Showing papers in "Ndt & E International in 2023"

Automatic defect depth estimation for ultrasonic testing in carbon fiber reinforced composites using deep learning

Abstract: Ultrasonic testing (UT) is commonly used to inspect the geometric shape of internal damage in composite materials and the test results need to be interpreted by trained experts. In this work, an automatic signal classification method based on deep learning is proposed for depth estimation of the detects introduced by low-velocity impact (LVI) in carbon fiber reinforced plastics (CFRPs). Three kinds of neural networks, LSTM, CNN, and CNN-LSTM are used to analyze the attributes with different depths. Then, trained models are applied to identify the depth information of impact damage. The results show that the CNN-LSTM model is a more accurate in-depth classification for LVI defects in CFRP based on A-scan signals than the other two structures.

Miniaturized multi-modality field-ready sensing system for defect detection of CFRP materials

Abstract: As infrastructure decays rapidly due to structural and material aging and failures, the development of new improved, and efficient nondestructive evaluation (NDE) techniques is vital for system health monitoring and preventive maintenance. Composite material, such as carbon fiber reinforced polymer (CFRP) plays a significant role in energy and transportation infrastructure due to the advantages of corrosion resistance, durability, and lightweight, which contributes to minimal maintenance and long service life. However, due to their unique anisotropic dielectric and mechanical characteristics, detecting composite material defects by NDE techniques is still challenging. This paper presents a novel, low-cost miniaturized multi-modality imaging system combining low-frequency capacitive shortwave and high-frequency microwave NDE technologies to detect various types of defects in CFRP materials. Based on the governing electromagnetic theory behind multi-modality electromagnetic NDE methods, the merit of combining low-frequency shortwave and near-field microwave imaging for dielectric characterization of composite materials is thoroughly investigated and demonstrated. Then, a miniaturized imaging system is developed to operate at ultra-wide bands: 10 kHz to 200 MHz for the capacitive shortwave probes and 1 GHz to 9 GHz for the microwave probes. Customized multi-material joining samples of CFRP and steel with different defect types, locations, depths, and sizes are tested by the developed imaging system, and the experimental results of the miniaturized system are compared with the existing table-top systems, which demonstrates comparatively accurate results for the developed multi-modality imaging system. The compact and practical nature of the presented imaging system makes it an optimal tool that can be utilized in field conditions with constrained operational spaces and NDE uncertainties.

Determination of the process-induced microstructure of woven glass fabric reinforced polyamide 6.6/6 composite using terahertz pulsed imaging

Abstract: Terahertz pulsed imaging, combined with spatial and temporal signal and image processing, is performed to visualize the woven fabric in the various plies of glass-fiber-reinforced polymer laminates and to determine quantitative parameters characterizing the microstructure such as fiber-bundle orientation and the distance between yarns. The results are in quantitative agreement with the microstructure features provided by the manufacturer. In addition, the employed signal processing shows an excellent capability to reveal the weave pattern from noisy C-scans - where a visual-based analysis can be problematic - and to assess the process-induced microstructure.

Simulation of ultrasonic TFM/FMC imaging for porosity clusters using multiple scattering modelling: Quantitative analyses and experimental comparisons

Abstract: Ultrasonic imaging using the Total Focusing Method (TFM) is very useful for locating and sizing defects accurately. However, when imaging clusters of pores, the results may be distorted by multiple scattering phenomena. Several simulations are performed in a 2D context considering different scattering model approaches: single scattering, single scattering including defect shadowing, and multiple scattering. In order to reproduce clusters of pores, we use sets of side-drilled holes to impose well-controlled properties (position and size) for comparison with experimental tests. Comparisons of the simulated TFM images with the experimental ones show qualitative and quantitative improvements when using the multiple scattering model, thanks in particular to a better consideration of shadowing and other interaction effects between defects.

Development of a linear array electromagnetic acoustic transducer for shear horizontal guided wave inspection

Abstract: Ultrasonic guided wave screenings have proven to be fast and reliable in detecting various types of defects in plate-like structures. Today, low frequency ultrasonic guided waves are routinely used to screen long sections of pipelines. For many years, ultrasonic guided waves at frequencies beyond the cutoff of the first high-order mode attracted interest in the research community due to the plurality of modes that can propagate. During operation at a frequency beyond the cutoff of the first high-order mode, the transduction mechanism becomes crucial for selectively exciting and detecting a single high-order mode or a group of high-order modes. Ultrasonic comb transducers allow to selectively excite and detect high-order ultrasonic guided waves at a desired wavelength. Linear array transducers are even more flexible, and allow virtually full control in the frequency wavenumber space. High-order shear horizontal (SH) modes have multiple potential applications including, for example, remote thickness gauging and crack monitoring. However, these modes are notoriously difficult to excite and detect using conventional piezoelectric transducers. Electromagnetic acoustic transducers (EMAT) make the excitation and detection of SH modes relatively simple. Periodic permanent magnet (PPM) EMAT can be used to selectively excite and detect high-order SH modes based on a desired wavelength. However, in some applications, it may be necessary to excite and detect high-order SH modes with more control in the frequency wavenumber space. In this paper, a novel EMAT linear array is proposed using a configuration similar to a PPM EMAT but with individual coils wrapped around the magnets. The performance of the EMAT linear array in transmission and reception is demonstrated in simulations and experimentally on a 9.53 mm steel plate. The results show a successful transmission and detection of SH0 to SH4 for a 300 to 800 kHz frequency range.

Learning defects from aircraft NDT data

Abstract: Non-destructive evaluation of aircraft production is optimised and digitalised with Industry 4.0. The aircraft structures produced using fibre metal laminate are traditionally inspected using water-coupled ultrasound scans and manually evaluated. This article proposes Machine Learning models to examine the defects in ultrasonic scans of A380 aircraft components. The proposed approach includes embedded image feature extraction methods and classifiers to learn defects in the scan images. The proposed algorithm is evaluated by benchmarking embedded classifiers and further promoted to research with an industry-based certification process. The HoG-Linear SVM classifier has outperformed SURF-Decision Fine Tree in detecting potential defects. The certification process uses the Probability of Detection function, substantiating that the HoG-Linear SVM classifier detects minor defects. The experimental trials prove that the proposed method will be helpful to examiners in the quality control and assurance of aircraft production, thus leading to significant contributions to non-destructive evaluation 4.0.

Sensitivity of acoustic nonlinearity and loss to residual porosity in additively manufactured aluminum

Abstract: Acoustic nonlinearity and loss are found to be positively correlated with porosity at industrially relevant levels of less than half a percent in commercially pure aluminum produced by laser powder bed fusion (L-PBF) with several different power levels. The technique employed for acoustic measurements involves nonlinear reverberation spectroscopy (NRS) with noncontacting electromagnetic-acoustic transduction, which offers advantages of adaptability to complex part geometries and short inspection times for industrial qualification of additively manufactured (AM) parts of arbitrary size. Porosity and microstructure are characterized with the Archimedes technique, X-ray computed tomography, and scanning electron microscopy. Fit parameters of nonlinearity and loss vs. porosity are found to vary significantly with the height of material in the build, consistent with an hypothesis that the correlations are indirect and involve dislocations as the principal nonlinear/anelastic elements. Nonlinearity and loss decrease with time under acoustic excitation, while being relatively insensitive to pauses in excitation of similar duration, indicating that acoustic excitation at inspection levels induces changes in nonlinear/anelastic defects without predominant involvement of thermal excitation. This remarkable behavior is not seen as a fundamental impediment for the application of the technique to nondestructive AM part qualification because of the brief time required for a measurement.

RCF crack direction assessment in moving ferromagnetic material by DC electromagnetic NDT technique

Abstract: The rolling contact fatigue (RCF) cracks that initiate from the surface of ferromagnetic materials in aviation bearings and high-speed rails are typically inclined at varying angles, posing a serious threat to safety. Therefore, a rapid and quantitative characterization of inclined cracks is of great importance. This paper investigates the distribution of magnetic fields and motion-induced eddy current (MIEC) in direct current (DC) electromagnetic non-destructive testing (NDT), and proposes an approach for measuring the direction of RCF cracks in moving ferromagnetic materials. The magnetic field and MIEC distributions in the moving ferromagnetic material are initially investigated, followed by a numerical simulation exploring the impact of crack inclination angle and depth on detection signals. The resulting relationship between crack propagation angle, depth, and detection signal is then obtained. Furthermore, a DC electromagnetic NDT platform has been designed and a quantitative detection approach for inclined cracks has been proposed and validated through experiments. Finally, a real scenario detection is carried out. The investigation presented in this paper demonstrates that the DC electromagnetic NDT technique is applicable not only for assessment RCF crack direction in moving ferromagnetic materials, such as rotating aviation bearings, but also for characterizing inclined cracks under conditions of fast relative motion between the probe and the ferromagnetic material (e.g., high-speed rail, pipeline, etc.).

Ultrasonic defect characterization using Bayesian inversion and scattering matrix denoising neural networks

Abstract: The use of scattering matrix and deep neural networks for ultrasonic characterization of inclined crack-like defects in noisy environments was explored. A distortion model was utilized to simulate coherent noises that could contaminate the experimental measurements in practice. Given a test scattering matrix, we first developed an approach for estimating the parameters of the distortion model based on the structural similarity index. Subsequently, a Bayesian inversion approach was adopted to determine the proportion of positive-angled cracks that should be included in the scattering matrix database. Based on this result, a deep neural network model was constructed and used in the denoising procedure, which can effectively reduce the characterization error induced by measurement noise. The simulation showed that the proposed approach can be reliably used for characterization of crack-like defects with large orientation angles relative to the array direction (e.g., 75∘) and small sizes (e.g., 0.8λ). In experiments, six crack-like defects with orientation angles of 60∘ and 75∘ were characterized with errors within 0.1λ (i.e., 0.25 mm) and 5∘ in size and angle, respectively. In addition, the characterization uncertainty measured by the root-mean-squared error was reduced by 44.7% in size compared with the conventional Bayesian approach.

On the ultrasonic characterization of the stacking sequence of CFRP laminates

Abstract: Ultrasound in pulse-echo scan mode has already been demonstrated for the extraction of local fiber angle distribution in composite laminates. Depending on the ultrasonic (scan) parameters employed, different sensitivity, spatial resolution, and dynamic depth range can be reached. This study investigates the use of pulse-echo ultrasound testing in different frequency ranges to characterize the in-plane fiber angle distribution and ply stacking sequence in a multi-layer composite laminate. Minimization of the Mumford-Shah functional is performed as an edge-preserving smoothing procedure for the recorded ultrasonic dataset, followed by the application of a Gabor Filter-based Information Diagram approach to extract a 3D tomographic image of the fiber angles. The performance of the method is experimentally studied on ultrasonic datasets, recorded at various center frequencies, of a 5.5 mm thick 24-layer quasi-isotropiccarbon fiber reinforced polymer laminate with stacking sequence [+45/0/−45/90]3s. For quantitative analysis, statistical metrics are employed to evaluate the accuracy and precision of the extracted angles over depth, and to better understand the interfering influence of adjacent plies. For the given case study, a 15 MHz ultrasound frequency is recommended as it provides a good balance between precision, accuracy, and depth range. To understand the influence of scan parameters on the fiber angle characterization, the 15 MHz ultrasonic dataset is analyzed for various noise conditions as well as different spatial step size of the scan.

Characterization of semi-infinite delaminations using lock-in thermography: Theory and numerical experiments

Abstract: Delaminations are buried defects parallel to the sample surface. In the last decades infrared thermography with optical excitation has been used to detect and size the depth of this kind of defects. However, sizing the delamination thickness has been usually disregarded. In a recent paper we proposed a method to size both depth and thickness of ideal delaminations (infinite area) using modulated excitation. Here, we extend the previous work to approach more realistic situations, tackling the case of semi-infinite delaminations. First, we calculate analytically the surface temperature oscillation of a sample containing a semi-infinite delamination using the thermal quadrupoles formalism. Then, we corroborate the analytical results by solving the same problem numerically. Finally, we perform an inverse parametric estimation of synthetic temperature amplitude and phase data with added Gaussian noise to retrieve the three geometrical parameters characterizing the delamination: length, depth and thickness.

Detectability of delamination in laminated CFRPs with diverse stacking sequences using eddy current method with T-R pancake coil

Abstract: A T-R probe composed of two non-coaxial pancake-based coils is used to identify delamination at different depths of laminated CFRPs with diverse stacking sequences. This type of probe is capable of inspecting delamination, since an interactional in-plane currents across the adjacent and non-adjacent layers of CFRPs stacked by different fibers can be induced using pancake-type coil. For optimizing the probe performance, a frequency sweep method is conducted to obtain the appropriate working frequency. To fabricate the imperfect samples containing the delamination-based damage, a tailored Teflon film is inserted into the appointed interfaces of 16-layer CFRPs. The physical mechanism of delamination detection using pancake coil is analyzed by using FE method. After then, a set of ECT experiments for various CFRPs are performed to verify the sensitivity to delamination detected by pancake coil. Correspondingly, both shape and location of the delamination are compared with those obtained from a well-established UT device. Experimental results indicate that the sensitivity to delamination is high at the interface formed by different stacked fibers, but it lacks sensitivity to delamination in unidirectional one due to the same aligned fibers, which are qualitatively consistent with the numerical results. Unexpectedly, delamination at the interfaces between adjacent layers stacked by the same fibers of the other two CFRPs can still be distinguished from the non-damage region, while this phenomenon fails to explain by the numerical analysis. Quantitatively, the visualized damage region agrees with the UT C-scan results.

Improved imaging technique for nondestructive evaluation using arbitrary virtual array source aperture (AVASA)

Abstract: In this paper, we propose a new phased array imaging technique called Arbitrary Virtual Array Source Aperture (AVASA) to image deeper defects with an improved SNR with fewer transmissions. The approach is to transmit the ultrasound waves by electronic beamforming at several arbitrary virtual source positions to achieve higher focal depth to increase the SNR of the received A-scans. Backscattered signals are recorded with all the array elements. A high-resolution image is obtained on reception by virtually focusing on every point in the region of interest by signal coherence summation. In this paper, the proposed AVASA and TFM methods are employed for scanning the larger thickness structure with an unknown defect nature to contrast the defect SNR and the number of defect imaging. Compared with TFM imaging, the AVASA method shows a significantly increasing defect-detecting range with higher amplitude. To further improve the imaging quality and reduce the reconstruction time, the influence of the virtual source parameters on the AVASA imaging and a scanning strategy is demonstrated. A good agreement between the AVASA and TFM is observed, and the number of transmissions is required to inspect the test specimen using AVASA reduced by a factor of four to eight.

Quality assessment of SUS316L fabricated by metal additive manufacturing with eddy current inspection

Abstract: Guaranteeing the quality of additive manufacturing (AM) components after fabrication is very important. In this study, a discrete non-destructive evaluation (NDE) system that includes an eddy current testing (ECT) probe and a fluxgate sensor was developed. In conventional ECT, the defect detection depth is limited by the skin depth of the material under investigation and requires a high frequency of the order of MHz. However, in the NDE system, the high sensitivity of the fluxgate sensor and the use of an ECT coil make it possible to detect subsurface defects at a relatively low frequency. A double-excitation coil was used to excite eddy currents in the specimen at relatively low frequencies, whereas a planar differential coil was used as a pick-up loop connected to an input coil that was inductively coupled to the fluxgate sensor. Based on the developed NDE system, five stainless steel (SUS316L) specimens fabricated by powder bed fusion were inspected. A strong correlation was observed between the detected signals and internal porosity, which was confirmed using X-ray computed tomography images. Although this approach cannot detect the local existence of defects, it can be applied for quality assessment, such as density and distribution of porosity in metallic parts fabricated by AM.

Non-destructive evaluation of corrosion degree of thermal barrier coatings using fluorescence

Abstract: With the increasing application of thermal barrier coatings (TBCs) in marine environments, the failure judgment of TBCs is becoming increasingly important. This paper introduces a method for non-destructive testing of the degree of corrosion of TBCs under alternating high and low temperatures and a Cl−-enriched environment. A Y2O3-8 wt.% ZrO2 + 1 wt% Tb2O3 (YSZ+Tb) coating (where YSZ is yttria-stabilised zirconia) was prepared by atmospheric plasma spraying, and its green fluorescence attributed to the 5D4→7F5 transition of Tb3+ ions was investigated. Moreover, the fluorescence intensity of the YSZ+Tb coating was reduced after corrosion due to a change in the Tb3+ crystal field structure caused by phase transition of the ceramic layer. The relationship between the fluorescence and degree of corrosion of the coating in alternating high and low temperatures was established.

Guided waves based debonding classification in lap-joints using modified Fisher discriminant criterion

Abstract: In this paper, a modified fisher discriminant criterion is used to identify and rank damage sensitive features for debonding classification. For this purpose, the analysis of the correlation between features (ACF) based on Pearson correlation coefficient and the Fisher discriminant ratio (FDR) criterion were combined to select the most relevant features. The debondings classification was evaluated in a multiclass classification by considering three different classes of damage and one class of undamaged. The nondestructive inspection was performed in a composite-adhesive-composite lap-joint using guided Lamb wave testing with angle beam transducers mounted in a pitch-catch configuration. From the collected data, twelve features were extracted by using statistical functions and signal processing techniques. From the extracted features, the most damage sensitive features were selected based on the proposed method (ACF and FDR) and the results were compared using the conventional FDR method. For three different classifiers, the feature selection strategies were tested and their performance evaluated using the metrics: accuracy, precision, recall and F1 score. For the dataset collected in this study, the results support that the proposed method yields an improvement of the performance of the classifiers in comparison with the use of the conventional FDR-based feature selection.

Reference-free fatigue crack detection using deep long short-term memory network (DLSTM) and nonlinear ultrasonic modulation

Abstract: Nonlinear ultrasonic modulation is sensitive to fatigue crack, but a reference signal or user-specified threshold is often required for crack diagnosis, easily causing false alarms in noisy environments. In this study, a reference-free damage detection method was developed by applying a deep long short-term memory network (DLSTM) to nonlinear ultrasonic modulation signals. First, an ultrasonic signal was generated and measured using piezoceramic ultrasonic transducers. Subsequently, a DLSTM network was constructed and trained to learn the inherent sequential patterns of the measured ultrasonic signals. Then, an absolute damage index (ADI) was defined and computed using only the current ultrasonic signal without any reference ultrasonic signal obtained from the intact condition. Finally, the crack was automatically detected using the ADI and without any user-specified threshold. The performance of the proposed method was examined using data from a submerged floating tunnel model and an actual long-span bridge. The results highlight the feasibility of the proposed method for automatic fatigue crack detection.

Sensitivity distribution optimization method for planar array capacitive imaging

Abstract: Planar array capacitive imaging is an emerging detection technique that has the advantages of being non-invasive, having a fast response time, and the ability to approach an object being measured from one side. However, the presence of soft field effects leads to the non-uniform distribution of sensitivity and the existence of negative sensitivity regions caused by the presence of the guard electrodes, which affects the quality of image reconstruction. To address this problem, an iterative average pooling method for sensitivity distribution optimization is proposed. A three-dimensional model of the sensor is established, the initial sensitivity coefficient matrix is calculated using the finite element method and the distribution characteristics of the sensitivity matrix are analysed. Inspired by neural networks, the convolution operation is introduced into the original sensitivity matrix. Then the 3 × 3 convolution operation is utilized to extract sensitivity data features. Iterative average pooling is proposed to reduce the uneven distribution caused by the soft field effect. The strategy of using the average pooled sensitivity values as parameters in the next calculation is proposed to prevent sudden changes in the sensitivity matrix and ensure a consistent trend with the original matrix. The experimental results show that the proposed method can attenuate the influence of soft field effects and effectively improve the homogeneity of the sensitivity distribution and quality of image reconstruction.

Characterization of semi-infinite delaminations using lock-in thermography: Experimental results

Abstract: Delaminations are flat subsurface flaws parallel to the sample surface. Lock-in infrared thermography with optical stimulation is proposed to size the geometrical parameters of a delamination. Here we deal with semi-infinite delaminations, which are characterized by three geometrical parameters: length, depth and thickness. In a recent paper we calculated analytically and numerically the temperature oscillation of a sample containing a semi-infinite delamination when illuminated by a homogeneous and modulated laser beam. In this work, we present lock-in infrared thermography experiments performed on calibrated delaminations manufactured on AISI-304 stainless steel. We fit the numerical model to the experiments to retrieve the delamination length, depth and thickness. The agreement between nominal and retrieved values confirms the validity of the method and paves the way for characterization of finite delaminations.

Research on non-destructive testing of stress in ferromagnetic components based on metal magnetic memory and the Barkhausen effect

Abstract: In this paper, the relationship between the effective field of metal magnetic memory (MMM) emission and the structural stress of the ferromagnetic components is studied. The relationship between the voltage of the received magnetic Barkhausen noise (MBN) and the excitation source is also studied. Moreover, the characteristics of the emission spectrum of the MMM and MBN detection signals, and the influences of the lift-off distance on the stress detection accuracy of the two detection methods are studied. The advantages and disadvantages of the two magnetic non-destructive testing (NDT) techniques are analyzed separately, and their functions are combined to enhance the advantages and weaken the disadvantages. Modern signal analysis methods such as wavelet packet transform and signal feature extraction are introduced to process MMM signals, and a classifier for stress grading is established in combination with machine learning methods. On this basis, graded detection of the temperature stress of the continuous welded rail (CWR) was carried out. The detection accuracies of the 30–50 MPa, 50–70 MPa and 70–90 MPa stress grade ranges of CWR reach 71.43%, 82.76% and 75.00% respectively. The rapid grading detection of the temperature stress of the CWR and the rapid judgment of the structural stability of the CWR are realized based on the MBN-MMM detection technique.

Non-destructive characterization on multiphase structures of duplex stainless steel using multi-frequency electromagnetic sensor

Abstract: Signals of multi-frequency electromagnetic sensor is of great significance for in-line monitoring microstructure transformations during duplex stainless steel (DSS) manufacture. The key is to accurately evaluate the relative permeability value of DSS as it dominates sensor signals at low frequencies and can be used to characterize microstructure transformations. In this paper, a micro finite element (FE) model of DSS microstructures for analyzing and calculating relative permeability is proposed. The relative permeability value is input into a macro multi-frequency electromagnetic sensor model and the modelling sensor signal shows a good agreement with the experimental validation. Effects of phase composition, phase distribution and grain numbers on the relative permeability value are investigated using the model. A 3D microstructural model is also established and results compared with the 2D model are discussed. Results show that the relative permeability value of DSS can be determined using the model and steel microstructures can be characterized using the multi-frequency electromagnetic sensor.

Towards a multi-fidelity deep learning framework for a fast and realistic generation of ultrasonic images in complex geometries with the multi-modal Total Focusing Method

Abstract: This paper presents a deep-learning surrogate model tailored for a fast generation of realistic ultrasonic images in the Multi-modal Total Focusing Method (M-TFM) framework. The method employs both physics- and data- driven data-sets. To this end, we propose a Conditional U-Net (cU-Net) to perform a controlled generative process of high-resolution M-TFM images by spanning the set of inspection parameters, employing both the experimental data (high-fidelity acquisitions) and the simulated ones (a low-fidelity counterpart). Once trained on experimental and simulated images, the cU-Net embodies an enhanced realism, learnt from the experimental data, coupled with a quasi-real-time prediction that prevents the need for extra simulations. Moreover, our surrogate model provides a controlled M-TFM generation conditioned by the steering parameters of the simulation as well as by the physics underlying the ultrasonic testing schema. The performances of our approach are demonstrated in a case study of M-TFM images of a component with planar defects in a complex weld-like profile. Furthermore, we consider uncertainties in M-TFM image parameters reconstruction in both numerical and experimental data to reproduce the on-site inspection. Additionally, we show how the trained neural network can learn its inner layers (i.e., the cU-Net layers) according to the physical parameters at stake so that it can be considered an open-box model enabling a qualitative interpretation of the generative process.

A scheme for a priori generation of the optical patterns for magnetic nanoemulsion based defect detection in carbon steel specimens

Abstract: A numerical scheme is proposed for a priori generation of the optical patterns for magnetic nanoemulsion (MNE) based defect detection in ferromagnetic steel specimens. External static magnetic field (HDC) induced tunable optical contrast in MNE has been exploited for the development of the wide-area non-contact optical sensor, which utilizes the magnetic flux leakage (MFL) from the defect regions to generate visually discernible optical patterns, where the image contrast varies with the severity of the defects. For theoretically generating the optical patterns, a three-step numerical scheme is proposed, where the first step involves the physical characterization of the MNEs. In the presence of a HDC, the MNE droplets exhibit an oriented linear arrangement, where the inter-droplet separation decreases with increasing HDC, resulting in a blue shift of the wavelength corresponding to the maxima of the Bragg's reflection peaks (λmax). Unique λmax-HDC calibration curves are obtained for various types of MNEs depending on the nature of the stabilizing moieties. In the second step, the surface distributions of the MFL are obtained using finite element modelling. Subsequently, in the third stage, using the λmax-HDC calibration curves, the surface distributions of MFL are converted to surface distributions of λmax values, which are then pseudo colour-coded to theoretically generate the optical patterns. The presence of the rectangular slots, double rectangular, cylindrical, and buried defects are clearly discernible from the simulated optical patterns, which are found to be in good agreement with the experimentally recorded patterns. Defect depth-dependent intensity variations and estimated defect widths from the simulated images are in good agreement with the data obtained from the experimental images, thereby validating the efficacy of the proposed scheme for a priori prediction of the optical patterns for MNE-based non-contact wide area defect detection. Further, the proposed scheme will be beneficial for the selection of appropriate MNE-based sensors, reducing inspection time, and developing automated inspection routines with enhanced detection capabilities.