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PurposeThe acousto-ultrasonic approach is used for propagating stress waves through different configurations of CORTEN steel specimens. The propagated waves are recorded and analysed by piezoelectric sensors. The purpose of the study is to study the characteristics of the CORTEN steel by analysing the propagated waves.Design/methodology/approachTo investigate the attenuation in acoustic wave propagation due to the corrosion formation in CORTEN steel specimens and to train a neural network model to classify the attenuated acoustic waves automatically.FindingsDue to the corrosion formation in CORTEN steel specimens, attenuation is observed in amplitude, energy, counts and duration of the propagated waves. When the waves are analysed in their time-frequency characteristics, attenuation is observed in their frequency and spectral energy.Originality/valueThe corrosion formation in CORTEN steel can automatically be analysed by using the acousto-ultrasonic approach and the trained deep learning neural network.

Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with a high degree of customization of equipment and machines. Structures such as bolted joints are a key part of the industry; therefore, monitoring their health is critical to maintaining structural integrity. Despite this, there has been little research on the detection of bolt loosening in rotating joints. In this study, vibration-based detection of bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission was performed using support vector machines (SVM). Different failures were analyzed for various vehicle operating conditions. Several classifiers were trained to evaluate the influence of the number and location of accelerometers used and to determine the best approach between specific models for each operating condition or a single model for all cases. The results showed that using a single SVM model with data from four accelerometers mounted both upstream and downstream of the bolted joint resulted in more reliable fault detection, with an overall accuracy of 92.4%.

A multiparameter approach is preferred while utilizing Acoustic Emission (AE) technique for mechanical characterization of composite materials. It is essential to utilize a statistical parameter, which is independent of the sensor characteristics, for this purpose. Thus, a new information–theoretics parameter, Lempel–Ziv (LZ) complexity, is used in this research work for mechanical characterization of Carbon Fibre Reinforced Plastic (CFRP) composites. CFRP specimens in plain weave fabric configurations were tested and the acoustic activity during the loading was recorded. The AE signals were classified based on their peak amplitudes, counts, and LZ complexity indices using k-means++ data clustering algorithm. The clustered data were compared with the mechanical results of the tensile tests on CFRP specimens. The results show that the clustered data are capable of identifying critical regions of failure. The LZ complexity indices of the AE signal can be used as an AE descriptor for mechanical characterization. This is validated by studying the clustered signals in their time–frequency domain using wavelet transform. Finally, a neural network framework based on SqueezeNet was trained using the wavelet scalograms for a quantitative validation of the data clustering approach proposed in this research work. The results show that the proposed method functions at an efficiency of more than 85% for three out of four clustered data. This validates the application of LZ complexity as an AE descriptor for AE signal data analysis.

In this research paper, the acoustic emission technique and a deep learning framework based on two types of pre-trained CNN models (alexNet and squeezeNet) and a new model are proposed to characterize and classify the mechanical behavior of AlSi10Mg components. Specimens are built in a Selective Laser Melting machine with different bed orientations along X, Y, Z, and 45 degrees. Tensile tests are performed, and AE signals are recorded from these tests. To characterize the elastic and plastic deformation stages, a time-frequency domain analysis was performed using CWT-based spectrograms. Three different categories of damage classification strategies were implemented, and CNN models were trained for each strategy. CNN models including AlexNet, SqueezeNet, and the new model were used. Several training modes were performed to determine the CNN model that can accurately classify AE data. Understanding the minimum set of AE signals needed to train the CNN while having 100% accuracy and understanding the parameters affecting the accuracy of a CNN and the training time for the efficient classification of AE signals are the main objectives of this work. The results obtained demonstrated that the new simplified CNN model proposed can accurately classify the AE signals in a short time compared to AlexNet and SqueezeNet.

Signal-based acoustic emission data are analysed in this research work for identifying the damage modes in carbon fibre–reinforced plastic (CFRP) composites. The research work is divided into three parts: analysis of the shifting in the spectral density of acoustic waveforms, use of waveform entropy for selecting the best wavelet and implementation of wavelet packet transform (WPT) for identifying the damage process. The first two methodologies introduced in this research work are novel. Shifting in the spectral density is introduced in analogous to ‘flicker noise’ which is popular in the field of waveform processing. The entropy-based wavelet selection is refined by using quadratic Renyi’s entropy and comparing the spectral energy of the dominating frequency band of the acoustic waveforms. Based on the method, ‘dmey’ wavelet is selected for analysing the waveforms using WPT. The slope values of the shifting in spectral density coincide with the results obtained from WPT in characterising the damage modes. The methodologies introduced in this research work are promising. They serve the purpose of identifying the damage process effectively in the CFRP composites.

In this work the response of soft open-cell polyurethane foams with three different densities, respectively of 85, 63 and 46 kg/m3, were obtained by quasi-static tensile, shear and cyclic compressi...

In this study, the damage evolution stages in testing AlSi10Mg specimens manufactured using Selective Laser Melting (SLM) process are identified using Acoustic Emission (AE) technique and Convolutional Neural Network (CNN). AE signals generated during the testing of AlSi10Mg specimens are recorded and analysed to identify their time-frequency features in three different damage evolution stages: elastic stage, plastic stage, and fracture stage. Continuous Wavelet Transform (CWT) spectrograms are used for the processing of the AE signals. The AE signals from each of these stages are then used for training a CNN based on SqueezeNet. Moreover, k-fold cross validation is implemented while training the modified SqueezeNet to improve the classification efficiency of the network. The trained network shows promising results in classifying the AE signals from different damage evolution stages.

Three-point bending tests on Short Beam Shear (SBS) specimens are performed to investigate the interlaminar shear properties of plain weave fabric CFRP composites. The tests are performed in a controlled environmental chamber at two different elevated temperatures. The interlaminar shear properties of the specimens remain largely unaffected by the testing temperature. However, the SEM micrographs show different damage progressions between the specimens tested at 100 °C and 120 °C. Fibre ruptures and longer delamination between the plies, as a result of a high temperature, are observed in the specimens tested at 120 °C, which are not observed in the specimens tested at 100 °C. In addition, the acoustic emission activities during the tests are investigated by using piezoelectric sensors. The information-theoretic parameter, the Lempel-Ziv (LZ) complexity, is calculated for the recorded acoustic signals. The LZ Complexities are used for identifying the occurrence of the first delamination failure in the specimens. Additionally, the two features of the acoustic signals, LZ complexity and Weighted Peak Frequency (W.P-Freq), are used for distinguishing the different damage sources in the CFRP specimens. The results are well-supported by the time-frequency analysis of the acoustic signals using a Continuous Wavelet Transform (CWT).

Modern technological development has made the designing and characterization of materials sophisticated [...]

Lempel–Ziv (LZ) complexity has been used predominantly in solving the information theoretic problems. However, its application is way beyond information theoretics. It has been used successfully in different biomedical applications. In this research work, this LZ complexity is used for identifying the damage modes in a plain weave fabric carbon fibre-reinforced polymer (CFRP) specimens tested at an elevated temperature. The acoustic signals generated during the tensile test of CFRP specimens at an elevated temperature are recorded. Their LZ complexity values are extracted and compared with different acoustic descriptors. When LZ complexity is used along with peak amplitude of the acoustic signal and its weighted peak frequency, it is able to distinguish the different damage sources of the signal. The acoustic signals with LZ complexity above 0.6 generally indicate their source as delamination or debonding of the fibres in the CFRP specimen. This multiparameter approach is more efficient in distinguishing the different damage modes in the CFRP specimens.

In this paper results on the evolution of Poisson’s ratio under tensile and cyclic compression on low-density soft open-cell polyurethane foams (85, 63, 46 kg/m3) up to 80% of strain are presented ...

The need to use components with improved surface characteristics in relation to severe operating conditions, together with the aim of cost reduction associated with the replacement of damaged components, has led to an increasing use of coatings and repairing processes. The most common deposition processes are generally characterized by high equipment costs and, sometimes, by long deposition time. Furthermore, some repair technologies, especially those characterized by high heat input, are not suitable for alloys used in aerospace applications due to the degradation of their mechanical characteristics. In the last decades, a novel eco-friendly method capable of overcoming the limits set out above emerged: the electrospark deposition (ESD) technology. Thanks to its efficiency, simplicity, cost-effectiveness, and low heat input, this technology has proved to be suitable both for improving surface properties, such as thermo and wear resistance, higher hardness and corrosion resistance, and for the repair of high-value components. The aim of this review is to describe in detail some aspects of the ESD technique to understand the ESD processing preparation of alloys normally considered difficult to weld by traditional processes and to give some important clues to the readers to contribute to the defect-free repair of damaged areas and coatings deposition.

The aim was to investigate the full-field mechanical-deformation behavior of clear aligners made by polyethylene terephthalate glycol (PET-G) subjected to cyclic compression tests.Digital Image Correlation (DIC) (Chu et al., 1985; Schreier et al., 2009), a contactless full-field measurement technique, and Optical Microscope (OM) analysis were applied to study two PET-G aligners thermoformed from discs of a thickness of 0.75 mm and 0.88 mm. The clear aligners were placed on dental shape resin casts and were subjected to cyclic compression up to 13000 load cycles from 0 to 50 N at room temperature. The chosen number of load cycles simulates the average load history to which an aligner is subjected for one week. Local displacements and strains were measured for each test at 2, 6, 10, 20, 1000, 5000 and 13000 loading cycles.Both aligners showed greater displacements in the early stages of loading, more pronounced for the 0.88 mm one. Local displacement and strain maps are derived both along the load direction and to the transverse one (never done from other researchers). Load-displacement cyclic curves allowed to evaluate the evolution of the stored energy and the stiffness during the test. The OM analyses showed significant morphological variations on the aligners' surface, such as wear and tear, high depressions and cracks, especially for the 0.75 mm specimen.Full-field analysis allowed to understand the mechanical behavior of device with complex geometry and complex load distribution, like invisible aligners. The knowledge of the stiffness and the direction of the total displacement helps the orthodontist to implement the best strategy to improve the patient's comfort and the treatment time.

In this research work, the acoustic emission results obtained from testing double cantilever beam specimens with carbon fibre reinforced plastic laminates are analysed. The acoustic emission descriptors such as amplitude, frequency centroid, counts, duration and risetime are clustered using k-means++ algorithm. An unconventional and innovative way of using the acoustic emission descriptors, after the clustering, is introduced. This method can favourably be used for relating the different damage progression modes in fibre reinforced plastics. Apart from this, the cumulative acoustic energy is used for predicting the crack length of the specimens. The predicted crack length is almost identical to the actual crack length opening recorded in each specimen. Finally, analytical and finite element models are used for validating the experimental results under the mode I delamination. The finite element studies are carried out using cohesive zone modelling in Comsol Multiphysics® platform.

The Acoustic Emission (AE) technique is one of the powerful Non-Destructive Evaluation (NDE) tools for predicting and assessing damages in composite structures. In this research work, the energy released during each acoustic event, as a function of the total number of counts, is used for evaluating the damage modes in CFRP hybrid joints. The damage modes identified using the AE parameter, Energy per Count ( E A E n ) is compared with the waveforms of the acoustic events. Generally, the type of damage mode can be identified by postprocessing the acoustic waveforms. By comparing the waveforms with the E A E n , the damage mode has been evaluated. The technique introduced in this work will reduce the data storage and computation time for analyzing the waveforms. The damage modes can be identified during loading using this technique. In addition to that, the location of the generated acoustic events is used for identifying the critical failure areas in the CFRP hybrid joints. The attenuation of the AE events due to the distance between the sensors and the AE source is also considered for this study. From the observed results, the AE technique proves to be one of the formidable techniques for evaluating damage modes in composite structures.

An innovative way of using the Acoustic Emission (AE) technique is introduced in this research work. The ratio of recorded acoustic energy and the counts recorded for each acoustic event were used for characterizing Carbon Fiber Reinforced Plastic (CFRP) laminates adhesively bonded with and without mechanical fasteners. The cumulative counts and cumulative energy of the recorded acoustic events were used for identifying the critical points of failure under loading of these hybrid joint specimens. The peak amplitude distribution was used for identifying the different damage modes such as delamination, matrix cracking and fiber breakage, albeit, ineffectively. The new parameter energy per count was introduced in this work, which can successfully identify the different damage modes under loading. To differentiate the damage modes using the energy per count, they were clustered using k-means++ pattern recognition technique. The method introduced in this work can estimate the damage modes of the CFRP specimens.

Acoustic emission technique is used to characterize the damage modes in AlSi10Mg specimens prepared using Selective Laser Melting technique. Three specimens built in different orientations with respect to the building platform were tested. The parameter-based acoustic parameter, peak amplitude was clustered to identify the different damage zones. Davies-Bouldin Index (DBI) is used to optimize the number of clusters and k-means++ algorithm was used to cluster the peak amplitude data. The linear relation between two other acoustic parameters, cumulative counts and cumulative energy, is used to exploit the damage mode under the loading condition. The slope of the linear relation, bAE is estimated for this study. Finally, the signal-based acoustic parameter, wavelets are transformed using Continuous Wavelet Transform (CWT) and Wavelet Packet Transform (WPT) in an attempt to identify the frequency bands associated with the damage modes. With the aid of proper in-situ characterization tool to support the Acoustic Emission Technique, it can be a powerful tool in damage monitoring and characterization in metal specimens.

The acousto-ultrasonic approach is experimented on CFRP laminates. The test was organized in two modes: before the impact event and after the impact event; along longitudinal and transverse directions. Finally, the Compression after impact (CAI) test is performed on the impacted specimens to characterize its residual compressive strength . The AE descriptors loss along the longitudinal and transverse directions of the specimen are utilized to characterize the interlaminar strength of the specimen. With the help of Continuous Wavelet Transform (CWT) analysis, a hypothesis is created to discriminate the loss variation of AE descriptors to characterize the interlaminar strength of the material and the presence of damage or flaw in the material. The hypothesis holds true that the direction along which the relative loss of AE descriptors is low, has the lowest interlaminar strength. The mechanical results support the hypothesis created. With the proper channelling of acoustic signals and discrimination of data, the acousto-ultrasonic approach can be extended to characterize the interlaminar strength of the mechanical structures.

Determination of thermal dissipation capability and thermomechanical behavior of electronic components is becoming a relevant topic in view of the fact that thermal management can strongly affect reliability and lifetime of the component. The level of miniaturization, which is achieved in current electronics, requires that no-contact analysis must be privileged in view of the fact that any contact with the chip can significantly alter the heat capacity of the component and to introduce a systematic bias in the measurements. Moreover, the high complexity of electronics components suggests that full-field methods should be adopted in order to take into account about differences in terms of thermomechanical response in different areas of the same sample. In this article, an original optical set-up including a speckle interferometer and a projection moiré branch was developed; this system comprises two different laser sources emitting at two different wavelengths. Recording by a color camera allows detecting simultaneously the speckle pattern and the projected fringes. The whole information is successively separated in the post-processing stage and this allows obtaining in-plane and out-of-plane displacement fields. The system demonstrated its capability to determine dynamic response of the LM1084 analyzed component and, moreover, to detect the presence of a functional damage.

Carbon Fiber Reinforced Polymer (CFRP) composites, adhesively bonded in a Single Lap Shear (SLS) configuration, are tested in this study. The damage characteristics are analysed using Acoustic Emission (AE) data recorded during the test. Among the different features of the AE data, the most suitable features are selected using a methodology developed based on the Laplacian scores for feature selection. The selected features are then analysed using k-means clustering algorithm for establishing a relationship among them. Amplitude of the recorded AE signals and their Frequency Centroid (C-Freq) had a strong relationship, which is used for characterizing the damage modes. The cumulative counts of the clustered data of Amplitude vs C-Freq are used for characterizing the damage modes. A comprehensive characterization of the damage modes in each stage of loading and the final failure is made using this method.

Corrosion detection and monitoring is a relevant task in many different fields including automotive, naval, civil etc. In the case of artifacts, corrosion is a relevant topic because this process can introduce huge damages on really ancient objects so that preventive procedures and long term monitoring are required. In this specific field the monitoring task presents, in addition, some extra requirements. In fact, monitoring the surface condition without contact, without sampling the object and, possibly, without diminishing the fruition of the handwork is preferable. Optical techniques, in general, are successfully used in many fields where non-contact, high accuracy measurements are required. In particular, an approach based on fringe projection will be explored in this paper. This method mainly consists in projecting a given intensity pattern on the object and in observing the fringe modulation introduced by the object itself; modulation contains information about the contour of the object itself. In this study corrosion tests on bronze alloy were performed both under salt spray and acid rain condition. A sinusoidal fringe pattern was projected on the analyzed samples before starting the measurement and at each advancement step of the corrosion test. Fringe patterns were analyzed using two approaches. Fast Fourier Transform of the recorded patterns was studied to detect if formation of corrosion patina can be related to some variation in the frequency spectrum. Moreover, continuous wavelet analysis was performed to detect where corrosive patina forms.

Fused Deposition Modelling (FDM) has become one of the most employed technologies to build complex 3D prototypes directly from a computerized solid model. In this process, the model is built as a layer-by-layer deposition of a feedstock wire. One of the most important issues in the FDM process is the distortion of the part during the printing. This issue is due to the rapid heating and cooling cycles of the feedstock material that could produce accumulation of residual stress during part building up. The aim of this work is to measure the residual stress in FDM parts made of ABS employing the hole-drilling method. In order to avoid the local reinforcement of the strain gage, an optical technique, i.e. ESPI (electronic speckle pattern interferometry), is employed to measure the displacement of the surface due to the stress relaxation. Furthermore, the effect of the stacking sequence and the residual stress distribution on each side of the specimen have been investigated.

Mispositioning of teeth is a medical condition which is treated by the dentist by the application of a properly designed system of forces that progressively move the teeth to the correct position. Nowadays this is often obtained by the so called invisible aligners. Those are devices which are put in contact with the dental structures and act in the direction of progressively shifting the teeth. The passage from the initial position to the final desired one is obtained by a series of intermediate steps which are achieved by a specific aligner so that, usually, each aligner is made to work for about two weeks. Based on this important consideration, in this paper, the mechanical properties of Polyethylene Terephthalate-Glycol (PET-G), one of the most used materials for manufacturing aligners, were studied. In particular, the fatigue behavior was analyzed both in normal environment and under simulated intraoral conditions. In this work, moreover, two different thicknesses of the aligners were analyzed. Typically, during their lifetime, aligners are subjected to 22500 loading cycles, this condition was replicated in our study and during each cycle aligners were loaded up to 50 N. For testing purposes, the aligners were mounted directly on a dental cast and this was clamped to a loading machine. Results of the test show that aligners exhibit an increasing stiffness as the cycling loading progresses, this is observable by a reduction of the crosshead displacement indicating the occurrence of cyclic hardening phenomena. Moreover, at least for the analyzed range of cycles and loads, the aligners display a tendency to strain accumulation. Finally, an effect of residual strain recovery was also observed subsequent to removal of the applied load.