Smart Products for Smart Production – A Use Case Overview

Industry 4.0 is driven by Cyber-Physical Systems and Smart Products. Smart Products provide a value to both its users and its manufacturers in terms of a closer connection to the customer and his data as well as the provided smart services. However, many companies, especially SMEs, struggle with the transformation of their existing product portfolio into smart products. In order to facilitate this process, this paper presents a set of smart product use-cases from a manufacturer’s perspective. These use-cases can guide the definition of a smart product and be used during its architecture development and realization. Initially the paper gives an introduction in the field of smart products. After that the research results, based on case-study research, are presented. This includes the methodological approach, the case-study data collection and analysis. Finally, a set of use-cases, their definitions and components are presented and highlighted from the perspective of a smart product manufacturer

Investigating the Evolving Knowledge Structures in New Technology Development

Part 8: Knowledge Management and Information SharingInternational audienceThe development of new technology has been identified as one of the key enablers to support business and economic growth in developed countries. For example, the United Kingdom (UK) has invested £968 Million into the creation of Catapult centres to provide ‘pull through’ of low Technology Readiness Level (TRL) research and science. While these Catapults have been instrumental in developing new technologies, the uptake of new technology within industry remains a considerable challenge.One of the reasons for this is that of skills and competencies, and in particular, defining the new skills and competencies necessary to effectively apply and operate the new technology within the context of the business. Addressing this issue is non-trivial because the skills and competencies cannot be defined a priori and will evolve with the maturity of the technology. Therefore, there is a need to create methods that enable the elicitation and definition of skills and competencies that co-evolve with new technology development, and what are referred to herein as knowledge structures.To meet this challenge, this paper reports the results from a dynamic co-word network analysis of the technical documentation from New Technology Development (NTD) programmes at the National Composites Centre (NCC). Through this analysis, emerging knowledge structures can be identified and monitored, and be used to inform industry on the skills & competencies required for a technology

INTELLIGENCE ANALYSIS THAT INFLUENCES ON CONFLICT MANAGEMENT IN THE DIGITAL ERA

This study analyzes intelligence influencing conflict management in the digital era. This research is descriptive-explorative. The research location at the PKL Center Jl. Benteng Pancasila City of Mojokerto. The population of 369 MSME actors with the Slovin formula of 0.01 was determined by 79 respondents. Data collection using a questionnaire. Data analysis using SmartPLS version 3.3.7. Test the hypothesis by comparing the T-Statistics and P-Values. The results of this study are: communication intelligence, creative intelligence, and emotional intelligence have a significant effect on conflict management, social intelligence, and strategic intelligence has no effect on conflict management, communication intelligence, creative intelligence, emotional intelligence, social intelligence, and strategic intelligence simultaneously influence conflict management. Research recommendation: Combining this intelligence can resolve conflicts effectively and efficiently and avoid further damage to relationships.This study analyzes intelligence influencing conflict management in the digital era. This research is descriptive-explorative. The research location at the PKL Center Jl. Benteng Pancasila City of Mojokerto. The population of 369 MSME actors with the Slovin formula of 0.01 was determined by 79 respondents. Data collection using a questionnaire. Data analysis using SmartPLS version 3.3.7. Test the hypothesis by comparing the T-Statistics and P-Values. The results of this study are: communication intelligence, creative intelligence, and emotional intelligence have a significant effect on conflict management, social intelligence, and strategic intelligence has no effect on conflict management, communication intelligence, creative intelligence, emotional intelligence, social intelligence, and strategic intelligence simultaneously influence conflict management. Research recommendation: Combining this intelligence can resolve conflicts effectively and efficiently and avoid further damage to relationships

Systemic Conception of the Data Acquisition of Digital Twin Solutions for Use Case-Oriented Development and Its Application to a Gearbox

Digital Twins are being used more and more frequently and provide information from the Real Twin for different applications. Measurements on the Real Twin are required to obtain information, which in many cases requires the installation of supplementary sensors. For their conception and design, it is particularly important that the measuring principles are selected purposefully and the appropriate sensors are integrated at the goal-oriented measuring positions without impairing the functions and other properties of the Real Twin by the integration of these sensors. In this article, a “Design for Digital Twin” approach is discussed for the systematic procedure and demonstrated using a multi-staged gearbox as a concrete example. The approach focuses on the mechanical and hardware side of the Real Twin. For the systematic conception and design of the Digital Twin solution, an understanding of the stakeholder demands and the expected use cases is necessary. Based on the stakeholder demands and use cases, the relevant product properties can be determined. Using the relevant properties, an iterative process of conception, design, and analysis takes place. The conception is carried out by means of target-oriented cause–effect analyses, taking into account systemic interrelations of the Real Twin components and systematics for the selection of measurement principles. Systemic considerations, combined with an effect graph, allow for the analysis and evaluation of disturbing factors

Product Lifecycle Management to Support Industry 4.0: 15th IFIP WG 5.1 International Conference, PLM 2018, Turin, Italy, July 2-4, 2018, Proceedings

International audienceBook Front Matter of AICT 54

Digital Twins in Industry

Digital Twins in Industry is a compilation of works by authors with specific emphasis on industrial applications. Much of the research on digital twins has been conducted by the academia in both theoretical considerations and laboratory-based prototypes. Industry, while taking the lead on larger scale implementations of Digital Twins (DT) using sophisticated software, is concentrating on dedicated solutions that are not within the reach of the average-sized industries. This book covers 11 chapters of various implementations of DT. It provides an insight for companies who are contemplating the adaption of the DT technology, as well as researchers and senior students in exploring the potential of DT and its associated technologies

Aplicación de la metodología MfM (Models for Manufacturing) para la fabricación de piezas aeronáuticas de chapa metálica

La evolución de los productos aeronáuticos, cada vez con mayor número de componentes, mayores exigencias en los plazos de entrega y costes reducidos, llevan a las empresas a optar por sistemas de gestión cada vez más integrados. El futuro de la industria aeronáutica está ligado inevitablemente al desarrollo de tecnologías que favorezcan la digitalización y estandarización, como la Ingeniería de Sistemas Basada en Modelos (MBSE). En las dos últimas décadas, el uso de estructuras semánticas, como los modelos ontológicos, con el objetivo de mejorar los sistemas de información ha demostrado ser muy eficaz en el ámbito industrial. Models for Manufacturing (MfM) es una reciente metodología OBE (Ontology Based Engineering) para definir y reproducir datos, funciones y comportamientos de sistemas de fabricación complejos mediante modelos gráficos. Se trata de una metodología novedosa, aun en construcción, basada en herramientas agnósticas y que se estructura en una arquitectura de 3 capas independientes, siendo la principal la capa de ontología compuesta a su vez por 4 modelos: alcance, datos, comportamiento y semántico En este proyecto se aplica la metodología MfM para caracterizar el proceso de fabricación de piezas metálicas en dos modelos diferentes. Un primer modelo general, esto es, incluyendo las diferentes tecnologías de fabricación de piezas metálicas, del que únicamente se desarrolla el modelo de alcance de la capa de ontología; y otro más específico enfocado en el conformado de chapa metálica. En este último, se desarrollan los 4 modelos de la capa de ontología y además, se propone una estructura para realizar las capas de datos y de servicios en trabajos futuros. Para mejorar la comprensión del modelo, se muestran dos ejemplos prácticos para dos piezas fabricadas con los procesos de conformado de chapa más utilizados en la industria aeronáutica: plegado e hidroconformado. El objetivo del modelo es caracterizar el proceso de conformado, esto es, reconocer el proceso de fabricación más apropiado, y posteriormente definir las operaciones y recursos necesarios, partiendo del modelo CAD de la pieza. Además, el modelo se complementa con otras herramientas MfM ya existentes para el conformado de chapa.The evolution of aeronautical products, with an increasing number of components, more demanding delivery times and reduced costs, is leading companies to opt for increasingly integrated management systems. The future of the aeronautics industry is inevitably linked to the development of technologies that favour digitalisation and standardisation, such as Model-Based Systems Engineering (MBSE). In the last two decades, the use of semantic structures, such as ontological models, with the aim of improving information systems has proven to be very effective in the industrial field. Models for Manufacturing (MfM) is a recent OBE (Ontology Based Engineering) methodology for defining and reproducing data, functions and behaviours of complex manufacturing systems using graphical models. It is a novel methodology, still under construction, based on agnostic tools and structured in an architecture of 3 independent layers, the main one being the ontology layer composed of 4 models: scope, data, behaviour and semantics. In this project, the MfM methodology is applied to describe the manufacturing process of metal parts in two different models. The first one is a general model, that is, including the different technologies for manufacturing metal parts. In this case, only the scope model of the ontology layer is considered. The second one is focused on the sheet metal forming process. In the second one, all 4 models of the ontology layer are developed and,also, a proposed framework for the data and services layers is proposed for future works. To improve the understanding of the model, two practical examples are shown for two parts manufactured with the most commonly used sheet metal forming processes in the aeronautical industry: bending and hydroforming. The aim of the model is to characterise the forming process, i.e. to recognise the most appropriate manufacturing process, and subsequently to define the necessary operations and resources, based on the CAD model of the part. In addition, the model is complemented by other existing MfM tools for sheet metal forming.Universidad de Sevilla. Máster en Ingeniería Aeronáutic

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems