CERES :: Browsing by Author "Addepalli, Sri"

Browsing by Author "Addepalli, Sri"

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Open Access
Assessment of an emerging aerospace manufacturing cluster and its dependence on the mature global clusters.
(Elsevier, 2018-02-08) Luna, José; Addepalli, Sri; Salonitis, Konstantinos; Makatsoris, Harris
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.
Open Access
Automation of knowledge extraction for degradation analysis
(Elsevier, 2023-07-13) Addepalli, Sri; Weyde, Tillman; Namoano, Bernadin; Oyedeji, Oluseyi Ayodeji; Wang, Tiancheng; Erkoyuncu, John Ahmet; Roy, Rajkumar
Degradation analysis relies heavily on capturing degradation data manually and its interpretation using knowledge to deduce an assessment of the health of a component. Health monitoring requires automation of knowledge extraction to improve the analysis, quality and effectiveness over manual degradation analysis. This paper proposes a novel approach to achieve automation by combining natural language processing methods, ontology and a knowledge graph to represent the extracted degradation causality and a rule based decision-making system to enable a continuous learning process. The effectiveness of this approach is demonstrated by using an aero-engine component as a use-case.
Open Access
Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection
(Springer, 2023-01-31) Deng, Kailun; Liu, Haochen; Yang, Lichao; Addepalli, Sri; Zhao, Yifan
With the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation of these materials have become very important and draw significant research attention. During the past 10 years, Artificial Intelligence (AI) technologies have been attractive in this area due to their outstanding ability in complex data analysis tasks. Most current AI-based studies on damage characterisation in this field focus on damage segmentation and depth measurement, which also faces the bottleneck of lacking adequate experimental data for model training. This paper proposes a new framework to understand the relationship between Barely Visible Impact Damage features occurring in typical CFRP laminates to their corresponding controlled drop-test impact energy using a Deep Learning approach. A parametric study consisting of one hundred CFRP laminates with known material specification and identical geometric dimensions were subjected to drop-impact tests using five different impact energy levels. Then Pulsed Thermography was adopted to reveal the subsurface impact damage in these specimens and recorded damage patterns in temporal sequences of thermal images. A convolutional neural network was then employed to train models that aim to classify captured thermal photos into different groups according to their corresponding impact energy levels. Testing results of models trained from different time windows and lengths were evaluated, and the best classification accuracy of 99.75% was achieved. Finally, to increase the transparency of the proposed solution, a salience map is introduced to understand the learning source of the produced models.
Open Access
A coefficient clustering analysis for damage assessment of composites based on pulsed thermographic inspection
(Elsevier, 2016-06-11) Zhao, Yifan; Tinsley, Lawrence; Addepalli, Sri; Mehnen, Jorn; Roy, Rajkumar
This paper introduces a coefficient clustering analysis method to detect and quantitatively measure damage occurring in composite materials using pulsed thermographic inspection. This method is based on fitting a low order polynomial model for temperature decay curves, which (a) provides an enhanced visual confirmation and size measurement of the damage, (b) provides the reference point for sound material for further damage depth measurement, (c) and reduces the burden in computational time. The performance of the proposed method is evaluated through a practical case study with carbon fibre reinforced polymer (CFRP) laminates which were subjected to a drop impact test with varying energy levels. A novel method for reducing an entire thermogram sequence into a single image is introduced, which provides an enhanced visualisation of the damage area.
Open Access
Cognitive digital twin: an approach to improve the maintenance management
(Elsevier, 2022-06-23) D’Amico, Rosario Davide; Erkoyuncu, John Ahmet; Addepalli, Sri; Penver, Steve
Digital twin (DT) technology allows the user to monitor the asset, specifically over the operation and service phase of the life cycle, which is the longest-lasting phase for complex engineering assets. This paper aims to present a thematic review of DTs in terms of the technology used, applications, and limitations specifically in the context of maintenance. This review includes a systematic literature review of 59 articles on semantic digital twins in the maintenance context. Key performance indicators and explanations of the main concepts constituting the DT have been presented. This article contains a description of the evolution of DTs together with their characterisation for maintenance purposes. It provides an ontological approach to develop DT and improve the maintenance management leading to the creation of a structured DT or a Cognitive Twin (CT). Moreover, it points out that using a top-level ontology approach should be the starting point for the creation of CT. Enabling the creation of the digital framework that will break down silos, ensuring a perfect integration in a network of twins’ scenario.
Open Access
Compound uncertainty quantification and aggregation (CUQA) for reliability assessment in industrial maintenance
(MDPI, 2023-05-16) Grenyer, Alex; Erkoyuncu, John Ahmet; Addepalli, Sri; Zhao, Yifan
The mounting increase in the technological complexity of modern engineering systems requires compound uncertainty quantification, from a quantitative and qualitative perspective. This paper presents a Compound Uncertainty Quantification and Aggregation (CUQA) framework to determine compound outputs along with a determination of the greatest uncertainty contribution via global sensitivity analysis. This was validated in two case studies: a bespoke heat exchanger test rig and a simulated turbofan engine. The results demonstrated the effective measurement of compound uncertainty and the individual impact on system reliability. Further work will derive methods to predict uncertainty in-service and the incorporation of the framework with more complex case studies.
Open Access
Conceptual framework of a digital twin to evaluate the degradation status of complex engineering systems
(Elsevier, 2019-02-18) D’Amico, Davide; Ekoyuncu, John; Addepalli, Sri; Smith, Christopher; Keedwell, Ed; Sibson, Jim; Penver, Steven
Degradation of engineering structures and systems often comes in the form of wear, corrosion, and fracture. These factors progressively bring about performance decay, until the system fails to function satisfactorily. Complex engineering systems (CES) need regular maintenance throughout their operation, along with continuous checks on the health status of components and equipment, within regulatory frameworks. A digital twin paradigm is able to continuously monitor CES, to use this data to update a virtual model of the CES and thus make real-time predictions about future functionality. The purpose of this paper is to introduce a conceptual framework of a digital twin to be applied within the degradation assessment process of a CES. The digital twin framework will aim to gather digital data through a network to plan through-life requirements of the system. Data-driven approaches can be used to predict how degradation evolves over time. The proposed framework will help the decision-making process to better handle maintenance operations and achieve targets such as asset availability and minimised cost.
Open Access
A confidence map based damage assessment approach using pulsed thermographic inspection
(Elsevier, 2017-10-07) Zhao, Yifan; Addepalli, Sri; Sirikham, Adisorn; Roy, Rajkumar
In the context of non-destructive testing, quantification of uncertainty caused by various factors such as inspection technique, testing environment and the operator is important and challenge. This paper introduces a concept of contour-based confidence map and an application framework for pulsed thermography that offers enhanced flexibility and reliability of inspection. This approach has been successfully applied to detect three flat-bottom holes of diameter 32, 16 and 8 mm drilled onto a 5 mm thick aluminium plate with a high accuracy of damage detection (R > 0.97). Its suitability and effectiveness in assessing impact damage occurring in composites have also been demonstrated.
Open Access
A deep-learning-based approach for aircraft engine defect detection
(MDPI, 2023-02-01) Upadhyay, Anurag; Li, Jun; King, Steve; Addepalli, Sri
Borescope inspection is a labour-intensive process used to find defects in aircraft engines that contain areas not visible during a general visual inspection. The outcome of the process largely depends on the judgment of the maintenance professionals who perform it. This research develops a novel deep learning framework for automated borescope inspection. In the framework, a customised U-Net architecture is developed to detect the defects on high-pressure compressor blades. Since motion blur is introduced in some images while the blades are rotated during the inspection, a hybrid motion deblurring method for image sharpening and denoising is applied to remove the effect based on classic computer vision techniques in combination with a customised GAN model. The framework also addresses the data imbalance, small size of the defects and data availability issues in part by testing different loss functions and generating synthetic images using a customised generative adversarial net (GAN) model, respectively. The results obtained from the implementation of the deep learning framework achieve precisions and recalls of over 90%. The hybrid model for motion deblurring results in a 10× improvement in image quality. However, the framework only achieves modest success with particular loss functions for very small sizes of defects. The future study will focus on very small defects detection and extend the deep learning framework to general borescope inspection.
Open Access
Degradation study of heat exchangers
(Elsevier, 2015-10-27) Addepalli, Sri; Eiroa, David; Lieotrakool, Suphansa; Francois, Anne-Laure; Guisset, Juliette; Sanjaime, David; Kazarian, Michele; Duda, Julia; Roy, Rajkumar; Phillips, Paul
Abstract This study mainly deals with the evaluation of various degradation mechanisms that heat exchangers are susceptible to with an aim of evaluating future design requirements. A heat exchanger is a heat management system that uses fluids to transfer heat from one medium to the other; the most common types of fluids being air, water, oil or specialised coolant mixtures. As part of this study a failure analysis of heat exchangers was carried out on selected heat exchangers used in both aerospace and automotive sectors. This study was then extended to designing test-rigs supporting two types of heat exchangers. For this study, an air-to-air and an oil-to-air heat exchanger test rigs were designed. Temperature, pressure and flow sensors were introduced in the test rig designs to monitor the flow characteristics in order to determine if degradations occurring as a result of operation have an impact on them. As part of the initial evaluation both visual inspection and pulsed thermography inspection were selected as suitable inspection methods to evaluate their in-service condition. Some heat exchanger units where then subjected to accelerated corrosion tests and their performance was monitored using scanning electron microscopy (SEM) measurements. The outcomes of the study presented in this paper confirm the suitability and adaptability of thermography in detecting degradations occurring in heat exchangers.
Open Access
Design for Digitally Enabled Industrial Product-Services Systems
(Elsevier, 2023-07-08) Erkoyuncu, John Ahmet; Farsi, Maryam; Addepalli, Sri; Latsou, Christina
Planning the life cycle of industrial product-service systems (IPS2) is highly challenging due to uncertainties experienced in predicting supply (e.g. spares) and demand (e.g. availability) related factors. Whilst digitalisation offers numerous exciting avenues, industry is finding it challenging to realise the potential benefits. This paper focuses on how to design the set of digital technologies and methodologies that serve as enabling capabilities to optimise value across the life cycle. This involves offering a step by step process to compare alternative improvement opportunities (e.g. data modelling, digital twins) with the justification to support investment decisions. The systematic design methodology is tested on an aerospace component, demonstrating the added value of digitally enabled IPS2.
Open Access
Designing a semantic based common taxonomy of mechanical component degradation to enable maintenance digitalisation
(Elsevier, 2023-07-08) Addepalli, Sri; Namoano, Bernadin; Oyedeji, Oluseyi Ayodeji; Farsi, Maryam; Erkoyuncu, John Ahmet
Digital data management and enterprise systems have become key to support the digitalisation of maintenance activities. With traditional maintenance activities still striving for efficiencies, platforms such as the natural language processing (NLP) are supporting industries to mine textural data, not just extracting degradation terminologies but providing the maintainer with holistic insights on the degradation process. Traditionally, the degradation analysis, the first step in maintenance, is a manual process for defect characterisation, followed by failure investigation and a remaining useful life estimation. To enable digitalisation, transfer of human cognitive decision making from the physical world to the digital world is key. This paper enables this cognitive knowledge transfer through the design of a common degradation taxonomy and extracting terminology relationships to produce degradation causality with an NLP knowledge extraction approach. Further, this paper proposes and demonstrates a framework to present the data in the form of a knowledge graph populated using an application-level ontology. Use cases in the aerospace context have been used to show the power of the NLP and conceptual journey into the digitalisation of maintenance.
Open Access
Detectability evaluation of attributes anomaly for electronic components using pulsed thermography
(Elsevier, 2020-09-16) Liu, Haochen; Tinsley, Lawrence; Addepalli, Sri; Liu, Xiaochen; Starr, Andrew; Zhao, Yifan
Counterfeit Electronic Components (CECs) pose a serious threat to all intellectual properties and bring fatal failure to the key industrial systems. This paper initiates the exploration of the prospect of CEC detection using pulsed thermography (PT) by proposing a detectability evaluation method for material and structural anomalies in CECs. Firstly, a numerical Finite Element Modelling (FEM) simulation approach of CEC detection using PT was established to predict the thermal response of electronic components under the heat excitation. Then, by experimental validation, FEM simulates multiple models with attribute deviations in mould compound conductivity, mould compound volumetric heat capacity and die size respectively considering experimental noise. Secondly, based on principal components analysis (PCA), the gradients of the 1st and 2nd principal components are extracted and identified as two promising classification features of distinguishing the deviation models. Thirdly, a supervised machine learning-based method was applied to classify the features to identify the range of detectability. By defining the 90% of classification accuracy as the detectable threshold, the detectability ranges of deviation in three attributes have been quantitively evaluated respectively. The promising results suggest that PT can act as a concise, operable and cost-efficient tool for CECs screening which has the potential to be embedded in the initial large scale screening stage for anti-counterfeit.
Open Access
Detecting failure of a material handling system through a cognitive twin
(Elsevier, 2022-10-26) D'Amico, Davide R.; Sarkar, A.; Karray, H.; Addepalli, Sri; Erkoyuncu, John Ahmet
This paper describes a methodology for developing a digital twin (DT) based on a rich semantic model and principles of system engineering. The aim is to provide a general model of digital twins (DT) that can improve decision making based on semantic reasoning on real-time system monitoring. The methodology has been tested on a laboratory pilot plant that acts as a material handling system. The key contribution of this research is to propose a generic information model for DT using foundational ontology and principles of systems engineering. The efficacy of the proposed methodology is demonstrated by the automatic detection of a component level failure using semantic reasoning.
Open Access
Effect of extrusion and compression moulding on the thermal properties of nylon-6/silica aerogel composites
(SAGE, 2017-10-03) Krishnaswamy, Suryanarayanan; Tinsley, Lawrence; Marchante, Veronica; Addepalli, Sri; Huang, Zhaorong; Abhyankar, Hrushikesh
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.
Open Access
Effect of extrusion and compression moulding on the thermal properties of Nylon-6/Silica Aerogel Composites: Experimental Data
(Cranfield University, 2017-10-05 16:38) Krishnaswamy, Surya; Tinsley, Lawrence; Marchante, Veronica; Addepalli, Sri; Huang, Zhaorong; Abhyankar, Hrushikesh
The paper presents the effect of a lower extrusion speed and compression moulding on the thermal properties of PA-6/Aerogel composite. SEM/EDX and optical microscope images showed that although most of the aerogel was destroyed during extrusion at 65 rpm, extrusion at 5 rpm showed better retention of the aerogel structure. However, when subjected to moulding in a compression press, both composites suffered significant damage. Nevertheless, the final thermal conductivity and damage coefficient values did show an improvement in the thermal insulation properties of the samples extruded at 5 rpm compared to the samples extruded at 65 rpm and the virgin polymer (PA-6) with the former losing around 33% of the structure of the aerogel particles compared to 41% for the later.
Open Access
Global motion based video super-resolution reconstruction using discrete wavelet transform
(Springer, 2018-04-11) Witwit, Wasnaa; Zhao, Yifan; Jenkins, Karl W.; Addepalli, Sri
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.
Open Access
Identifying challenges in quantifying uncertainty: case study in infrared thermography
(Elsevier, 2018-07-03) Grenyer, Alex; Addepalli, Sri; Zhao, Yifan; Oakey, Luke; Erkoyuncu, John Ahmet; Roy, Rajkumar
Complex engineering systems present a wealth of uncertainties concerning aspects ranging from performance measurements to maintainability and through-life characteristics. A quantifiable understanding of these uncertainties is vital to system optimisation and plays a key role in decision-making processes for manufacturing organisations worldwide; impacting profit, product availability and manufacturing efficiency. The aim of this paper is to examine challenges and complications that arise when quantifying uncertainties in complex engineering systems that rely on expert opinion. A thermographic inspection system is utilised as a use case. Contractor-client and supervisor-maintainer relationships are examined. Key challenges highlighted involve accurate depiction of error margins and corresponding uncertainties of components where data is only heuristically obtainable, as well as the influence of environmental conditions and skill of the maintainer.
Open Access
‘In-situ’Inspection Technologies: Trends in Degradation Assessment and Associated Technologies
(Elsevier, 2017-03-02) Addepalli, Sri; Roy, Rajkumar; Axinte, D.; Mehen, Jorn
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.
Open Access
Industrial insights on digital twins in manufacturing: application landscape, current practices, and future needs
(MDPI, 2023-06-29) D'Amico, Davide R.; Addepalli, Sri; Erkoyuncu, John Ahmet
The digital twin (DT) research field is experiencing rapid expansion; yet, the research on industrial practices in this area remains poorly understood. This paper aims to address this knowledge gap by sharing feedback and future requirements from the manufacturing industry. The methodology employed in this study involves an examination of a survey that received 99 responses and interviews with 14 experts from 10 prominent UK organisations, most of which are involved in the defence industry in the UK. The survey and interviews explored topics such as DT design, return on investment, drivers, inhibitors, and future directions for DT development in manufacturing. This study’s findings indicate that DTs should possess characteristics such as adaptability, scalability, interoperability, and the ability to support assets throughout their entire life cycle. On average, completed DT projects reach the breakeven point in less than two years. The primary motivators behind DT development were identified to be autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability. Meanwhile, the main obstacles include a lack of expertise, funding, and interoperability. This study concludes that the federation of twins and a paradigm shift in industrial thinking are essential components for the future of DT development.