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ꡬ쑰 μΆμ κΈ°λ²κ³Ό μΈκ³΅μ κ²½ νλ‘λ§μ μ΄μ©ν μ νμμ ꡬ쑰물μ λͺ¨λΈ κ°±μ κΈ°λ²
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν κΈ°κ³κ³΅νκ³Ό, 2020. 8. μ‘°λ§Ήν¨.Model updating methods for structural systems have been introduced in various numerical processes. To improve the updating method, the process must require an accurate analysis and minimized experimental uncertainties. Finite element model was employed to describe structural system. Structural vibration behavior of a plate model is expressed as a combination of the initial state behavior of the structure and its associated perturbations. The dynamic behavior obtained from a limited number of accessible nodes and their associated degrees of freedom is employed to detect structural changes that are consistent with the perturbations. The equilibrium model is described in terms of the measured and unmeasured modal data. Unmeasured information is estimated using an iterated improved reduction scheme. Because the identification problem depends on the measured information, the quality of the measured data determines the accuracy of the identified model and the convergence of the identification problem. The accuracy of the identification depends on the measurement/sensor location. We propose a more accurate identification method using the optimal sensor location selection method. Experimental examples are adopted to examine the convergence and accuracy of the proposed method applied to an inverse problem of system identification. Model updating methods for structural systems have been introduced in various fields. Model updating processes are important for improving a models accuracy by considering experimental data. Structural system identification was achieved here by applying the degree of freedom-based reduction method and the inverse perturbation method. Experimental data were obtained using the specific sensor location selection method. Experimental vibration data were restored to a full finite element model using the reduction method to compare and update the numerical model. Applied iteratively, the improved reduced system method boosts model accuracy
during full model restoration; however, iterative processes are time-consuming. The calculation efficiency was improved using the system equivalent reduction-expansion process in concert with the proper orthogonal
decomposition. A convolutional neural network was trained and applied to the updating process. We propose the use of an efficient model updating method using a convolutional neural network to reduce calculation time. Experimental and numerical examples were adopted to examine the efficiency and accuracy of the model updating method using a convolutional neural network. A more complex model is applied for model updating method and validated with proposed methods. A bolt assembly modeling is introduced and simplified with verified methodologies.ꡬ쑰 μμ€ν
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νκ³ μ€νμ ν΅ν λͺ¨λΈ κ°±μ μΌλ‘ λμ± λ¨μνλ λͺ¨λΈλ§μ μ μν©λλ€.Chapter 1. Introduction 1
1.1 Frequency model updating method . 1
1.2 Reduction methods . 3
1.2.1 Degree of freedom-based reduction method 3
1.2.2 Iterated improved reduced system 4
1.2.3 Proper orthogonal decomposition 8
1.2.4 System equivalent reduction-expansion process 9
1.3 Structural system identification . 11
1.3.1 Balance equation for system identification . 15
1.3.2 Inverse perturbation method . 16
1.4 Machine learning in identification process . 20
Chapter 2. Sensor location selection method 21
2.1 Vibration test setup . 21
2.1.1 Vibration test setup for system identification 21
2.1.2 Vibration data rebuilt for in-house code . 22
2.2 Nodal point consideration . 26
2.2.1 Sequential elimination method 26
2.2.2 Energy method 27
2.2.3 Nodal point consideration 28
2.2.4 Numerical examples . 28
2.3 Sensor location selection method 32
Chapter 3. Residual error equation for identificataion process 36
3.1 Parameter optimizing equation setup 36
3.2 Convergence criterion . 38
3.3 Weighting factor for parameter evaluation 39
3.4 Identification examples 42
Chapter 4. Convolutional neural networks-based system identification method 54
4.1 Introduction . 54
4.2 The balance equation of the model updating method . 57
4.2.1 The IPM method 58
4.2.2 The DOF-based reduction method 59
4.2.3 Experimental data for the model updating method 63
4.3 Convolutional neural network-based identification 67
4.3.1 The SEREP and POD . 67
4.3.2 The 2D-CNN 72
4.4 Experimental examples 77
Chapter 5. A model updating of complex models 94
5.1 The model updating and digital twin . 94
5.2 A complex model example 95
5.2.1 The tank bracket model 95
5.2.2 The sensor location selection 98
5.3 The bolt joint assembly simplification . 102
Chapter 6. Conclusion 109
Appendix A. Structural design of soft robotics using a joint structure of photo responsive polymers 113
A.1 Overview 113
A.2 Structural desing of soft robotics . 114
A.3 Experimental setup 117
A.3.1 Systhesis process 117
A.3.2 Sample preparation 118
A.3.3 Spectrometer characterization 118
A.4 Structural modeling . 121
A.4.1 Multiscale mechanincs 121
A.4.2 Nonlinear FEM with a co-rotational formulation 123
A.5 Results and discussion 128
A.6 Summary of Appendix A 142
Bibliography 145
Abstract in Korean 158Docto