Machining-based coverage path planning for automated structural inspection

The automation of robotically delivered nondestructive evaluation inspection shares many aims with traditional manufacture machining. This paper presents a new hardware and software system for automated thickness mapping of large-scale areas, with multiple obstacles, by employing computer-aided drawing (CAD)/computer-aided manufacturing (CAM)-inspired path planning to implement control of a novel mobile robotic thickness mapping inspection vehicle. A custom postprocessor provides the necessary translation from CAM numeric code through robotic kinematic control to combine and automate the overall process. The generalized steps to implement this approach for any mobile robotic platform are presented herein and applied, in this instance, to a novel thickness mapping crawler. The inspection capabilities of the system were evaluated on an indoor mock-inspection scenario, within a motion tracking cell, to provide quantitative performance figures for positional accuracy. Multiple thickness defects simulating corrosion features on a steel sample plate were combined with obstacles to be avoided during the inspection. A minimum thickness mapping error of 0.21 mm and a mean path error of 4.41 mm were observed for a 2 m² carbon steel sample of 10-mm nominal thickness. The potential of this automated approach has benefits in terms of repeatability of area coverage, obstacle avoidance, and reduced path overlap, all of which directly lead to increased task efficiency and reduced inspection time of large structural assets

Fibre volume fraction screening of pultruded carbon fibre reinforced polymer panels based on analysis of anisotropic ultrasonic sound velocity

Composites have become the material of choice in a wide range of manufacturing applications. Whilst ultrasound inspection is a well-established non-destructive testing (NDT) technique, the application to composite imaging presents significant challenges stemming from the inherent anisotropy of the material. The fibre-volume fraction (FVF) of a composite plays a key role in determining the final strength and stiffness of a part as well as influencing the ultrasonic bulk velocity. In this work, a novel FVF determination technique, based on the angular dependence of the sound velocity with respect to the composite fibre direction, is presented. This method is introduced and validated by inspection of pultruded carbon fibre reinforced polymer (CFRP) panels commonly used in the manufacture of high-power wind turbine blades. Full matrix capture (FMC) data acquired from a phased array (PA) ultrasonic probe is used to generate calibration data for samples ranging in FVF from 60.5 % to 69.9 %. Sample velocity, as a function of propagation angle, is used to estimate the FVF of samples and ensure they fall within the desired range. Experimental results show values of 61.1, 66.1 and 68.3 %, comparing favourably to the known values of 60.5, 66.3 and 69.9 % respectively. The work offers significant potential in terms of factory implementation of NDT procedures to ensure final parts satisfy standards and certification by ensuring any FVF inconsistencies are identified as early in the manufacturing process as possible

A framework of using customized LIDAR to localize robot for nuclear reactor inspections

While remote inspection of industrial structures, such as nuclear reactors, using robotic crawlers currently presents significant advantages in terms of safety, accuracy and cost, other challenges emerge due to poor context-awareness and positional accuracy. This results in a lack of visibility for path planning and difficulty in precise localization of NDE (Non-Destructive Evaluation) inspection data. LIDAR (Light Detection and Ranging) are one form of sensors that estimate distances at various angles to map the surrounding environment using optical techniques. Existing commercial LIDARs offer a long range of measurement, allowing mapping of the surroundings. However, such sensors often have centimeter accuracy and a minimum scan range, resulting in a blind area and are generally unsuitable for compact spaces and areas with high density of neighboring objects. This paper presents a framework for using a customized 2D laser scanner, an IMU (Inertial Measurement Unit) and a data fusion approach for localization inside high-density volumes such as nuclear reactors. The laser scanner offers precise measurements with submillimeter accuracy for items located in the short range. The IMU calculates the robot attitude angles, which are critical for inclination angle corrections. The facilities are often made of metallic materials with highly reflective surfaces, which remains problematic for the laser scanner. A mock-up nuclear dome, of realistic material construction, was utilized to benchmark the performance of this framework. The distance and orientation error observed were below 2 mm and 1°, respectively. The framework will be further processed to produce a close-range environment mapping

Modelling of echo amplitude fidelity for transducer bandwidth and TFM pixel resolution

The unrectified (RF) A-scan contains additional information in the phase component compared to its envelope-detected equivalent, but requires higher temporal sampling rates for accurate representation and measurement. The same is true for the pixel density in images reconstructed by the Total Focusing Method (TFM). However the time to calculate each pixel means there is a drive to minimise the total number of pixels for faster real-time imaging rates. This conflicts with the ASME V requirement for the amplitude measurement error, usually a result of under-sampling, to be less than 2dB [1, 2]. There is therefore a need to model the process and to optimise the pixel density trade-off. Quadrature-sampling is a standard approach for optimised representation of band-limited signals [3-6] and is a suitable candidate for ultrasound A-scans. This greatly reduces the size of the Full Matrix Capture (FMC) data set and hence the data transfer rate and storage requirements. A parametric analysis model of the FMC and TFM processes has been created. This has been used to evaluate the amplitude fidelity on both A-scans and TFM images as the fractional bandwidth of the transducer increases and as the pixel density reduces. The results from quadrature-sampling are compared with those using conventional high temporal sampling rates. Results from the parametric analysis modelling are presented for targets at varying scan angles and depths into the material. These show that, for typical transducer fractional bandwidths (up to 75%), quadrature-sampling of the FMC A-scans gives the required amplitude fidelity with TFM image resolutions of around 4 pixels/wavelength. This is a similar result to those reported for conventional FMC acquisition and envelope detection TFM [7]. Extensions of the quadrature-sampling approach for higher fractional bandwidth are also presented. These results are currently being compared with those from real data acquired from Side-Drilled Hole (SDH) targets in a calibration block. An advantage of the FMC+TFM approach is the separation of the acquisition and reconstruction processes. This allows the latter to be redone at higher pixel resolutions for better human interpretation when an indication is observed in the lower pixel resolution images. High resolution TFM reconstructions have been performed on the quadrature-sampled data to confirm that this capability is still possible. A key outcome from this work is the range of optimised pixel resolution values that are safe to use for fast FMC+TFM imaging, as well as the supporting evidence needed to justify them. References [1] ASME Committee, ASME BPVC.V Article 4 Mandatory Appendix XI Full Matric Capture, ASME, 2019. [2] ASME Committee, ASME BPVC.V Article 4 Nonmandatory Appendix F - Examination of Welds Using Full Matric Capture., ASME, 2019. [3] C. E. Shannon, “Communication in the presence of noise,” Proceedings of the Institute of Radio Engineers, vol. 37, no. 1, p. 10–21, Jan 1949. [4] J. L. Brown Jnr., “On Quadrature Sampling of Bandpass Signals,” IEEE Transactions on Aerospace and Electronic Systems, Vols. AES-15, no. 3, pp. 366-371, May 1979. [5] J. L. Brown Jnr., “A simplified approach to optimum quadrature sampling,” The Journal of the Acoustical Society of America, vol. 67, no. 5, pp. 1659-1662, May 1980. [6] C. M. Rader, “A Simple Method for Sampling In-Phase and Quadrature Components,” IEEE Transactions on Aerospace and Electronic Systems, Vols. AES-20, no. 6, pp. 821-824, Nov 1984. [7] N. Badeau, G. Painchaud-April and A. Le Duff, “Use of the Total Focusing Method with the Envelope Feature,” Olympus NDT Canada, 30 March 2020. [Online]. Available: https://www.olympus-ims.com/en/resources/white-papers/use-of-the-total-focusing-method-with-the-envelope-feature/. [Accessed 30 March 2020]

Structure-from-motion based image unwrapping and stitching for small bore pipe inspections

Visual inspection is one of the most ubiquitous forms of non-destructive testing, being widely used in routine pipe inspections. For small bore pipes (centimetre diameter), inspectors often have a restricted field of view limiting overall image and inspection quality. Stitching multiple unwrapped images is a common inspection technique to provide a full view inspection image by combining multiple video frames together. A key challenge of this method is knowing the camera pose of each frame. Consequently, mechanical centralisers are often utilised to ensure the camera is located centrally. For the inspection of small-bore pipes, such mechanical centralisers are often too large to fit. This paper presents a post-processing, Structure-from-Motion (SfM) based approach to unwrap and stitch inspection images, captured by a manually deployed commercial videoscope. It advances state-of-the-art approaches which rely on the projection of a laser pattern into the field of view, thus reducing the equipment size. The process consists of camera pose estimation, preliminary point cloud generation, secondary fitting, images unwrapping and stitching to form an undistorted view of the pipe interior. Two industrial focussed demonstrators verified the successful implementation for small-bore pipe inspections. Whereby the new approach does not rely on image features to create the surface texture and is less sensitive to the image quality, more areas can be retrieved from inspections. The reconstructed area was increased by up to 87% using the new approach versus the conventional 3D model

Autonomous ultrasonic inspection using unmanned aerial vehicle

In terms of safety and convenience, an Unmanned Aerial Vehicle (UAV) offers significant benefits when conducting remote NDT evaluations by mitigating hazards and inefficiencies associated with manned access. Traditionally, UAV remote inspections rely on high-resolution cameras, providing a visual overview of surface condition. This photogrammetric inspection, however, cannot distinguish minute discontinuities or deformations beneath a surface coating. Ultrasonic inspection is a Non-Destructive Testing (NDT) method conventionally used in corrosion mapping. Surface contacting ultrasonic transducers offer the potential for internal inspection of an industrial asset, providing enhanced structural integrity information. However, manually piloting a UAV with sufficient surface proximity to perform a detailed, contact-based examination requires a highly developed skillset and intense concentration. Limitations of payload mass and electronic interference also represent significant challenges to be overcome. Addressing such issues, this paper demonstrates the implementation of an autonomous UAV system with an integrated ultrasonic contact measurement payload. The prototype is autonomously guided and undertakes the contact thickness measurement process without manual intervention

A novel visual pipework inspection system

The interior visual inspection of pipelines in the nuclear industry is a safety critical activity conducted during outages to ensure the continued safe and reliable operation of plant. Typically, the video output by a manually deployed probe is viewed by an operator looking to identify and localise surface defects such as corrosion, erosion and pitting. However, it is very challenging to estimate the nature and extent of defects by viewing a large structure through a relatively small field of view. This work describes a new visual inspection system employing photogrammetry using a fisheye camera and a structured light system to map the internal geometry of pipelines by generating a photorealistic, geometrically accurate surface model. The error of the system output was evaluated through comparison to a ground truth laser scan (ATOS GOM Triple Scan) of a nuclear grade split pipe sample (stainless steel 304L, 80mm internal diameter) containing defects representative of the application – the error was found to be submillimetre across the sample

Transfer learning for classification of experimental ultrasonic non-destructive testing images from synthetic data

Lack of experimental training data is a significant challenge for the use of Deep Learning algorithms in Non-Destructive Testing. This work provides a Transfer Learning solution to the challenge of low training data volumes in Non-Destructive Ultrasonic Testing of carbon fibre reinforced polymer composites, which are known for their high structural ultrasonic noise. The performance of Convolutional Neural Networks for classification was initially tested on experimental data when trained on simulated data. The results demonstrated that due to inaccurate noise production the simulated data domain was too far from the experimental test data to provide accurate classification. Different synthetic datasets were then generated using a variety of methods and their effect on classification performance was studied. The primary focus of these datasets were different methods of noise generation for more experimentally accurate simulated images. To allow for the direct comparison of the different synthetic data generation methods, a standardized custom Convolutional Neural Network was developed. To make sure that the Neural Network was complex enough for the solution space hyperparameter optimization was performed on the network using a secondary experimental dataset. The hyperparameter optimization was a variant of Regularized Evolution [1] which was adapted for continuous and integer valued hyperparameters. The algorithm was initialized with a Population of 128 configurations generated via a random search. At each iteration of Regularized Evolution, a parent model was selected from a sample of configurations from the population, with the highest F1 score. A new child configuration was generated by mutating one of the parents hyperparameters. This child model was then trained and prepended to the population with the ‘oldest’ model discarded. The best performing model was then used for comparisons of classification accuracy for different synthetic datasets. The best performing synthetic dataset saw an F1 score increase of 0.34 (0.738-0.394) from the simulated dataset

Guided wave based-occupancy grid robotic mapping

Asset inspection of large structures such as storage tanks in the oil, gas and petrochemical industry is challenging, either requiring labour-intensive manual measurements or using robotic deployment to make the measurements. Current robotic systems employ point-by-point scanning, which is time-consuming. Using guided waves for such inspections is attractive as they provide a mechanism for monitoring the inaccessible areas and simultaneously providing structural location data to speed up the inspection process. In this research, shear horizontal (SH) guided waves generated by electromagnetic acoustic transducers (EMATs) are used to screen a large area using a crawler. EMATs with 22 mm wavelength are used to generate the first two SH modes: a non-dispersive SH0 and highly dispersive SH1 on a 10 mm thick steel sample. Previously, we have demonstrated the feasibility of guided wave-based occupancy grid mapping (GW-OGM) for mapping a structure's edges. In this work, the GW-OGM technique is generalised to identify and estimate the location of a flat bottom hole in a pitch-catch mode. The simulation and empirical data demonstrate that the location of damage can be identified as the robot navigates on the component, with full coverage. Moreover, the simulated data are in good agreement with the experimental results on the generation of SH wave modes

Application of eddy currents for inspection of carbon fibre composites

Carbon Fibre Reinforced Plastics (CFRP) have diverse industrial applications due to their unique mechanical and structural properties. The manufacturing cycle of CFRP can be summarised into three stages: Preforming, moulding and post cure. During the preforming stage of the composites where there is cutting, handling and layup of carbon fibre fabrics, defects such as fibre waviness, missing bundles and in-plane waviness can occur. These defects are usually detected when the component is inspected after the post cure stage. Hence there is a need to inspect these components before the resin is infused into the dry layup. Currently there is no standardised NDE protocols for the inspection of these dry fabrics and preforms in the aerospace manufacturing industry. This study investigates the inspection of Dry Carbon Fabrics (DCF) for fibre orientation, density, and defects such as missing fibre bundles, in and out of plane fibre waviness, before the resin infusion manufacturing stage, using Eddy Current Testing (ECT). Initial experiments were conducted to test the penetration depth of eddy currents in DCF. A sample was built using biaxial fibre cloth with fibre orientation at 0° and 90°. Six layers were used where layers 2,3,4 and 5 had a strip of aluminium foil to detect the penetration depth of eddy currents through the sample. A total of four stripes were used within the sample. The inspection was carried out at frequencies of 500 and 800 kHz using an eddy current array probe attached to a KUKA robotic arm. Data was gathered in absolute mode for pairs of transmit-receive coils in two transversal and axial topologies. The scans displayed all four stripes, indicating that the eddy current had penetrated through all six layers at both test frequencies. To identify the sensitivity to internal defects, a second experiment was conducted. The inspection sample was made by stacking 10 sheets of DCF with a piece of preformed carbon fibre to induce fibre waviness. Initial results show that the waviness can be detected at 500 kHz with a strong accuracy in every repetition of the scans. Orientation of the fibres could not be detected at this frequency. To conclude, initial experiments were conducted on dry carbon fibre fabrics using eddy current testing to detect fibre waviness and penetration depth of eddy currents. The results show an indication of fibre waviness in a 10-layer sample at 500 KHz in every repetition of the scans. Although the orientation of the fibres could not be detected at this frequency