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Robotic Non-destructive Testing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (25 July 2022) | Viewed by 37636

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Dr. Carmelo Mineo

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Guest Editor

Researcher, Institute for High-Performance Computing and Networking (ICAR), National Research Council (CNR), Via Ugo La Malfa, 153, 90146 Palermo, Italy
Interests: automated and autonomous robotic inspection systems; metrology; non-destructive testing; instrument interfacing; real-time control; data-driven robotic inspection; adaptive robotic inspections; in-process inspections; ultrasonics; phased array technology
Special Issues, Collections and Topics in MDPI journals

Dr. Yashar Javadi

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Guest Editor

Centre for Ultrasonic Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Interests: welding technology; NDT; residual stress; additive manufacturing; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Non-destructive testing (NDT) is notoriously referred to as the wide group of analysis techniques used in research, civil, medical, and industrial sectors to evaluate the properties of materials, components or structures, without causing any damage. NDT is of vital importance to ensure the integrity of critical parts and social safety. Automation offers many benefits for NDT, to cope with increasing demands, including improved reliability and higher inspection speeds. Also, robots enable reaching inspection positions not easily accessible to human operators and remove humans from potentially dangerous environments. However, the perceived complexity and high costs has meant the adoption of automation for NDT has been limited. The seamless integration of industrial robotic arms with sensors, actuators, and software can revolutionize the way automated NDT is performed and conceived. Robotic manipulators have typically been operated by predefined tool-paths generated through off-line path-planning software applications. The recent advancements in electronics, robotics, sensor technology and software pave the way to new developments in automated NDT and data-driven autonomous robotic inspections, in several civil and industrial sectors.

The upcoming technologies are expected to encompass all aspects of networking and compatibility with the Internet of Things and to adopt new manufacturing techniques such as 3D printing and custom manufacturing and assembly of individual components based on the customer’s needs. Moreover, robotic inspections systems are able to acquire huge data volumes. As a result, NDT must be able to adapt to the new trends by introducing new solutions and approaches. The aim of this Special Issue is to attract the latest outcomes of research in the field of robotic NDT. We invite research authors to submit their manuscripts introducing novel developments in one or more of the following aspects:

Robotic NDT
Robotic enabled sensing
Novel integrations of robotic systems for hybrid manufacturing and inspection tasks
Transition from automated to autonomous robotic NDT
Modelling of robotic NDT approaches, remote inspection and interpretation
Real time monitoring of production and structures
Management and processing of big data
Machine learning, artificial intelligence, image recognition and data mining in NDT

The goal of this Special Issue is to present how NDT is being updated and transformed to address the issues raised by the challenging new frontiers in civil and medical fields and by Industry 4.0, which is the ongoing automation of traditional manufacturing and industrial practices using modern smart technology.

Dr. Carmelo Mineo
Dr. Yashar Javadi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Robotic non-destructive testing
Robotic enabled sensing
Remote inspection
Adaptive inspection
Data interpretation
Real time monitoring
Data-driven autonomous inspection
Data management, Data processing
Machine learning, Artificial intelligence, Image recognition

Published Papers (12 papers)

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Editorial

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3 pages, 165 KiB

Open AccessEditorial

Robotic Non-Destructive Testing

by Carmelo Mineo and Yashar Javadi

Sensors 2022, 22(19), 7654; https://doi.org/10.3390/s22197654 - 09 Oct 2022

Viewed by 1427

Abstract

Non-destructive testing (NDT) and evaluation (NDE) are commonly referred to as the vast group of analysis techniques used in civil, medical, and industrial sectors to evaluate the properties of materials, tissues, components, or structures without causing any damage [...] Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

Research

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19 pages, 14906 KiB

Open AccessArticle

Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries

by Carmelo Mineo, Nicola Montinaro, Mario Fustaino, Antonio Pantano and Donatella Cerniglia

Sensors 2022, 22(16), 6267; https://doi.org/10.3390/s22166267 - 20 Aug 2022

Cited by 2 | Viewed by 1429

Abstract

Increasing the efficiency of the quality control phase in industrial production lines through automation is a rapidly growing trend. In non-destructive testing, active thermography techniques are known for their suitability to allow rapid non-contact and full-field inspections. The robotic manipulation of the thermographic instrumentation enables the possibility of performing inspections of large components with complex geometries by collecting multiple thermographic images from optimal positions. The robotisation of the thermographic inspection is highly desirable to improve assessment speed and repeatability without compromising inspection accuracy. Although integrating a robotic setup for thermographic data capture is not challenging, the application of robotic thermography has not grown significantly to date due to the absence of a suitable approach for merging multiple thermographic images into a single presentation. Indeed, such an approach must guarantee accurate alignment and consistent pixel blending, which is crucial to facilitate defect detection and sizing. In this work, an innovative inspection platform was conceptualised and implemented, consisting of a pulsed thermography setup, a six-axis robotic manipulator and an algorithm for image alignment, correction and blending. The performance of the inspection platform is tested on a convex-shaped specimen with artificial defects, which highlights the potential of the new combined approach. This work bridges a technology gap, making thermographic inspections more deployable in industrial environments. The proposed fine image alignment approach can find applicability beyond thermographic non-destructive testing. Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

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19 pages, 4456 KiB

Open AccessArticle

Automated Real-Time Eddy Current Array Inspection of Nuclear Assets

by Euan Alexander Foster, Gary Bolton, Robert Bernard, Martin McInnes, Shaun McKnight, Ewan Nicolson, Charalampos Loukas, Momchil Vasilev, Dave Lines, Ehsan Mohseni, Anthony Gachagan, Gareth Pierce and Charles N. Macleod

Sensors 2022, 22(16), 6036; https://doi.org/10.3390/s22166036 - 12 Aug 2022

Cited by 7 | Viewed by 2734

Abstract

Inspection of components with surface discontinuities is an area that volumetric Non-Destructive Testing (NDT) methods, such as ultrasonic and radiographic, struggle in detection and characterisation. This coupled with the industrial desire to detect surface-breaking defects of components at the point of manufacture and/or maintenance, to increase design lifetime and further embed sustainability in their business models, is driving the increased adoption of Eddy Current Testing (ECT). Moreover, as businesses move toward Industry 4.0, demand for robotic delivery of NDT has grown. In this work, the authors present the novel implementation and use of a flexible robotic cell to deliver an eddy current array to inspect stress corrosion cracking on a nuclear canister made from 1.4404 stainless steel. Three 180-degree scans at different heights on one side of the canister were performed, and the acquired impedance data were vertically stitched together to show the full extent of the cracking. Axial and transversal datasets, corresponding to the transmit/receive coil configurations of the array elements, were simultaneously acquired at transmission frequencies 250, 300, 400, and 450 kHz and allowed for the generation of several impedance C-scan images. The variation in the lift-off of the eddy current array was innovatively minimised through the use of a force–torque sensor, a padded flexible ECT array and a PI control system. Through the use of bespoke software, the impedance data were logged in real-time (

\leq

7 ms), displayed to the user, saved to a binary file, and flexibly post-processed via phase-rotation and mixing of the impedance data of different frequency and coil configuration channels. Phase rotation alone demonstrated an average increase in Signal to Noise Ratio (SNR) of 4.53 decibels across all datasets acquired, while a selective sum and average mixing technique was shown to increase the SNR by an average of 1.19 decibels. The results show how robotic delivery of eddy current arrays, and innovative post-processing, can allow for repeatable and flexible surface inspection, suitable for the challenges faced in many quality-focused industries. Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

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16 pages, 13583 KiB

Open AccessArticle

A Robotic Experimental Setup with a Stewart Platform to Emulate Underwater Vehicle-Manipulator Systems

by Kamil Cetin, Harun Tugal, Yvan Petillot, Matthew Dunnigan, Leonard Newbrook and Mustafa Suphi Erden

Sensors 2022, 22(15), 5827; https://doi.org/10.3390/s22155827 - 04 Aug 2022

Cited by 8 | Viewed by 2112

Abstract

This study presents an experimental robotic setup with a Stewart platform and a robot manipulator to emulate an underwater vehicle–manipulator system (UVMS). This hardware-based emulator setup consists of a KUKA IIWA14 robotic manipulator mounted on a parallel manipulator, known as Stewart Platform, and a force/torque sensor attached to the end-effector of the robotic arm interacting with a pipe. In this setup, we use realistic underwater vehicle movements either communicated to a system in real-time through 4G routers or recorded in advance in a water tank environment. In addition, we simulate both the water current impact on vehicle movement and dynamic coupling effects between the vehicle and manipulator in a Gazebo-based software simulator and transfer these to the physical robotic experimental setup. Such a complete setup is useful to study the control techniques to be applied on the underwater robotic systems in a dry lab environment and allows us to carry out fast and numerous experiments, circumventing the difficulties with performing similar experiments and data collection with actual underwater vehicles in water tanks. Exemplary controller development studies are carried out for contact management of the UVMS using the experimental setup. Full article

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13 pages, 725 KiB

Open AccessArticle

Defects Recognition Algorithm Development from Visual UAV Inspections

by Nicolas P. Avdelidis, Antonios Tsourdos, Pasquale Lafiosca, Richard Plaster, Anna Plaster and Mark Droznika

Sensors 2022, 22(13), 4682; https://doi.org/10.3390/s22134682 - 21 Jun 2022

Cited by 6 | Viewed by 1909

Abstract

Aircraft maintenance plays a key role in the safety of air transport. One of its most significant procedures is the visual inspection of the aircraft skin for defects. This is mainly carried out manually and involves a high skilled human walking around the aircraft. It is very time consuming, costly, stressful and the outcome heavily depends on the skills of the inspector. In this paper, we propose a two-step process for automating the defect recognition and classification from visual images. The visual inspection can be carried out with the use of an unmanned aerial vehicle (UAV) carrying an image sensor to fully automate the procedure and eliminate any human error. With our proposed method in the first step, we perform the crucial part of recognizing the defect. If a defect is found, the image is fed to an ensemble of classifiers for identifying the type. The classifiers are a combination of different pretrained convolution neural network (CNN) models, which we retrained to fit our problem. For achieving our goal, we created our own dataset with defect images captured from aircrafts during inspection in TUI’s maintenance hangar. The images were preprocessed and used to train different pretrained CNNs with the use of transfer learning. We performed an initial training of 40 different CNN architectures to choose the ones that best fitted our dataset. Then, we chose the best four for fine tuning and further testing. For the first step of defect recognition, the DenseNet201 CNN architecture performed better, with an overall accuracy of 81.82%. For the second step for the defect classification, an ensemble of different CNN models was used. The results show that even with a very small dataset, we can reach an accuracy of around 82% in the defect recognition and even 100% for the classification of the categories of missing or damaged exterior paint and primer and dents. Full article

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14 pages, 3539 KiB

Open AccessArticle

Collaborative Robotic Wire + Arc Additive Manufacture and Sensor-Enabled In-Process Ultrasonic Non-Destructive Evaluation

by Rastislav Zimermann, Ehsan Mohseni, Momchil Vasilev, Charalampos Loukas, Randika K. W. Vithanage, Charles N. Macleod, David Lines, Yashar Javadi, Misael Pimentel Espirindio E Silva, Stephen Fitzpatrick, Steven Halavage, Scott Mckegney, Stephen Gareth Pierce, Stewart Williams and Jialuo Ding

Sensors 2022, 22(11), 4203; https://doi.org/10.3390/s22114203 - 31 May 2022

Cited by 8 | Viewed by 2969

Abstract

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part. This work presents a synchronized multi-robot WAAM and NDE cell aiming to achieve (1) defect detection in-process, (2) enable possible in-process repair and (3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, was performed on a titanium WAAM straight sample containing an intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data were acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient signal-to-noise ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair. Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

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16 pages, 4627 KiB

Open AccessArticle

A Novel Complete-Surface-Finding Algorithm for Online Surface Scanning with Limited View Sensors

by Alastair Poole, Mark Sutcliffe, Gareth Pierce and Anthony Gachagan

Sensors 2021, 21(22), 7692; https://doi.org/10.3390/s21227692 - 19 Nov 2021

Cited by 7 | Viewed by 1917

Abstract

Robotised Non-Destructive Testing (NDT) has revolutionised the field, increasing the speed of repetitive scanning procedures and ability to reach hazardous environments. Application of robot-assisted NDT within specific industries such as remanufacturing and Aersopace, in which parts are regularly moulded and susceptible to non-critical deformation has however presented drawbacks. In these cases, digital models for robotic path planning are not always available or accurate. Cutting edge methods to counter the limited flexibility of robots require an initial pre-scan using camera-based systems in order to build a CAD model for path planning. This paper has sought to create a novel algorithm that enables robot-assisted ultrasonic testing of unknown surfaces within a single pass. Key to the impact of this article is the enabled autonomous profiling with sensors whose aperture is several orders of magnitude smaller than the target surface, for surfaces of any scale. Potential applications of the algorithm presented include autonomous drone and crawler inspections of large, complex, unknown environments in addition to situations where traditional metrological profiling equipment is not practical, such as in confined spaces. In simulation, the proposed algorithm has completely mapped significantly curved and complex shapes by utilising only local information, outputting a traditional raster pattern when curvature is present only in a single direction. In practical demonstrations, both curved and non-simple surfaces were fully mapped with no required operator intervention. The core limitations of the algorithm in practical cases is the effective range of the applied sensor, and as a stand-alone method it lacks the required knowledge of the environment to prevent collisions. However, since the approach has met success in fully scanning non-obstructive but still significantly complex surfaces, the objectives of this paper have been met. Future work will focus on low-accuracy environmental sensing capabilities to tackle the challenges faced. The method has been designed to allow single-pass scans for Conformable Wedge Probe UT scanning, but may be applied to any surface scans in the case the sensor aperture is significantly smaller than the part. Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

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17 pages, 2759 KiB

Open AccessArticle

Detection of the Deep-Sea Plankton Community in Marine Ecosystem with Underwater Robotic Platform

by Jiaxing Wang, Mingqiang Yang, Zhongjun Ding, Qinghe Zheng, Deqiang Wang, Kidiyo Kpalma and Jinchang Ren

Sensors 2021, 21(20), 6720; https://doi.org/10.3390/s21206720 - 10 Oct 2021

Cited by 2 | Viewed by 1889

Abstract

Variations in the quantity of plankton impact the entire marine ecosystem. It is of great significance to accurately assess the dynamic evolution of the plankton for monitoring the marine environment and global climate change. In this paper, a novel method is introduced for deep-sea plankton community detection in marine ecosystem using an underwater robotic platform. The videos were sampled at a distance of 1.5 m from the ocean floor, with a focal length of 1.5–2.5 m. The optical flow field is used to detect plankton community. We showed that for each of the moving plankton that do not overlap in space in two consecutive video frames, the time gradient of the spatial position of the plankton are opposite to each other in two consecutive optical flow fields. Further, the lateral and vertical gradients have the same value and orientation in two consecutive optical flow fields. Accordingly, moving plankton can be accurately detected under the complex dynamic background in the deep-sea environment. Experimental comparison with manual ground-truth fully validated the efficacy of the proposed methodology, which outperforms six state-of-the-art approaches. Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

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16 pages, 7520 KiB

Open AccessArticle

Sensor-Enabled Multi-Robot System for Automated Welding and In-Process Ultrasonic NDE

by Momchil Vasilev, Charles N. MacLeod, Charalampos Loukas, Yashar Javadi, Randika K. W. Vithanage, David Lines, Ehsan Mohseni, Stephen Gareth Pierce and Anthony Gachagan

Sensors 2021, 21(15), 5077; https://doi.org/10.3390/s21155077 - 27 Jul 2021

Cited by 23 | Viewed by 5650

Abstract

The growth of the automated welding sector and emerging technological requirements of Industry 4.0 have driven demand and research into intelligent sensor-enabled robotic systems. The higher production rates of automated welding have increased the need for fast, robotically deployed Non-Destructive Evaluation (NDE), replacing current time-consuming manually deployed inspection. This paper presents the development and deployment of a novel multi-robot system for automated welding and in-process NDE. Full external positional control is achieved in real time allowing for on-the-fly motion correction, based on multi-sensory input. The inspection capabilities of the system are demonstrated at three different stages of the manufacturing process: after all welding passes are complete; between individual welding passes; and during live-arc welding deposition. The specific advantages and challenges of each approach are outlined, and the defect detection capability is demonstrated through inspection of artificially induced defects. The developed system offers an early defect detection opportunity compared to current inspection methods, drastically reducing the delay between defect formation and discovery. This approach would enable in-process weld repair, leading to higher production efficiency, reduced rework rates and lower production costs. Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

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19 pages, 1909 KiB

Open AccessArticle

Soft-Tentacle Gripper for Pipe Crawling to Inspect Industrial Facilities Using UAVs

by F. Javier Garcia Rubiales, Pablo Ramon Soria, Begoña C. Arrue and Anibal Ollero

Sensors 2021, 21(12), 4142; https://doi.org/10.3390/s21124142 - 16 Jun 2021

Cited by 10 | Viewed by 5064

Abstract

This paper presents a crawling mechanism using a soft-tentacle gripper integrated into an unmanned aerial vehicle for pipe inspection in industrial environments. The objective was to allow the aerial robot to perch and crawl along the pipe, minimizing the energy consumption, and allowing to perform contact inspection. This paper introduces the design of the soft limbs of the gripper and also the internal mechanism that allows movement along pipes. Several tests have been carried out to ensure the grasping capability on the pipe and the performance and reliability of the developed system. This paper shows the complete development of the system using additive manufacturing techniques and includes the results of experiments performed in realistic environments. Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

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14 pages, 3722 KiB

Open AccessArticle

A Novel Seam Tracking Technique with a Four-Step Method and Experimental Investigation of Robotic Welding Oriented to Complex Welding Seam

by Gong Zhang, Yuhang Zhang, Shuaihua Tuo, Zhicheng Hou, Wenlin Yang, Zheng Xu, Yueyu Wu, Hai Yuan and Kyoosik Shin

Sensors 2021, 21(9), 3067; https://doi.org/10.3390/s21093067 - 28 Apr 2021

Cited by 18 | Viewed by 4132

Abstract

The seam tracking operation is essential for extracting welding seam characteristics which can instruct the motion of a welding robot along the welding seam path. The chief tasks for seam tracking would be divided into three partitions. First, starting and ending points detection, then, weld edge detection, followed by joint width measurement, and, lastly, welding path position determination with respect to welding robot co-ordinate frame. A novel seam tracking technique with a four-step method is introduced. A laser sensor is used to scan grooves to obtain profile data, and the data are processed by a filtering algorithm to smooth the noise. The second derivative algorithm is proposed to initially position the feature points, and then linear fitting is performed to achieve precise positioning. The groove data are transformed into the robot’s welding path through sensor pose calibration, which could realize real-time seam tracking. Experimental demonstration was carried out to verify the tracking effect of both straight and curved welding seams. Results show that the average deviations in the X direction are about 0.628 mm and 0.736 mm during the initial positioning of feature points. After precise positioning, the average deviations are reduced to 0.387 mm and 0.429 mm. These promising results show that the tracking errors are decreased by up to 38.38% and 41.71%, respectively. Moreover, the average deviations in both X and Z direction of both straight and curved welding seams are no more than 0.5 mm, after precise positioning. Therefore, the proposed seam tracking method with four steps is feasible and effective, and provides a reference for future seam tracking research. Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

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20 pages, 8126 KiB

Open AccessArticle

Optimization Design and Flexible Detection Method of a Surface Adaptation Wall-Climbing Robot with Multisensor Integration for Petrochemical Tanks

by Minglu Zhang, Xuan Zhang, Manhong Li, Jian Cao and Zhexuan Huang

Sensors 2020, 20(22), 6651; https://doi.org/10.3390/s20226651 - 20 Nov 2020

Cited by 19 | Viewed by 3535

Abstract

Recently, numerous wall-climbing robots have been developed for petrochemical tank maintenance. However, most of them are difficult to be widely applied due to common problems such as poor adsorption capacity, low facade adaptability, and low detection accuracy. In order to realize automatic precise detection, an innovative wall-climbing robot system was designed. Based on magnetic circuit optimization, a passive adaptive moving mechanism that can adapt to the walls of different curvatures was proposed. In order to improve detection accuracy and efficiency, a flexible detection mechanism combining with a hooke hinge that can realize passive vertical alignment was designed to meet the detection requirements. Through the analysis of mechanical models under different working conditions, a hierarchical control system was established to complete the wall thickness and film thickness detection. The results showed that the robot could move safely and stably on the facade, as well as complete automatic precise detection. Full article

(This article belongs to the Special Issue Robotic Non-destructive Testing)

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