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

Automated deep learning for defect detection in carbon fibre reinforced plastic composites

Carbon Fibre Reinforced Polymers (CFRPs) are used extensively in the aerospace industry because of their unique physical properties and reduced weight that enables lower fuel consumption. This increase was especially rapid in the past decade, with CFRPs accounting for around 50% of the total material weight used in flagship models by Airbus and Boeing [1,2]. Before shipping, Non-Destructive Testing (NDT) methods are used to validate and control the quality of manufactured parts. Commonly used NDT technologies are radiographic testing, eddy current testing, and Ultrasonic Testing (UT). In the aerospace industry, UT is most prominent due to its flexibility and safety. However, when UT is done manually, reliability issues are often observed due to human inspector errors [3]. In addition to this, manufactured parts that need to be inspected are quite large (e.g., wing covers), resulting in slow inspection times. On the other hand, when NDT robotic inspection is deployed, large amounts of data can be captured in a short period of time. While this accelerates the acquisition of information, data interpretation is still done manually thus creating a bottleneck. Therefore, an automated data interpretation system would greatly improve the NDT process. To overcome these challenges, this project proposes a fully automated Deep Learning (DL) approach that leverages current technological advances in Machine Learning (ML) field for defect localization, sizing, and automatic report generation based on ultrasonic amplitude C-scans. Such an approach could decrease the processing time from approximately 6 hours for a 15-meter wing cover to just minutes, significantly benefiting the process throughput. In this research, a manually annotated semi-analytical simulated dataset in form of C-scans was used for training of "You Only Look Once" family of models for the detection and sizing of back-drilled holes and delamination defects in CFRPs. The purpose of using model-based simulations for training was the scarcity of real-world data, and a novel approach of image augmentation was introduced to ensure that the simulated scans closely mimic the experimental data. For NDT inspection, a force-torque-controlled 6-axis industrial robotic arm was used to deliver a phased array ultrasound roller probe to both defect-free and defective CFRP samples of varying thicknesses. The roller-probe array was connected to an array controller and water-coupled to the surface of the CFRPs. Raster scans were performed while the array was excited in linear-scan mode with a sub-aperture of 4 elements and an operating frequency of 5 MHz. Lastly, amplitude C-scan images of 64 x 64 resolution were extracted and used as an object detection validation dataset. These combined methods result in an accurate and precise deep learning network that enables rapid analysis of image data (with the possibility of real-time analysis)

A study of machine learning object detection performance for phased array ultrasonic testing of carbon fibre reinforced plastics

The growing adoption of Carbon Fibre Reinforced Plastics (CFRPs) in the aerospace industry has resulted in a significant reliance on Non-Destructive Evaluation (NDE) to ensure the quality and integrity of these materials. The interpretation of large amounts of data acquired from automated robotic ultrasonic scanning by expert operators is often time consuming, tedious, and prone to human error creating a bottleneck in the manufacturing process. However, with ever growing trend of computing power and digitally stored NDE data, intelligent Machine Learning (ML) algorithms have been gaining more traction than before for NDE data analysis. In this study, the performance of ML object detection models, statistical methods for defect detection, and traditional amplitude thresholding approaches for defect detection in CFRPs were compared. A novel augmentation technique was used to enhance synthetically generated datasets used for ML model training. All approaches were tested on real data obtained from an experimental setup mimicking industrial conditions, with ML models showing improvement over amplitude thresholding and statistical thresholding techniques. The advantages and limitations of all methods are reported and discussed

Advanced non-destructive testing of blade manufacturing defects

In response to the international climate crisis, governments across the globe have set target dates by which they aim to achieve net zero greenhouse gas emissions. These targets range between the years 2040 to 2060 depending on a range of environmental, technological, and political factors at play in each nation. The necessity to de-carbonise the electricity supply is key to this and due to its cost effectiveness, and technological maturity, wind power plays a major role. The size of installed wind capacity will continue to grow exponentially over the coming years thus making the manufacture of wind turbine blades a rapidly growing and developing sector [1]. Non-destructive testing (NDT) is used across a wide range of engineering fields to ensure a final component is defect-free and can be guaranteed to perform as certified in each application. In the framework of blade manufacture, NDT is often in the form of ultrasonic inspections that can identify faults and errors in the early production phase so to prevent failures and reduce the cost of operations and maintenance. This thesis reports on research carried out to develop future ultrasonic NDT techniques applied to composite turbine blades. To that end, three innovative developments to improve the manufacturing environment, resulting in significant overall benefits for clean energy production, are presented. An in-process ultrasonic inspection system, using dry-coupled phased array inspection has been designed and tested for the inspection of Carbon Fibre Reinforced Polymer (CFRP) blade subcomponents. Secondly, novel adaptive methods of ultrasonic phased array operation and data analysis, employed using a new Multi-Aperture (MA), ultrasonic beam transmission and reception strategy, have been shown to increase imaging frame rates, resulting in the ability for increased inspection speeds and/or resolutions. An adaptive and autonomous MA firing sequence generator has been designed, specific to the sample and target defect size, with frame rate increases by a factor of 6.7 reported. Finally, the nature of an ultrasonic wave’s interaction with a composite’s non-homogenous and anisotropic internal structure has been used to determine both the Fibre Volume Fraction (FVF) as well as fibre orientation, in the third major contribution reported in this thesis. Data obtained was used as an effective screening technique to guarantee that these parameters, and thereby the mechanical performance of a composite, fall within the desired range. FVF values determined by this method were typically within 2 % of reference values, measured by a third party using conventional testing methods.In response to the international climate crisis, governments across the globe have set target dates by which they aim to achieve net zero greenhouse gas emissions. These targets range between the years 2040 to 2060 depending on a range of environmental, technological, and political factors at play in each nation. The necessity to de-carbonise the electricity supply is key to this and due to its cost effectiveness, and technological maturity, wind power plays a major role. The size of installed wind capacity will continue to grow exponentially over the coming years thus making the manufacture of wind turbine blades a rapidly growing and developing sector [1]. Non-destructive testing (NDT) is used across a wide range of engineering fields to ensure a final component is defect-free and can be guaranteed to perform as certified in each application. In the framework of blade manufacture, NDT is often in the form of ultrasonic inspections that can identify faults and errors in the early production phase so to prevent failures and reduce the cost of operations and maintenance. This thesis reports on research carried out to develop future ultrasonic NDT techniques applied to composite turbine blades. To that end, three innovative developments to improve the manufacturing environment, resulting in significant overall benefits for clean energy production, are presented. An in-process ultrasonic inspection system, using dry-coupled phased array inspection has been designed and tested for the inspection of Carbon Fibre Reinforced Polymer (CFRP) blade subcomponents. Secondly, novel adaptive methods of ultrasonic phased array operation and data analysis, employed using a new Multi-Aperture (MA), ultrasonic beam transmission and reception strategy, have been shown to increase imaging frame rates, resulting in the ability for increased inspection speeds and/or resolutions. An adaptive and autonomous MA firing sequence generator has been designed, specific to the sample and target defect size, with frame rate increases by a factor of 6.7 reported. Finally, the nature of an ultrasonic wave’s interaction with a composite’s non-homogenous and anisotropic internal structure has been used to determine both the Fibre Volume Fraction (FVF) as well as fibre orientation, in the third major contribution reported in this thesis. Data obtained was used as an effective screening technique to guarantee that these parameters, and thereby the mechanical performance of a composite, fall within the desired range. FVF values determined by this method were typically within 2 % of reference values, measured by a third party using conventional testing methods