.triboint.2022.108140 ↗
19. Identification of damage stages in bolted metallic joints for different joint geometries and tightening torques using statistical analysis10.1177/1369433219886081 ↗
20. Effect of Tightening Speed on the Torque-Tension and Wear Pattern in Bolted Connections10.1115/1.2749290 ↗
21. Understanding the behaviour of silver as a low friction coating in aerospace fasteners10.1016/j.triboint.2015.12.050 ↗
22. Friction of threaded fasteners10.1016/j.triboint.2017.10.014 ↗
23. Tightening behavior of bolted joint with non-parallel bearing surface
24. Novel Formulation of Bolt Elastic Interaction in Gasketed Joints
25. Analysis of elastic interaction stiffness and its effect on bolt preloading10.1016/j.ijmecsci.2017.05.032 ↗
26. Research on preload of bolted joints tightening sequence-related relaxation considering elastic interaction between bolts10.1016/j.jcsr.2019.01.016 ↗
27. Hyperstatic mechanics analysis of guideway assembly and motion errors prediction method under thread friction coefficient uncertainties10.1016/j.triboint.2023.108275 ↗
28. Changing behavior of friction coefficient for high strength bolts during repeated tightening10.1016/j.triboint.2020.106486 ↗
29. Experimental study on initial loss of tension in bolted joints10.1177/0954406215583080 ↗
30. Initial losing behavior of pre-tightening force for threaded fastener during repeated tightening10.1016/j.engfailanal.2021.106021 ↗
31. A combined theoretical and experimental study on contact creep-induced clamping force relaxation of bolted joints at ambient temperature10.1016/j.marstruc.2022.103263 ↗
32. A multi-bolt tightening method based on elastic interaction coefficient and gasket creep relaxation10.1177/09544062221096242 ↗
33. Experimental and numerical studies of bolted joints subjected to axial excitation10.1016/j.wear.2015.10.012 ↗
34. Thorough understanding on the mechanism of vibration-induced loosening of threaded fasteners based on modified Iwan model10.1016/j.jsv.2020.115238 ↗
35. Experimental study on behavior of time-related preload relaxation for bolted joints subjected to vibration in different directions10.1016/j.triboint.2019.106005 ↗
36. Study on self-loosening mechanism of bolted joint under rotational vibration10.1016/j.triboint.2021.107074 ↗
37. The effect of tightening again on bolt loosening under transverse load: Experimental and finite element analysis10.1016/j.istruc.2022.08.049 ↗
38. A novel three-variable shear deformation plate formulation: Theory and Isogeometric implementation10.1016/j.cma.2017.07.024 ↗
39. NURBS-based postbuckling analysis of functionally graded carbon nanotube-reinforced composite shells10.1016/j.cma.2019.01.011 ↗
40. Artificial neural network combined with damage parameters to predict fretting fatigue crack initiation lifetime10.1016/j.triboint.2022.107854 ↗
41. An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime10.1016/j.triboint.2023.108411 ↗
42. Prediction of failure stages for double lap joints using finite element analysis and artificial neural networks10.1016/j.engfailanal.2019.01.042 ↗
43. An estimation method of fractal parameters on rough surfaces based on the exact spectral moment using artificial neural network10.1016/j.chaos.2022.112366 ↗
44. Artificial neural network methods for the solution of second order boundary value problems10.32604/cmc.2019.06641 ↗
45. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications10.1016/j.cma.2019.112790 ↗
46. Global sensitivity analysis using polynomial chaos expansions10.1016/j.ress.2007.04.002 ↗
47. Adaptive sparse polynomial chaos expansion based on least angle regression10.1016/j.jcp.2010.12.021 ↗
48. Uncertainty quantification of bladed disc systems using data driven stochastic reduced order models10.1016/j.ijmecsci.2020.106011 ↗
49. Williams · 2006 · vol. 2
50. An efficient size-dependent computational approach for functionally graded isotropic and sandwich microplates based on modified couple stress theory and moving Kriging-based meshfree method10.1016/j.ijmecsci.2018.04.040 ↗
51. An accelerated active learning Kriging model with the distance-based subdomain and a new stopping criterion for reliability analysis10.1016/j.ress.2022.109034 ↗
52. A surrogate-assisted stochastic optimization inversion algorithm: Parameter identification of dams10.1016/j.aei.2022.101853 ↗
53. Multi-parameter identification of concrete dam using polynomial chaos expansion and slime mould algorithm10.1016/j.compstruc.2023.107018 ↗
54. Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm10.1016/j.engstruct.2023.115891 ↗
55. Meta-model based stochastic isogeometric analysis of composite plates10.1016/j.ijmecsci.2020.106194 ↗
56. VDI 2230 Systematic calculation of high duty bolted joints Joints with one cylindrical bolt
57. ISO 898–1 Mechanical properties of fasteners made of carbon steel and alloy steel — Part 1: Bolts, screws and studs with specified property classes — Coarse thread and fine pitch thread
58. Bickford · 2022
59. Polynomial-chaos-based Kriging10.1615/Int.J.UncertaintyQuantification.2015012467 ↗
60. Sequential Design of Experiment for Sparse Polynomial Chaos Expansions10.1137/16M1103488 ↗
61. Polynomial chaos expansion for uncertainty quantification of dam engineering problems10.1016/j.engstruct.2019.109631 ↗
62. An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis10.1016/j.probengmech.2009.10.003 ↗
63. A new surrogate modeling technique combining Kriging and polynomial chaos expansions – Application to uncertainty analysis in computational dosimetry10.1016/j.jcp.2015.01.034 ↗
64. A tutorial on support vector machines for pattern recognition10.1023/A:1009715923555 ↗
65. LIBSVM: A Library for support vector machines10.1145/1961189.1961199 ↗
66. Artificial neural networks: A tutorial
67. Artificial neural networks: Fundamentals, computing, design, and application10.1016/S0167-7012(00)00201-3 ↗
68. Automated modal parameter estimation using correlation analysis and bootstrap sampling10.1016/j.ymssp.2017.07.004 ↗
69. Spearman’s rank correlation coefficient
70. Learning with Genetic Algorithms: An Overview10.1023/A:1022606120092 ↗
71. Particle Swarm Optimization Variants for Solving Geotechnical Problems: Review and Comparative Analysis10.1007/s11831-020-09442-0 ↗
72. Grey Wolf Optimizer10.1016/j.advengsoft.2013.12.007 ↗
73. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm10.1016/j.knosys.2015.07.006 ↗
74. Balancing composite motion optimization
75. The Arithmetic Optimization Algorithm10.1016/j.cma.2020.113609 ↗