Subsurface Defects Research Articles

The Machinability of Different Albromet W130 Plates Thicknesses by WEDM to the Required Surface Roughness Value

Wire Electrical Discharge Machining (WEDM) technology represents an unconventional but vital manufacturing technology in many different industrial branches. The automotive industry and its many significant requirements bring the need to manufacture inserts and mould segments for plastic injections from Albromet W130 material, with a required roughness, Ra, from 4.5 to 5 µm so that subsequent profile etching can be eliminated. A planned experiment of 60 rounds was carried out to discover the optimal machining parameters, namely, the pulse-off time, gap voltage, discharge current, pulse-on time, and wire speed in order for the thickness of 10 to 100 mm (after 10 mm) to demonstrate the required roughness. The goal was to evaluate the surface roughness, maximise the cutting speed, and manufacture it without surface or subsurface defects. The evaluation of the planned experiment led to the establishment of optimised WEDM machining parameters with which thicknesses of 10–100 mm will always be produced with the required roughness, Ra, from 4.5 to 5 µm and with the highest possible cutting speed. It was also proven that the machining does not lead to surface or subsurface defects, and thus, the service life of the manufactured parts will not be affected.

A New Method for Detecting Surface and Subsurface Defects in Carbon Fiber- Reinforced Polymer (CFRP) Unidirectional Laminates

A New Method for Detecting Surface and Subsurface Defects in Carbon Fiber- Reinforced Polymer (CFRP) Unidirectional Laminates

Automatic detection of subsurface defects of concrete slabs by domain adaptation algorithm

Automatic detection of subsurface defects of concrete slabs by domain adaptation algorithm

Corrosion behavior of additively manufactured M300-CuCr1Zr by multi-material laser-based powder bed fusion

Additive manufactured parts of thermally-conductive CuCr1Zr and high-hardness M300 tool steel were fabricated via a single step multi-material Laser-based Powder Bed Fusion (PBF-LB). The corrosion properties of two interface configurations, CuCr1Zr built on M300 and M300 on CuCr1Zr, were evaluated using the electrochemical polarization resistance measurement, weight loss method and galvanic corrosion current measurement via a Zero Resistance Ammeter. The interface microstructure of the dissimilar bi-metallic parts fabricated by multi-material PBF-LB is tunable. The printing configuration influences the defect size and density and interface microstructure, which affect the global corrosion rate of the fabricated parts. Electrochemical measurements were performed on the fabricated parts in oxygen-saturated and chloride-containing water at elevated temperature, complimented by post-mortem characterizations. The interface configuration with M300 on CuCr1Zr has a slightly lower corrosion rate than that of CuCr1Zr on M300. Unexpectedly, galvanic corrosion occurred only locally in the interface region between M300 and CuCr1Zr materials. Beyond the interface regions, there is no clear sign of galvanic corrosion. Pitting and intergranular corrosion in CuCr1Zr material is the dominant corrosion mechanism. Both base materials beyond the interface region underwent localized pitting, particular in CuCr1Zr. Intergranular corrosion in CuCr1Zr is governed by sub-surface defects like pores and microcracks formed during printing.

A quadrilateral inverse plate element for real-time shape-sensing and structural health monitoring of thin plate structures

The inverse finite element method (iFEM) emerged as a powerful tool in shape-sensing and structural health monitoring (SHM) applications with distinct advantages over existing methodologies. In this study, a quadrilateral inverse-plate element is formulated via a sub-parametric approach using bi-linear and non-conforming cubic Hermite basis functions for engineering structures, which can be modeled as thin plates. Numerical validation involves dense and assumed sparse sensor arrangements for in-plane, out-of-plane, and mixed general loading conditions. iFEM analysis reveals efficient monotonic convergence to analytical and high-fidelity finite element reference solutions. After successful numerical validation, defect detection analysis is performed considering minute geometric discontinuities and structural stiffness reduction because of latent subsurface defects under tensile and transverse loading conditions. The inverse formulation successfully resolves the presence of simulated defects under a sparse sensor arrangement. The proposed inverse-plate element is accurate in the full-field reconstruction of shape-sensing profiles and reliable in defect identification and quantification in thin plate structures.

Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets

This study reviews advanced methods for corrosion detection and characterization in pipes using thermography, with a focus on addressing the limitations posed by small datasets. Thermography captures temperature distributions on the surface of pipes to identify subsurface defects. The challenges of sequential data processing, neural network performance, feature extraction, and dataset size are discussed, with proposed solutions such as advanced algorithms, feature selection techniques, and data augmentation. Given the significant gap in the current literature, there is a need for larger, more diverse datasets to train more robust and accurate machine learning models. A case study combining experimental data with Finite Element Method (FEM) simulations demonstrates that augmenting datasets with synthetic data significantly improves defect detection accuracy. These findings highlight the potential of integrating thermography with machine learning to enhance defect detection, providing insights for future research and practical applications.

Filler processing and mixing effects of polyoxymethylene/graphene nanocomposite on tribo-mechanical performances

Binary nanocomposites based on polyoxymethylene (POM) and graphene nanoplatelet (GNP) were fabricated to improve the tribo-mechanical performances of high-precision sliding parts in linear drive system applications. Liquid-phase exfoliation technique and surface modification by 3-aminopropyltriethoxysilane (APTES) were used to process GNP filler for improved endowment of POM/e-GNP matrix-filler interfacial adhesion. Results show that processed e-GNP at optimal 0.5 wt% loading is distributed uniformly in the matrix and enables excellent stress-transfer during the mechanical loading, increasing flexural modulus, impact strength and elongation at break by 51.3 %, 41.9 % and 24.5 %, respectively, in contrast to neat POM. Simultaneously, coefficient of friction (COF) and specific wear rate (Ws) of POM in ambient temperature were also reduced substantially by e-GNP incorporation under both dry (COF by 19.5 %, Ws by 40.6 %) and grease lubricated (COF by 38.2 %, Ws by 76.4 %) sliding conditions. Smoothened wear surface and formation of homogeneous transfer film on the steel counterface were accounted for large specific surface coverage and low-shear strength of e-GNP at the contact interface, especially during the dry sliding. In addition, the accumulated energy dissipation by frictional work was calculated in the sliding interfaces based on friction force-displacement (F-D) hysteresis loop. However, pristine POM/GNP exhibited poor matrix-filler adhesion and deteriorated composite properties due to the aggregated structure which can cause subsurface defects and serve as a failure site. The proposed new material would extend the applications of precision parts by overcoming the tribo-mechanical related issues for quieter and accurate linear motion systems.

Non-destructive testing based on Unet-CBAM network for pulsed thermography

Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe.

Noise suppression in pulsed IR thermographic NDT: Efficiency of data processing algorithms

Various types of noise, which accompany active TNDT procedures using optical heating, have been analyzed, both numerically and experimentally. An emphasis has been made on the suppression of surface clutter, which represents local areas of varying absorptivity/emissivity. The concept of signal-to-noise that is typically used in defect detection has been applied to fixed pattern noise in order to compare capabilities of data processing algorithms in reducing surface clutter. The experimental investigation has been fulfilled on a special sample containing both subsurface air-filled defects and areas with varying emissivity/absorptivity. The best suppression of the fixed pattern noise was provided by the complex wavelet transform and principle component analysis. Because of 3D heat diffusion, clutter spot boundaries are often underlined by particular data processing algorithms thus producing specific contours. The test situations where subsurface defects are located under localized clutter spots have been analyzed to demonstrate an overshadowing effect of such spots when detecting hidden defects.

A zero-shot quantitative evaluation model for subsurface defects size based on ultrasonic nondestructive testing

Existing defect quantitative evaluation models requires collecting and labeling all types of defect data for training. However, such models have a poor ability for new defect, and collecting and labeling data is a costly task. A zero-shot evaluation model is proposed to solve the questions. First, a Wavelet Packet Transform-based method is developed to convert defect ultrasonic signals into time–frequency images. Second, we define defect semantics from length, width, and height to describe defects of different sizes. Third, the ResNet with embedded Brownian Distance Covariance matrix and multi-dimension parallel framework is designed to extract the image features. Then, an autoencoder is trained to embed the defect semantic into the feature space. Finally, the dimension of defect is evaluated by measuring the similarity between the features and the category centroids. Experimental results reveal that our model has better ability to evaluate the dimensions of new defect compared to others models.

Adaptation of an Eddy Current Model for Characterizing Subsurface Defects in CFRP Plates Using FEM Analysis Based on Energy Functional

In this work, a known Eddy Current (EC) model is adapted to characterize subsurface defects in carbon fiber-reinforced polymer (CFRP) plates intended for the civil aerospace industry. The considered defects include delaminations, microcracks, porosity, fiber breakage, and the simultaneous presence of these defects. Each defect is modeled as an additive variation in the material’s electrical conductivity tensor, allowing for a detailed mathematical representation of the defect’s influence on the CFRP’s electromagnetic behavior. The additivity of the variations in the conductivity tensor is justified by the assumption that the defects are not visible to the naked eye, implying that the material does not require non-destructive testing. The adapted EC model admits a unique and stable solution by verifying that all analytical steps are satisfied. To reconstruct 2D maps of the magnetic flux density amplitude, a FEM formulation is adopted, based on the energy functional because it ensures a stable and consistent numerical formulation given its coercivity. Moreover, the numerical approach allows precise and reliable numerical solutions, enhancing the capability to detect and quantify defects. The numerical results show that the obtained 2D maps are entirely superimposable on those highlighting the distribution of mechanical stress states known in the literature, offering a clear advantage in terms of detection costs. This approach provides an effective and economical solution for the non-destructive inspection of CFRP, ensuring accurate and timely defect diagnosis for maintaining structural integrity.

Research on the Detection of Steel Plate Defects Based on SimAM and Twin-NMF Transfer

Pulsed eddy current thermography can detect surface or subsurface defects in steel, but in the process of combining deep learning, it is expensive and inefficient to build a complete sample of defects due to the complexity of the actual industrial environment. Consequently, this study proposes a transfer learning method based on Twin-NMF and combines it with the SimAM attention mechanism to enhance the detection accuracy of the target domain task. First, to address the domain differences between the target domain task and the source domain samples, this study introduces a Twin-NMF transfer method. This approach reconstructs the feature space of both the source and target domains using twin non-negative matrix factorization and employs cosine similarity to measure the correlation between the features of these two domains. Secondly, this study integrates a parameter-free SimAM into the neck of the YOLOv8 model to enhance its capabilities in extracting and classifying steel surface defects, as well as to alleviate the precision collapse phenomenon associated with multi-scale defect recognition. The experimental results show that the proposed Twin-NMF model with SimAM improves the detection accuracy of steel surface defects. Taking NEU-DET and GC10-DET as source domains, respectively, in the ECTI dataset, mAP@0.5 reaches 99.3% and 99.2%, and the detection accuracy reaches 98% and 98.5%.

Eddy Currents Probe Design for NDT Applications: A Review.

Eddy current testing (ECT) is a crucial non-destructive testing (NDT) technique extensively used across various industries to detect surface and sub-surface defects in conductive materials. This review explores the latest advancements and methodologies in the design of eddy current probes, emphasizing their application in diverse industrial contexts such as aerospace, automotive, energy, and electronics. It explores the fundamental principles of ECT, examining how eddy currents interact with material defects to provide valuable insights into material integrity. The integration of numerical simulations, particularly through the Finite Element Method (FEM), has emerged as a transformative approach, enabling the precise modeling of electromagnetic interactions and optimizing probe configurations. Innovative probe designs, including multiple coil configurations, have significantly enhanced defect detection capabilities. Despite these advancements, challenges remain, particularly in calibration and sensitivity to environmental conditions. This comprehensive overview highlights the evolving landscape of ECT probe design, aiming to provide researchers and practitioners with a detailed understanding of current trends in this dynamic field.

Quantitative detection of the width and depth of subsurface defects in curved structures using ultrasonic Rayleigh waves

ABSTRACT Rayleigh waves are highly sensitive to surface and subsurface defects. However, the varying surface profiles of complex structures induce constant changes in the Rayleigh waveform. Furthermore, Rayleigh waves encompass multi-dimensional characteristics of subsurface defects, significantly increasing the difficulty of quantitatively detecting the sizes of subsurface defects in different dimensions. This paper presents a machine learning (ML)-based method for quantitatively detecting subsurface defect depth and width in curved structures using laser-generated Rayleigh waves. An experimentally validated finite element model is first established to construct a dataset of Rayleigh wave signals labeled with multidimensional subsurface defect sizes. The dataset is augmented from 188 to 368 samples using the Mix-up algorithm, with additional experimental signals incorporated. Feature parameters of Rayleigh waves are extracted in both time and frequency domains through waveform analysis and sliding window wavelet transform (SWT). These features are used to train ML models: Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Comparative results demonstrate GP achieves the highest prediction accuracy for subsurface defect depth and width on the test set, reaching 98.64% and 97.73%, respectively. The study highlights the integration of experimental and simulated data in training, which reduces costs while enhancing model robustness in practical applications.

Subsurface defect detection of high-temperatured components with laser induced surface wave

Abstract Subsurface defects are detrimental to the safety and integrity of critical components in various fields, particularly for those used in high-temperature environments. For this reason, a reliable non-destructive evaluation (NDE) method for in-situ inspection of subsurface defects of high-temperatured components is highly desired. Laser ultrasonic technique offers a promising potential to accommodate the demands in virtue of non-contact generation and detection of ultrasonic waves. In this work, laser ultrasosnic inspection of subsurface defects in high-temperatured components is proposed, which exploits the modal conversion of surface longitudinal wave to Rayleigh wave. The modal conversion was firstly investigated with finite element simulation, from which the phenomenon of modal conversion can be readily identified. Thereafter, an experimental setup is built to verify the feasibility and effectiveness of proposed method. Notably, a delay-and-sum method based on a scanning procedure is conducted to coherently increase the detection sensitivity of subsurface defects, since the surface longitudinal wave rapidly vanishes with propagation distance., and the temperature-dependant velocity variation can be adaptively compensated. This work provides a viable route for in-situ inspection of subsurface defects in high-temperatured components where conventional ultrasonic method fails, and it would find potential applications in broad fields, such as coating quality evaluation in fabrication, bearing assessment under load, and in-situ monitoring for additive manufacturing.

Surface integrity and high-cycle fatigue life of direct laser metal deposited AISI 431 alloys modified by plasticity ball burnishing

Laser metal deposition (LMD) as an additive manufacturing (AM) is widely used to repair and extend wear and fatigue life of the critical components. This paper has investigated the application of ball burnishing (BB) to improve surface integrity and high-cycle fatigue resistance of LMDed AISI 431 alloys. Results showed that the BB treated samples exhibited significant surface finish improvement by lowering roughness by 91 %. Microhardness increased from 490 to 530 HV0.1, an increase by 10 % with a modified depth of 400 µm from the top surface. XRD results showed a peak shift and increase in FWHM by up to 17 %. This had been corroborated by EBSD exhibiting a 20 % increase in dislocation density and 24 % increase in localised misorientation within microstructure. As a result, the overall high cycle fatigue strength of the burnished sample increased by 50 %, and the cracks initiated from sub-surface level defects at a depth of 350 μm below the top surface, delaying the crack propagation and fracture failure. The findings clearly highlight that the burnishing treatment can be a plausible approach in improving the dynamic fatigue resistance and the overall service life of LMDed AISI 431 steel alloys components in engineering applications.

Thermal Inspection of Subsurface Defects in Wind Turbine Blade Segments Under the Natural Solar Condition

Thermal Inspection of Subsurface Defects in Wind Turbine Blade Segments Under the Natural Solar Condition

Comparison of Skin Effects in Ferromagnetic and Nonferromagnetic Metals in Eddy Current Testing

ABSTRACT In eddy current (EC) testing, skin effect is a constraint factor of detecting subsurface defects and skin depth is an important parameter for probe design and frequency setup. This paper compares the skin effects in ferromagnetic and nonferromagnetic metals. It is well known that the high permeability of ferromagnetic metal makes the attenuation of EC faster due to the greater EC loss. In this paper, the other role of the high permeability of ferromagnetic metal on the skin effect is reported. The EC distributions in both the radial and depth directions in the two types of materials excited by pancake coils are computed. It shows that the EC is more dispersed in the radial direction in high-permeability material. More importantly, the high permeability of ferromagnetic metal makes the skin depth of EC under the excitation of pancake coil close to the standard skin depth of EC under the excitation of uniform magnetic field, whereas there is a big discrepancy in the skin depths of ECs under the two types of excitations. This difference is explained by the radial dispersion of EC based on the EC diffusion-cancellation theory. Finally, the EC distributions in ferromagnetic Q235 steel and nonferromagnetic brass are compared, and the simulation results are qualitatively verified by experiments.

Axial peak extraction in the measurement of subsurface structures with annular illumination confocal microscopy

Abstract Three-dimensional dark-field confocal microscopy (DFCM) using annular illumination and complementary detection apertures is capable of detecting subsurface defects in ultra-precision optical components. In this study, a DFCM tuning algorithm for large apertures is proposed based on the sinc2 tuning model. This algorithm provides a reliable and theoretically accurate method for extracting the axial position during the measurement of subsurface defects. The rationality and accuracy of this method are verified through simulations and experimental results.

Eddy Current Sensor Probe Design for Subsurface Defect Detection in Additive Manufacturing.

Pore and crack formation in parts produced by additive manufacturing (AM) processes, such as laser powder bed fusion, is one of the issues associated with AM technology. Surface and subsurface cracks and pores are induced during the printing process, undermining the printed part durability. In-situ detection of defects will enable the real-time or intermittent control of the process, resulting in higher product quality. In this paper, a new eddy current-based probe design is proposed to detect these defects in parts with various defects that mimic pores and cracks in additively manufactured parts. Electromagnetic finite element analyses were carried out to optimize the probe geometry, followed by fabricating a prototype. Artificial defects were seeded in stainless steel plates to assess the feasibility of detecting various flaws with different widths and lengths. The smallest defect detected had a 0.17 mm radius for blind holes and a 0.43 mm notch with a 5 mm length. All the defects were 0.5 mm from the surface, and the probe was placed on the back surface of the defects. The surface roughness of the tested samples was less than 2 µm. The results show promise for detecting defects, indicating a potential application in AM.