Automatic detection of L and T shaped cracks in semifinished casting products

Gillich, Gilbert-Rainer; Tufiși, Cristian; Korka, Zoltan-Iosif; Hamat, Codruta Oana; Gillich, Nicoleta

doi:10.1088/1757-899x/393/1/012016

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The method for determining the modal curvature and natural frequency caused by a crack, with a known position x, is given in papers [12,13,14,15]. In the current paper, the analysis was performed for damages reducing the rigidity of a cantilever beam subjected to the highest bending moment.…”

Section: Methodsmentioning

confidence: 99%

Evaluating the severity of transverse cracks in beam-like structures by using an energy loss method

Pop

Tufiși

Gillich

et al. 2022

Anal. Tech. Szeged.

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Over functioning time, structures can be affected by multiple types of damages caused by fatigue, improper production methods, or exceeding loads. The current paper describes a method for evaluating the severity of transverse cracks that are present in beam-like structures based on changes in the natural frequencies. Because the presence of damage has a negative impact on the energy that a beam can store in the affected section, it is possible to find the influence of the crack on any other position along the beam, considering the stored normalized energy in that location. The technique is based on a mathematical relationship that provides the exact solution to the frequency changes of the bending vibration modes, considering two terms. The first term is related to the tensile energy stored in the beam, and the second term considers the increase of flexibility due to cracks, for this reason, damage assessment is performed in two stages; first, the location of the crack is found and then an assessment of its severity is performed. In this study, the aim is to test the developed method for estimating the severity of transverse cracks for different sections and lengths of beams.

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Section: Methodsmentioning

confidence: 99%

Evaluating the severity of transverse cracks in beam-like structures by using an energy loss method

Pop

Tufiși

Gillich

et al. 2022

Anal. Tech. Szeged.

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“…To generate the training data, for several scenarios of the damaged beam, the relative frequency shift (RFS) values are used, according to Equation (4) [12,13]:…”

Section: Training Datasetmentioning

confidence: 99%

Detection of transversal cracks in prismatic cantilever beams with weak clamping using machine learning

David

Tufiși

Gillich

et al. 2022

Anal. Tech. Szeged.

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Because our infrastructure is aging and approaching the end of its intended functioning time, the detection of damage or loosening of joints is a topic of high importance in structural health monitoring. The most desired way to assess the health of engineering structures during operation is to use non-destructive vibration-based methods that can offer a global evaluation of the structure’s integrity. A comparison of using different modal data for training feedforward backpropagation neural networks for detecting transverse damages in beam-like structures that can also be affected by imperfect boundary conditions is presented in the current paper. The different RFS, RFSmin, and DLC training datasets are generated by applying an analytical method, previously developed by our research team, that uses a known relation, based on the modal curvature, severity estimation of the transverse crack, and the estimated severity for the weak clamping. The obtained dataset values are employed for training three feedforward backpropagation neural networks that will be used to locate transverse cracks in cantilever beams and detect if the structure is affected by weak clamping. The output from the three ANN models is compared by plotting the calculated error for each case.

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“…The relations for the modal curvatures depend on the boundary conditions and are well known; examples for different are presented in [12]. Here we discuss the case of the cantilever beam, but the method can be likewise applied for double-clamped beams if the right curvature relations for the curvatures are used.…”

Section: Rfs Databasementioning

confidence: 99%

Detection of weak joints and damages for beams using machine learning

Tufiși¹,

Gillich²,

David³

et al. 2022

Vib. proced.

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For maintaining the safe operation of structures, it is necessary to develop SHM methods that can detect not only the presence of cracks in the structure but also any alterations of its fastening conditions. The current paper presents a method for developing an Artificial Intelligent model that can detect if a beam is affected by transverse cracks and at the same time, by improper boundary conditions. To this aim, a cantilever steel beam is considered as the in the current study. The training data for the artificial neural network (ANN) is created using an original analytic method which allows calculating the natural frequency loss caused by the occurrence of transverse cracks even if the beam is improperly fastened. The intelligent model is trained by employing the MATLAB software and tested using data acquired from numerical simulations. The results show very high accuracy in determining the presence of transverse cracks, and the capability of detecting the presence and severity of improper clamping conditions.

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Automatic detection of L and T shaped cracks in semifinished casting products

Cited by 4 publications

References 13 publications

Evaluating the severity of transverse cracks in beam-like structures by using an energy loss method

Evaluating the severity of transverse cracks in beam-like structures by using an energy loss method

Detection of transversal cracks in prismatic cantilever beams with weak clamping using machine learning

Detection of weak joints and damages for beams using machine learning

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