Production Process Modelling Architecture to Support Improved Cyber-Physical Production Systems

With the proliferation of intelligent networks in industrial environments, manufacturing SME’s have been in a continuous search for integrating and retrofitting existing assets with modern technologies that could provide low-cost solutions for optimizations in their production processes. Their willingness to support a technological evolution is firmly based on the perception that, in the future, better tools will guarantee process control, surveillance and maintenance. For this to happen, the digitalization of valuable and extractable information must be held in a cost-effective manner, through contemporary approaches such as IoT, creating the required fluidity between hardware and software, for implementing Cyber-Physical modules in the manufacturing process. The goal of this work is to develop an architecture that will support companies to digitize their machines and processes through an MDA approach, by modeling their production processes and physical resources, and transforming into an implementation model, using contemporary CPS and IoT concepts, to be continuously improved using forecasting/predictive algorithms and analytics.authorsversionpublishe

A PLC Variable Identification Method by Manual Declaration of Time-Stamped Events

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On key technologies for realising digital twins for structural dynamics applications

The term digital twin has gained increasing popularity over the last few years. The concept, loosely based on a virtual model framework that can replicate a particular system for contexts of interest over time, will require the development and integration of several key technologies in order to be fully realised. This paper, focusing on vibration-related problems in mechanical systems, discusses these key technologies as the building blocks of a digital twin. The example of a simulation digital twin that can be used for asset management is then considered. After briefly discussing the building blocks required, the process of data-augmented modelling is selected for detailed investigation. This concept is one of the defining characteristics of the digital twin idea, and using a simple numerical example, it is shown how augmenting a model with data can be used to compensate for the inherent model discrepancy. Finally the implications of this type of data augmentation for future digital twin technology is discussed

Digitalized manufacturing logistics in engineer-to-order operations

This is a post-peer-review, pre-copyedit version of an article published in Advances in Production Management Systems. Production Management for the Factory of the Future. APMS 2019. IFIP Advances in Information and Communication Technology, vol. 566. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-30000-5_71. The high complexity in Engineer-To-Order (ETO) operations causes major challenges for manufacturing logistics, especially in complex ETO, i.e. one-of-a-kind production. Increased digitalization of manufacturing logistics processes and activities can facilitate more efficient coordination of the material and information flows for manufacturing operations in general. However, it is not clear how to do this in the ETO environment, where products are highly customized and production is non-repetitive. This paper aims to investigate the challenges related to manufacturing logistics in ETO and how digital technologies can be applied to address them. Through a case study of a Norwegian shipyard, four main challenges related to manufacturing logistics are identified. Further, by reviewing recent literature on ETO and digitalization, the paper identifies specific applications of digital technologies in ETO manufacturing. Finally, by linking manufacturing logistics challenges to digitalization, the paper suggests four main features of digitalized manufacturing logistics in ETO: (i) seamless, digitalized information flow, (ii) identification and interconnectivity, (iii) digitalized operator support, and (iv) automated and autonomous material flow. Thus, the paper provides valuable insights into how ETO companies can move towards digitalized manufacturing logistics

Digital Twin Requirements in the Context of Industry 4.0

Part 3: PLM for Digital Factories and Cyber Physical SystemsInternational audienceDigital Twin (DT) is being considered a significant enabler for Industry 4.0 initiatives. Within Industry 4.0, the amount of digital product information generated and collected over the entire lifecycle has been growing. Current information and communication technologies, including data storage, data processing, and wireless data transmission, may be leveraged to digitally mirror the lifecycle of a corresponding physical product with increasing level of detail. A DT creates a link between physical products and their virtual models with more comprehensive data and accumulation of knowledge. Therefore, a DT may be applied to enhance simulation, traceability and to support the offering of value-added services along the lifecycle. However, the definition of a DT and its requirements are not yet fully established. The characteristics a DT model should possess to be widely used in manufacturing remains an open question in the literature. The concept is still broad and dependent on the lifecycle stage and industry sector of application. Therefore, the objective of this paper is to propose an initial synthesis of DT requirements based on a literature review and industry interviews. The literature review focuses on the content analysis of papers published from 2010 to 2018 and indexed in the ISI Web of Science database. The interviews were conducted with industry representatives in Brazil. The results show that DT requirements are related to real-time data, integration, and fidelity. Besides, it shows that industry requirements are close to literature and the actual implementation of DT is the future of research in this field

The Transformation Towards Smart (er) Factories:Integration Requirements of the Digital Twin

The vision of the smart factory with its interconnected systems is based on the seamless (real-time) integration of data across the information system (IS) landscape. Yet, due to the existence of many legacy systems, this task is far from trivial. In industrial practice, the IS landscape comprises systems of different application functionality which can be characterized as technologically heterogeneous, e.g. transaction processing versus real-time systems. Integrating such systems has always been a major challenge and constraining force for many organizations. This problem is receiving renewed attention in the context of the implementation of the digital twin in manufacturing. Due to its central role in the IS landscape, the digital twin needs to communicate with a number of heterogeneous applications to achieve its full potential, i.e. achieving a complete virtual representation of an asset, process or product. This research analyses the integration requirements from the perspective of the digital twins’ application functionality. In particular, we provide an explicit mapping of the integrations needed between the digital twin and existing information systems (IS) in manufacturing, which serves as a basis to better understand integration issues. These findings provide an explanation for and a conceptualization of some of the challenges that emerge when transforming towards an interconnected smart factory

Digital Shadows as an Enabler for the Internet of Production

Part 5: Cyber-Physical Production Systems and Digital TwinsInternational audienceDue to increasing atomization, manufacturing companies generate increasing amounts of production data. Most of this data is domain-specific, heterogeneous and unstructured. This complicates the access, interpretation, analysis and usage for efficiency improvement, faster reaction to change and weaknesses identification. To overcome this challenge, the idea of an “internet of production” is to link all kind of production relevant data by a data lake. Based on this data lake, digital shadows aggregate data for a specific purpose. For example, digital shadows in production planning and control help to manage the dynamic changes like delays in production or machine break–downs. This paper examines the existing research in the field of digital twins and digital shadows in manufacturing and gives a brief overview of the historical development. In particular, the potential and possible applications of digital shadows in production planning and control are analyzed. A top–down–bottom–up approach is developed to support the design of digital shadows in production planning and control