Asset selection and optimisation for robotic assembly cell reconfiguration

With the development of Industry 4.0, the manufacturing industry has revolutionized a lot. Product manufacture becomes more and more customized. This trend is achieved by innovative techniques, such as the reconfigurable manufacturing system. This system is designed at the outset for rapid change in its structure, as well as in software and hardware components, to respond to market changes quickly. Robots are important in these systems because they provide the agility and precision required to adapt rapidly to new manufacturing processes and customization demands. Despite the importance of applying robots in these systems, there might be some challenges. For example, there is data from multiple sources, such as the technical manual sensor data. Besides, robot applications must react quickly to the ever-changing process requirements to meet customer's requirements. Furthermore, further optimization, especially layout optimization, is needed to ensure production efficiency after adaptation to the current process requirements. To address these challenges, this doctoral thesis presents a framework for reconfiguring robotic assembly cells in manufacturing. This framework consists of three parts: the experience databank, the methodology for optimal manufacturing asset selection, and the methodology for layout optimization. The experience databank is introduced to confront the challenge of assimilating and processing heterogeneous data from numerous manufacturing sources, which is achieved by proposing a vendor-neutral ontology model. This model is specifically designed for encapsulating information about robotic assembly cells and is subsequently applied to a knowledge graph. The resulting knowledge graph, constituting the experience databank, facilitates the effective organization and interpretation of the diverse data. An optimal manufacturing asset selection methodology is introduced to adapt to shifting processes and product requirements, which focuses on identifying potential assets and their subsequent evaluation. This approach integrates a modular evaluation framework that considers multiple criteria such as cost, energy consumption, and robot maneuverability, ensuring the selection process remains robust in changing market demands and product requirements. A scalable methodology for layout optimization within the reconfigurable robotic assembly cells is proposed to resolve the need for further optimization post-adaption. It introduces a scalable, multi-decision modular optimization framework that synergizes a simulation environment, optimization environment, and robust optimization algorithms. This strategy utilizes the insights garnered from the experience databank to facilitate informed decision-making, thereby enabling the robotic assembly cells to not only meet the immediate production exigencies but also align with the manufacturing landscape's evolving dynamics. The validation of the three methodologies presented in this doctoral thesis encompasses both software development and practical application through three distinct use cases. For the experience databank, an interface was developed using Protégé, Neo4j, and Py2neo, allowing for effective organization and processing of varied manufacturing data. The programming interface for the asset selection methodology was built using Python, integrating with the experience databank via Py2neo and Neo4j to facilitate dynamic and informed decision-making in asset selection. In terms of software for the layout optimization framework, two different applications were developed to demonstrate the framework's scalability and adaptability. The first application, combining Python and C# programming with Siemens Tecnomatix Process Simulate, is geared towards optimizing layouts involving multiple machines. The second application utilizes Python programming alongside the RoboDK API and RoboDK software, tailored for layout optimization in scenarios involving a single robot. Complementing these software developments, the methodologies were further validated through three use cases, each addressing a unique aspect of the framework. Use Case 1 focused on implementing asset selection and system layout optimization based on a single objective, leveraging the experience databank. The required assets are selected, and the required cycle time for executing the whole robotic assembly operation has been reduced by 15.6% from 47.17 seconds to 39.83 seconds. Use Case 2 extended the layout optimization to single-robot operations with an emphasis on multi-criteria decision-making. The energy consumption was minimized to 5613.59 Wh after implementing optimization strategies, demonstrating a significant enhancement in energy efficiency. Compared with the baseline of 6164.98 Wh, this represents an 8.9% reduction in energy usage. For minimized cycle time, a reduction of 6.0% from the baseline of 57.11 seconds is achieved, resulting in a cycle time of 53.15 seconds. Regarding the pursuit of a maximized robot maneuverability index, an increase of 140.8% from the baseline of 0.4891235 is achieved, resulting in a maximized value of 1.1786125. Lastly, Use Case 3 tested the modular and multi-objective asset selection methodology, demonstrating its efficacy across diverse operational scenarios. Evaluations conducted with two multi-objective optimization algorithms, Non-Dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm II, revealed interesting implications for selecting and optimizing robotic assets in response to new customer requests. Specifically, Strength Pareto Evolutionary Algorithm II identified a Pareto solution that was more cost-effective (£20,920) compared to Non-Dominated Sorting Genetic Algorithm II (£21,090), while maintaining a competitive specification efficiency score (0.865 vs. 0.879). Consequently, Strength Pareto Evolutionary Algorithm II is preferred for optimizing robotic asset selection in scenarios prioritizing cost. However, should the requirement shift towards maximizing specification efficiency, the Non-Dominated Sorting Genetic Algorithm II would be the more suitable choice. These use cases not only showcased the practical applicability of the developed software but also underlined the robustness and adaptability of the proposed methodologies in real-world manufacturing environments. In conclusion, this doctoral thesis presents a methodology for reconfiguring robotic assembly cells in manufacturing. By harnessing the capabilities of artificial intelligence, knowledge graphs, and simulation methodologies, it addresses the challenges of processing data from diverse sources, adapting to fluctuating market demands, and establishing further optimizations for enhanced operational efficiency in the modern manufacturing landscape. To affirm the viability of this framework, the thesis integrates software development procedures tailored to the proposed methodologies and furnishes evidence through three use cases, which are evaluated against well-defined criteria

Asset selection and optimisation for robotic assembly cell reconfiguration

With the development of Industry 4.0, the manufacturing industry has revolutionized a lot. Product manufacture becomes more and more customized. This trend is achieved by innovative techniques, such as the reconfigurable manufacturing system. This system is designed at the outset for rapid change in its structure, as well as in software and hardware components, to respond to market changes quickly. Robots are important in these systems because they provide the agility and precision required to adapt rapidly to new manufacturing processes and customization demands. Despite the importance of applying robots in these systems, there might be some challenges. For example, there is data from multiple sources, such as the technical manual sensor data. Besides, robot applications must react quickly to the ever-changing process requirements to meet customer's requirements. Furthermore, further optimization, especially layout optimization, is needed to ensure production efficiency after adaptation to the current process requirements. To address these challenges, this doctoral thesis presents a framework for reconfiguring robotic assembly cells in manufacturing. This framework consists of three parts: the experience databank, the methodology for optimal manufacturing asset selection, and the methodology for layout optimization. The experience databank is introduced to confront the challenge of assimilating and processing heterogeneous data from numerous manufacturing sources, which is achieved by proposing a vendor-neutral ontology model. This model is specifically designed for encapsulating information about robotic assembly cells and is subsequently applied to a knowledge graph. The resulting knowledge graph, constituting the experience databank, facilitates the effective organization and interpretation of the diverse data. An optimal manufacturing asset selection methodology is introduced to adapt to shifting processes and product requirements, which focuses on identifying potential assets and their subsequent evaluation. This approach integrates a modular evaluation framework that considers multiple criteria such as cost, energy consumption, and robot maneuverability, ensuring the selection process remains robust in changing market demands and product requirements. A scalable methodology for layout optimization within the reconfigurable robotic assembly cells is proposed to resolve the need for further optimization post-adaption. It introduces a scalable, multi-decision modular optimization framework that synergizes a simulation environment, optimization environment, and robust optimization algorithms. This strategy utilizes the insights garnered from the experience databank to facilitate informed decision-making, thereby enabling the robotic assembly cells to not only meet the immediate production exigencies but also align with the manufacturing landscape's evolving dynamics. The validation of the three methodologies presented in this doctoral thesis encompasses both software development and practical application through three distinct use cases. For the experience databank, an interface was developed using Protégé, Neo4j, and Py2neo, allowing for effective organization and processing of varied manufacturing data. The programming interface for the asset selection methodology was built using Python, integrating with the experience databank via Py2neo and Neo4j to facilitate dynamic and informed decision-making in asset selection. In terms of software for the layout optimization framework, two different applications were developed to demonstrate the framework's scalability and adaptability. The first application, combining Python and C# programming with Siemens Tecnomatix Process Simulate, is geared towards optimizing layouts involving multiple machines. The second application utilizes Python programming alongside the RoboDK API and RoboDK software, tailored for layout optimization in scenarios involving a single robot. Complementing these software developments, the methodologies were further validated through three use cases, each addressing a unique aspect of the framework. Use Case 1 focused on implementing asset selection and system layout optimization based on a single objective, leveraging the experience databank. The required assets are selected, and the required cycle time for executing the whole robotic assembly operation has been reduced by 15.6% from 47.17 seconds to 39.83 seconds. Use Case 2 extended the layout optimization to single-robot operations with an emphasis on multi-criteria decision-making. The energy consumption was minimized to 5613.59 Wh after implementing optimization strategies, demonstrating a significant enhancement in energy efficiency. Compared with the baseline of 6164.98 Wh, this represents an 8.9% reduction in energy usage. For minimized cycle time, a reduction of 6.0% from the baseline of 57.11 seconds is achieved, resulting in a cycle time of 53.15 seconds. Regarding the pursuit of a maximized robot maneuverability index, an increase of 140.8% from the baseline of 0.4891235 is achieved, resulting in a maximized value of 1.1786125. Lastly, Use Case 3 tested the modular and multi-objective asset selection methodology, demonstrating its efficacy across diverse operational scenarios. Evaluations conducted with two multi-objective optimization algorithms, Non-Dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm II, revealed interesting implications for selecting and optimizing robotic assets in response to new customer requests. Specifically, Strength Pareto Evolutionary Algorithm II identified a Pareto solution that was more cost-effective (£20,920) compared to Non-Dominated Sorting Genetic Algorithm II (£21,090), while maintaining a competitive specification efficiency score (0.865 vs. 0.879). Consequently, Strength Pareto Evolutionary Algorithm II is preferred for optimizing robotic asset selection in scenarios prioritizing cost. However, should the requirement shift towards maximizing specification efficiency, the Non-Dominated Sorting Genetic Algorithm II would be the more suitable choice. These use cases not only showcased the practical applicability of the developed software but also underlined the robustness and adaptability of the proposed methodologies in real-world manufacturing environments. In conclusion, this doctoral thesis presents a methodology for reconfiguring robotic assembly cells in manufacturing. By harnessing the capabilities of artificial intelligence, knowledge graphs, and simulation methodologies, it addresses the challenges of processing data from diverse sources, adapting to fluctuating market demands, and establishing further optimizations for enhanced operational efficiency in the modern manufacturing landscape. To affirm the viability of this framework, the thesis integrates software development procedures tailored to the proposed methodologies and furnishes evidence through three use cases, which are evaluated against well-defined criteria

i-DATAQUEST : a Proposal for a Manufacturing Data Query System Based on a Graph

During the manufacturing product life cycle, an increasing volume of data is generated and stored in distributed resources. These data are heterogeneous, explicitly and implicitly linked and they could be structured and unstructured. The rapid, exhaustive and relevant acquisition of information from this data is a major manufacturing industry issue. The key challenges, in this context, are to transform heterogeneous data into a common searchable data model, to allow semantic search, to detect implicit links between data and to rank results by relevance. To address this issue, the authors propose a query system based on a graph database. This graph is defined based on all the transformed manufacturing data. Besides, the graph is enriched by explicitly and implicitly data links. Finally, the enriched graph is queried thanks to an extended queries system defined by a knowledge graph. The authors depict a proof of concept to validate the proposal. After a partial implementation of this proof of concept, the authors obtain an acceptable result and a needed effort to improve the system response time. Finally, the authors open the topic on the subjects of right management, user profile/customization and data update.Chaire ENSAM-Capgemini sur le PLM du futu

Methodological approaches and techniques for designing ontologies in information systems requirements engineering

Programa doutoral em Information Systems and TechnologyThe way we interact with the world around us is changing as new challenges arise, embracing innovative business models, rethinking the organization and processes to maximize results, and evolving change management. Currently, and considering the projects executed, the methodologies used do not fully respond to the companies' needs. On the one hand, organizations are not familiar with the languages used in Information Systems, and on the other hand, they are often unable to validate requirements or business models. These are some of the difficulties encountered that lead us to think about formulating a new approach. Thus, the state of the art presented in this paper includes a study of the models involved in the software development process, where traditional methods and the rivalry of agile methods are present. In addition, a survey is made about Ontologies and what methods exist to conceive, transform, and represent them. Thus, after analyzing some of the various possibilities currently available, we began the process of evolving a method and developing an approach that would allow us to design ontologies. The method we evolved and adapted will allow us to derive terminologies from a specific domain, aggregating them in order to facilitate the construction of a catalog of terminologies. Next, the definition of an approach to designing ontologies will allow the construction of a domain-specific ontology. This approach allows in the first instance to integrate and store the data from different information systems of a given organization. In a second instance, the rules for mapping and building the ontology database are defined. Finally, a technological architecture is also proposed that will allow the mapping of an ontology through the construction of complex networks, allowing mapping and relating terminologies. This doctoral work encompasses numerous Research & Development (R&D) projects belonging to different domains such as Software Industry, Textile Industry, Robotic Industry and Smart Cities. Finally, a critical and descriptive analysis of the work done is performed, and we also point out perspectives for possible future work.A forma como interagimos com o mundo à nossa volta está a mudar à medida que novos desafios surgem, abraçando modelos empresariais inovadores, repensando a organização e os processos para maximizar os resultados, e evoluindo a gestão da mudança. Atualmente, e considerando os projetos executados, as metodologias utilizadas não respondem na totalidade às necessidades das empresas. Por um lado, as organizações não estão familiarizadas com as linguagens utilizadas nos Sistemas de Informação, por outro lado, são muitas vezes incapazes de validar requisitos ou modelos de negócio. Estas são algumas das dificuldades encontradas que nos levam a pensar na formulação de uma nova abordagem. Assim, o estado da arte apresentado neste documento inclui um estudo dos modelos envolvidos no processo de desenvolvimento de software, onde os métodos tradicionais e a rivalidade de métodos ágeis estão presentes. Além disso, é efetuado um levantamento sobre Ontologias e quais os métodos existentes para as conceber, transformar e representar. Assim, e após analisarmos algumas das várias possibilidades atualmente disponíveis, iniciou-se o processo de evolução de um método e desenvolvimento de uma abordagem que nos permitisse conceber ontologias. O método que evoluímos e adaptamos permitirá derivar terminologias de um domínio específico, agregando-as de forma a facilitar a construção de um catálogo de terminologias. Em seguida, a definição de uma abordagem para conceber ontologias permitirá a construção de uma ontologia de um domínio específico. Esta abordagem permite em primeira instância, integrar e armazenar os dados de diferentes sistemas de informação de uma determinada organização. Num segundo momento, são definidas as regras para o mapeamento e construção da base de dados ontológica. Finalmente, é também proposta uma arquitetura tecnológica que permitirá efetuar o mapeamento de uma ontologia através da construção de redes complexas, permitindo mapear e relacionar terminologias. Este trabalho de doutoramento engloba inúmeros projetos de Investigação & Desenvolvimento (I&D) pertencentes a diferentes domínios como por exemplo Indústria de Software, Indústria Têxtil, Indústria Robótica e Smart Cities. Finalmente, é realizada uma análise critica e descritiva do trabalho realizado, sendo que apontamos ainda perspetivas de possíveis trabalhos futuros

A manifesto for future generation cloud computing: research directions for the next decade

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing

A Semantic Information Management Approach for Improving Bridge Maintenance based on Advanced Constraint Management

Bridge rehabilitation projects are important for transportation infrastructures. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success bridge rehabilitation projects, on the other hand improves information management practices in the construction industry

Graafitietokantojen sovelluksia: systemaattinen kirjallisuuskatsaus

Tässä työssä kartoitetaan akateemisessa tutkimuksessa esiintyviä graafitietokantoja, niiden sovellusaloja sekä niihin liitettyjä hyötyjä ja haittoja. Tutkimusmenetelmänä on systemaattinen kirjallisuuskatsaus, jossa tunnistettiin 111 kriteerit täyttävää artikkelia vuosilta 2017–2021. Artikkeleja analysoitiin sisällönanalyysin keinoin. Graafitietokantojen sovellusaloja tunnistettiin 25. Sovellusaloilla tieto on tyypillisesti mallinnettavissa kompleksisina verkkoina. Yleisimpiä aloja olivat bioinformatiikka, sosiaaliset verkostot, tietoverkot ja geografinen tieto. Yksittäisistä graafitietokannoista ylivoimaisesti käytetyin oli Neo4j: se oli käytössä valtaosassa artikkelien sovelluksista. Muut graafitietokannat olivat edustettuna vähäisessä määrin aineistossa. Graafitietokantojen käytölle tunnistettiin kymmenen hyötyä. Yleisimmin mainitut hyödyt olivat graafikyselyiden ja algoritmien hyödyntäminen sekä graafitietokantojen soveltuvuus verkottuneelle datalle. Näiden jälkeen yleisimpinä hyötyinä tulivat selitysvoima erilaisissa analyyseissa, suorituskyky, visualisointiominaisuudet, tietokantakaavion joustavuus ja graafitietomallin ymmärrettävyys. Eri haittoja puolestaan tunnistettiin yhdeksän: haittoja mainittiin kuitenkin ylipäänsä huomattavasti hyötyjä harvemmin. Yleisimmin mainitut haitat olivat suorituskyky ja graafitietokantojen opettelu: molemmat oli mainittu kohtalaisen usein myös hyötynä. Tätä voi selittää sillä, että graafitietokantojen suorituskyvyssä on eroja eri sovellusten välillä: graafitietokantojen ja -kyselykielten koettu vaikeustaso taas riippuu tutkijoiden näkemyksistä. Lisäksi harvemmin mainittuja haittoja olivat muun muassa graafitietokantojen soveltumattomuus tietynlaiselle datalle ja alempi kypsyysaste verrattuna relaatiotietokantoihin