DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints

Improving depth resolution of ultrasonic phased array imaging to inspect aerospace composite structures

In this paper, we present challenges and achievements in development and use of a compact ultrasonic Phased Array (PA) module with signal processing and imaging technology for autonomous non-destructive evaluation of composite aerospace structures. We analyse two different sets of ultrasonic scan data, acquired from 5 MHz and 10 MHz PA transducers. Although higher frequency transducers promise higher axial (depth) resolution in PA imaging, we face several signal processing challenges to detect defects in composite specimens at 10 MHz. One of the challenges is the presence of multiple echoes at the boundary of the composite layers called structural noise. Here, we propose a wavelet transform-based algorithm that is able to detect and characterize defects (depth, size, and shape in 3D plots). This algorithm uses a smart thresholding technique based on the extracted statistical mean and standard deviation of the structural noise. Finally, we use the proposed algorithm to detect and characterize defects in a standard calibration specimen and validate the results by comparing to the designed depth information

Quality assessment of bonded primary CFRP structures by means of laser proof testing

The use of adhesive bonding as an assembly technology is still limited because of the absence of NDT method to assess the quality of the adhesion. This work evaluates the state-of-the-art of potential NDT technologies and focuses on laser proof test techniques. The theory of this approach aiming at debonding weak adhesive bond and leaving strong adhesive bond unaffected is introduced. A preparation technique and a characterization strategy for bonded CFRP specimens with defined adhesion levels is presented. Two laser proof test setups are then investigated experimentally. A first test focuses on the determination of threshold energy for debonding of the different adhesive bond states. Further tests (repetition of laser shocks, different laser energy levels, mechanical test after laser shock) are performed to evaluate the effects on the CFRP structures. The main objective is the evaluation of the NDT character of laser proof test. With both laser setups, a debonding intensity threshold was achieved, but not without affecting the CFRP substrates. Ultrasonic inspections and mechanical tests conducted before and after laser shocks are compared to analyze the role of each laser setting in the observations. This study shows the feasibility of the concept with a high potential of improvements for the laser technologies and for approaches towards the industrialization

Eddy current pulsed thermography for non-destructive evaluation of carbon fibre reinforced plastic for wind turbine blades

PhD ThesisThe use of Renewable energy such as wind power has grown rapidly over the past ten years. However, the poor reliability and high lifecycle costs of wind energy can limit power generation. Wind turbine blades suffer from relatively high failure rates resulting in long downtimes. The motivation of this research is to improve the reliability of wind turbine blades via non-destructive evaluation (NDE) for the early warning of faults and condition-based maintenance. Failure in wind turbine blades can be categorised as three types of major defect in carbon fibre reinforced plastic (CFRP), which are cracks, delaminations and impact damages. To detect and characterise those defects in their early stages, this thesis proposes eddy current pulsed thermography (ECPT) NDE method for CFRP-based wind turbine blades. The ECPT system is a redesigned extension of previous work. Directional excitation is applied to overcome the problems of non-homogeneous and anisotropic properties of composites in both numerical and experimental studies. Through the investigation of the multiple-physical phenomena of electromagnetic-thermal interaction, defects can be detected, classified and characterised via numerical simulation and experimental studies. An integrative multiple-physical ECPT system can provide transient thermal responses under eddy current heating inside a sample. It is applied for the measurement and characterisation of different samples. Samples with surface defects such as cracks are detected from hot-spots in thermal images, whereas internal defects, like delamination and impact damage, are detected through thermal or heat flow patterns. For quantitative NDE, defect detection, characterisation and classification are carried out at different levels to deal with various defect locations and fibre textures. Different approaches for different applications are tested and compared via samples with crack, delamination and impact damage. Comprehensive transient feature extraction at the three different levels of the pixel, local area and pattern are developed and implemented with respect to defect location in terms of the thickness and complexity of fibre texture. Three types of defects are detected and classified at those three levels. The transient responses at pixel level, flow patterns at local area level, and principal or independent components at pattern level are derived for defect classification. Features at the pixel and local area levels are extracted in order to gain quantitative information about the defects. Through comparison of the performance of evaluations at those three levels, the pixel level is shown to be good at evaluating surface defects, in particular within uni- directional fibres. Meanwhile the local area level has advantages for detecting deeper defects such as delamination and impact damage, and in specimens with multiple fibre orientations, the pattern level is useful for the separation of defective patterns and fibre texture, as well as in distinguishing multiple defects.Engineering and Physical Sciences Research Council(EPSRC), Frame Programme 7(FP7

Transient thermography for detection of micro-defects in multilayer thin films

Delamination and cracks within the multilayer structure are typical failure modes observed in microelectronic and micro electro mechanical system (MEMS) devices and packages. As destructive detection methods consume large numbers of devices during reliability tests, non-destructive techniques (NDT) are critical for measuring the size and position of internal defects throughout such tests. There are several established NDT methods; however, some of them have significant disadvantages for detecting defects within multilayer structures such as those found in MEMS devices. This thesis presents research into the application of transient infrared thermography as a non-destructive method for detecting and measuring internal defects, such as delamination and cracks, in the multilayer structure of MEMS devices. This technique works through the use of an infrared imaging system to map the changing temperature distribution over the surface of a target object following a sudden change in the boundary conditions, such as the application of a heat source to an external surface. It has previously been utilised in various applications, such as damage assessment in aerospace composites and verification of printed circuit board solder joint manufacture, but little research of its applicability to MEMS structures has previously been reported. In this work, the thermal behaviour of a multilayer structure containing defects was first numerically analysed. A multilayer structure was then successfully modelled using COMSOL finite element analysis (FEA) software with pulse heating on the bottom surface and observing the resulting time varying temperature distribution on the top. The optimum detecting conditions such as the pulse heating energy, pulse duration and heating method were determined and applied in the simulation. The influences of thermal properties of materials, physical dimensions of film, substrate and defect and other factors that will influence the surface temperature gradients were analytically evaluated. Furthermore, a functional relationship between the defect size and the resulting surface temperature was obtained to improve the accuracy of estimating the physical dimensions and location of the internal defect in detection. Corresponding experiments on specimens containing artificially created defects in macro-scale revealed the ability of the thermographic method to detect the internal defect. The precision of the established model was confirmed by contrasting the experimental results and numerical simulations

Cracking assessment in concrete structures by distributed optical fiber

In this paper, a method to obtain crack initiation, location and width in concrete structures subjected to bending and instrumented with an optical backscattered reflectometer (OBR) system is proposed. Continuous strain data with high spatial resolution and accuracy are the main advantages of the OBR system. These characteristics make this structural health monitoring technique a useful tool in early damage detection in important structural problems. In the specific case of reinforced concrete structures, which exhibit cracks even in-service loading, the possibility to obtain strain data with high spatial resolution is a main issue. In this way, this information is of paramount importance concerning the durability and long performance and management of concrete structures. The proposed method is based on the results of a test up to failure carried out on a reinforced concrete slab. Using test data and different crack modeling criteria in concrete structures, simple nonlinear finite element models were elaborated to validate its use in the localization and appraisal of the crack width in the testing slab.Peer ReviewedPostprint (author’s final draft

White paper on the future of plasma science and technology in plastics and textiles

This is the peer reviewed version of the following article: “Uros, C., Walsh, J., Cernák, M., Labay, C., Canal, J.M., Canal, C. (2019) White paper on the future of plasma science and technology in plastics and textiles. Plasma processes and polymers, 16 1 which has been published in final form at [doi: 10.1002/ppap.201700228]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."This white paper considers the future of plasma science and technology related to the manufacturing and modifications of plastics and textiles, summarizing existing efforts and the current state‐of‐art for major topics related to plasma processing techniques. It draws on the frontier of plasma technologies in order to see beyond and identify the grand challenges which we face in the following 5–10 years. To progress and move the frontier forward, the paper highlights the major enabling technologies and topics related to the design of surfaces, coatings and materials with non‐equilibrium plasmas. The aim is to progress the field of plastics and textile production using advanced plasma processing as the key enabling technology which is environmentally friendly, cost efficient, and offers high‐speed processingPeer ReviewedPostprint (author's final draft

In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

Nanoscale Sensing Using Point Defects in Single-Crystal Diamond: Recent Progress on Nitrogen Vacancy Center-Based Sensors

Individual, luminescent point defects in solids so called color centers are atomic-sized quantum systems enabling sensing and imaging with nanoscale spatial resolution. In this overview, we introduce nanoscale sensing based on individual nitrogen vacancy (NV) centers in diamond. We discuss two central challenges of the field: First, the creation of highly-coherent, shallow NV centers less than 10 nm below the surface of single-crystal diamond. Second, the fabrication of tip-like photonic nanostructures that enable efficient fluorescence collection and can be used for scanning probe imaging based on color centers with nanoscale resolution.Comment: Overview paper on sensing with defects in diamond, we focus on creation of shallow NV centers and nanostructures, Final Version published in Crystal