Service Knowledge Capture and Reuse

The keynote will start with the need for service knowledge capture and reuse for industrial product-service systems. A novel approach to capture the service damage knowledge about individual component will be presented with experimental results. The technique uses active thermography and image processing approaches for the assessment. The paper will also give an overview of other non-destructive inspection techniques for service damage assessment. A robotic system will be described to automate the damage image capture. The keynote will then propose ways to reuse the knowledge to predict remaining life of the component and feedback to design and manufacturing

Assessment of an emerging aerospace manufacturing cluster and its dependence on the mature global clusters.

This study assesses the aerospace manufacturing industry of an emerging cluster by using Porter’s Diamond model. The assessment is used to identify its dependence from mature global markets and the elements that are behind its dependence. In the first part of the paper, an introduction to the current landscape, the market trends and challenges of the aerospace industry is presented. Then, a case study of an emerging aerospace manufacturing cluster is undertaken: the case of Mexico. The results indicated that the aerospace industry in this country has positively developed, however, it is still highly dependent on mature global markets. Recently launched strategies and programs from the government, evidence that it is aiming to impulse the growth of the aerospace industry and to reduce its dependence on foreign markets

‘In-situ’Inspection Technologies: Trends in Degradation Assessment and Associated Technologies

The advent of advanced, innovative and complex engineered systems has established new technologies that are far more superior and perform well even in harsh environments. It is well established that such next generation systems need to be maintained regularly to prevent any catastrophic failure as a result of regular wear and tear. Non-destructive and structural monitoring technologies have been supporting maintenance activities for over a century and industries still continue to rely on such technologies for effective degradation assessment. Maintenance ‘in-situ’ has been adopted for decades where the health of system or component needs to be inspected in its natural environment, especially those safety critical systems that need in-field inspection to determine its health. This paper presents selective case studies adopted in the area of damage assessment that qualify for both field and ‘in-situ’ inspection. The future directions in the applicability of traditional and advanced inspection techniques to inspect multiple materials and in the area of inaccessible area degradation assessment have also been presented as part of this study

The effect of thermal heat treatment on the corrosion performance of some commercial and advanced magnesium alloys.

"The research titled "The effect of thermal heat treatment on the corrosion performance of some commercial and advanced magnesium alloys" deals with the use of heat treatment to change the characteristics of some commercial magnesium alloys. The alloys were first subjected to homogeneous solution treatment and quenched in cold water in order to retain the homogeneous state of the material. Then the samples were aged at temperatures as suggested in the literature for different time periods. Their corrosion performance was then assessed using Time lapse photography, Hydrogen evolution experiment and Scanning Vibrating Electrode Technique (SVET). The data obtained from the time lapse photography was assessed using Sigma plot to characterize the corroded rate in terms of area (m.;2) over a period of time. The bulk corrosion rates by the amount ofhydrogen released was estimated volumetrically over a period of time. Finally the data obtained form the SVET analysis was assessed using Surfer to acquire the local current density rates due to corrosion. From the current density data, the approximate loss of material during SVET was estimated quantitatively. This thesis compares three different magnesium alloys, AZ31, AZ91 and Elektron 21 (E21). It was noticed that heat treatment changed the microstructural characteristics of the alloys which in turn affected the corrosion performance of those alloys. The results show that solution treatment was preferred for AZ31 and Elektron 21 alloy and age hardening for AZ91 alloy. It was also noticed that all the results obtained using various experimental techniques were similar to each other.

A critical investigation of the use of infrared thermography in determining the condition of composite materials

Since the introduction of synthetic composite materials as primary airframe structures in aircrafts, especially the Carbon Fibre Reinforced Polymers (CFRP) in the late 1990’s, there has been increased use of these advanced materials that have completely replaced key metallic parts of the aircraft contributing to overall reduction in the weight of the aircraft. These innovative materials are increasingly preferred due to their material properties and better strength-to-weight ratio offering not just weight savings but increased resistance to issues such as abrasion and corrosion. As these composite materials are non-metallic in nature, their behaviour especially in the presence of defects and damage is less understood as they do not follow properties exhibited by metals and their alloy systems. This study thus focusses on establishing methods that could detect these defects and damage in a non-destructive manner such that the inspection systems do not cause further damage to the component. This study is primarily experimental in nature and has been presented in two parts. The first section looks at establishing pulsed thermography as a key technique capable of detecting sub-surface defects and its applicability to detect them. This has been presented by inspecting field representative samples and by introducing commonly occurring materials as inserts during the layup stage of the CFRP at controlled depths to determine the detection capability of the system. The second part of the work presented a parametric low-energy impact study where laminates were subject to modified Charpy and ballistic testing to create barely visible impact damage (BVID). The damaged parts were then subjected to inspection using techniques such as pulsed thermography, thermoelastic stress analysis (TSA), immersion ultrasonic testing, microscopy and laser doppler vibrometer (LDV). The aim was to establish TSA method as an alternative tool to detect surface breaking damage. It was found that pulsed thermography, though capable of detecting subsurface damage, was less sensitive to near surface damage. Further, it was noticed that the TSA method showed a positive response when it came to detecting surface breaking damage created during ballistic impact, thus establishing the technique as an in-situ technique

Global motion based video super-resolution reconstruction using discrete wavelet transform

Different from the existing super-resolution (SR) reconstruction approaches working under either the frequency-domain or the spatial- domain, this paper proposes an improved video SR approach based on both frequency and spatial-domains to improve the spatial resolution and recover the noiseless high-frequency components of the observed noisy low-resolution video sequences with global motion. An iterative planar motion estimation algorithm followed by a structure-adaptive normalised convolution reconstruction method are applied to produce the estimated low-frequency sub-band. The discrete wavelet transform process is employed to decompose the input low-resolution reference frame into four sub-bands, and then the new edge-directed interpolation method is used to interpolate each of the high-frequency sub-bands. The novelty of this algorithm is the introduction and integration of a nonlinear soft thresholding process to filter the estimated high-frequency sub-bands in order to better preserve the edges and remove potential noise. Another novelty of this algorithm is to provide flexibility with various motion levels, noise levels, wavelet functions, and the number of used low-resolution frames. The performance of the proposed method has been tested on three well-known videos. Both visual and quantitative results demonstrate the high performance and improved flexibility of the proposed technique over the conventional interpolation and the state-of-the-art video SR techniques in the wavelet- domain

The spatial resolution enhancement for a thermogram enabled by controlled sub-pixel movements

The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial resolution of the thermal imager. Using a high-end camera to achieve high spatial resolution can be costly or infeasible due to a high sample rate required. Furthermore, the system miniaturisation becomes an inevitable trend with the continuous development of Internet of Things and their suitability to in-situ inspection scenarios. However, a miniaturised sensor usually suffers a low spatial resolution. Addressing this challenge, the paper reports a novel Spatial Resolution Enhancement for a Thermogram (SRE4T) system to significantly improve the spatial resolution without upgrading the sensor. A high-resolution thermal image is reconstructed by fusing a sequence of low-resolution images with sub-pixel movements. To achieve the best image quality, instead of benefiting from natural movements of existing studies, this paper proposes to use a high-resolution xy translation stage to produce a sequence of controlled sub-pixel movements. The performance of the proposed system was tested on both high-end and low-end thermal imagers. Both visual and quantitative results successfully demonstrated the considerable improvement of the quality of thermal images (up to 30.5% improvement of peak signal to noise ratio). This technique allows improving the measurement accuracy of thermography inspection without upgrading sensors. It also has the potential to be applied on other imaging systems

Practical options for adopting recurrent neural network and its variants on remaining useful life prediction

The remaining useful life (RUL) of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators. Recently, different deep learning (DL) techniques have been used for RUL prediction and achieved great success. Because the data is often time-sequential, recurrent neural network (RNN) has attracted significant interests due to its efficiency in dealing with such data. This paper systematically reviews RNN and its variants for RUL prediction, with a specific focus on understanding how different components (e.g., types of optimisers and activation functions) or parameters (e.g., sequence length, neuron quantities) affect their performance. After that, a case study using the well-studied NASA’s C-MAPSS dataset is presented to quantitatively evaluate the influence of various state-of-the-art RNN structures on the RUL prediction performance. The result suggests that the variant methods usually perform better than the original RNN, and among which, Bi-directional Long Short-Term Memory generally has the best performance in terms of stability, precision and accuracy. Certain model structures may fail to produce valid RUL prediction result due to the gradient vanishing or gradient exploring problem if the parameters are not chosen appropriately. It is concluded that parameter tuning is a crucial step to achieve optimal prediction performance

Recurrent neural networks and its variants in Remaining Useful Life prediction

Data-driven techniques, especially artificial intelligence (AI) based deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector because of the rapid growth of the industrial Internet of Things (IoT) and Big Data. Tremendous researches of DL techniques have been applied in machine health monitoring, but still very limited works focus on the application of DL on the Remaining Useful Life (RUL) prediction. Precise RUL prediction can significantly improve the reliability and operational safety of industrial components or systems, avoid fatal breakdown and reduce the maintenance costs. This paper reviews and compares the state-of-the-art DL approaches for RUL prediction focusing on Recurrent Neural Networks (RNN) and its variants. It has been observed from the results for a publicly available dataset that Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks outperform the basic RNNs, and the number of the network layers affects the performance of the prediction

Effect of extrusion and compression moulding on the thermal properties of nylon-6/silica aerogel composites

The article presents the effect of a lower extrusion speed and compression moulding processes on the thermal properties of polyamide 6 (PA-6)/aerogel composite. Scanning electron and optical microscope images showed that although most of the aerogel was destroyed during extrusion at 65 r/min, extrusion at 5 r/min showed a better retention of the aerogel structure. However, when subjected to moulding in a compression press, both composites extruded at different speeds suffered significant damage. Nevertheless, the extruded samples did show a lower thermal conductivity compared to the virgin polymer. Further, it was observed that the sample extruded at 5 r/min had a lower damage coefficient value with an overall loss of around 33% to the aerogel structure when compared to the material extruded at 65 r/min, which endured a structural loss of 41% to the aerogel in it