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Quality Data Management in the Next Industrial Revolution : A Study of Prerequisites for Industry 4.0 at GKN Aerospace SwedenErkki, Robert, Johnsson, Philip January 2018 (has links)
The so-called Industry 4.0 is by its agitators commonly denoted as the fourth industrial revolution and promises to turn the manufacturing sector on its head. However, everything that glimmers is not gold and in the backwash of hefty consultant fees questions arises: What are the drivers behind Industry 4.0? Which barriers exists? How does one prepare its manufacturing procedures in anticipation of the (if ever) coming era? What is the internet of things and what file sizes’ is characterised as big data? To answer these questions, this thesis aims to resolve the ambiguity surrounding the definitions of Industry 4.0, as well as clarify the fuzziness of a data-driven manufacturing approach. Ergo, the comprehensive usage of data, including collection and storage, quality control, and analysis. In order to do so, this thesis was carried out as a case study at GKN Aerospace Sweden (GAS). Through interviews and observations, as well as a literature review of the subject, the thesis examined different process’ data-driven needs from a quality management perspective. The findings of this thesis show that the collection of quality data at GAS is mainly concerned with explicitly stated customer requirements. As such, the data available for the examined processes is proven inadequate for multivariate analytics. The transition towards a data-driven state of manufacturing involves a five-stage process wherein data collection through sensors is seen as a key enabler for multivariate analytics and a deepened process knowledge. Together, these efforts form the prerequisites for Industry 4.0. In order to effectively start transition towards Industry 4.0, near-time recommendations for GAS includes: capture all data, with emphasize on process data; improve the accessibility of data; and ultimately taking advantage of advanced analytics. Collectively, these undertakings pave the way for the actual improvements of Industry 4.0, such as digital twins, machine cognition, and process self-optimization. Finally, due to the delimitations of the case study, the findings are but generalized for companies with similar characteristics, i.e. complex processes with low volumes.
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Sheet metal forming in the era of industry 4.0 : using data and simulations to improve understanding, predictability and performanceTatipala, Sravan January 2019 (has links)
A major issue within automotive Sheet Metal Forming (SMF) concerns ensuring desired output product quality and consistent process performance. This is fueled by complex physical phenomena, process fluctuations and complicated parameter correlations governing the dynamics of the production processes. The aim of the thesis is to provide a deeper understanding of the challenges and opportunities in this regard within automotive SMF. The research is conducted in collaboration with a global automotive manufacturer. The research shows that systematic investigations using process simulation models allow exploration of the product-process parameter interdependencies and their influence on the output product quality. Furthermore, it is shown that incorporating in-line measured data within process simulation models enhance model prediction accuracy. In this regard, automating the data processing and model configuration tasks reduces the overall modelling effort. However, utilization of results from process simulations within a production line requires real-time computational performance. The research hence proposes the use of reduced process models derived from process simulations in combination with production data, i.e. a hybrid data- and model-based approach. Such a hybrid approach would benefit process performance by capturing the deviations present in the real process while also incorporating the enhanced process knowledge derived from process simulations. Bringing monitoring and control realms within the production process to interact synergistically would facilitate the realization of such a hybrid approach. The thesis presents a procedure for exploring the causal relationship between the product-process parameters and their influence on output product quality in addition to proposing an automated approach to process and configure in-line measured data for incorporation within process simulations. Furthermore, a framework for enhancing output product quality within automotive SMF is proposed. Based on the thesis findings, it can be concluded that in-line measured data combined with process simulations hold the potential to unveil the convoluted interplay of process parameters on the output product quality parameters. / <p>Related work:</p><p>1) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14412</p><p>2) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14388</p><p>3) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18935</p>
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A customer-centric evaluation of a smart manufacturing concept : Development of a continuous improvement strategy for improving the productivity of a small and medium-sized enterprisesBenediktsdóttir, Laufey January 2019 (has links)
Nytt AB is a startup focusing on simplifying the concept of smart manufacturing to small and medium-sized companies providing an add-on machine monitoring solution for data analyzing. The product is currently under development with the final product soon to be launched. In the next phase of Nytt AB, a marketing plan has to be strategized. This thesis, which is built on previous Nytt AB’s work, focuses on addressing issues that will be important when creating a marketing and sales strategy. The customer is put as the focal point and the customer values analyzed including discussions on productivity improvements within the machines and how to standardize the changes to satisfy every customer. Using research questions as a base for the study, the customer values were analyzed by first understanding the main threats, weaknesses, strengths, and opportunities for the product and then analyzing data from an installed prototype and the improvements that can be achieved based on the data. The customers can, by using the product get statistical facts about their machines which can be the first step to understand the need for changes within their company. With future development, further customer values will appear, such as providing aid on how to improve KPIs such as availability. Providing this information to the customers will help them obtain a better insight into the field of smart manufacturing, the manufacturing of the future. / Nytt AB är ett nystartat företag som fokuserar på att förenkla konceptet smart tillverkning för små och medelstora företag och erbjuder en maskinövervakningslösning för dataanalys. Produkten är just nu under utveckling, slutprodukten planeras lanseras snart. Nästa steg för Nytt AB är att strukturera en marknadsplan. Detta examensarbete, som bygger på tidigare arbete i Nytt AB, fokuserar på frågor som kommer att vara viktiga när man skapar en marknadsförings- och försäljningsstrategi. Kunden blir i fokus när kundvärdena är analyserade inklusive i diskussioner om produktivitetsförbättringar för maskiner och hur man standardiserar förändringarna för att uppfylla kundens krav. Med hjälp av forskningsfrågor, analyseras kundvärdena genom att först förstå de viktigaste hoten, svagheterna, styrkorna och möjligheterna för produkten och sedan analyseras data från prototypen och de förbättringar som kan uppnås baserat på given data. Kunderna kan genom att använda produkten få statistik om sina maskiner vilket kan vara första steget att inse behovet av förändringar inom kundföretaget. Vid utveckling kommer ytterligare kundvärden att dyka upp, till exempel metoder på hur man förbättrar olika nyckeltal, såsom tillgänglighet. Att ge denna information till kunderna hjälper dem att få en bättre inblick i smart tillverkning, framtidens tillverkning.
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Data-driven Decision-making for Efficient & Sustainable Production / Datadrivet beslutsfattande för effektiv och hållbar produktionBroms, Arvid, Liljenberg Olsson, Simon January 2021 (has links)
As a result of digitalization, previously analog systems in the manufacturing industry have become digitalized, including the decision-making processes. Companies are, therefore,becoming more dependent on data for strategic decisions. However, because of the rapid development of digitalization, companies are left blindfolded in the path towards smarter manufacturing which often leads to unsuccessful technological implementations. Therefore, the thesis will explore this problem by asking: What are the required initiatives for successfully implementing digital data-driven decision-making to improve efficiency and sustainability by Swedish manufacturing companies? To answer the research questions, an exploratory multiple case study approach was conducted, where interviews with informants from the industry as well as researchers within the context of smarter manufacturing were made. The findings were then used to derive propositions which worked as the foundation of a conceptual model which functionality would be to illuminate the results in the form of a strategy map. Findings suggest that it is not always necessary for companies to implement technologies linked to large investments to enable digital data-driven decision-making. However, for those that do, there needs to be a clear organizational plan and agenda before executing theprojects since they otherwise often lead to insufficient results. That means, the technological aspects are often not the culprit in failed digital data-driven decision-makingprojects. Additional findings suggest that there are synergies connected to digital data-driven decision-making such as data-sharing possibilities that have the potential of becoming a major aspect within the context of sustainability and efficiency. / Som ett resultat av ökad digitalisering har analoga system i tillverkningsindustrin blivit digitaliserade, vilket inkluderar beslutsfattandet. Företag har därför börjat förlita sig alltmer på data för sina strategiska beslut. Men på grund av den snabba utveckling av digitalisering har tillverkningsföretagen lämnats utan klara riktlinjer för hur de bör gå tillväga för att implementera digitalt datadrivet beslutsfattande på ett effektivt men hållbart sätt. Avhandlingen kommer därför att undersöka detta problem genom att fråga: Vilka är de initiativ som krävs för att framgångsrikt implementera digital datadrivet beslutsfattande med målet att förbättra effektiviteten och hållbarheten hos svenska tillverkningsföretag? För att svara på forskningsfrågorna användes en undersökande metod med flerafallstudier, där intervjuer gjordes med informanter från industrin såväl som forskare inom ramen för smartare tillverkning. Resultaten användes sedan för att härleda förslag som därefter användes till konstruktionen av en konceptuell model vars huvuduppgift var att illustrera resultaten i form av en strategikarta. Slutsatserna pekar på att det inte alltid är nödvändigt för företag att implementera teknik kopplad till stora investeringar för att möjliggöra digitalt datadrivet beslutsfattande. Men för de som valt att implementera sådana system behövs en tydlig organisationsplan innan projekten genomförs eftersom de annars ofta leder till ofördelaktiga resultat. Detta tyder på att de tekniska aspekterna oftast inte är vad som orsakar misslyckade datadrivna beslutsprojekt. Dessutom tyder resultaten på att det finns synergier kopplade till digitalt datadrivet beslutsfattande, till exempel möjligheter att dela data som har potential att bli en viktig aspekt inom hållbarhet och effektivitet.
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Chemical Manufacturing in Developing Markets: Analysis and Cost EstimationsWasiu Peter Oladipupo (8669685) 28 July 2023 (has links)
<p>Developed countries have built wealth and prosperity on the strength of their manufacturing sectors, with China’s success story of lifting 800 million people out of extreme poverty in 30 years a sterling and most recent example of how manufacturing-led industrialization can foster economic development. Sub-Saharan Africa, unfortunately, find itself today in a similar situation as China did in 1990, with over 50% of the world’s desperately poor 719 million people living in the region. But unlike China, Sub-Saharan Africa is faced with the additional challenge of overcoming poverty in a world with stricter constraints to global trade and climate change limitations to modern-day industrialization. Compounding the challenges further is the region's limited know-how and human capital — a consequence of years of underdevelopment, creating a classic chicken and egg dilemma where the lack of industrialization perpetuates the dearth of know-how and human capital, and vice versa.</p><p>Considering these challenges, we investigate how chemical manufacturing and what chemical manufacturing approaches can be leveraged to effectively drive industrialization and economic development in Sub-Saharan Africa. We propose chemicals manufacturing using prefabricated modules – which are constructed offsite in places with available human capital and transported to be assembled in places where they are needed – as a flexible and needed approach. However, Economy of Scale, which generally favors large-scale chemical manufacturing, poses as a major constraint to such modularization approach, especially given the presently small serviceable market sizes in Sub-Saharan Africa due to low purchasing power parity. We thus utilize mathematical modeling techniques to determine and establish scenarios for economic viability of the proposed approach, providing modeling frameworks and introducing measures for further studies in the process. We also provide and analyze exemplary flowsheets synthesized for a net-zero carbon emissions chemical manufacturing paradigm in the region.</p><p>This work concludes with a prefeasibility study of a chemical manufacturing project in Nigeria, as part of the author’s quest to build prefabricated modular plants across Africa. <i>Modular plants are attractive as they can be tuned to market demand of a developing market and region that needs them, putting less capital at risk.</i></p><p>This thesis is intended to be a vanguard of potential solutions to the complex challenges to industrialization in Sub-Saharan Africa. It endeavors to pave the way for addressing these issues through chemical manufacturing, offering valuable insights for sustainable progress.</p>
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ENHANCING INTERPRETABILITY AND ADAPTABILITY OF MANUFACTURING EQUIPMENT HEALTH MODELS AND ESTABLISHMENT OF COST MODELS FOR MAINTENANCE DECISIONSHaiyue Wu (15100972) 05 April 2023 (has links)
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<p>The integration of Industry 4.0 technologies such as cyber-physical systems, the internet of things, and artificial intelligence has revolutionized the traditional manufacturing systems, making them smart and digital. Maintenance, a critical component of manufacturing, has been incorporated with data-driven strategies such as prognostic and health management (PHM) to improve production efficiency and reliability. This is achieved by real-time sensing and AI-based modeling, which monitor the health condition of operational equipment for fault detection or failure prediction. The results generated by these models provide crucial support for decision-making processes in manufacturing, ranging from maintenance scheduling to production management.</p>
<p>This research focuses on data-driven machine health models based on deep learning in manufacturing systems and explores three directions towards the practical implementation of PHM: model interpretation, model adaptability and robustness enhancement, and cost-benefit analysis of maintenance strategies. In terms of model interpretation, the RNN-LSTM-based model prediction on bearing health estimation was analyzed, and the relationship between the model input and output was investigated. The adoption of the LRP technique improved the explainability of the LSTM model beyond predictive maintenance applications. To enhance model adaptability and robustness, a Transformer-based method was developed for fault diagnosis and novel fault detection, which achieved superior performance compared to conventional fault classification AI-based models. The decision-making aspect of PHM was addressed by conducting a cost-benefit analysis on different maintenance strategies, which provided a new perspective for decision-makers in maintenance management.</p>
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Kundkrav för service av mobilnät i stora industrier / Customer requirements for service of mobile networks within large industriesAlyas, Lailee January 2021 (has links)
Detta är ett examensarbete inom Industriell teknik och produktionsunderhåll som handlar om kundkrav för service av mobilnät i stora industrier. De stora industrierna, som exempelvis Scania och Boliden, har fokus på att underlätta underhåll och hantering av produktionen med hjälp av automation och digitalisering i sina företag. Idag är det nya mobilnätet, 5G, en efterfrågad teknologi i stora industrier med stora produktionsanläggningar, som gör det möjligt att koppla upp de fysiska komponenterna och därmed effektivisera sitt företag. I detta arbete har intervjuer genomförts med tre svenska företag, Ericsson, Scania och Boliden, där de stora kraven med implementering och användning av 5G tagits fram. Utöver den kvalitativ studien har en litteraturstudie genomförts, där kravet var att välja information som inriktar sig på 5G och dess implementering i stora produktionsanläggningar. Målet med detta arbete är att identifiera kundens behov av egen service och kundkrav på operatören vid implementering och användning av 5G, samt komma fram med ett förslag på ett SLA. Resultaten, från intervjuer och litteraturstudie, visar att kundens krav på operatören överväger kundkrav av egen service. Vid implementering av 5G måste industrikunder ställa om hela produktionen, vilket kräver ny utrustning, ny teknik och kompetent personal. Dessa krav ska operatörer, som implementerar och levererar mobilnät, ta hand om. Industrikunder har också krav att kunna själv hantera vissa delar i 5G-mobilnätet, exempelvis att ha tekniker som kan ta hand om enkel underhåll. Detta gör nätet säkrare och effektivare men även billigare, då operatören inte behöver genomföra enklar underhållsarbete. Vidare har ett förslag på ett SLA tagits fram, där vissa krav som industrikunder hade på ett serviceavtal dokumenterades. / The conducted project within the program of Industrial Technology and Production Maintenance concerns the industrial customer requirements for service of mobile networks within large industries. The large industrial customers, such as Scania and Boliden, have an immense focus on supporting the maintenance processes and managing production with the help of automation and digitalization within their companies. Today, the new mobile network of 5G, is a sought-after technology within large industries with large production facilities, that enables the opportunity to connect physical components and thereby increasing the efficiency within the operations. In this thesis work, interviews have been conducted with three Swedish companies, Ericsson, Scania, and Boliden. This is where the requirements of implementation and usage of 5G network have been determined. Moreover, a literature study has also been carried out with the requirement of selecting information that emphasizes on 5G and implementation within large production facilities. The objective of this thesis work is to identify the requirements of large industrial customers, such as their needs for self-maintenance, their requirements on the network operator during implementation and usage of 5G. Finally, the objective is to also present a Service Level Agreement or SLA. The result from the interview and the literature study shows that the customer requirements on the network operator are higher than the requirements the network operator has on thecustomers. During the implementation of 5G, industrial customers must adapt and change their production facilities, which requires new equipment, technology, and competent workers. These aspects should be considered by the and handled by the network operator. Industrial customers also have the condition of handling certain areas of the 5G network themselves, for example having technicians ready that can conduct simple maintenance. This in turn will enable more secure and efficient network and on the same time, at a lesser cost, due to the network operator no longer must conduct simple maintenance. Furthermore, a proposition for a Service Level Agreement have been composed, where the requirements from the industrial customers were documented.
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Deviation management in high-mix low-volume production : A case study conducted in the defense industryGyllenberg, Jacob, Nilsson, Mathilda January 2024 (has links)
In a period marked by escalating demands for defense equipment, this master thesis aims to explore deviation management practices within HMLV (High-Mix Low-Volume) productions, a common practice among firms operating in the defense sector. To achieve this objective, a case study methodology has been employed, drawing insights from a representative company within the industry. The study adopts a qualitative approach, incorporating internal interviews and on-site observations, thereby grounding its findings in firsthand data. Concurrently, a comprehensive literature review was conducted to contextualize the findings, examining theories such as ISO standards, lean principles, and Smart Manufacturing in the specific context of HMLV productions, facilitating the derivation of informed conclusions. The analysis of gathered data revealed four primary areas posing challenges to effective deviation management within the case company: process development, organizational development, resource management, and data management and analysis. Building upon these identified challenges, an action plan was developed to ensure deviations are addressed in proportion to their impact on production, emphasizing a proactive stance. Drawing from the current state of affairs within the case company, the study underscores the imperative to prioritize deviation management, particularly given the heightened production demands within the defense industry. Furthermore, it suggests that many of the identified issues across the aforementioned areas could be mitigated by fostering a culture of continual improvement. By implementing strategies aligned with this and enhancing their deviation management practices, the company can transition from a reactive to a proactive approach, leading to increased efficiency and quality within the HMLV-production.
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<b>ENERGY CONSERVATION THROUGH INTERNET-OF-THING FRAMEWORK</b>Da Chun Wu (20391771) 06 December 2024 (has links)
<p dir="ltr">Improving the energy efficiency of buildings and manufacturing plants involves a continuous cycle of real-time monitoring, analysis, decision-making, action, and assessment, which are essential components of a smart manufacturing approach. Achieving this requires a comprehensive platform that integrates data storage and sharing; incorporates models to interpret sensor data and algorithms to analyze it; provides actionable options with projected benefits and trade-offs; executes selected actions and evaluates their outcomes; and retains knowledge for ongoing enhancement. However, many commercially available solutions are designed for large-scale institutions, making them expensive and requiring significant customization by specialized professionals, which limits accessibility for smaller companies and building owners. This research aims to address these limitations by developing an IoT-based platform that integrates all essential functions while remaining affordable and user-friendly for small and medium-sized businesses and individual building owners. The platform supports the seamless integration of sensors, software, hardware models, decision-making algorithms, actuators, and a structured knowledge repository, with data communication and sharing managed via the internet through cloud services to ensure accessibility and flexibility. The platform was applied in real-world settings to verify its performance and usability, focusing on three core implementations to establish an advanced energy management framework. The first implementation involved ventilation optimization using IoT sensors to monitor parameters such as temperature, differential pressure, and airflow, combined with neural networks to predict system behavior under varying conditions. A genetic algorithm was used to identify optimal operational settings for make-up air units, ensuring energy-efficient ventilation while maintaining indoor air quality and temperature standards. This approach resulted in a 20% reduction in annual ventilation energy consumption and a 60% decrease in power demand on weekends. The second implementation focused on optimizing compressed air system pressure settings, addressing the high energy intensity and inefficiencies caused by leaks, heat, and pressure drops. IoT-enabled sensors captured real-time data on pressure, flow, and power consumption, which were analyzed using machine learning models, achieving a 7% energy saving for every 1 bar reduction in pressure. The final implementation addressed the detection of unwanted air demand using unsupervised k-means classification to distinguish between normal operating hours and non-operating periods. Unexpected air usage patterns during non-operating hours were identified and analyzed through histogram and heatmap techniques, enabling corrective measures that saved approximately 393 kWh weekly, equivalent to 10% of the compressor’s weekly energy consumption. The innovation of this study lies in the integration of model-based intelligence within an IoT system, enhancing real-time energy management capabilities and enabling continuous learning and improvements in operational efficiency. This work demonstrates how advanced IoT frameworks can bridge the gap between energy efficiency and practicality for smaller enterprises, fostering sustainable and cost-effective operations.</p>
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Enhancing interoperability for IoT based smart manufacturing : An analytical study of interoperability issues and case studyWang, Yujue January 2020 (has links)
In the era of Industry 4.0, the Internet-of-Things (IoT) plays the driving role comparable to steam power in the first industrial revolution. IoT provides the potential to combine machine-to-machine (M2M) interaction and real time data collection within the field of manufacturing. Therefore, the adoption of IoT in industry enhances dynamic optimization, control and data-driven decision making. However, the domain suffers due to interoperability issues, with massive numbers of IoT devices connecting to the internet despite the absence of communication standards upon. Heterogeneity is pervasive in IoT ranging from the low levels (device connectivity, network connectivity, communication protocols) to high levels (services, applications, and platforms). The project investigates the current state of industrial IoT (IIoT) ecosystem, to draw a comprehensive understanding on interoperability challenges and current solutions in supporting of IoT-based smart manufacturing. Based upon a literature review, IIoT interoperability issues were classified into four levels: technical, syntactical, semantic, and organizational level interoperability. Regarding each level of interoperability, the current solutions that addressing interoperability were grouped and analyzed. Nine reference architectures were compared in the context of supporting industrial interoperability. Based on the analysis, interoperability research trends and challenges were identified. FIWARE Generic Enablers (FIWARE GEs) were identified as a possible solution in supporting interoperability for manufacturing applications. FIWARE GEs were evaluated with a scenario-based Method for Evaluating Middleware Architectures (MEMS). Nine key scenarios were identified in order to evaluate the interoperability attribute of FIWARE GEs. A smart manufacturing use case was prototyped and a test bed adopting FIWARE Orion Context Broker as its main component was designed. The evaluation shows that FIWARE GEs meet eight out of nine key scenarios’ requirements. These results show that FIWARE GEs have the ability to enhance industrial IoT interoperability for a smart manufacturing use case. The overall performance of FIWARE GEs was also evaluated from the perspectives of CPU usage, network traffic, and request execution time. Different request loads were simulated and tested in our testbed. The results show an acceptable performance in terms with a maximum CPU usage (on a Macbook Pro (2018) with a 2.3 GHz Intel Core i5 processor) of less than 25% with a load of 1000 devices, and an average execution time of less than 5 seconds for 500 devices to publish their measurements under the prototyped implementation. / I en tid präglad av Industry 4.0, Internet-of-things (IoT) spelar drivande roll jämförbar med ångkraft i den första industriella revolutionen. IoT ger potentialen att kombinera maskin-till-maskin (M2M) -interaktion och realtidsdatainsamling inom tillverkningsområdet. Därför förbättrar antagandet av IoT i branschen dynamisk optimering, kontroll och datadriven beslutsfattande. Domänen lider dock på grund av interoperabilitetsproblem, med enorma antal IoT-enheter som ansluter till internet trots avsaknaden av kommunikationsstandarder på. Heterogenitet är genomgripande i IoT som sträcker sig från de låga nivåerna (enhetskonnektivitet, nätverksanslutning, kommunikationsprotokoll) till höga nivåer (tjänster, applikationer och plattformar). Projektet undersöker det nuvarande tillståndet för det industriella IoT (IIoT) ekosystemet, för att få en omfattande förståelse för interoperabilitetsutmaningar och aktuella lösningar för att stödja IoT-baserad smart tillverkning. Baserat på en litteraturöversikt klassificerades IIoT-interoperabilitetsfrågor i fyra nivåer: teknisk, syntaktisk, semantisk och organisatorisk nivå interoperabilitet. När det gäller varje nivå av driftskompatibilitet grupperades och analyserades de nuvarande lösningarna för adressering av interoperabilitet. Nio referensarkitekturer jämfördes i samband med att stödja industriell driftskompatibilitet. Baserat på analysen identifierades interoperabilitetstrender och utmaningar. FIWARE Generic Enablers (FIWARE GEs) identifierades som en möjlig lösning för att stödja interoperabilitet för tillverkningstillämpningar. FIWARE GEs utvärderades med en scenariebaserad metod för utvärdering av Middleware Architectures (MEMS). Nio nyckelscenarier identifierades för att utvärdera interoperabilitetsattributet för FIWARE GEs. Ett smart tillverkningsfodral tillverkades med prototyper och en testbädd som antog FIWARE Orion Context Broker som huvudkomponent designades. Utvärderingen visar att FIWARE GE uppfyller åtta av nio krav på nyckelscenarier. Dessa resultat visar att FIWARE GE har förmågan att förbättra industriell IoT-interoperabilitet för ett smart tillverkningsfodral. FIWARE GEs totala prestanda utvärderades också utifrån perspektivet för CPU-användning, nätverkstrafik och begär exekveringstid. Olika förfrågningsbelastningar simulerades och testades i vår testbädd. Resultaten visar en acceptabel prestanda i termer av en maximal CPU-användning (på en Macbook Pro (2018) med en 2,3 GHz Intel Core i5-processor) på mindre än 25% med en belastning på 1000 enheter och en genomsnittlig körningstid på mindre än 5 sekunder för 500 enheter att publicera sina mätningar under den prototyperna implementateringen.
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