A Cloud-based Framework for Shop Floor Big Data Management and Elastic Computing Analytics

Advanced digitalization together with the rise of disruptive Internet technologies are key enablers of a fundamental paradigm shift observed in industrial production. This is known as the fourth industrial revolution (Industry 4.0) which proposes the integration of the new generation of ICT solutions for the monitoring, adaptation, simulation, and optimisation of factories. With the democratization of sensors and actuators, factories and machine tools can now be sensorized and the data generated by these devices can be exploited, for instance, to optimise the utilization of the machines as well as their operation and maintenance. However, analyzing the vast amount of generated data is resource demanding both in terms of computing power and network bandwidth, thus requiring highly scalable solutions. This paper presents a novel big data approach and analytics framework for the management and analysis of machine generated data in the cloud. It brings together standard open source technologies and the exploitation of elastic computing, which, as a whole, can be adapted to and deployed on different cloud computing platforms. This enables reducing infrastructure costs, minimizing deployment difficulty and providing on-demand access to a virtually infinite set of computing power, storage and network resources.H202

How artificial intelligence can be used to improve lean manufacturing and production processes A case study of Hennig Olsen

The implementation of Lean and Artificial Intelligence has demonstrated a positive correlation across different industries. By integrating AI techniques, the efficiency and effectiveness of Lean processes can be enhanced. The combination of Lean and AI contributes to improved decision-making, increased productivity, and reduced waste. Moreover, AI can identify and rectify process errors, enabling streamlined and more efficient operations. In 2014, Hennig Olsen initiated the implementation of lean thinking, which yielded mixed results initially. However, they decided to adopt lean principles according to their specific requirements, leading to significantly improved outcomes. With the rapid advancement of technology, Hennig Olsen ventured into experimenting with artificial intelligence, particularly in the realm of vision control, starting in 2019. Subsequently, they have consistently embraced and integrated increasingly advanced technologies to continuously enhance their production lines. This case study examined the impact of implementing artificial intelligence on the company’s performance. The findings revealed that as Hennig Olsen incorporated more artificial intelligence into their production lines, they experienced a significant reduction in customer complaints. However, they continue to face challenges in meeting their overall equipment effectiveness goals. The thesis also identified potential areas for improvement, emphasizing the potential benefits of integrating six sigma processes through AI initiatives. More specifically, implementing predictive maintenance to minimize unexpected downtime and improve OEE emerged as a key opportunity. Leveraging AI to analyze vast amounts of data could also prove advantageous in optimizing cycle time and reducing waste within the organization. Finally, this report has examined the readiness of Hennig Olsen to further integrate AI tools into their operations. To fully capitalize on the potential benefits of AI and evolve into a comprehensive smart factory, the company needs to invest in additional technologies such as the Internet of Things, big data analytic, and cloud computing. However, a significant hurdle arises from the limitations of their existing machinery, which cannot gather extensive data or establish interconnectivity. Moreover, sourcing qualified personnel proficient in developing these technologies poses a challenge. A more effective strategy, along with support from stakeholders, is necessary to encourage investments in new technologies. This will facilitate the successful implementation of AI technologies and foster improved acceptance of new technology among employees

How artificial intelligence can be used to improve lean manufacturing and production processes

The implementation of Lean and Artificial Intelligence has demonstrated a positive correlation across different industries. By integrating AI techniques, the efficiency and effectiveness of Lean processes can be enhanced. The combination of Lean and AI contributes to improved decision-making, increased productivity, and reduced waste. Moreover, AI can identify and rectify process errors, enabling streamlined and more efficient operations. In 2014, Hennig Olsen initiated the implementation of lean thinking, which yielded mixed results initially. However, they decided to adopt lean principles according to their specific requirements, leading to significantly improved outcomes. With the rapid advancement of technology, Hennig Olsen ventured into experimenting with artificial intelligence, particularly in the realm of vision control, starting in 2019. Subsequently, they have consistently embraced and integrated increasingly advanced technologies to continuously enhance their production lines. This case study examined the impact of implementing artificial intelligence on the company's performance. The findings revealed that as Hennig Olsen incorporated more artificial intelligence into their production lines, they experienced a significant reduction in customer complaints. However, they continue to face challenges in meeting their overall equipment effectiveness goals. The thesis also identified potential areas for improvement, emphasizing the potential benefits of integrating six sigma processes through AI initiatives. More specifically, implementing predictive maintenance to minimize unexpected downtime and improve OEE emerged as a key opportunity. Leveraging AI to analyze vast amounts of data could also prove advantageous in optimizing cycle time and reducing waste within the organization. Finally, this report has examined the readiness of Hennig Olsen to further integrate AI tools into their operations. To fully capitalize on the potential benefits of AI and evolve into a comprehensive smart factory, the company needs to invest in additional technologies such as the Internet of Things, big data analytic, and cloud computing. However, a significant hurdle arises from the limitations of their existing machinery, which cannot gather extensive data or establish interconnectivity. Moreover, sourcing qualified personnel proficient in developing these technologies poses a challenge. A more effective strategy, along with support from stakeholders, is necessary to encourage investments in new technologies. This will facilitate the successful implementation of AI technologies and foster improved acceptance of new technology among employees

Background, Systematic Review, Challenges and Outlook

Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.publishersversionpublishe

Smart Factory e a indústria 4.0: uma revisão sistemática de literatura

A mudança de uma fábrica tradicional para uma Smart Factory estimula o efeito profundo e duradouro da manufatura futura em todo o mundo. Como o coração da Indústria 4.0, a Smart Factory integra estruturas físicas com tecnologias dessa indústria, tornando-as mais precisas, com o propósito de melhorar o desempenho, qualidade, controle, gerenciamento e transparência dos processos de manufatura. Nessa perspectiva, o principal objetivo deste estudo é apresentar os desafios para implementação da Smart Factory no contexto da Indústria 4.0. Para o propósito desta pesquisa, foi elaborada uma Revisão Sistemática da Literatura (RSL), metodologia que agrupa trabalhos anteriores sobre um tema especifico, promovendo a identificação, a avaliação e a interpretação de estudos em uma determinada área por meio da análise de conceitos e práticas. Com base nos resultados obtidos, verificou-se que as principais indústrias começaram a jornada para implementar a Smart Factory, no entanto, a maioria ainda carece de compreensão sobre os desafios e recursos para implementá-la. Smart Factory não significa fábrica sem seres humanos, mas sim visa atender as necessidades individuais do mercado, tanto quanto possível, com custos razoáveis. Portanto, este artigo contribui para o corpo de conhecimento atual sobre a Smart Factory, identificando os seus requisitos e os principais desafios, investigando as principais tecnologias da Indústria 4.0 para implementação de uma Smart Factory, bem como também indicam os rumos de possíveis pesquisas futuras

An Approach to QoS-based Task Distribution in Edge Computing Networks for IoT Applications

abstract: Internet of Things (IoT) is emerging as part of the infrastructures for advancing a large variety of applications involving connections of many intelligent devices, leading to smart communities. Due to the severe limitation of the computing resources of IoT devices, it is common to offload tasks of various applications requiring substantial computing resources to computing systems with sufficient computing resources, such as servers, cloud systems, and/or data centers for processing. However, this offloading method suffers from both high latency and network congestion in the IoT infrastructures. Recently edge computing has emerged to reduce the negative impacts of tasks offloading to remote computing systems. As edge computing is in close proximity to IoT devices, it can reduce the latency of task offloading and reduce network congestion. Yet, edge computing has its drawbacks, such as the limited computing resources of some edge computing devices and the unbalanced loads among these devices. In order to effectively explore the potential of edge computing to support IoT applications, it is necessary to have efficient task management and load balancing in edge computing networks. In this dissertation research, an approach is presented to periodically distributing tasks within the edge computing network while satisfying the quality-of-service (QoS) requirements of tasks. The QoS requirements include task completion deadline and security requirement. The approach aims to maximize the number of tasks that can be accommodated in the edge computing network, with consideration of tasks’ priorities. The goal is achieved through the joint optimization of the computing resource allocation and network bandwidth provisioning. Evaluation results show the improvement of the approach in increasing the number of tasks that can be accommodated in the edge computing network and the efficiency in resource utilization.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Emergent Alternative Home 2050

Designing for a time beyond the immediate future has potentials for new dialogues. This approach challenges the designer to reconsider the point of departure, societal, contextual and other dynamics, techniques and available materials. Endeavors such as this also promote the opportunity to envision architectural inventions, and new concepts better suited for future. My project aims to find a prototype home for the future 2050, envisioning a design solution for a tropical urban environment. A series of chronological scenarios across a selected time line investigates the preferred future home. An architectural genealogy and emerging techniques suitable for the context are presented to solidify the overarching theme. My design proposal provides the necessary proof or evidence for this doctorate project, and tests my hypothesis for a synthetic organic Emergent Alternative Home 2050. Biotechnology, nanotechnology and biomimetic manifestations propagate a reductive, performative, and generative design. In turn, this leads to investigation of accurate, efficient, near intelligent, mass fabricated, low cost materials, components and systems. Self-assembly, flexibility and positive environmental footprint are forecasted for the future home 2050

3d food printing: study and applications to produce innovative food products.

La stampa 3D degli alimenti rappresenta una tecnologia innovativa ed emergente capace di costruire un oggetto tridimensionale partendo da un modello CAD creato su software di disegno grafico. Durante gli ultimi anni molti studi hanno dimostrato come questa tecnologia sia stata applicata per la produzione di alimenti nuovi. L’obiettivo principale di questa tesi è stato l’approfondimento e il miglioramento della tecnologia di stampa 3D nel settore alimentare contribuendo alla creazione di alimenti dalle proprietà mai esplorate prima. Dopo un’analisi dell’evoluzione temporale della tecnologia di stampa 3D nel settore alimentare, una varietà di altri aspetti sono stati studiati, tra cui la capacità di creare e modificare alimenti dalle nuove texture attraverso la progettazione di nuovi design, inoltre è stata oggetto di studio la stampa 3D ad alta velocità, tema interessante dal punto di vista dell’applicazione in campo industriale. Gli studi si sono focalizzati sull’utilizzo di due diverse matrici di stampa: impasto a base di cereali e gel a base d’amido, stampando strutture geometriche (cubi, parallelepipedi) e design ispirati alla natura (tessuti interni delle mele). La tesi è strutturata in 8 capitoli: una breve introduzione (capitolo 1), obiettivi e linee di ricerca (capitolo 2) e altri cinque capitoli corrispondenti alle 5 pubblicazioni su riviste internazionali; Drawing the scientific landscape of 3D Food Printing. Maps and interpretation of the global information in the first 13 years of detailed experiments, from 2007 to 2020’(capitolo 3) and ‘Rheological properties, dispensing force and printing fidelity of starchy-gels modulated by concentration, temperature and resting time’ (capitolo 4). I capitoli 5 e 6 sono dedicati alla creazione di alimenti stampati in 3D con proprietà meccaniche desiderate e personalizzabili: Programmable texture properties of cereal-based snack mediated by 3D printing technology’ (capitolo 5), ‘Extending 3D food printing application. Apple tissues microstructure as CAD model to create innovative cereal-based snacks’ (capitolo 6). Il capitolo 7 si è focalizzato sulla stampa 3D ad alta velocità: ‘Extending the 3D food printing tests at high speed. Material deposition and effect of non-printing movements on the final quality of printed structures’. E infine il capitolo 8 racchiude le conclusioni e alter discussioni generali riguardanti la tesi.3D printing (3DP) represents an innovative and emerging technology aiming to build three-dimensional objects starting from the computer-aided model. During last years main studies showed the application of this technology to produce innovative foods. The main aim of this research was the better understanding and the implementation of 3D Printing in the food sector aiming to contribute to the creation of food with unprecedented properties. After an analysis on the temporal evolution of 3D Food Printing (3DFP) in scientific field, a variety of relevant aspects have been studied: the capability of modifying the texture properties of the end products by means of accurate design of the digital models and the printing at high speed that could open for a more practical application at industrial level. Moreover, the studies have focused on two different matrix: cereals based and starchy gels, printing geometric structures (cube, parallelepiped) and design inspired by nature (apple tissue). The thesis is structured in 8 chapters: a brief introduction (chapter 1), objects and outlines of research (chapter 2) and the other sections consists of five published papers in international peer reviewed journals ‘Drawing the scientific landscape of 3D Food Printing. Maps and interpretation of the global information in the first 13 years of detailed experiments, from 2007 to 2020’(chapter 3); ‘Rheological properties, dispensing force and printing fidelity of starchy-gels modulated by concentration, temperature and resting time’ (chapter 4). The chapters 5 and 6 has been dedicated to the creation of 3D-printed food with desired and programmable mechanical properties: ‘Programmable texture properties of cereal-based snack mediated by 3D printing technology’ (chapter 5), ‘Extending 3D food printing application. Apple tissues microstructure as CAD model to create innovative cereal-based snacks’ (chapter 6). Chapter 7 focused on speed up of 3DFP: ‘Extending the 3D food printing tests at high speed. Material deposition and effect of non-printing movements on the final quality of printed structures’. Finally chapter 8 contains the conclusions and some general discussion of the thesis