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ScienceDirect Procedia Manufacturing 9 (2017) 222 – 228

7th Conference on Learning Factories, CLF 2017

A Web-based Application for Classifying Teaching and Learning Factories Dimitris Mavrikiosa, Konstantinos Sipsasa, Konstantinos Smparounisa, Loukas Rentzosa, George Chryssolourisa* a

Laboratory for Manufacturing Systems and Automation, Dept. of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26500 Greece

Abstract The importance of leveraging manufacturing teaching and training, up to the standards of current and future needs, is quite evident. Recent studies have shown the urgency for future engineers and knowledge workers to adopt new teaching curricula in order to cope with the increasing industrial requirements. Teaching and Learning Factories aim at aligning manufacturing teaching and training standards with the needs of modern industrial practice. Both paradigms comprise an infrastructure that is required for efficient operation despite its different nature. Learning Factories depend on industrial-grade equipment, installed in academic sites, for the educational implementation of their curriculum. On the other hand, Teaching Factories, which aim to bring the real industrial practice into the academic setting, rely on modern ICT technologies for the facilitation of interaction and knowledge transfer. This paper presents an approach to the classification of Teaching and Learning Factories infrastructures. Specifically, these infrastructures refer to installations that offer industrial-grade equipment and a modern ICT installation for the operation of both paradigms, either in a local or distributed mode. These environments comprise various types of equipment with a vast range of specifications and characteristics. The proposed web-based application offers a way of storing the knowledge, related to such installations and creates relations that can be easily classified. © 2017 2016The TheAuthors. Authors. Published Elsevier © Published by by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 7th Conference on Learning Factories. Peer review under responsibility of the scientific committee of the 7th Conference on Learning Factories Keywords: Teaching Factory, Learning Factory

* Corresponding author. Tel.: +30-2610-910160; fax: +30-2610-997744. E-mail address: [email protected]

2351-9789 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer review under responsibility of the scientific committee of the 7th Conference on Learning Factories doi:10.1016/j.promfg.2017.04.002

Dimitris Mavrikios et al. / Procedia Manufacturing 9 (2017) 222 – 228

1. Introduction Considering the importance of manufacturing as a wealth generating activity for any nation, the promotion of excellence in manufacturing education will become a strategic target in the years to come [1]. However, teaching and training have not kept pace with the advances in technology. Modern concepts of training, industrial learning and knowledge transfer schemes can contribute to improving the innovation performance of European manufacturing [2]. Manufacturing is a subject that cannot be treated effectively only inside a classroom, whilst industry can only evolve through the adoption of new research results. To effectively address the emerging challenges for the delivery of manufacturing education and skills ([3], [4]) the educational paradigm in manufacturing, should be revised. Many educational institutions have tried to bring their educational practice closer to industry ([2], [5], [6], [7]) with the concept of Teaching and Learning Factories (LF). The Teaching Factory approach is based on the knowledge triangle notion, as suggested in [8], [9], [10] and [11]. The aim is to effectively integrate education, research and innovation activities into a single initiative, involving industry and academia. Towards that end, the Teaching Factory paradigm focuses on integrating industry and academia, through novel adaptations to the teaching / training curricula, achieved by the deployment of ICT-based delivery mechanisms. The current study presents an approach for the classification of the existing Teaching and Learning Factories through a web-based application. The classification is based on an effort for the creation of a common norm, towards the necessary ingredients characterizing a Learning Factory. Since Learning and Teaching Factories can bring great value to the educational and training practices, both at academic and industrial business levels, having a unified database of the existing facilities and paradigms, can facilitate the wide implementation of the manufacturing of the future. This uniform description focuses on the different aspects of a Learning and Teaching Factory that have to do with its operation, the applications involved, the products and processes related to its function, the didactics and learning contents, as well as the metrics for the measuring of its efficiency and outputs. The following sections of this paper give an insight to all the aforementioned aspects and present the implementation of this uniform description in a web-application. Finally, the use-case of the “Learning Factories Morphology Application” is presented in the context of the relevant CIRP (The International Academy for Production Engineering) working group. 2. Database Model The developed database model constitutes several entities, aiming to define the knowledge existing in a Learning and Teaching Factory, in a complete context, based on the morphology suggested in [12]. The main entities of the database model, presented in this paper, are the user, the associated facility and the application scenarios, associated with each facility (see Figure 1).

Figure 1: Entity–relationship diagram of the Learning Factories Morphology database.

The application scenarios entity contains all the associated knowledge, describing the Learning and Teaching Factories. It consists of a set of eight associated entities, namely the operating model, product, learning factory

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metrics, purpose target, didactics, process, setting and videos (see Figure 2).

Figure 2: Entity–relationship diagram of the Learning Factories Morphology database.

The ‘operating model’ entity describes the operation of the LF, in terms of personnel, financing and business model. The ‘product’ of the LF is described in technical terms and the ‘process’ stores data are related to the way that the product is manufactured within the LF and its lifecycle. Each application scenario is also related to data describing the ‘purpose target’ of the LF, mainly regarding the industry and research relevance. The ‘didactics’ entity, deals with the learning conditions and the theoretical foundations that are used during a single application scenario. The knowledge for the efficiency and output of the application scenario for the LF are stored under the ‘metrics’ entity. The ‘setting’ entity describes the LF as a system, while the ‘videos’ entity stores pertinent multimedia material to the LF. 3. Technical Implementation On top of the database schema, described in section 2 of this paper, a web application has been developed, called “Learning Factories Morphology Application” or abbreviated LF application that facilitates data visualization and editing. The LF application is deployed on a Java servlet container, following a three-tier multi-layer architecture (see Figure 3). The LF application design is based on an open architecture and follows the Client/Server approach, based on the three-tier example, comprising the data tier, the business tier, and the presentation tier. These tiers communicate through the internet or an intranet, depending on the type of communication. Based on the 3-tier example, the platform operates in a user-friendly Windows-based environment and the connection among the users complies with the Browser User Interface (BUI), which allow the exploitation of all net-place capabilities with the use of any desired web browser.


The user interface, as a part of the presentation tier, is rendered through a standard Web browser (i.e. Internet Explorer, Mozilla, etc.). It is developed using HTML, CSS, and JavaScript and is responsible for the provision of a series of elements for accessing, editing, and searching the data that reside in the persistence tier. The business tier comprises the groups together with the backend side of the application, the presentations, the business logic, and the data access layers. The architecture of this level can be further divided into a series of layers that provide different kinds of functionalities and are deployed in the application server. 1. The presentation layer groups along with the functionality for the generation of the user interface elements that provide information to the user and facilitate data input (i.e. LF graphs, search forms, etc.). In particular, the main visualization component is the Infrastructure graph visualization, which is responsible for rendering graphs (see Figure 4) with all the details of each learning factory being available. In addition, this layer is in support of the access rights control by enabling/disabling editing functionality according to the infrastructure ownership. 2. The business logic layer is responsible for the provision of data access/processing and the exception handling functionality, managing user access rights, and providing search facilities. 3. The data access layer is responsible for providing access to the database, through high level Java objects. The data tier includes the persistence layer and serves as the application’s data store. It provides connections to the database for the recovery and storage of data through query execution mechanisms.

Figure 3: Three-tier multi-layer architecture of the Learning Factories Morphology web-application.

The technologies used in the implementation of this web application are Java, JSP, Spring Framework, HTML, CSS, and JavaScript. The database schema was deployed on the MySQL server. The use of such technologies,

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enables the separation of the rational presentation of the applications from that of business. Apache Tomcat is used for deploying the web application and making it accessible to the users, over the Internet. 4. CIRP Use Case The use-case of this web-application is for storing, searching and viewing Learning and Teaching Factories, in the respective CIRP collaborative working group. It aims at having a common understanding of the existing Learning and Teaching Factories. The web application provides the user with a navigation menu atthe top of the screen (see Figure 4). The user, through this menu, can browse all the applications’ functionalities.

Figure 4: Home screen of the Learning Factories Morphology web application, depicting the interactive graph for visualizing a LF.

Starting from left to right, the first element is the Home link, which classifies the existing facilities. The user has multiple views of the way that these infrastructures are presented, such as a) by facility b) by country and c) by application scenario. The next two are the Browse link, which directs the user to the browse functionality of the application and the Map link that directs the user to an interactive map with a complete geographical overview of the stored LF facilities. Each LF facility may have one or more application scenarios. There are two visualization components: First, there is the interactive graph, which can provide navigation through the existing instantiations and visualize the concepts and associated information. Second, is the table view, which shows all the values of a specific instance in a table format. There are also two search components present. The ‘Map’ is an interactive map, where the available LFs are indicated. The user is able to navigate a particular LF by clicking the corresponding icon on the map. Through the ‘Print’ functionality, the user can have the LF information in a printable format. Finally, the last two elements of the menu are the Help, which directs the user to the user’s manual, the Login that enables the user to add a new infrastructure or edit an existing one and the Register in order to create a new user account. The application administrator has the system’s overall security management. In order for a user to have the right


of accessing the private part of the web application, it is required that he/she be registered. All the application users, according to their speciality, are categorized by the administrator into different roles, so as for each user to have the proper rights of accessing the infrastructure data of the application. The visitor of the web application is able to navigate through the existing instantiations of LFs through an interactive graph. The latter functionality (see Figure 4) visualizes the concepts in a tree form. Once the user has selected a specific entity, the properties table is shown with the values of the specific instance. The current statistics of the Learning Factories Morphology web application show that, so far, eighteen (18) Learning and Teaching Factories have been created by an equal number of organizations, from nine (9) countries. 5. Conclusions and Outlook The current paper has presented a web-based application for the classification of Learning and Teaching Factories. The development of the database was made on the basis of a morphology of Learning and Teaching Factories. This database stores knowledge related to the operating model, product, learning factory metrics, purpose target, didactics, process, setting and videos. Its objective is to have a unified and structural way of defining facilities used for Learning and Teaching Factories. The developed web-application is used in the context of a collaborative working group of CIRP, in order to be made a common description of the existing facilities, throughout eighteen organizations so far. As future work, the web application will be enhanced in order to support the operations of the Learning and Teaching Factories registered in the form of a collaborative platform. Furthermore, the knowledge existing in the web-application, could be used in a new functionality, for the selection of the optimal match between the skills/requirements for learning/training and the available facilities that can support them. 6. Acknowledgements The authors would like to express their gratitude to the colleagues from TU Darmstadt / PTW, for their contribution and support during this work. 7. References [1] Manufuture High Level Group and Implementation Support Group (2006). ManuFuture PlaTeaching Factoryorm - Strategic Research Agenda, assuring the future of manufacturing in Europe. Manufuture PlaTeaching Factoryorm. [2] Chryssolouris, G., Mavrikios, D., Mourtzis, D. (2013). Manufacturing Systems: Skills & Competencies for the Future. Procedia CIRP Keynote paper of the 46th CIRP Conference on Manufacturing Systems, 7, 17-24. [3] Hanushek, A.E., Wößmann, L. (2007). The Role of Education Quality in Economic Growth. Policy Research WP 4122, World Bank. Washington, D.C. [4] Ifo Institute, Cambridge Econometrics, Danish Technological Institute (2012). Study on the Competitiveness of the EU Mechanical Engineering Industry. Final Report. [5] Dinkelmann, M., Riffelmacher, P., Westkämper, E. (2011). Training concept and structure of the Learning Factory advanced Industrial Engineering. In H. ElMaraghy (Ed.), Enabling Manufacturing Competitiveness and Economic Sustainability: Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual Production CARV 2011 (pp. 624-629). Montreal. [6] Tisch, M., Hertle, C., Cachay, J., Abele, E., Metternich, J., Tenberg, R. (2013). A systematic approach on developing action-oriented, competency-based Learning Factories. Procedia CIRP, 7, 580-585. [7] Wagner, U., AlGeddawy, T., ElMaraghy, H., Müller, E. (2012). The State-of-the-Art and Prospects of Learning Factories. Procedia CIRP, 3, 109-114. [8] Mavrikios, D., Papakostas, N., Mourtzis, D., Chryssolouris, G. (2011). On industrial learning & training for the Factories of the Future: A conceptual, cognitive & technology framework. Journal of Intelligent Manufacturing Special Issue on Engineering Education. 24/3, 473-485. [9] Rentzos, L., Doukas, M., Mavrikios, D., Mourtzis, D., Chryssolouris, G. (2014). Integrating Manufacturing Education with Industrial Practice using Teaching Factory Paradigm: A Construction Equipment Application. Procedia CIRP, Accepted for Publication. [10] Chryssolouris, G., Mavrikios, G., Rentzos, L. (2014). On a new educational paradigm for manufacturing: The Teaching Factory. Proceeding of WPK (Wiener Produktionstechnik Kongress) 2014: Industrie 4.0 – die intelligente Fabrik der Zukunft. Vienna, Austria. [11] Chryssolouris, G., Mavrikios, D., Papakostas, N., Mourtzis, D. (2006). Education in Manufacturing Technology & Science: A view on Future Challenges & Goals. Proceedings of the International Conference on Manufacturing Science and Technology Inaugural Keynote, Melaka, Malaysia.

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[12] Tisch M., Ranz F., Abele E., Metternich J., Hummel V., Learning Factory Morphology – Study Of Form And Structure Of An Innovative Learning Approach In The Manufacturing Domain, TOJET: The Turkish Online Journal of Educational Technology – July 2015, Special Issue 2 for INTE 2015, pp. 356-363