Featured projects (3)
To improve the attractiveness for investment and to meet market requirements of competitivity, in terms of performance, quality, and sustainability, both Morocco and Tunisia need to support industrialization. Therefore, both countries have adopted sustained strategies to support industrial companies to modernize their hardware, software and “humanware” infrastructures through embracing the industry 4.0 paradigm and technologies. The ENHANCE project (2021-2024) is co-funded by the Erasmus+ Programme of the European Union and focuses on developing a knowledge transfer framework and mechanisms to address three main topics, which we refer to by MPQ4.0 (Maintenance, Production, Quality).
vf-OS is a Research and Innovation Action within the European Commission's H2020 program and will contribute to the state of the art in Factories of the Future by providing new research results, concrete applications and demonstrations of the technological advancements in concrete use cases and environments. The project is not limited to providing theory-driven concepts, but will deliver the technological means to realise these concepts in software.
The initiative for Fostering DIHs for Embedding Interoperability in Cyber-Physical Systems of European SMEs (DIH4CPS) will help European enterprises overcome the innovation hurdles and establish Europe as a world leading innovator of the Fourth Industrial Revolution. DIH4CPS will create an embracing, interdisciplinary network of DIHs and solution providers, focussed on cyber-physical and embedded systems, interweaving knowledge and technologies from different domains, and connecting regional clusters with the pan-European expert pool of DIHs.
Featured research (20)
This work aims to review literature related to the latest cyber-physical systems (CPS) for manufacturing in the revolutionary Industry 4.0 for a comprehensive understanding of the challenges, approaches, and used techniques in this domain. Different published studies on CPS for manufacturing in Industry 4.0 paradigms through 2010 to 2019 were searched and summarized. We, then, analyzed the studies at a different granularity level inspecting the title, abstract, and full text to include in the prospective study list. Out of 626 primarily extracted relevant articles, we scrutinized 78 articles as the prospective studies on CPS for manufacturing in Industry 4.0. First, we analyzed the articles’ context to identify the major components along with their associated fine-grained constituents of Industry 4.0. Then, we reviewed different studies through a number of synthesized matrices to narrate the challenges, approaches, and used techniques as the key-enablers of the CPS for manufacturing in Industry 4.0. Although the key technologies of Industry 4.0 are the CPS, Internet of Things (IoT), and Internet of Services (IoS), the human component (HC), cyber component (CC), physical component (PC), and their HC-CC, CC-PC, and HC-PC interfaces need to be standardized to achieve the success of Industry 4.0.
Enterprise IT performance can be improved by providing reactive and predictive monitoring tools that anticipate problem detection. It requires advanced approaches for creating more agile, adaptable, sustainable and intelligent information systems. Service-oriented architecture (SOA) has been used in significant performance-based approaches by information system practitioners. Organizations are interested in performance-based decision support along the layers of SOA to maintain their sustainability for service reuse. Reusability is a very important aspect of Service-based systems (SBS) to analyze service or process reuse. This helps in achieving business agility to meet changing marketplace needs. However currently, there are many challenges pertaining tothe complexities of service reuse evolution along SBS. These challenges are related to the sustainability of service behavior during its lifecycle and the limitations of existing monitoring tools. There is a need for a consolidated classified knowledge-based performance profile, analytical assessment, prediction and recommendation. Therefore, this paper provides a semantic performance-oriented decision support system (SPODSS) for SOA. SPODSS provides recommendations for suggesting service reuse during its evolution. SPODSS is supported by five building blocks. These blocks are data, semantic, traces, machine learning, and decision. SPODSS classify data, validate (analytical assessment, traces, semantic enrichment) at different time intervals and increased consumption and prediction based on consolidated results. It handles the dynamic evolution of SBS and new or changed user requirements by ontology development. Finally, SPODSS generates recommendations for atomic service, composite service, and resourceallocation provisioning. To motivate this approach, we illustrate the implementation of the proposed algorithms for all the five blocks by a business process use case and public data set repositories of shared services. Sustainability and adaptability of service-based systems are ensured by handling new business requirements, dynamicity issues and ensuring performance. Performance criterion includes functional suitability, time behavior, resource utilization, and reliability in terms of availability, maturity, and risk.
Open source and software reuse become more and more challenging for companies to save time and development cost. Collecting existing service-oriented solutions and qualifying them helps to reuse them directly or via orchestration. Our objective in this research work targets the consolidation of an advanced service repository able to be directly mapped to end-user requirements for new business application development. In this perspective, we define a model-based capability profile based on Enterprise Architecture offering a wider view qualification for service-oriented software. We propose a meta-model to gather architecture artifacts used to guide the development of existing solutions. The generated capability profiles help to create a repository of software capabilities. Furthermore, an ontology is proposed to exploit the resulted capability profiles to guide the future business needs to reuse the qualified software in an efficient way. This contribution aims to upgrade research on dynamic services consumption and orchestration to the level of end-users’ requirements mapped with advanced service assets as an enabler for accelerating business application development.
The purpose of this work is to efficiently design a disassembly line, as disassembly system, handling variability of End-of-Life (EoL) product quality as well as uncertainty of disassembly process operations and provides an optimal revenue of such a system. The main finding of this work is the development of a decision tool that allows decision-makers to choose the best disassembly process and disassembly depth, while assigning the corresponding tasks to the line workstations under precedence and cycle time constraints. Task times are assumed to be random variables with known normal probability distributions. Presence of hazardous material is considered and line cycle time constraints are to be jointly satisfied with at least a certain service (probability) level set by the decision-maker. The revenue of a product part depends explicitly on its EoL state or quality. Industrial applicability is shown using an industrial case focused on remanufacturing of mechatronic parts in the automotive industry.