Solution For Inspection Research Articles

CBS-YOLOv5: fault detection algorithm of electrolyzer plate with low-resolution infrared images based on improved YOLOv5

Abstract In the process of copper electrorefining, accurate detection of electrode plate faults is extremely challenging due to the low resolution of captured infrared images, significant noise interference, and dense electrode plate arrangements. To address these challenges, this paper proposes an improved YOLOv5-based electrode plate fault detection algorithm called CBS-YOLOv5. This algorithm introduces several innovations over the original YOLOv5, including: the incorporation of coordinate attention to enhance the ability of the feature extraction network to separate target information from noise; the construction of a small object detection module to improve the detection of dense small objects by increasing the resolution of the feature map; the replacement of the traditional path aggregation network with a Bi-directional Feature Pyramid Network (BiFPN) for more flexible multi-scale feature fusion; and the integration of the swin transformer to optimize the cross-stage partial bottleneck structure, significantly enhancing the model’s ability to detect densely packed small objects. Experimental results show that the proposed CBS-YOLOv5 model achieves an accuracy of 88.1%, which is an improvement of 5.7% over the base model. Furthermore, this algorithm demonstrates exceptional detection capabilities for dense small objects in low-resolution infrared images while maintaining real-time detection speed, making it suitable for various complex industrial scenarios, including fault detection in non-ferrous metal electrolysis processes. CBS-YOLOv5 not only improves detection accuracy and robustness but also has broad application prospects, offering a new solution for intelligent manufacturing and industrial inspection.

Implementation strategy for launch and performance improvement of high throughput manufacturing inspection systems

Product technologies are changing rapidly in advanced automotive propulsion systems. These products are driving the need for new manufacturing processes and new inspection methods. To keep new propulsion systems affordable and ensure these new products are introduced with high quality, automotive manufacturers are seeking automated inspection solutions with low cost and near-zero error rates to inspect 100% of the items. In this paper, a progressive deployment strategy of a hybrid inspection system is presented and studied in the context of technology development and rapid deployment. It enabled us to begin with human inspection and gradually phase-in automated inspection technology, while almost never failing to identify a bad item. This strategy was applied successfully to inspect ultrasonic welds in lithium ion battery packs. At the time of this study, a 75% reduction in human inspection was achieved with prospects for further reduction. Actual results from the implementation of this strategy in production are presented. Recommendations are made regarding the most appropriate time to employ this strategy and how it could increase the use of advanced automated in-line inspection technologies.

Characterization of internal defects in cylindrical components using laser ultrasonic method with a modified SAFT algorithm

The health monitoring of cylindrical elements is particularly important for the safe operation of industrial production, because cylinders bear a lot of support and transmission work. Normal non-destructive evaluation techniques need special designs to suit high curvature surfaces of cylinders. Laser ultrasonic (LU) method can provide a remote and non-destructive inspection solution to solid cylinders due to its flexible adaptability to complex structures and strong penetration depth. However, constrained by the common problems of optical detection systems, the detected ultrasonic signals will suffer low signal-to-noise ratio and bad resolution from poor sample surface quality. Thus, a modified synthetic aperture focusing technique (SAFT) optimized for cylindrical components is proposed to improve the detectability of LU on small and buried defects. Mode conversion wave signals obtained by multiple separate excitations are used in SAFT imaging for the reconstruction of the defects under surface profile correction. To reduce the influence of incident waves such as direct surface acoustic wave, besides common difference method, adjacent wave subtraction algorithm based on cross-correlation is used for signal preprocessing, suppressing the incident waves and exaggerating the mode conversion waves. In numerical simulation, internal defects with diameters from 0.6 to 0.2 mm with various buried depths are visualized and accurately located via the optimized SAFT algorithm using mode conversion waves. For validation, a circumferential scanning system is established in LU experiment and internal defects from 0.8 to 0.4 mm in diameter inside solid cylinders are successfully detected with precise location. The results elucidate the reliability of characterizing the location of small internal buried defects in solid cylinder structures through LU-SAFT imaging.

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.

Vision-based mixed color detection of plastic particles.

In the production process of high-end PP-R pipes, mixing different colored raw material particles can result in uneven color in the final product, affecting its appearance quality. in addition, color mixing can reduce the physical properties of the pipes, impacting their durability and safety. To address this issue, we propose a visual, non-destructive inspection solution based on image processing technology. The solution aims to enhance detection efficiency and accuracy by reducing background interference and enabling adaptive adjustments in various environments. Initially, the K-Means image segmentation algorithm is employed to eliminate complex background factors from the original image, significantly improving image segmentation accuracy. Subsequently, the Gaussian mixture model algorithm is utilized to automatically extract the color threshold of the foreground image after background removal, facilitating adaptive algorithm adjustments. Finally, the mean value algorithm is introduced to swiftly and accurately identify plastic particles of different colors using the automatically obtained color thresholds. Experimental results demonstrate that this method can quickly and accurately identify different color particles and effectively support the rejection of impurity particles. Through this approach, the algorithm achieves an average detection accuracy of 99.3%.

Hyperspectral imaging system for pre- and post-harvest defect detection in paprika fruit

External defects in paprika fruit (Capsicum annuum, L. var. angulosum) directly impact their storage and marketability, potentially leading to internal decay and altering fruit appearance. However, automatically detecting various paprika defects, including cracks, scars, diseases, and bruises, poses a significant challenge due to variations in defect size and visual similarities among different types of defects. This study proposed a comprehensive, accurate, and rapid defect detection method utilizing hyperspectral imaging (HSI) alongside multivariate analysis and imaging technologies. The reflective surface of fruit poses a primary limitation to efficient defect detection using imaging technologies. Therefore, a polarized HSI visible–near-infrared (VIS-NIR, 397–1000 nm) system was developed, and over 500 paprika fruit with various types of defects were imaged using the developed system. Additionally, fruit were manually bruised in different sizes to ensure spectral data variability and measured every 12 hours for two days. Spectral data (over 4580 spectral data) from the regions of interest (ROIs) of the paprika fruit samples, including manually bruised fruit, were extracted. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was developed to distinguish between normal and defective fruit. Remarkably, the developed model achieved an accuracy of over 99 % in predicting normal and defective fruit. Moreover, the chemical images generated enhance the inspection process. The successive projection algorithm (SPA) and variable importance in projection (VIP) were further utilized for critical band selection, yielding an accuracy of 93 % and 98 % in the prediction set, respectively. Furthermore, a classification algorithm based on analysis of variance (ANOVA) was employed to determine the optimal wavelength band ratios (F-values). This approach yielded an impressive accuracy rate of over 88.8 % and further enhanced the performance of the band ratio by an improved watershed segmentation algorithm (IWSA) to reach an accuracy of 91.3 % in multi-defect detection. These findings offer a practical and efficient solution for the external quality inspection of paprika fruit, particularly in cases with naturally occurring defects. By integrating hyperspectral imaging, PLS-DA, band selection methods, ANOVA-based classification, and IWSA, this comprehensive analysis provides a reliable and practical means for identifying and categorizing defects in paprika fruit, ultimately enhancing product quality and consumer satisfaction.

Underwater quantitative thickness mapping through marine growth for corrosion measurement using shear wave EMAT with high lift-off performance

Underwater inspection is important to ensure the safety, integrity and functionality of underwater structures. Although numerous conventional methods have been adopted for underwater inspection, successful application of most methods relies on the surface condition of the object, which, however, is typically covered by marine growth. Consequently, routine inspection requires thorough cleaning of marine growth, which is time-consuming and costly. Hence a method which can inspect objects without the need for extensive surface cleaning is necessary. Two methods have the potential to achieve this: pulse eddy current (PEC) and electromagnetic acoustic transducer (EMAT). PEC attains a significant lift-off distance, enabling inspection through marine growth. However, it suffers from high sensitivity to environmental conditions and low inspection accuracy due to ‘relative’ property which means its results are interpreted by comparing received signals to reference values. In contrast to PEC, EMAT provides ‘absolute’ measurements, ensuring precise results in the inspection. But it is limited by a small lift-off distance (<2∼3 mm), rendering it unsuitable for underwater applications with marine growth. Therefore, if the lift-off distance can be enhanced to a specific value, this method may offer a superior solution for underwater inspections. In this paper, a quantitative measurement method is proposed through employing a shear wave EMAT with high lift-off performance. A repelling configuration of magnets is introduced to achieve a significantly improved maximum effective lift-off distance of up to 5 mm in both air and seawater conditions with only 400 Vpp applied. This EMAT is then demonstrated to measure thickness through marine growth, showing excellent underwater performance in quantitative thickness mapping for corrosion inspection.

Optical tomography by laser line scanning and digital twinning for in-process inspection of lattice structures in material extrusion

One of the challenges of manufacturing hollow parts featuring internal lattice structures by using material extrusion is to achieve geometric accuracy of the internal geometries. In this work a solution for in-process geometric inspection and monitoring is presented, based on combining on-machine part measurement by laser line scanning and digital twinning. In the solution, a laser line scanner is used to acquire a two-dimensional map of material and void distribution within the deposited layer. Layer inspection is carried out by comparing the 2D map with a reference one obtained by simulating the deposition process (digital twin of the layer); discrepancies are automatically identified and quantified. The evolution of anomalies across layers can be tracked by vertically stacking both layer measurements and 2D digital twins and by investigating the resulting 3D voxel models. The models are updated after the fabrication of each new layer, to allow geometric monitoring over time. The proposed inspection and monitoring solution is particularly suitable for hollow parts and/or lattice or otherwise reticular internal structures, which would otherwise be inaccessible when using conventional measurement methods on the final part.

A graph learning approach for automatic subsurface defects segmentation in building façade using RGB and infrared images

Infrared thermal imaging technology provides a non-destructive and automated inspection solution for analyzing building facades. However, conventional methods that rely on detecting subsurface defects through abnormal heat loss often suffer from the limitation of misidentifying other materials present on the building facade. To address this challenge, this study proposes a graph learning approach for segmentation of subsurface defects by integrating both visual and thermal information. The proposed methodology encompasses several key steps. Firstly, the infrared image is partitioned into internally connected superpixels with similar characteristics using a linear iterative clustering algorithm. Subsequently, these superpixels are employed as nodes to construct a weighted undirected graph. The edges of the graph are defined based on the connectivity of superpixels in the image plane, while the weights are determined by the Wasserstein distance between each node. Furthermore, a multimodal graph reuse technique is introduced to construct a new graph based on an RGB image, thereby incorporating additional visual information. By defining the defect as tightly connected in the RGB graph but loosely connected in the infrared graph, the segmentation of defects involving multiple materials is formulated as a multi-graph N-cut problem. The feasibility and effectiveness of the proposed method are demonstrated through laboratory testing conducted on concrete specimens, as well as on-site monitoring carried out on a tiled building façade.

Lightweight Oriented Detector for Insulators in Drone Aerial Images

Due to long-term exposure to the wild, insulators are prone to various defects that affect the safe operation of the power system. In recent years, the combination of drones and deep learning has provided a more intelligent solution for insulator automatic defect inspection. Positioning insulators is an important prerequisite step for defect detection, and the accuracy of insulator positioning greatly affects defect detection. However, traditional horizontal detectors lose directional information and it is difficult to accurately locate tilted insulators. Although oriented detectors can predict detection boxes with rotation angles to solve this problem, these models are complex and difficult to apply to edge devices with limited computing power. This greatly limits the practical application of deep learning methods in insulator detection. To address these issues, we proposed a lightweight insulator oriented detector. First, we designed a lightweight insulator feature pyramid network (LIFPN). It can fuse features more efficiently while reducing the number of parameters. Second, we designed a more lightweight insulator oriented detection head (LIHead). It has less computational complexity and can predict rotated detection boxes. Third, we deployed the detector on edge devices and further improved its inference speed through TensorRT. Finally, a series of experiments demonstrated that our method could reduce the computational complexity of the detector by approximately 49 G and the number of parameters by approximately 30 M while ensuring almost no decrease in the detection accuracy. It can be easily deployed to edge devices and achieve a detection speed of 41.89 frames per second (FPS).

Application of image processing technology based on field programmable gate array in mechanical part inspection

Introduction: In the field of industrial manufacturing, accurate inspection of mechanical components such as gears and bearings is of Paramount importance. However, the traditional mechanical testing methods are often disturbed by human factors, which not only affects the stability of the test results, but also leads to low efficiency and large error. In order to solve these problems, this research focuses on developing a new edge detection model.Methods: A novel edge detection model based on field-programmable gate array image processing technology was used in this study. The model uses adaptive threshold multi-directional edge detection technology to identify the edge features of mechanical gears and bearings, aiming at improving the precision of detection.Results and Discussion: After performance verification, the running time of the model was controlled within 11 s, and the detection error was limited to less than 9%. Compared with the control group and the experimental group, their performance was superior. Further analysis data show that the detection accuracy of this model is as high as 0.9004, its internal resource utilization rate is 88%, and the detection rate is as high as 91%, which are better than the comparison model.Conclusion: The proposed test model not only significantly improves the efficiency and accuracy of the test, but also fully meets the requirements of the test. This new edge detection model has potential application value in industrial manufacturing field, and provides a new solution for industrial manufacturing quality inspection.

Comprehensive Inspection Solution for Boiler Water Wall Tube Corrosion

A substantial number of domestic coal-fired boilers, most of which have been in operation for numerous years, are susceptible to corrosion and tube ruptures within their high-temperature environment, often resulting in unplanned shutdowns. Despite the annual planned maintenance shutdowns, which involve routine inspections of water wall and furnace tubes to detect potential corrosion and erosion, the current non-destructive testing methods are generally localized. This approach, particularly with the considerable height variation of the water wall tubes (ranging from approximately 10 to 25 meters), may overlook segments with severe corrosion during localized ultrasonic thickness gauging. Although the experience of inspection teams reduces the chance of defect oversight, the demand for a comprehensive and effective inspection technique remains a priority for boiler operators. This study utilizes the characteristic of guided waves to propagate over a certain distance along pipelines, initiating an exploration into a comprehensive inspection solution for water wall tubes. Finite element simulation results demonstrate the suitability of utilizing a 120 kHz frequency for shear horizontal mode (SH0) testing on the fire side of water wall tubes. This mode effectively screens for localized corrosion within a 3-meter range. In the field of on-site boiler inspection, electromagnetic coils are employed to generate guided waves within water wall tubes. The gathered signals from each electromagnetic guided wave testing (EMGWT) are consolidated into a distribution map, indicating suspicious signal locations. Pulse eddy current testing (PECT) is employed to identify potential issues with guided waves without requiring surface preparation. If PECT reveals anomalies, the affected surface is polished, and phased array ultrasonic testing (PAUT) is conducted to quantitatively measure the remaining wall thickness due to corrosion. The theoretical exploration and on-site inspection outcomes affirm the feasibility of this approach, aimed at enhancing operational safety for boiler water wall tubes and mitigating the risks of tube ruptures and leaks. This comprehensive inspection solution amalgamates three advanced detection technologies, each capitalizing on its unique inspection characteristics to facilitate corrosion screening, corrosion scanning, and wall thickness sizing. Through the integration of these technologies, the overall inspection strategy ensures the utmost level of reliability.

Learning-Based Approach for Automated Surface Inspection with Industrial Tomography Imaging

Abstract In recent years, advanced deep learning techniques have emerged as pivotal tools in enabling the development of robust vision-based solutions for steel surface inspection. This resulted in enhanced inspection accuracy, all while significantly reducing costs in the manufacturing industry. However, the lack of actual steel surface defects datasets currently places a certain constraint on further research into classifying those anomalies. As a consequence, the Convolutional Neural Network (CNN) technique, known for its prowess in image-related tasks, faces certain challenges, especially in classifying less common defects. This work proposes a novel hybrid CNN model with a Support Vector Machine (SVM) classifier at the output layer for surface defects classification. The features extracted from the pre-trained ResNet152 and EfficientB0 CNN algorithms are concatenated and fed to the SVM layer for classification. Extensive experiments on a merged dataset consisting of the publicly available Northeastern University (NEU) dataset and Xsteel surface defect dataset (X-SDD) are carried out and the accuracy and F1 scores are calculated for performance evaluation. The merged dataset contains eleven typical defect types with a total of 2660 defect images. Then, the adopted algorithm is compared with ten fine-tuned deep learning models to evaluate the performance of transfer learning for steel defect detection and identification. The evaluation results show that the deep feature extraction and SVM classification produced better results than the transfer learning. Finally, the proposed classifier model is validated on a newly collected dataset from a Computed Tomography scanner with an accuracy reaching over 96%.

An element perception and state recognition method oriented to complex assembled product

Quality inspection during assembly process has an important impact on the performance and service life of the final product. Automated inspection solutions based on machine vision and artificial intelligence greatly improve the efficiency of inspection. However, the current mainstream large-scene inspection solutions, such as mobile vision platforms, are difficult to apply widely due to cost issues. Therefore, portable vision inspection solutions have gradually gained attention. However, the bottleneck technologies that limit this solution are full-element perception and occlusion judgment under free viewing angle, robust object matching method. To address the above problems, This paper proposes a free-viewpoint-based element perception and state recognition scheme. An improved structure estimation model is adopted to establish a bridge between virtual and actual mapping under the support of small-sample data, and all elements are located and occlusion analyzed by a 3D model-constrained view evolution algorithm. In addition, a hierarchy matching and optimization method is established to complete visual analysis. Experimental results show that this scheme performs well in structure estimation, element localization, occlusion judgment, state recognition, and work efficiency, demonstrating high application value.

Q3D: a complete solution for quality control and inspection in additive manufacturing processes

PurposeThis paper aims to present an integrated system designed for quality control and inspection in additive manufacturing (AM) technologies.Design/methodology/approachThe study undertakes a comprehensive examination of the process in three distinct stages. First, the quality of the feedstock material is inspected during the preprocessing step. Subsequently, the main research topic of the study is directed toward the 3D printing process itself with real-time monitoring procedures using computer vision methods. Finally, an evaluation of the 3D printed parts is conducted, using measuring methods and mechanical experiments.FindingsThe main results of this technical paper are the development and presentation of an integrated solution for quality control and inspection in AM processes.Originality/valueThe proposed solution entails the development of a promising tool for the optimization of the quality in 3D prints based on machine learning algorithms.

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Microelectronics and electronic products are integral to our increasingly connected world, facing constant challenges in terms of quality, security, and provenance. As technology advances and becomes more complex, the demand for automated solutions to verify the quality and origin of components assembled on printed circuit boards (PCBs) is skyrocketing. This paper proposes an innovative approach to detecting and classifying microelectronic components with impressive accuracy and reliability, paving the way for a more efficient and safer electronics industry. Our approach introduces significant advancements by integrating optical and X-ray imaging, overcoming the limitations of traditional methods that rely on a single imaging modality. This method uses a novel data fusion technique that enhances feature visibility and detectability across various component types, crucial for densely packed PCBs. By leveraging the WaferCaps capsule network, our system improves spatial hierarchy and dynamic routing capabilities, leading to robust and accurate classifications. We employ decision-level fusion across multiple classifiers trained on different representations—optical, X-ray, and fused images—enhancing accuracy by synergistically combining their predictive strengths. This comprehensive method directly addresses challenges surrounding concurrency, reliability, availability, and resolution in component identification. Through extensive experiments, we demonstrate that our approach not only significantly improves classification metrics but also enhances the learning and identification processes of PCB components, achieving a remarkable total accuracy of 95.2%. Our findings offer a substantial contribution to the ongoing development of reliable and accurate automatic inspection solutions in the electronics manufacturing sector.

Edge-Computing Oriented Real-Time Missing Track Components Detection

Track integrity is critical for railroad safety. Traditional track inspections are either labor-intensive or require centralized data processing, making them susceptible to human error and lapses between data collection and situation awareness. The advent of deep learning and computer vision provides promising potential for automated track inspections. However, few existing systems are edge-computing oriented or provide inspection results in real time. In this study, a novel ultra-portable system for real-time detection of track components, such as spikes, bolts, and clips, is developed by integrating the cutting-edge YOLOv8 object detection model with a tailored template matching algorithm. In this system, YOLOv8 serves to recognize track components, while the template matching algorithm discerns missing components based on predefined patterns. Field blind testing results verified the exceptional performance of the model in detecting track components and a remarkable speed of 98.12 frames per second. Leveraging these detection results, the proposed template matching technique displayed an impressive recall rate of 90% and an accuracy rate of 90.77% in identifying missing components. The proposed system provides an affordable and versatile solution for track inspection, aiming to improve railway safety.

Cable-stayed bridge all-round morphology identification based on 3D point cloud model

Traditional inspection methods, that is, total station, can be labor-intensive, time-consuming, and efficiently challenging, especially for long-span cable-supported bridges. Recently, Terrestrial Laser Scanning (TLS) has been attractive in bridge digitization because of its non-contact and high-efficiency features. It can provide a comprehensive and all-round morphology assessment through establishing the bridge’s 3D point cloud model. Based on our previous research on point cloud registration of long-span bridges, this article focuses on spatial form identification using the registered point cloud model, specifically the identification of cable sag and girder spatial volume. In detail, precisely monitoring cable sag is critical for cable force identification, while accurate girder volume is essential bridge information, which should be tracked regularly and used for long-term performance evaluation and maintenance guidance. To evaluate the precision of the proposed TLS-based inspection solution, a case study is conducted on a 152 + 370 + 152 m cable-stayed bridge. Results show the identifying error of TLS-based solution remains at 2% for cable sag identification and 5% for girder volume recognition. The traffic-induced sag variance can also be captured by the point cloud data. Therefore, the TLS-based inspection method is thoroughly evaluated and demonstrated as a highly efficient and trustworthy solution.

ESTUDIO COMPARATIVO TERMOGRÁFICO MEDIANTE SMARTPHONE Y CÁMARA EN EDIFICIOS

The cost of thermographic cameras has significantly decreased in recent years, making it an accessible and cost-effective solution for building and infrastructure inspection. With advancements in technology, low-cost thermographic devices have now become readily available in the market. These cost-effective devices offer a more convenient and accessible option for individuals and businesses to perform onsite temperature measurements. In this paper, we examine the capabilities and limitations of a thermal-equipped smartphone and the potential impact on the thermography industry by comparison with a dedicated thermographic camera. The advantages of thermography still hold true for recent low-cost devices: non-invasive inspections, quick and efficient measurements, and hidden defects easily detected. The low-cost thermal-equipped smartphone proved to be a highly maneuverable system, easy to use and with option of overlapping with real color image, what facilitates the interpretation. The low-cost system is also characterized by a larger field of view but also by a lower resolution, what makes it impracticable for measuring small objects. In any case, integration of non-destructive technologies in smartphones opens up a whole new world of possibilities for spreading the inspection of as-built buildings and infrastructures and democratizing the technology. Keywords: Low-Cost; inspection; thermal bridges; energy sustainability; energy efficiency; digital twins.

Practical Applications of a Set-Based Camera Deployment Methodology.

This work establishes a complete methodology for solving continuous sets of camera deployment solutions for automated machine vision inspection systems in industrial manufacturing facilities. The methods presented herein generate constraints that realistically model cameras and their associated intrinsic parameters and use set-based solving methods to evaluate these constraints over a 3D mesh model of a real part. This results in a complete and certifiable set of all valid camera poses describing all possible inspection poses for a given camera/part pair, as well as how much of the part's surface is inspectable from any pose in the set. These methods are tested and validated experimentally using real cameras and precise 3D tracking equipment and are shown to accurately align with real imaging results according to the hardware they are modelling for a given inspection deployment. In addition, their ability to generate full inspection solution sets is demonstrated on several realistic geometries using realistic factory settings, and they are shown to generate tangible, deployable inspection solutions, which can be readily integrated into real factory settings.