Reference Models for Digital Manufacturing Platforms

[EN] This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g., design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform.This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825631 and from the Operational Program of the European Regional Development Fund (ERDF) of the Valencian Community 2014-2020 IDIFEDER/2018/025.Fraile Gil, F.; Sanchis, R.; Poler, R.; Ortiz Bas, Á. (2019). Reference Models for Digital Manufacturing Platforms. Applied Sciences. 9(20):1-25. https://doi.org/10.3390/app9204433S125920Pedone, G., & Mezgár, I. (2018). Model similarity evidence and interoperability affinity in cloud-ready Industry 4.0 technologies. Computers in Industry, 100, 278-286. doi:10.1016/j.compind.2018.05.003Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. doi:10.3390/app9183865Tran, Park, Nguyen, & Hoang. (2019). Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context. Applied Sciences, 9(16), 3325. doi:10.3390/app9163325Fernandez-Carames, T. M., & Fraga-Lamas, P. (2019). A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories. IEEE Access, 7, 45201-45218. doi:10.1109/access.2019.2908780Moghaddam, M., Cadavid, M. N., Kenley, C. R., & Deshmukh, A. V. (2018). Reference architectures for smart manufacturing: A critical review. Journal of Manufacturing Systems, 49, 215-225. doi:10.1016/j.jmsy.2018.10.006Sutherland, W., & Jarrahi, M. H. (2018). The sharing economy and digital platforms: A review and research agenda. International Journal of Information Management, 43, 328-341. doi:10.1016/j.ijinfomgt.2018.07.004Corradi, A., Foschini, L., Giannelli, C., Lazzarini, R., Stefanelli, C., Tortonesi, M., & Virgilli, G. (2019). Smart Appliances and RAMI 4.0: Management and Servitization of Ice Cream Machines. IEEE Transactions on Industrial Informatics, 15(2), 1007-1016. doi:10.1109/tii.2018.2867643Gerrikagoitia, J. K., Unamuno, G., Urkia, E., & Serna, A. (2019). Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives. Applied Sciences, 9(14), 2934. doi:10.3390/app9142934Digital Manufacturing Platforms, Factories 4.0 and beyondhttps://www.effra.eu/digital-manufacturing-platformsZero Defect Manufacturing Platform Project 2019https://www.zdmp.eu/Zezulka, F., Marcon, P., Vesely, I., & Sajdl, O. (2016). Industry 4.0 – An Introduction in the phenomenon. IFAC-PapersOnLine, 49(25), 8-12. doi:10.1016/j.ifacol.2016.12.002Announcing the IoT Industrie 4.0 Reference Architecturehttps://www.ibm.com/cloud/blog/announcements/iot-industrie-40-reference-architectureVelásquez, N., Estevez, E., & Pesado, P. (2018). Cloud Computing, Big Data and the Industry 4.0 Reference Architectures. Journal of Computer Science and Technology, 18(03), e29. doi:10.24215/16666038.18.e29Pisching, M. A., Pessoa, M. A. O., Junqueira, F., dos Santos Filho, D. J., & Miyagi, P. E. (2018). An architecture based on RAMI 4.0 to discover equipment to process operations required by products. Computers & Industrial Engineering, 125, 574-591. doi:10.1016/j.cie.2017.12.029Calvin, T. (1983). Quality Control Techniques for «Zero Defects». IEEE Transactions on Components, Hybrids, and Manufacturing Technology, 6(3), 323-328. doi:10.1109/tchmt.1983.113617

Automated Experiments for Deriving Performance-relevant Properties of Software Execution Environments

The execution environment can play a crucial role when analyzing the performance of a software system. However, detecting execution environment properties and integrating such properties into performance analyses is a manual, error-prone task. In this thesis, a novel approach for detecting performance-relevant properties of the software execution environment is presented. These properties are automatically detected using predefined experiments and integrated into performance prediction tools

A Process Model for the Integrated Reasoning about Quantitative IT Infrastructure Attributes

IT infrastructures can be quantitatively described by attributes, like performance or energy efficiency. Ever-changing user demands and economic attempts require varying short-term and long-term decisions regarding the alignment of an IT infrastructure and particularly its attributes to this dynamic surrounding. Potentially conflicting attribute goals and the central role of IT infrastructures presuppose decision making based upon reasoning, the process of forming inferences from facts or premises. The focus on specific IT infrastructure parts or a fixed (small) attribute set disqualify existing reasoning approaches for this intent, as they neither cover the (complex) interplay of all IT infrastructure components simultaneously, nor do they address inter- and intra-attribute correlations sufficiently. This thesis presents a process model for the integrated reasoning about quantitative IT infrastructure attributes. The process model’s main idea is to formalize the compilation of an individual reasoning function, a mathematical mapping of parametric influencing factors and modifications on an attribute vector. Compilation bases upon model integration to benefit from the multitude of existing specialized, elaborated, and well-established attribute models. The achieved reasoning function consumes an individual tuple of IT infrastructure components, attributes, and external influencing factors to expose a broad applicability. The process model formalizes a reasoning intent in three phases. First, reasoning goals and parameters are collected in a reasoning suite, and formalized in a reasoning function skeleton. Second, the skeleton is iteratively refined, guided by the reasoning suite. Third, the achieved reasoning function is employed for What-if analyses, optimization, or descriptive statistics to conduct the concrete reasoning. The process model provides five template classes that collectively formalize all phases in order to foster reproducibility and to reduce error-proneness. Process model validation is threefold. A controlled experiment reasons about a Raspberry Pi cluster’s performance and energy efficiency to illustrate feasibility. Besides, a requirements analysis on a world-class supercomputer and on the European-wide execution of hydro meteorology simulations as well as a related work examination disclose the process model’s level of innovation. Potential future work employs prepared automation capabilities, integrates human factors, and uses reasoning results for the automatic generation of modification recommendations.IT-Infrastrukturen können mit Attributen, wie Leistung und Energieeffizienz, quantitativ beschrieben werden. Nutzungsbedarfsänderungen und ökonomische Bestrebungen erfordern Kurz- und Langfristentscheidungen zur Anpassung einer IT-Infrastruktur und insbesondere ihre Attribute an dieses dynamische Umfeld. Potentielle Attribut-Zielkonflikte sowie die zentrale Rolle von IT-Infrastrukturen erfordern eine Entscheidungsfindung mittels Reasoning, einem Prozess, der Rückschlüsse (rein) aus Fakten und Prämissen zieht. Die Fokussierung auf spezifische Teile einer IT-Infrastruktur sowie die Beschränkung auf (sehr) wenige Attribute disqualifizieren bestehende Reasoning-Ansätze für dieses Vorhaben, da sie weder das komplexe Zusammenspiel von IT-Infrastruktur-Komponenten, noch Abhängigkeiten zwischen und innerhalb einzelner Attribute ausreichend berücksichtigen können. Diese Arbeit präsentiert ein Prozessmodell für das integrierte Reasoning über quantitative IT-Infrastruktur-Attribute. Die grundlegende Idee des Prozessmodells ist die Herleitung einer individuellen Reasoning-Funktion, einer mathematischen Abbildung von Einfluss- und Modifikationsparametern auf einen Attributvektor. Die Herleitung basiert auf der Integration bestehender (Attribut-)Modelle, um von deren Spezialisierung, Reife und Verbreitung profitieren zu können. Die erzielte Reasoning-Funktion verarbeitet ein individuelles Tupel aus IT-Infrastruktur-Komponenten, Attributen und externen Einflussfaktoren, um eine breite Anwendbarkeit zu gewährleisten. Das Prozessmodell formalisiert ein Reasoning-Vorhaben in drei Phasen. Zunächst werden die Reasoning-Ziele und -Parameter in einer Reasoning-Suite gesammelt und in einem Reasoning-Funktions-Gerüst formalisiert. Anschließend wird das Gerüst entsprechend den Vorgaben der Reasoning-Suite iterativ verfeinert. Abschließend wird die hergeleitete Reasoning-Funktion verwendet, um mittels “What-if”–Analysen, Optimierungsverfahren oder deskriptiver Statistik das Reasoning durchzuführen. Das Prozessmodell enthält fünf Template-Klassen, die den Prozess formalisieren, um Reproduzierbarkeit zu gewährleisten und Fehleranfälligkeit zu reduzieren. Das Prozessmodell wird auf drei Arten validiert. Ein kontrolliertes Experiment zeigt die Durchführbarkeit des Prozessmodells anhand des Reasonings zur Leistung und Energieeffizienz eines Raspberry Pi Clusters. Eine Anforderungsanalyse an einem Superrechner und an der europaweiten Ausführung von Hydro-Meteorologie-Modellen erläutert gemeinsam mit der Betrachtung verwandter Arbeiten den Innovationsgrad des Prozessmodells. Potentielle Erweiterungen nutzen die vorbereiteten Automatisierungsansätze, integrieren menschliche Faktoren, und generieren Modifikationsempfehlungen basierend auf Reasoning-Ergebnissen

Reusability in manufacturing, supported by value net and patterns approaches

The concept of manufacturing and the need or desire to create artefacts or products is very, very old, yet it is still an essential component of all modem economies. Indeed, manufacturing is one of the few ways that wealth is created. The creation or identification of good quality, sustainable product designs is fundamental to the success of any manufacturing enterprise. Increasingly, there is also a requirement for the manufacturing system which will be used to manufacture the product, to be designed (or redesigned) in parallel with the product design. Many different types of manufacturing knowledge and information will contribute to these designs. A key question therefore for manufacturing companies to address is how to make the very best use of their existing, valuable, knowledge resources. […] The research reported in this thesis examines ways of reusing existing manufacturing knowledge of many types, particularly in the area of manufacturing systems design. The successes and failures of reported reuse programmes are examined, and lessons learnt from their experiences. This research is therefore focused on identifying solutions that address both technical and non-technical requirements simultaneously, to determine ways to facilitate and increase the reuse of manufacturing knowledge in manufacturing system design. [Continues.

An integrated product and process information modelling system for on-site construction

The inadequate infrastructure that exists for seamless project team communications has its roots in the problems arising from fragmentation, and the lack of effective co-ordination between stages of the construction process. The use of disparate computer-aided engineering (CAE) systems by most disciplines is one of the enduring legacies of this problem and makes information exchange between construction team members difficult and, in some cases, impossible. The importance of integrating modelling techniques with a view to creating an integrated product and process model that is applicable to all stages of a construction project's life cycle, is being recognised by the Construction Industry. However, improved methods are still needed to assist the developer in the definition of information model structures, and current modelling methods and standards are only able to provide limited assistance at various stages of the information modelling process. This research investigates the role of system integration by reviewing product and process information models, current modelling practices and modelling standards in the construction industry, and draws conclusions with similar practices from other industries, both in terms of product and process representation, and model content. It further reviews various application development tools and information system requirements to support a suitable integrated information structure, for developing an integrated product and process model for design and construction, based on concurrent engineering principles. The functional and information perspectives of the integrated model, which were represented using IDEFO and the unified modelling language (UML), provided the basis for developing a prototype hyper-integrated product and process information modelling system (HIPPY). Details of the integrated conceptual model's implementation, practical application of the prototype system, using house-building as an example, and evaluation by industry practitioners are also presented. It is concluded that the effective integration of product and process information models is a key component of the implementation of concurrent engineering in construction, and is a vital step towards providing richer information representation, better efficiency, and the flexibility to support life cycle information management during the construction stage of small to medium sized-building projects

Metaheuristic Design Patterns: New Perspectives for Larger-Scale Search Architectures

Design patterns capture the essentials of recurring best practice in an abstract form. Their merits are well established in domains as diverse as architecture and software development. They offer significant benefits, not least a common conceptual vocabulary for designers, enabling greater communication of high-level concerns and increased software reuse. Inspired by the success of software design patterns, this chapter seeks to promote the merits of a pattern-based method to the development of metaheuristic search software components. To achieve this, a catalog of patterns is presented, organized into the families of structural, behavioral, methodological and component-based patterns. As an alternative to the increasing specialization associated with individual metaheuristic search components, the authors encourage computer scientists to embrace the ‘cross cutting' benefits of a pattern-based perspective to optimization algorithms. Some ways in which the patterns might form the basis of further larger-scale metaheuristic component design automation are also discussed