Dynamic Study and Detection of Edge Crack in Composite Laminates Using Vibration Parameters

Senthilkumar, M.; Reddy, S. Manikanta; Sreekanth, T. G.

doi:10.1007/s12666-021-02419-y

Cited by 8 publications

(6 citation statements)

References 21 publications

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“…Artificial neural networks, optimisation techniques, and other artificial intelligence tools have been used in damage detection (Tran-Ngoc et al, 2019;Sreekanth et al, 2022aSreekanth et al, , 2022b. Feedforward neural networks were used to detect damages such as cracks and delamination in composite laminates (Senthilkumar et al, 2022;Sreekanth et al, 2021). Another kind of approach is employing optimisation algorithms to detect the damage.…”

Section: Introductionmentioning

confidence: 99%

Vibration-based crack detection in cantilever beams using Rao-1 algorithm

Reddy,

Senthilkumar,

Sreekanth

2023

IJPSQ

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The prediction of crack location and crack depth in a cantilever beam is considered as an optimisation problem. In this paper, the location and depth of the crack are predicted by minimising the cost function. The cost or objective function is formulated based on the difference between the first three experimental and calculated natural frequencies. The natural frequencies are calculated from the equations which are developed based on the numerical analysis. Rao-1 algorithm is used for predicting the crack location and depth. The Rao-1 algorithm does not have any algorithm-specific parameters to tune them to get optimised performance. Results obtained from the Rao-1 algorithm are compared with those obtained by particle swarm optimisation (PSO). PSO algorithm detected the crack with an average error of 4.94% and the crack depth with 5.96% whereas the Rao-1 algorithm has predicted the location and depth of the crack with an average error of 4.85% and 6.08%. Rao-1 algorithm is also robust and can be used in damage detection to avoid tuning algorithmspecific parameters for better prediction.

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Section: Introductionmentioning

confidence: 99%

Vibration-based crack detection in cantilever beams using Rao-1 algorithm

Reddy,

Senthilkumar,

Sreekanth

2023

IJPSQ

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“…Despite successful assessment of damage, it is worth mentioning that the damage considered in most studies presented in the literature either manifests on an element with significant extent of damage along the beam [ 22 ] or has significant severity: 22% to 68% in [ 26 ] and 20% to 80% in [ 27 ]. For skeletal structures, the damage manifests on one or more structural elements, see, for example, [ 19 , 20 , 21 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Gillich

Tufiși

Săcărea

et al. 2022

Sensors

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Damage detection based on modal parameter changes has become popular in the last few decades. Nowadays, there are robust and reliable mathematical relations available to predict natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF), and the artificial neural network (ANN), as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment was made in two steps. First, a coarse damage location was found from the networks trained for scenarios comprising the whole beam. Afterwards, the assessment was made involving a particular network trained for the segment of the beam on which the crack was previously found. Using the two machine learning methods, we succeeded in estimating the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which was the main goal of the practitioners, the errors were less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

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“…Recent research focuses on detecting damage by involving artificial intelligence (AI). Examples of current methods aiming to detect damage in beams can be found in [21][22][23], while in [24][25][26] are exemplified methods applicable to complex structures. These approaches are based on the analysis of the vibration signal parameters in the time domain (acceleration, damping), or in the frequency domain (mode shapes and curvatures, frequencies).…”

Section: Introductionmentioning

confidence: 99%

Determining the Severity of Open and Closed Cracks Using the Strain Energy Loss and the Hill-Climbing Method

et al. 2022

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Evaluating the integrity of structures is an important issue in engineering applications. The use of vibration-based techniques has become a common approach to assessing cracks, which are the most frequently occurring damage in structures. When involving an inverse method, it is necessary to know the influence of the position and the geometry of the crack on the modal parameter changes. The geometry of the crack, both in size and shape, defines the damage severity (DS). In this study, we present a method (DS-SHC) used for estimating the DS for closed and open transverse cracks in beam-like structures using the intact and damaged beam deflections under its weight and a Stochastic Hill Climbing (SHC) algorithm. After describing the procedure of applying DS-SHC, we calculate for a prismatic cantilever beam the severities for different crack types and depths. The results are tested by comparing the DS obtained with DS-SHC with those acquired from dynamic tests made using professional simulation software. We obtained a good fit between the severities determined in these two ways. Subsequently, we performed laboratory experiments and found that the severities obtained with the DS-SHC method can accurately predict the frequency changes due to the crack. Hence, these severities are a valuable tool for damage detection.

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Dynamic Study and Detection of Edge Crack in Composite Laminates Using Vibration Parameters

Cited by 8 publications

References 21 publications

Vibration-based crack detection in cantilever beams using Rao-1 algorithm

Vibration-based crack detection in cantilever beams using Rao-1 algorithm

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Determining the Severity of Open and Closed Cracks Using the Strain Energy Loss and the Hill-Climbing Method

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