Elasticity is concerned with determining the strength and load carrying ability of engineering structures including buildings, bridges, cars, planes, and thousands of machine parts that most of us ...never see. It is especially important in the fields of mechanical, civil, aeronautical and materials engineering. This book provides a concise and organized presentation and development of the theory of elasticity, moving from solution methodologies, formulations and strategies into applications of contemporary interest, including fracture mechanics, anisotropic/composite materials, micromechanics and computational methods. Developed as a text for a one or two-semester graduate elasticity course, this new Second Edition is the only elasticity text to provide coverage in the new area of non-homogenous, or graded, material behavior. End of chapter exercises throughout the book are fully incorporated with the use of MATLAB software.
Wire ropes operate at high stress levels and are almost invariably subject to fluctuating loads. The continuous degradation of wire rope affected with operative services will ultimately lead to ...failure. Study of the causes of failure of two wire ropes from two different Indian coal mines have been carried out and discussed here. The investigating parameters comprises of physical examination, wear & corrosion, lubrication, macro & micro-examination and chemical composition. The investigation revealed that the major cause of failure was due to excessive wear and corrosion resulted in high reduction in diameter ranging from 50%–90% and poor lubrication condition of wire rope. Micro-examination also revealed intergranular corrosion with cracks and pittings, uniform corrosion with pitting along the grain. Further elongated grain which was indicative of stress due to excessive corrosion with sudden impact resulted in its failure.
•Failed winding rope of 25mm diameter 6X8FS construction and failed guide rope of 32mm diameter of BCCL, Dhanbad have been studied.•The investigation revealed that excessive reduction in wire diameter along with sudden load on wire rope may responsible for its failure.•Chemical compositions and poor lubrication are also the factors for the failure because poor lubrication leads to intermittent friction of wires prone to corrosion of the wires.
Steel wire rope (SWR) is widely used in industrial scenarios because of its high strength and toughness. To avoid accidents that local flaws (LFs) can cause, SWRs should be inspected regularly. ...Magnetic flux leakage (MFL) image detection, which is a common SWR inspection method, is inevitably influenced by shaking noise and strand noise. Although noise-reduction methods for these two types of noise have been proposed, the phenomenon of noise distortion has not received enough attention. This paper investigates the noise distortion phenomenon on the MFL image and finds that the distorted noise severely affects the performance of existing noise-feature-oriented (NFO) denoising methods. To solve this problem, we adopt a target-feature-oriented (TFO) denoising method. Specifically, the removal of noise is avoided, instead, an LF-enhancement-based denoising process is proposed. Moreover, to localize LFs in denoised images, a three-stage adaptive localization method mainly based on disjoint region analysis is proposed. The experiment results show that the proposed TFO denoising method significantly improves the denoising performance for images that distorted noise influences. In addition, the proposed adaptive localization method improves the intelligence of the localization process and has better localization performance for images with distorted noise.
•Deep learning is used in the SWR damage detection.•Data acquisition apparatus is designed for reliable collection of the SWR damage data.•Two surface damage types of the SWR (broken wire and wear) ...are studied.•Deep learning has superiority over the conventional machine learning on the SWR damage data.
Steel wire rope (SWR) is of great importance to its many industrial applications. When SWR is damaged, it is likely to result in serious consequences. Therefore, it is important to do research in the field of SWR damage detection. Computer vision-based surface damage detection methods for SWR can operate with high detection accuracy and good adaptation for different types of SWR. Conventional machine learning methods with manual feature extraction have strong subjectivity. If the discriminant information cannot be extracted accurately, the detection accuracy decreases. To address this problem, this paper proposes an intelligent SWR damage detection method, based on a convolutional neural network, which has powerful learning ability and can automatically extract discriminant features by training surface images of the SWR. The experiment results show that the proposed method, based on deep learning, has a higher F1 score and a higher detection speed than four other conventional machine learning methods.
•Twisting enhances the ductility by inhibiting the elongation of helical filaments.•Analytical solutions develop to describe the interaction between layers.•Stretching can more significantly densify ...the fiber than free-twisting.•Binormal force contributes little to the axial stretching of twisted structure.
Twisting plays an important role in fabricating twisted actuators and energy harvesters, which require excellent microstructural deformation and interaction properties between different layers. However, the cross-layer microstructural evolution and interaction mechanism of helical structures under twisting and stretching are still unclear. Herein, a multi-layer model is established to theoretically investigate the response of the filaments under twisting and stretching. The results quantitatively indicate that the filaments with a higher twist have more structural evolution and less elongation deformation in the tensile process, which contributes to the increase of ductility. The evolution of the helical angle is also verified by in-situ experiments and finite element simulations. Besides, a theoretical method is provided for investigating the mechanical behavior of anisotropic twisted fibers, and the contact pressure is also promoted to understand the twisting-induced densification. The interlayer extrusion reveals that appropriate stretching of twisted fiber is an effective way to densify the loose fibers, and it also provides the theoretical reason for twisting and stretching simultaneously when wringing out a wet towel. We believe that this work would shed light on the interaction mechanism of twisted filaments and provide mechanistic insights into the twisting design of multi-layer helical structures.
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•The behavior of wire strand under varying axial tension and torsion is analyzed.•An interwire full contact model considering different contact statuses is built.•The coupled contact status happens ...to the strands under some certain axial loads.•The stiffness at a large lay angle increases obviously with increasing axial load.
A wire rope is often subjected to a varying axial tensile force or torsional moment, resulting in its unstable performances. However, this problem has not been studied to date due to the lack of a scheme effectively determining the interwire contact status. For this, an interwire full contact model considering different contact statuses is established based on contact mechanics and thin rods theory. Then the model is solved with the semi-analytical method (SAM), into which the conjugate gradient method (CGM) and fast Fourier transform (FFT) are employed for analyzing the contact behavior. With the above contact dealing, the full contact performances are achieved for the core-wire contact, the wire-wire contact and the coupled contact statuses of the strand subjected to varying axial loads. And it is found that the interwire contact status of the strand may change with the varying loads, resulting in the unstable distributions of the interwire contact pressure and deformation. Meanwhile, the validity of the proposed full contact model is verified.
•It is one of the first applications of MFAM sensor for steel wire rope diagnostics.•The MFAM method is future-proof, but requires further research.•The appropriate position of the tested object in ...relation to the Earth magnetic field lines is very important.•The biggest challenge in usings MFAM sensors is the separation of the Earth's magnetic field.•Comprehensive application: to combine various methods to characterize and detect damages.
Passive techniques, which do not require magnetization of the tested object with a strong external field, are becoming increasingly popular. This article investigates the influence of the Earth's magnetic field on the diagnostic result. A detailed analysis of the steel wire rope test results in a polyurethane cover set in different directions - both geographic and magnetic - was performed. The rope was tested for two cases: in the delivered condition and with damages. The authors analyzed three different magnetic positions for each subject. Researchers analyzed the collected values in terms of the error obtained while indicating which position the results are the most promising. The authors used for the study the optically pumped magnetometer with a laser pumped caesium module.
Steel wire rope can be frequently damaged by various factors. Although weak defect signal detection is the key to guarantee safety, current classic stochastic resonance (SR) models and methods are ...unavailable due to the complicated interference of noise. Thus, in this article, a novel E-exponential SR model and weak signal detection method are proposed. The principle of this model is first introduced from the perspective of Taylor expansion and nonlinear system analysis. Then, the model performance is simulated and evaluated by output signal analysis and chaos characterizations. Afterward, model comparison with different system parameters, experiments, and case studies regarding three typical wire rope defects inspection and weak signal recognition is conducted to demonstrate the validity of the novel method. Finally, the advantages and limitations of the proposed SR model are discussed.
Nowadays, steel wire rope (SWR) plays a more and more crucial role in modern facilities including but not limited to goods transmission, elevators, and dams. Due to the external environment, local ...flaws (LFs) on the SWR may cause deadly accidents. Thus, its health condition should be monitored. When applying the magnetic flux leakage (MFL) based nondestructive testing method to collect the LF signals in multichannel, the shaking noise inevitably exists, and greatly influences the accuracy of LF detection. This article focuses on analyzing shaking sources and the representation of shaking noise in MFL signals based on the lift-off distance. Then, after analyzing morphological features of shaking noise, strand noise, and LF signals, the morphological image processing based method is proposed to suppress shaking noise, especially when LF signals are covered and surrounded by strong shaking noise. In comparison with the state-of-the-art denoising method, this proposed method not only suppresses both strand noise and strong shaking noise but also improves the signal-to-noise ratio of MFL signals for better LF detection.
Because of its flexibility, high strength, and durability, steel wire rope (SWR) is widely used in irrigation works, bridges, harbors, tourism, and many industrial fields as a vital component. Thus, ...it can cause accidents and economic losses if local flaws (LFs) of the SWR in service are not detected in time. This article points out two major problems in magnetic flux leakage (MFL) imaging-based nondestructive testing for fault diagnosis of SWR and proposes an integrated signal-processing method specifically designed for addressing the two problems. In this article, the MFL signals are collected by a detector that is formed by a set of permanent magnets and a Hall sensor array. Based on these multichannel MFL signals obtained from the Hall sensor array, we use the principle of multichannel signal fusion to determine rich information from all MFL signals. We solve the strand noise problem by an oblique-directional resampling and filtering method, which avoids severe attenuation in the LF signal. Moreover, the shaking noise is effectively removed by the proposed antishaking filtering based on the median filter. According to our simulation and experiment, the proposed fault diagnosis method for SWR significantly improves the performance of LF detection and localization under strong shaking and strand noises.
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