Study of carbon fiber reinforced polymers technology and market

The growing knowledge and awareness of the environmental condition have driven to governments to implement restrictions on energy use, levels of efficiency and resources use, aiming for higher sustainability targets. Transportation an energy sectors have found on CFRP (Carbon Fiber Reinforced Polymers) technology a solution to achieve the highest levels of mechanical performance under low weights and is therefore a key enabling technology that makes possible the electric and hydrogen vehicles, the wind energy production and reduced levels of air transport emissions among others. This Master Thesis comprehends the study of the CFRP technological from a technical and market perspective pursuing the holistic comprehension of the system. This analysis will employ bibliographic references, quantitative and qualitative analysis and graphical visualizations to decompose complexity and to compile key data for the reader. For market forecasting time series forecasting techniques will be employed. At technical level the study covers the definition of the CFRP systems and subsystems/components, their characterization, production, manufacturing, the state of the art on the design methodology, recyclability and life-cycle assessment. This study will also detail current applications of the CFRP technology on multiple products. The market study involves the analysis of the European political context and regulations affecting CFRP adoption, the stakeholders of the system, the cost analysis of the CFRP, market quantification and the future market forecasting. The study produced has compiled relevant information from multiple sources in a summarized, relational and organized way, it has also produced useful datasets of CFRP technology and market; qualitative and quantitative analysis together with data visualization techniques have revealed multiple insights. The lifecycle assessment has quantified the environmental effects of CFRP production. A forecast of CFRP sells, prices and market value has been produced. Studies performed have revealed CFRP as an outstanding technical material at an advanced level of Technological Readiness; with well stablished manufacturing methods, characterization, mechanical modelization, failure modes understanding, and with available commercial software supporting design workflows and simulation. Technological developments and downwards prices allow for companies to change classical materials such as steel with CFRP, not only as a way to achieve higher levels of performance but also as a way to produce products with less parts in a lean and flexible way, it entails, therefore a paradigm shift; CFRP environmental impact is conditioned by high levels of energy needed to produce fibers and difficulties to recover fibers from thermoset matrices, to achieve environmental improvements versus other engineering materials more than one life cycle is needed, to achieve that, new methods and approaches are in development such as Supercritical Water or design for End Of Life strategies. CFRP and Carbon Fiber markets are supported as a key enabling technology to achieve the efficiency and clean energy targets imposed by Europe being key market drivers on automotive, aerospace and energetic markets; CFRP cost structure find it highest contributions on the energy and labor needed to carbon PAN (Polyacrylonitrile) based fibers, the latter costs are driven by petroleum feedstock prices; time series forecasting reveals CF sells to double in the 2022-2027 perio

Hybrid Unsupervised Exploratory Plots: A Case Study of Analysing Foreign Direct Investment

The curse of dimensionality has been an open issue for many years and still is, as finding nonobvious and previously unknown patterns in ever-increasing amounts of high-dimensional data is not an easy task. Advancing in descriptive data analysis, the present paper proposes Hybrid Unsupervised Exploratory Plots (HUEPs) as a new visualization technique to combine the outputs of Exploratory Projection Pursuit and Clustering methods in a novel and informative way. As a case study, HUEPs are validated in a real-world context for analysing the internationalization strategy of companies, by taking into account bilateral distance between home and host countries. As a multifaceted concept, distance encompasses multiple dimensions. Together with data from both the countries and the companies, various psychic distances are analysed by means of HUEPs, to gain deep knowledge of the internationalization strategy of large Spanish companies. Informative visualizations are obtained from the analysed dataset, leading to useful business implications and decision making.The work was conducted during Álvaro Herrero’s research stay at KEDGE Business School in Bordeaux (France). Some results of this ongoing research, from the same dataset, have been presented in the 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, as a paper entitled “Visualizing Industrial Development Distance to Better Understand Internationalization of Spanish Companies”

Hybridization of machine learning for advanced manufacturing

Tesis por compendio de publicacioines[ES] En el contexto de la industria, hoy por hoy, los términos “Fabricación Avanzada”, “Industria 4.0” y “Fábrica Inteligente” están convirtiéndose en una realidad. Las empresas industriales buscan ser más competitivas, ya sea en costes, tiempo, consumo de materias primas, energía, etc. Se busca ser eficiente en todos los ámbitos y además ser sostenible. El futuro de muchas compañías depende de su grado de adaptación a los cambios y su capacidad de innovación. Los consumidores son cada vez más exigentes, buscando productos personalizados y específicos con alta calidad, a un bajo coste y no contaminantes. Por todo ello, las empresas industriales implantan innovaciones tecnológicas para conseguirlo. Entre estas innovaciones tecnológicas están la ya mencionada Fabricación Avanzada (Advanced Manufacturing) y el Machine Learning (ML). En estos campos se enmarca el presente trabajo de investigación, en el que se han concebido y aplicado soluciones inteligentes híbridas que combinan diversas técnicas de ML para resolver problemas en el campo de la industria manufacturera. Se han aplicado técnicas inteligentes tales como Redes Neuronales Artificiales (RNA), algoritmos genéticos multiobjetivo, métodos proyeccionistas para la reducción de la dimensionalidad, técnicas de agrupamiento o clustering, etc. También se han utilizado técnicas de Identificación de Sistemas con el propósito de obtener el modelo matemático que representa mejor el sistema real bajo estudio. Se han hibridado diversas técnicas con el propósito de construir soluciones más robustas y fiables. Combinando técnicas de ML específicas se crean sistemas más complejos y con una mayor capacidad de representación/solución. Estos sistemas utilizan datos y el conocimiento sobre estos para resolver problemas. Las soluciones propuestas buscan solucionar problemas complejos del mundo real y de un amplio espectro, manejando aspectos como la incertidumbre, la falta de precisión, la alta dimensionalidad, etc. La presente tesis cubre varios casos de estudio reales, en los que se han aplicado diversas técnicas de ML a distintas problemáticas del campo de la industria manufacturera. Los casos de estudio reales de la industria en los que se ha trabajado, con cuatro conjuntos de datos diferentes, se corresponden con: • Proceso de fresado dental de alta precisión, de la empresa Estudio Previo SL. • Análisis de datos para el mantenimiento predictivo de una empresa del sector de la automoción, como es la multinacional Grupo Antolin. Adicionalmente se ha colaborado con el grupo de investigación GICAP de la Universidad de Burgos y con el centro tecnológico ITCL en los casos de estudio que forman parte de esta tesis y otros relacionados. Las diferentes hibridaciones de técnicas de ML desarrolladas han sido aplicadas y validadas con conjuntos de datos reales y originales, en colaboración con empresas industriales o centros de fresado, permitiendo resolver problemas actuales y complejos. De esta manera, el trabajo realizado no ha tenido sólo un enfoque teórico, sino que se ha aplicado de modo práctico permitiendo que las empresas industriales puedan mejorar sus procesos, ahorrar en costes y tiempo, contaminar menos, etc. Los satisfactorios resultados obtenidos apuntan hacia la utilidad y aportación que las técnicas de ML pueden realizar en el campo de la Fabricación Avanzada

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

An integrated approach to value chain analysis of end of life aircraft treatment

Dans cette thèse, on propose une approche holistique pour l’analyse, la modélisation et l’optimisation des performances de la chaîne de valeur pour le traitement des avions en fin de vie (FdV). Les recherches réalisées ont débouché sur onze importantes contributions. Dans la première contribution, on traite du contexte, de la complexité, de la diversité et des défis du recyclage d’avions en FdV. La seconde contribution traite du problème de la prédiction du nombre de retraits d’avions et propose une approche intégrée pour l’estimation de ce nombre de retraits. Le troisième et le quatrième articles visent à identifier les parties prenantes, les valeurs perçues par chaque partenaire et indiquent comment cette valeur peut affecter les décisions au stade de la conception. Les considérations relatives à la conception et à la fabrication ont donné lieu à quatre contributions importantes. La cinquième contribution traite des défis et opportunités pouvant résulter de l’application des concepts de la chaîne logistique verte, pour les manufacturiers d’avions. Dans la sixième contribution, un outil d’aide à la décision a été développé pour choisir la stratégie verte qui optimise les performances globales de de toute la chaîne de valeur en tenant compte des priorités et contraintes de chaque partenaire. Dans la septième contribution, un modèle mathématique est proposé pour analyser le choix stratégique des manufacturiers en réponse aux directives en matière de FdV de produits comme le résultat des interactions des compétiteurs dans le marché. La huitième contribution porte sur les travaux réalisés dans le cadre d’un stage chez le constructeur d’avions, Bombardier. Cette dernière traite de l’apport de « l’analyse du cycle de vie » au stade de la conception d’avions. La neuvième contribution introduit une méthodologie d’analyse de la chaîne de valeur dans un contexte de développement durable. Finalement, les dixième et onzième contributions proposent une approche holistique pour le traitement des avions en FdV en intégrant les concepts du « lean », du développement durable et des contraintes et opportunités inhérentes à la mondialisation des affaires. Un modèle d’optimisation intégrant les modèles d’affaires, les stratégies de désassemblage et les structures du réseau qui influencent l’efficacité, la stabilité et l’agilité du réseau de récupération est proposé. Les données requises pour exploiter le modèle sont indiquées dans l’article. Mots-clés: Fin de vie des avions, analyse de la chaîne de valeurs, développement durable, intervenants.The number of aircrafts at the end of life (EOL) is continuously increasing. Dealing with retired aircrafts considering the environmental, social and economic impacts is becoming an emerging problem in the aviation industry in near future. This thesis seeks to develop a holistic approach in order to analyze the value chain of EOL aircraft treatment in the context of sustainable development. The performed researches have led to eleven main contributions. In the first contribution, the complexity and diversity of the EOL aircraft recycling including the challenges and problem context are discussed. The second contribution addresses the challenges for estimation of retired aircrafts and proposes an integrated approach for prediction of EOL aircrafts. The third and fourth contributions aim to identify the players involved in EOL recycling context, values perceived by different shareholders and formulate that how such value can affect design decisions. Design stage consideration and manufacture’s issues are discussed and have led to four main contributions. The fifth contribution addresses the opportunities and challenges of applying green supply chain for aircraft manufacturers. In the sixth contribution, a decision tool is developed to aid manufactures in early stage of design for their green strategy choices. In the seventh contribution, a mathematical model is developed in order to analyze the strategic choice of manufacturers in response to EOL directives as the result of the interaction of competitors in the market. An internship project has been also performed in Bombardier and led to the eighth contribution, which addresses life cycle approach and incorporating the sustainability in early stage of design of aircraft. The ninth contribution introduces a methodology for analyzing the value chain in the context of sustainable development. Finally, the tenth and eleventh contributions propose a holistic approach to EOL aircraft treatment considering lean principals, sustainable development, and global business environment. An optimization model is developed to support decision making in both strategic and managerial level. The analytical approaches, decision tools and step by step guidelines proposed in this thesis will aid decision makers to identify appropriate strategies for the EOL aircraft treatment in the sustainable development context. Keywords: End of life aircraft, value chain analysis, sustainable development, stakeholders

Innovation for the digitization process of the AECO sector

In this article I will develop the following points: 1. The imagination is structurally technological as it is entangled with historically dominant technologies; 2. These orientate the reconfiguration of its multimodality, i.e. the fact that the imagination does not work only on the optical and visual level but extends its action to all of our sensorimotor system; 3. How this re-modeling is influenced by digital technologies remains to be clarified; 4. In this problematic field there are two opposing lines of development, which I will treat with some examples

A proposed framework for supply chain analytics using customer data

Thesis (PhD (Business Management))--University of Pretoria, 2022.The COVID-19 pandemic and recent geopolitical events have called for a need to re-evaluate methodologies for Supply Chain Risk management. Significant investment in supply chain technology has resulted in data being generated throughout the value chain. Customer data, specifically, is of interest in order to establish customer-centricity and an enhanced customer journey. However, the transformation of this data to insight is not obvious for some organisations. Forecasting models are typically used to inform decision-making, mitigate risks and enlighten policymakers. This thesis aims to address this challenge by proposing a set of capabilities that will enhance the integration of the supply chain network to its customer data. Given this context, two methodologies were used to address the research problem; (i) multinational petrochemicals company was considered for our case study and a web-based survey was distributed among key stakeholders at their head offices in South Africa. A structured equation model (SEM) was constructed to empirically test the proposed relationships among the constructs, specifically: People, Process and Technology capabilities; (ii) The macro-economic factors that drive customer demand also considered. Increasing crude oil prices have increased logistics costs and have incited the deglobalization of supply chain operations. A novel petroleum forecasting model is also proposed, particularly focusing on the forecasting on South Africa’s petrol and diesel consumptions. The model uses indices for Brent crude oil price (ZAR), Gross Domestic Product (GDP), Rand to Dollar exchange rate, Consumer Confidence Index (CCI) and Business Confidence Index (BCI) data as input data. Overall, this study suggests that in order to effectively serve their customers, organisations need to establish a culture of customer centricity that is underpinned by appropriate supply chain analytics techniques. The predictive model further highlights the need to establish the relationship between the organisation’s supply chain and micro and macro-economic drivers.Business ManagementPhD (Business Management)Unrestricte