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Linking Industry 4.0, Learning Factory, and Simulation: testbeds and proof-of-concept experiments
Abstract
Learning factories have an important role in the development of Industry 4.0, providing a rich environment where researchers and companies can collaborate and test (in multiple scenarios) the application of cutting-edge technologies (e.g. the internet of things, big data, collaborative robots, additive manufacturing, simulation modeling). The purpose of this paper is to identify and describe testbeds and proof-of-concept experiments, mainly related to simulation modeling, developed in learning factories to support Industry 4.0 deployment. It also aims to present the lab shared by Produtique Québec and the Center of Excellence in Innovative Manufacturing Enterprise Management (CEGEMI), located at Sherbrooke, Canada, identifying its potential to conduct simulation modeling research and to promote the digitalization of manufacturing companies. This research combines a
literature review with a case presentation. For the literature review, data were collected through electronic data sources to analyze the development of Industry 4.0 learning factories and to identify existing Industry 4.0 testbeds and proof-of-concept experiments documented in the scientific literature. As for the case presentation, site visits and interviews were conducted to understand how the lab can support applied research and companies’ transition to Industry 4.0. The literature review and case presentation show the relevance of leaning factories to develop applied research and to deploy Industry
4.0, especially in small and medium-sized enterprises (SME) that tend incrementally to move towards digitalization. Moreover, it describes how learning factories can incorporate and test a wide range of Industry 4.0’ technologies through testbeds and proof-of-concept experiments, supporting experimental validation of different artifacts, such as simulation modeling frameworks.
... architecture model, to serve as a reference for targeted development of problem-specific competencies. Ferreira et al. [9] identified and characterized test beds and proof-of-concept experiments developed in learning factories to support Industry 4.0 adoption, with a focus on simulation modeling. ...
Manufacturing industries are facing radical changes under the technological acceleration of Industry 4.0. The manufacturing workforce is not ready for such disruptions due to the lack of vertical skills on digital technologies. Production planning and control
of manufacturing systems is often an experience-based art. Further, the companies need of offering training paths for long-life
learning of their employees finds several obstacles in the availability of skilled trainers and the trainee’s low engagement with
traditional learning models. This paper presents how the FactoryBricks project aims at overcoming the aforementioned issues. The
project delivers effective training courses to enable the uptake of industrial technologies and smart manufacturing systems for professionals, either executives or technicians. Beside digital learning contents, the learners are offered an interaction with lab-scale
models of production systems built with modular components such as LEGO®. The courses are designed in a modular way, and
aim to teach manufacturing concepts in three main topics: (1) the physical system and its dynamics, (2) the physical-digital data
connections for smart online analytics, and (3) the exploitation of digital models for production. The paper also presents the results
of the prototypical implementation of the project.
Industry 4.0 is a central strategy to strengthen the competitiveness of the manufacturing sector over the next years. Nevertheless, there is a lack of common understanding of Industry 4.0 and tools to help companies’ transformation to Industry 4.0, especially for small and medium-sized enterprises (SMEs). To address these research gaps, this study proposes a framework to characterise and evaluate Industry 4.0 scenarios to aid companies’ transition towards Industry 4.0. For this, a design science (multi-methodological) approach is adopted, including an Industry 4.0 use case survey, modelling and simulation and two proof-of-concept cases developed in collaboration with a Canadian college centre for technology transfer. The results indicate that the proposed framework can help companies identify Industry 4.0 scenarios more intuitively to assist project conception, portfolio selection and planning during Industry 4.0 roadmap development. Finally, the results of this study suggest that Industry 4.0 can be implemented incrementally while companies increase their digital capabilities and maturity.
Fundamental changes of business processes in railway transport are the outcomes of implementing large-scale research and technical projects known today as “digital railways”. The adoption of digital technologies has enabled more effective management of the industry intangible assets which are defined in the article as industry-specific knowledge. In particular, there is increasing pragmatic interest in ontologies as a form of industry-specific knowledge representation and Semantic Web standards relevant to the challenges facing digital railways in a global scale. This is a powerful argument in favour of an ontology-based model of interaction between the railway industry and engineering education (railway universities). Such an ontology-based model is the core of knowledge factory didactic concept proposed by the authors. Addressing the ontologies provides developing a model of highly structured, open conceptual space organized through Semantic Web standards for industry-specific knowledge interoperability and their machine processing. The study used a bottom-up approach to constructing Russian-English ontologies as the basis of e-learning training courses intended for future railway engineers. The Onto.plus software with an embedded multi-user ontology editor supporting the Semantic Web standards and the way of knowledge representation in Controlled Natural Languages (in particular, the author's version of Controlled Russian Language) was applied for that purpose. The constructed ontologies are considered as a key element of didactic tools to introduce theoretical foundations of knowledge factory concept in the learning and teaching process. The integration of an ontology-based model into the competence-based paradigm of engineering education is ensured through a method for developing students’ cognitive skills focusing on the step-by-step formation of concepts.
One of the best-known tools from Lean Thinking is the Value Stream Mapping (VSM), which stands for a visual modelling approach allowing the identification of critical steps in a supply chain, thus helping to identify organisational bottlenecks and wastes. Despite its relevance, VSM is based on a static report from the time the mapping was performed. To add dynamism to VSM analysis, computer-based simulations can be employed; however, little is done in the domain of Systems Dynamics (SD), a known modelling approach usually employed to get insights about stock and flows behaviour. In this context, this research aims to propose a practical framework called VSM-to-SD to help modellers easily translate VSM into System Dynamics quantitative models using the notation of two known modelling tools on the market, namely Stella© and Vensim©. To demonstrate its usefulness and ease-of-use, a proof-of-concept is developed in an industrial case study. Results suggest the efficiency of the VSM-to-SD framework, including its ability to straightforwardly generate simulation-based VSM, allowing analytic comparisons of different simulation scenarios, and expanding the analytic decision-making capacity of managers desiring to go further than traditional and static VSM.
In the context of digitization and industry 4.0, the production-related disciplines developed powerful simulation models with different scopes and varying levels of detail. As these simulation systems are usually not built in a compatible way, the models cannot be combined easily. Co-simulation techniques provide a promising basis for combining these models into one superordinate model and utilizing it for planning new factories, adapting existing ones or for production planning. However, today’s co-simulation systems do not benefit from the inherent flexibility of the represented production systems. Simulation-based optimization is carried out inside each discipline’s simulation system, which means that interdisciplinary, global optima are often impossible to reach. Additionally, the aspect of human interaction with such complex co-simulation systems is often disregarded. Addressing these two issues, this paper presents a concept for combining different simulation models to interdisciplinary multi-level simulations of production systems. In this concept, the inherent flexibilities are capitalized to enhance the flexibility and performance of production systems. The concept includes three hierarchical levels of production systems and allows human interaction with the simulation system. These three levels are the Process Simulation level, the Factory Simulation level, and the Human Interaction level, but the concept is easily extendable to support additional levels. Within the multi-level structure, each simulation system carries out a multi-objective optimization. Pareto-optimal solutions are forwarded to simulations on higher hierarchical levels in order to combine them and meet flexibly adaptable objectives of the entire production system. The concept is tested by means of a simplified production system, to optimize it in terms of throughput time and electric energy consumption. Results show that the presented interdisciplinary combination of heterogeneous simulation models in multi-level simulations has the potential to optimize the productivity and efficiency of production systems.
Rapid advances in new generation information technologies, such as big data analytics, internet of things (IoT), edge computing and artificial intelligence, have nowadays driven traditional manufacturing all the way to intelligent manufacturing. Intelligent manufacturing is characterised by autonomy and self-optimization, which proposes new demands such as learning and cognitive capacities for manufacturing cell, known as the minimum implementation unit for intelligent manufacturing. Consequently, this paper proposes a general framework for knowledge-driven digital twin manufacturing cell (KDTMC) towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimizing and controlling strategy. Three key enabling technologies including digital twin model, dynamic knowledge bases and knowledge-based intelligent skills for supporting the above strategy are analysed, which equip KDTMC with the capacities of self-thinking, self-decision-making, self-execution and self-improving. The implementing methods of KDTMC are also introduced by a thus constructed test bed. Three application examples about intelligent process planning, intelligent production scheduling and production process analysis and dynamic regulation demonstrate the feasibility of KDTMC, which provides a practical insight into the intelligent manufacturing paradigm.
The recent manufacturing trend toward mass customization and further personalization of products requires factories to be smarter than ever before in order to: (1) quickly respond to customer requirements, (2) resiliently retool machinery and adjust operational parameters for unforeseen system failures and product quality problems, and (3) retrofit old systems with upcoming new technologies. Furthermore, product lifecycles are becoming shorter due to unbounded and unpredictable customer requirements, thereby requiring reconfigurable and versatile manufacturing systems that underpin the basic building blocks of smart factories. This study introduces a modular factory testbed, emphasizing transformability and modularity under a distributed shop-floor control architecture. The main technologies and methods, being developed and verified through the testbed, are presented from the four aspects of rapid factory transformation: self-layout recognition, rapid workstation and robot reprogramming, inter-layer information sharing, and configurable software for shop-floor monitoring.
In this age of digital disruption, every industry is undergoing digital transformation and manufacturing is no exception. Since Internet-of-Things and Big-Data have immense transformational potential, manufacturing companies are in a race to implement IoT based solutions to innovate, improve productivity, reduce costs and improve their market share. As companies try to embrace IoT, one of the key challenges that they face is to come up with an “Integrated Approach”. “Integrated Approach” includes fitting IoT in the overall system landscape, selecting the right use case, evaluating and deciding on implementation approach. This paper provides guidance to transform legacy manufacturing unit to a smart factory conforming to Industry 4.0 principles. It presents a reference architecture and offers various practical approaches for consideration to embark on digital journey. It details each approach along with its own benefits and challenges, and helps organizations to realistically determine faster time-to-market vs its long-term goals. This paper attempts to perform a deep dive on various aspects of integrated approach which will be immensely helpful for organizations and researchers alike.
The concept of Industrie 4.0 has initiated an evolutionary process in the manufacturing industry. Especially small and medium sized companies lack the capacities and knowledge to accomplish this process autonomously. The learning factory concept offers the possibility to second companies within this process. This paper will present a concept of how enterprises can be trained within the realm of a learning factory based on scenarios of different Industrie 4.0 evolutionary steps regarding the three dimensions of the sociotechnical approach. These scenarios focus on demonstrating and assessing the dependencies of assorted characteristics of Industrie 4.0 category groups. An important supporting tool during this process of assessment is a holistic Industrie 4.0 maturity model, which will assist the enterprises in recognizing the dependencies during different evolutionary steps. The concept was developed based on the results of the research project Adaption, which are outlined beforehand.
The existing learning factories cover a variety of learning environments. Each implementation of a learning factory looks differently and is used for a different purpose. Several of the newer learning factories have a focus on Industry 4.0 and demonstrate different implementation aspects, but some of them lack a significant hands-on component. Only a few learning factories implement several Industry 4.0 components and have a strong hands-on aspect. Any new implementation needs to identify the aspects on Industry 4.0 that are addressed and to decide the ratio between demonstrations and hands-on processes that answer the goal of the learning factory. A new implementation of a learning factory that uses several technologies included in the Industry 4.0 vision and has a strong experiential learning approach is presented. This implementation provides modern design and simulation, prototyping, manufacturing processes, including 3D metal printing resources, and automated assembly and testing systems that incorporate Industry 4.0 implementation strategy technologies such as Cyber-Physical Systems, Internet of Things, and Industrial Internet of Things.
The concept of smart factory includes collection and distribution of information in real time to help human operators in their work. Machine setup instructions are notable examples of such information. This paper advocates the use of augmented reality (AR) to present such information addressing in an intuitive manner the demonstration of setup tasks on the variety of machine tools that are available in a factory and for the multitude of parts that are produced. Turning and machining centres are used as typical machine tools, for which setup instruction applications were created on a widely used computer game development platform enhanced by standard AR functionality based on target markers present on the scene. This AR demonstrator has been primarily created as a proof of concept that highlights the feasibility of such low-cost, efficient and user friendly AR applications of industrial guidance and training. The emphasis of the work is on technical aspects of AR application development, which is lacking in literature. In particular, interfacing of distance sensors that impart intelligence, scenario structuring in terms of states and state threads, as well as techniques ensuring expansibility, flexibility and ease of authoring of such applications are demonstrated.
The manufacturing industry is moving “digital” and the changes required are numerous and extensive. In general, there is a lack of understanding of how new technologies are beneficial to the manufacturing industry. This trans-formation, also known as the 4th industrial revolution or Industry 4.0, created the need of a research platform that could enable practitioners and academia collaboration on new technologies. Aalborg University developed a Learning Factory that is a facility equipped with machines, materials and tools established to support research projects. This paper presents Aalborg University’s "small Industry 4.0 factory" and its contribution to Industry 4.0 research by creating a platform for developing technologies to satisfy manufacturing requirements and by demonstrating their value in a production environment. The Leaning Factory is contributing to research by enabling the development of manufacturing technology. At the same time, it is providing practitioners with a platform for solving industrial problems.
Full text available at http://ceur-ws.org/Vol-1898/paper4.pdf
Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed firstly, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the Overall Equipment Effectiveness (OEE) of the equipment is significantly improved.
The fourth industrial revolution involves the advanced topics such as industrial Internet of Things (IIoT), Cyber-physical system (CPS) and smart manufacturing that address increasing demands for mass customized manufacturing. The agent-based manufacturing is a highly distributed control paradigm that can cope with these challenges well. This paper gives an overview of agent-based architectures for manufacturing systems. Besides, a cloud-assisted self-organized architecture (CASOA) is presented by comprising smart agents and cloud to communicate and negotiate through networks. Ontological representations of knowledge base are constructed to provide the information basis for decision-making of agents, which enables dynamic reconfiguration among agents in a collaborative way to achieve agility and flexibility. Furthermore, the agents’ interaction behavior is modeled to structure the agents hierarchically to reduce the complexity, because the interactions among agents in distributed system are difficult to understand and predict. The experimental results show that the presented architecture can be easily deployed to build smart manufacturing system and can improve the adaptiveness and robustness of the manufacturing system when dealing with mixed multi-product tasks.
The main objective of the current study is to analyse and develop production monitoring system (PMS) for small and medium enterprises (SME) with predictive functionality. The development of planned PMS covers design of hardware architecture, building data analysis and prediction tools. New tool wear forecast model has been developed. The proposed model extends traditional widely used models by introducing the effects of working regimes and multiple passes on tool/component wear.
Industry 4.0 provides new paradigms for the industrial management of SMEs. Supported by a growing number of new technologies, this concept appears more flexible and less expensive than traditional enterprise information systems such as ERP and MES. However, SMEs find themselves ill-equipped to face these new possibilities regarding their production planning and control functions. This paper presents a literature review of existing applied research covering different Industry 4.0 issues with regard to SMEs. Papers are classified according to a new framework which allows identification of the targeted performance objectives, the required managerial capacities and the selected group of technologies for each selected case. Our results show that SMEs do not exploit all the resources for implementing Industry 4.0 and often limit themselves to the adoption of Cloud Computing and the Internet of Things. Likewise, SMEs seem to have adopted Industry 4.0 concepts only for monitoring industrial processes and there is still absence of real applications in the field of production planning. Finally, our literature review shows that reported Industry 4.0 projects in SMEs remained cost-driven initiatives and there in still no evidence of real business model transformation at this time.
Nowadays, there are plenty of studies that seek to determine which are the skills that should be met by an engineer. Communication and teamwork are some of the most recurrent ones associated with a knowledge of the engineering sciences. However, their application is not straight forward, due to the lack of educational approaches that contributes to develop experience-based knowledge. Learning Factories (LF) have shown to be effective for developing theoretical and practical knowledge in a real production environment. This article describes the transformation process of a training-addressed manufacturing workshop, in order to structure a Learning Factory for the production engineering program at EAFIT University. The proposed transformations were based on the definition of three pillars (didactic, integrative and engineering) for the development of an LF.
The large product variety driven by customers’ preferences and fluctuation in number of product variants produced annually impose manufacturing challenges. Changeable, reconfigurable, adaptable and smart manufacturing (Industry 4.0) paradigms aim at dealing with these challenges. The implementation of such paradigms presents many challenges to industry. Learning factories can be used as a research test bed and in educating engineering students and practitioners with the required knowledge and continuing professional development. This paper demonstrates the steps involved in introducing a new product family to an existing changeable learning factory characterized by changeability enablers including mobility, modularity, scalability and convertibility. A new product family of belt tensioners is introduced as a new product for the assembly learning factory, the iFactory, in the Intelligent Manufacturing Systems (IMS) Center, which initially assembled a family of desk sets with 265 variants. All required steps starting with the rationale of selecting the new product family, process planning, redesign of fixtures, pallets, and system re-configuration are discussed. The ability of the modular learning factory to change and adapt to the new product family, the involved experiential learning objectives and benefits and research projects along with the experience with the transition to new products family are discussed.
In this paper, potentials and limits of learning factories are described regarding its key application fields: Education and training, research, and innovation transfer. On the one hand it is shown how learning factories support the methodical modelling of effective competency development, how they enable important feedback processes for the learner, how they open up possibilities in production research, and how the concept is used in recent years for innovation transfer purposes. On the other hand, for the learning factory concept as it is implemented today several limitations can be identified. The limits of learning factories are systematized in limits regarding the resources needed, the mapping ability, the scalability, the mobility, and the effectiveness of learning factories. In order to overcome the identified limitations, various learning factory concept advancements are identified.
The Institute of Innovation and Industrial Management operates a learning factory (LeanLab) since 2014 to enhance academic education, company trainings and hands-on research in the fields of industrial engineering and logistics. The aim is to enable practice oriented learning in an environment close to industrial reality for facilitating effective knowledge-transfer. An extension with state of the art technologies relevant for future manufacturing sites is currently planned. Learning factories are suitable platforms for applying, testing and spreading technologies and should thus guarantee better and more targeted technology-development and transfer. Moreover, training sessions for workers will be designed to prepare them for the Industry 4.0 environment in the factory of the future. These trainings will focus on the development of the skills and qualifications necessary for workers to cope with the increasing complexity in industrial environments. This paper presents the actual state of the LeanLab and illustrates the next concrete steps towards an Industry 4.0 learning factory.
Cyber-physical systems are integrations of computation, networking, and physical processes and they are increasingly finding applications in manufacturing. Cloud manufacturing integrates cloud computing and service-oriented technologies with manufacturing processes and provides manufacturing services in manufacturing clouds. A cyber physical system for manufacturing is not a manufacturing cloud if it does not use virtualization technique in cloud computing and service oriented architecture in service computing. On the other hand, a manufacturing cloud is not cyber physical system if it does not have components for direct interactions with machine tools and other physical devices. In this paper, a new paradigm of Cyber-Physical Manufacturing Cloud (CPMC) is introduced to bridge gaps among cloud computing, cyber physical systems, and manufacturing. A CPMC allows direct operations and monitoring of machine tools in a manufacturing cloud over the Internet. A scalable and service-oriented layered architecture of CPMC is developed. It allows publication and subscription of manufacturing web services and cross-platform applications in CPMC. A virtualization method of manufacturing resources in CPMC is presented. In addition, communication mechanisms between the layers of the CPMC using communication protocols such as MTConnect, TCP/IP, and REST are discussed. A CPMC testbed is developed and implemented based on the proposed architecture. The testbed is fully operational in two geographically distributed sites. The developed testbed is evaluated using several manufacturing scenarios. Its testing results demonstrate that it can monitor and execute manufacturing operations remotely over the Internet efficiently in a manufacturing cloud.
The concept of “Industry 4.0” that covers the topics of Internet of Things, cyber-physical system, and smart manufacturing, is a result of increasing demand of mass customized manufacturing. In this paper, a smart manufacturing framework of Industry 4.0 is presented. In the proposed framework, the shop-floor entities (machines, conveyers, etc.), the smart products and the cloud can communicate and negotiate interactively through networks. The shop-floor entities can be considered as agents based on the theory of multi-agent system. These agents implement dynamic reconfiguration in a collaborative manner to achieve agility and flexibility. However, without global coordination, problems such as load-unbalance and inefficiency may occur due to different abilities and performances of agents. Therefore, the intelligent evaluation and control algorithms are proposed to reduce the load-unbalance with the assistance of big data feedback. The experimental results indicate that the presented algorithms can easily be deployed in smart manufacturing system and can improve both load-balance and efficiency.
Best Paper Award 2018 of IJPR (55th Volume Anniversary: 15 Free to Access Papers IJPR)
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In the context of Industry 4.0, it is necessary to meet customization manufacturing demands on a timely basis. Based on the related concepts of Industry 4.0, this paper intends to introduce mobile services and cloud computing technology into the intelligent manufacturing environment. A customization manufacturing system is designed to meet the demands of personalization requests and flexible production mechanisms. This system consists of three layers, namely a manufacturing device layer, cloud service system layer, and mobile service layer. The manufacturing device layer forms the production platform. This platform is composed of a number of physical devices, such as a flexible conveyor belt, industrial robots, and corresponding sensors. The physical devices are connected to the cloud via the support of a wireless module. In the cloud, the manufacturing big data is processed, and the optimization decision-making mechanism pertaining to customization manufacturing is formed. Then, mobile services running in a mobile terminal are used to receive orders from customers and to inquire the necessary production information. To verify the feasibility of the proposed customization manufacturing system, we also established a customizable candy production system.
Digitalization and the informational interconnection of value-adding-processes promise a multitude of opportunities to increase the competitiveness of production companies. This article introduces a research project, which takes an already existent production setting of a learning factory as a starting point and aims to optimize it using Industrie 4.0 approaches. The given value chain and IT-infrastructure are thereby used as a foundation, which is not rebuild, but retrofitted for an increased efficiency and flexibility. Additionally, an outlook on a competence center will be given, which aims to holistically transfer the vision of digitalization into company practice.
Industry 4.0 has been proposed to address personalized consumption demands by building cyber-physical production systems for smart manufacturing. Although cloud manufacturing and some integrated frameworks for smart factory have been presented in literatures, it still lacks industrial applications. In this paper, we use personalized candy packing application as a demonstration to illustrate our smart factory design. We first describe the component layers of the smart factory, i.e., physical devices, private cloud, client terminals, and network, to enable the smart factory to be integrated with other systems, such as banks and logistical network, to cope with personalized consumption demands. Then, we present a scheme for inter-layered interaction. As for the physical devices, we also design an intra-layered negotiation mechanism to implement dynamic reconfiguration, so that the system can support hybrid production of multi-typed products. Finally, we give experimental results to verify efficiency, self-organized process, and hybrid production paradigm of the proposed system.
In recent years, there have been great advances in industrial Internet of Things (IIoT) and its related domains, such as industrial wireless networks (IWNs), big data, and cloud computing. These emerging technologies will bring great opportunities for promoting industrial upgrades and even allow the introduction of the fourth industrial revolution, namely, Industry 4.0. In the context of Industry 4.0, all kinds of intelligent equipment (e.g., industrial robots) supported by wired or wireless networks are widely adopted, and both real-time and delayed signals coexist. Therefore, based on the advancement of software-defined networks technology, we propose a new concept for industrial environments by introducing software-defined IIoT in order to make the network more flexible. In this paper, we analyze the IIoT architecture, including physical layer, IWNs, industrial cloud, and smart terminals, and describe the information interaction among different devices. Then, we propose a software-defined IIoT architecture to manage physical devices and provide an interface for information exchange. Subsequently, we discuss the prominent problems and possible solutions for software-defined IIoT. Finally, we select an intelligent manufacturing environment as an assessment test bed, and implement the basic experimental analysis. This paper will open a new research direction of IIoT and accelerate the implementation of Industry 4.0.
In the last decade, numerous learning factories for education, training, and research have been built up in industry and academia. In recent years learning factory initiatives were elevated from a local to a European and then to a worldwide level. In 2014 the CIRP Collaborative Working Group (CWG) on Learning Factories enables a lively exchange on the topic “Learning Factories for future oriented research and education in manufacturing”. In this paper results of discussions inside the CWG are presented. First, what is meant by the term Learning Factory is outlined. Second, based on the definition a description model (morphology) for learning factories is presented. The morphology covers the most relevant characteristics and features of learning factories in seven dimensions. Third, following the morphology the actual variance of learning factory manifestations is shown in six learning factory application scenarios from industrial training over education to research. Finally, future prospects of the learning factory concept are presented.
"Proof of concept" is a phrase frequently used in descriptions of research sought in program announcements, in experimental studies, and in the marketing of new technologies. It is often coupled with either a short definition or none at all, its meaning assumed to be fully understood. This is problematic. As a phrase with potential implications for research and technology, its assumed meaning requires some analysis to avoid it becoming a descriptive category that refers to all things scientifically exciting. I provide a short analysis of proof of concept research and offer an example of it within synthetic biology. I suggest that not only are there activities that circumscribe new epistemological categories but there are also associated normative ethical categories or principles linked to the research. I examine these and provide an outline for an alternative ethical account to describe these activities that I refer to as "extended agency ethics". This view is used to explain how the type of research described as proof of concept also provides an attendant proof of principle that is the result of decision-making that extends across practitioners, their tools, techniques, and the problem solving activities of other research groups.
Developing competencies of employees on all hierarchy levels is a crucial prerequisite to enable fast problem solving and adaption to changing market conditions. Action-oriented learning approaches in learning factories show promising results, though a systematic approach for the design of learning factory courses and systems is missing. This article presents such a systematic approach for the competency-oriented development of learning factories integrating the conceptual design levels ‘learning factory’, ‘teaching module’ and ‘learning situation’. The presented approach enables an effective competency development in learning factories by addressing problems of intuitively designed learning systems. As a result learning factories, teaching modules and single teaching–learning situations meeting industries’ requirements can be realised with less effort and an increased success in applied competencies in real situations.
This paper presents an approach to how existing production systems that are not Industry 4.0-ready can be expanded to participate in an Industry 4.0 factory. Within this paper, a concept is presented how production systems can be discovered and included into an Industry 4.0 (I4.0) environment, even though they did not have I4.0-interfaces when they have been manufactured.
The concept is based on a communication gateway and an information server. Besides the concept itself, this paper presents a validation that demonstrates applicability of the developed concept.
The advancement of Industry 4.0 and its enabling technologies (e.g., the Internet of Things and Big Data analytics) pose new challenges to the field of simulation due to the increasing complexity of systems to be modelled. Despite several advances in this domain, the literature still lacks a state-of-the-art review of simulation-based approaches applied in the context of the Fourth Industrial Revolution. This paper therefore aims to provide a systematic literature review and meta-analysis of peer-reviewed publications between 2011 and 2018 in the field of simulation modelling applied in Industry 4.0, with a focus on manufacturing and logistics systems. This review contributes to the literature in two significant ways. First, it provides a broad coverage of current publications on the theme by reviewing 112 papers, using a meta-analysis approach. Moreover, it presents a detailed evaluation of different elements from these publications, including research activities (application area, methods, frameworks and software tools), gaps and directions. Around 80% of the papers analysed were published in the last two years. Finally, the results suggest that the main simulation approaches used in Industry 4.0 are agent-based or mixed methods and identify Digital Twin (DT) as the most promising simulation technology.
In this study, we have identified that a Proof of Concept can be characterized as a research practice and instrument of knowledge creation, based on a set of activities that are applied to the study and understanding of certain objects by the actors involved. In Information Systems Development, we have characterized a Proof of Concept as a system that creates socio-technical phenomena and, with the aim of understanding these phenomena, we use the Context Engineering approach. Context Engineering represents the relationship between a set of essential movements in a new framework of activities of Information Systems Development. Furthermore, we highlight Information Science, which allows us to study in formal and rigorous ways the processes, techniques, conditions, and effects that are entailed in improving the efficacy of information, which is used for a range of purposes related to individual, social and organizational needs, as well as new methods of scientific communication. To our knowledge, and following a review of the literature, there is a lack of studies combined with gaps in the knowledge of Proof of Concept practices. The misunderstanding of these practices may strengthen the probability of compromising the reliability, reproducibility, and reusability of the knowledge consumed or constructed in a Proof of Concept, which may affect its appropriate utilization by the organizations, their actors, or communities of practice. In this context, this paper aims to promote a preliminary study of the state-of-the-art scientific knowledge and to contribute to the body of published literature on Proof of Concept practices.
urpose
The purpose of this paper is to conduct a state-of-the-art review of the ongoing research on the Industry 4.0 phenomenon, highlight its key design principles and technology trends, identify its architectural design and offer a strategic roadmap that can serve manufacturers as a simple guide for the process of Industry 4.0 transition. The study performs a systematic and content-centric review of literature based on a six-stage approach to identify key design principles and technology trends of Industry 4.0. The study further benefits from a comprehensive content analysis of the 178 documents identified, both manually and via IBM Watson’s natural language processing for advanced text analysis.
Industry 4.0 is an integrative system of value creation that is comprised of 12 design principles and 14 technology trends. Industry 4.0 is no longer a hype and manufacturers need to get on board sooner rather than later. The strategic roadmap presented in this study can serve academicians and practitioners as a stepping stone for development of a detailed strategic roadmap for successful transition from traditional manufacturing into the Industry 4.0. However, there is no one-size-fits-all strategy that suits all businesses or industries, meaning that the Industry 4.0 roadmap for each company is idiosyncratic, and should be devised based on company’s core competencies, motivations, capabilities, intent, goals, priorities and budgets.
The first step for transitioning into the Industry 4.0 is the development of a comprehensive strategic roadmap that carefully identifies and plans every single step a manufacturing company needs to take, as well as the timeline, and the costs and benefits associated with each step. The strategic roadmap presented in this study can offer as a holistic view of common steps that manufacturers need to undertake in their transition toward the Industry 4.0. The study is among the first to identify, cluster and describe design principles and technology trends that are building blocks of the Industry 4.0. The strategic roadmap for Industry 4.0 transition presented in this study is expected to assist contemporary manufacturers to understand what implementing the Industry 4.0 really requires of them and what challenges they might face during the transition process.
This paper introduces a framework to assess the performance of manufacturing systems using hybrid simulation in real time. Continuous and discrete variables of different machines are monitored to analyze performance using a virtual environment running synchronous to plant floor equipment as a reference. Data are extracted from machines using industrial Internet of Things solutions. Productivity and reliability of a physical system are compared in real time with data from a hybrid simulation. The simulation uses discrete-event systems to estimate performance metrics at a system level, and continuous dynamics at a machine level to monitor input and output variables. Simulation outputs are used as a reference to detect abnormal conditions based on deviations of real outputs in different stages of the process. This monitoring method is implemented in a fully automated manufacturing system testbed with robots and CNC machines. Machines are integrated on an Ethernet/IP control network using a programmable logic controller to coordinate actions and transfer data. Results demonstrated the capacity to perform real-time monitoring and capture performance errors within confidence intervals.
The objective of this research is to demonstrate through simulation techniques and analyses performed in production systems of a company located in the city of Guarulhos, which produces an electronic component that has plastic, acrylic and steel, the improvements that can be acchieved with the use of a specialist software to assist the manager in decision making. For the study, concepts of simulation, Monte Carlo method, queueing theory and the software Arena were used. By simulating processes and evaluating performance, the software offers reports that assist the manager to see more clearly potential bottlenecks and points of improvement in process, thus effectively contributing to the company's competitiveness on the market. With the study presented in this article it was possible to verify the importance of the use of this tool such as barriers they currently face to grow faster, and to find evidences of how collaboration between organizations could facilitate the process of acquiring competitive advantage.
This paper provides an abstract view of the Industry 4.0 as the next industrial revolution. Cyber Physical Systems (CPS) as smart connected solutions are considered to be the key answer to the needs of future industry. Effects of this revolution on the logistics sector is analysed and integration of CPS in this field is presented. To evaluate the quality of CPS solutions in the field of logistics, PhyNetLab and its subcomponents are presented as a physical testbed for testing CPS nodes, structural designs, communication platform and protocols in addition to the energy challenges for materials handling and warehousing application. inBin and P-ink as two CPS solutions are reviewed in the context of the order-picking. Also, iCon as an alternative outdoor asset tracking solution is presented.
Learning factories present a promising environment for education, training and research, especially in manufacturing related areas which are a main driver for wealth creation in any nation. While numerous learning factories have been built in industry and academia in the last decades, a comprehensive scientific overview of the topic is still missing. This paper intends to close this gap by establishing the state of the art of learning factories. The motivations, historic background, and the didactic foundations of learning factories are outlined. Definitions of the term learning factory and the corresponding morphological model are provided. An overview of existing learning factory approaches in industry and academia is provided, showing the broad range of different applications and varying contents. The state of the art of learning factories curricula design and their use to enhance learning and research as well as potentials and limitations are presented. Conclusions and an outlook on further research priorities are offered.
Smart factory, as one of key future for our industry, requires logistics automation within a manufacturing site such as a shop floor. Automated guided vehicle (AGV) systems may be one solution, whose accuracy will be influenced by some factors. This paper presents a radio frequency identification (RFID)-enabled positioning system in AGV for smart factory. Key impact factors on AGV's accuracy such as magnetic field in circular antenna, circular magnetic field, and circular contours stability are examined quantitatively. Based on the examinations, simulation studies and a testbed are carried out to evaluate the feasibility and practicality of the proposed approach. It is observed that large diameter antennas are used in driving zone and small diameter antennas are used in parking zone. This approach was compared with another method using passive RFID tags and it is superior to that method with greatly reduced tags’ deployment. Observations and lessons from simulation and testbed studies could be used for guiding automatic logistics within a smart manufacturing shop floor.
Computer Systems Performance Evaluation and Prediction bridges the gap from academic to professional analysis of computer performance.This book makes analytic, simulation and instrumentation based modeling and performance evaluation of computer systems components understandable to a wide audience of computer systems designers, developers, administrators, managers and users. The book assumes familiarity with computer systems architecture, computer systems software, computer networks and mathematics including calculus and linear algebra.
Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry
- H Kagermann
- J Helbig
- A Hellinger
- W Wahlster
H. Kagermann, J. Helbig, A. Hellinger, and W. Wahlster, "Recommendations for
implementing the strategic initiative INDUSTRIE 4.0: Securing the future of
German manufacturing industry; final report of the Industrie 4.0 Working Group,"
Forschungsunion, 2013.
Learning integrated product and manufacturing systems
- H Elmaraghy
- W Elmaraghy
H. ElMaraghy and W. ElMaraghy, "Learning integrated product and
manufacturing systems," Procedia CIRP, vol. 32, pp. 19-24, 2015.
Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production
- S Erol
- A Jäger
- P Hold
- K Ott
- W Sihn
S. Erol, A. Jäger, P. Hold, K. Ott, and W. Sihn, "Tangible Industry 4.0: A
Scenario-Based Approach to Learning for the Future of Production," Procedia
CIRP, vol. 54, pp. 13-18, 2016.
Building an Industry 4.0-compliant lab environment to demonstrate connectivity between shopflor and IT levels of an enterprise
- M Zarte
- A Pechmann
- J Wermann
- F Gosewehr
M. Zarte, A. Pechmann, J. Wermann, and F. Gosewehr, "Building an
Industry 4.0-compliant lab environment to demonstrate connectivity between
shopflor and IT levels of an enterprise," IECON 2016 -42nd Annu. Conf. IEEE
Ind. Electron. Soc., pp. 6590-6595, 2016.
Experimentable Digital Twins-Streamlining Simulation-Based Systems Engineering for Industry 4.0
- M Schluse
- M Priggemeyer
- L Atorf
- J Rossmann
M. Schluse, M. Priggemeyer, L. Atorf, and J. Rossmann, "Experimentable
Digital Twins-Streamlining Simulation-Based Systems Engineering for Industry
4.0," IEEE Trans. Ind. Informatics, vol. 14, no. 4, pp. 1722-1731, 2018.
Simulation-based Control of Reconfigurable Robotic Workcells : Inter-active Planning and Execution of Processes in Cyber-Physical Systems State-of-the-Art
- M Priggemeyer
- J Rossmann
M. Priggemeyer and J. Rossmann, "Simulation-based Control of
Reconfigurable Robotic Workcells : Inter-active Planning and Execution of
Processes in Cyber-Physical Systems State-of-the-Art," in ISR 2018; 50th
International Symposium on Robotics, 2018, pp. 1-8.
Human-machine collaboration in virtual reality for adaptive production engineering
- A De Giorgio
- M Romero
- M Onori
- L Wang
A. De Giorgio, M. Romero, M. Onori, and L. Wang, "Human-machine
collaboration in virtual reality for adaptive production engineering," Procedia
Manuf., vol. 11, pp. 1279-1287, 2017.
Centre d'excellence en gestion de l'entreprise manufacturière innovante (CEGEMI). 720 Rue Longpré, Sherbrooke, QC J1G 4L3
- Canada Qc
Université de Sherbrooke. 2500 boul. de l'Université, Sherbrooke, QC J1K2R1, Canada.
6 Centre d'excellence en gestion de l'entreprise manufacturière innovante (CEGEMI). 720 Rue Longpré,
Sherbrooke, QC J1G 4L3, Canada
3 rd International Symposium on Supply Chain 4.0: Challenges and Opportunities of Digital
Transformation, Intelligent Manufacturing and Supply Chain Management 4.0,
ISSC4 -2019, October 24-28 th, Indianapolis, USA.
http://supplychain4.org/