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TU Wien Pilot Factory Industry 4.0
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Available Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Procedia Manufacturing 00 (2019) 000–000 Procedia Manufacturing 00 (2019) 000–000
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Procedia Manufacturing 31 (2019) 200–205 Procedia Manufacturing 00 (2017) 000–000
9th Conference on Learning Factories 2019 9th Conference on Learning Factories 2019
www.elsevier.com/locate/procedia
TU Wien Pilot Factory Industry 4.0 TU Wien Pilot Factory Industry 4.0
a 2017, MESIC Manufacturing Society a,International Conference Junea, Martin Henniga,Engineering Gerhard Reisinger *, Thomas Trautner , Philipp Holda,2017, Detlef28-30 Gerhard a a,Vigo (Pontevedra),bSpain a a 2017, Martin Hennig , Gerhard Reisinger Alexandra *, ThomasMazak Trautner , Philipp Hold , Detlef Gerharda, Alexandra Mazakb Wien Pilot Factory Industry 4.0, Seestadtstraße 27, 1220 Vienna, Austria Costing models TU for optimization inVienna, Industry 4.0: Trade-off TU capacity Wien, Favoritenstrasse 9-11, 1040 Austria TU Wien Pilot CDL-MINT, Factory Industry 4.0, Seestadtstraße 27, 1220 Vienna, Austria TU Wien, CDL-MINT, Favoritenstrasse 9-11, 1040 Vienna, Austria between used capacity and operational efficiency a a
b b
Abstract A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb Abstract a Driven by present megatrends like digitization demographic Learning Universityand of Minho, 4800-058change, Guimarães, PortugalFactories all over the world are gaining b Unochapecó, 89809-000 Chapecó, Brazil Factories recognition in science, industry like and society. In these learning environments, students from Driven by present megatrends digitization and collaborative demographic change,SC, Learning all and overinterested the worldtrainees are gaining industry acquire methodological, social and personal competencies learning for future challenges instudents production. TU Wien trainees Pilot Factory recognition in science, industry and society. In these collaborative environments, and interested from Industry 4.0 (in short TU PF) is a Pilot, and Learning for Factory, to provide companies a fundamental insight industry acquire methodological, socialDemonstration and personal competencies futureaiming challenges in production. TU Wien Pilot Factory into Industry 4.0short techniques, and associated challenges implementation of aa digitized production Industry 4.0 (in TU PF) applications is a Pilot, Demonstration and Learningthrough Factory,exemplary aiming to provide companies fundamental insight Abstract environment as techniques, well as subsequent research, workshops and presentations. Thereby a focus is set information and into Industry 4.0 applications and associated challenges through exemplary implementation of a on digitized production communication (ICT) particularly, to realize the of customer-specific products lot sizes.and In environment as technologies well as subsequent research, workshops andproduction presentations. Thereby a focus is set inonsmall information Under the concept of "Industry 4.0", production processes will be pushed tobutbealsoincreasingly interconnected, contrast to other Learning Factories, TU PF emphasizes not onlyproduction on teaching on the demonstration of novel communication technologies (ICT) particularly, to realize the ofknowledge, customer-specific products in small lot sizes. In information based onwell a real time basis necessarily, much more efficient. In this context, production concepts as as the development, implementation and of prototypes industryoptimization partners. This contrast to other Learning Factories, TU PFand, emphasizes not only on evaluation teaching knowledge, buttogether also on with thecapacity demonstration of novel goes beyond traditional of capacity maximization, contributing also for organization’s profitability and value. paper givesconcepts anthe insight current status of TU PF and its consistent information flow from engineering to production. production asinto wellthe asaim the development, implementation and evaluation of prototypes together with industry partners. This Indeed, lean management and continuous approaches suggest capacity optimization instead of paper gives an insight into the current status of TU improvement PF and its consistent information flow from engineering to production. © 2019 The Authors. Published by Elsevier B.V. maximization. The study of capacity optimization and costing models is an important research topic that deserves © 2019 The Authors. Published by B.V. Peer review theboth responsibility of the and scientific committee of the 9th Conference on Learning Factories. © 2019 Theunder Authors. Published by Elsevier Elsevier B.V. contributions from the practical theoretical perspectives. This paper presents and discusses a mathematical Peer review under the responsibility of the scientific committee of the th9th Conference on Learning Factories. Peer review under the responsibility of the scientific committee of themodels 9 Conference on Learning Factories. model for capacity management based on different costing (ABC and TDABC). A generic model has been Keywords: Pilot Factory, Demonstration Factory, Learning Factory, Continuous Information Flow, Industry 4.0, Digitization, Digital Factory developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s Keywords: Pilot Factory, Demonstration Factory, Learning Factory, Continuous Information Flow, Industry 4.0, Digitization, Digital Factory value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency. 1. Introduction
© 2017 The Authors. Published by Elsevier B.V. 1. Introduction Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference Present mega trends like digitization and demographic change present enormous challenges for established 2017.
Present mega trendsin like digitization and In demographic change present enormous challenges for established production companies high-wage countries. order to obtain flexible production processes, companies have to production companies in high-wage countries. In order to obtain flexible production processes, companies have to make fundamental changes to their business processes and achieve the ability to create disruptive technical innovations Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency make fundamental changes to their business processes and achieve the ability to create disruptive technical innovations
1. Introduction * The Corresponding author. Tel.: +43 888 616 36. information for companies and their management of extreme importance cost of idle capacity is 676 a fundamental E-mail address:author. [email protected] * Corresponding Tel.: +43 676 888 616 36. in modern production systems. In general, it is defined as unused capacity or production potential and can be measured E-mail address: [email protected] in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity 2351-9789 © 2019 The Authors. Published by Elsevier B.V. * Paulo Afonso. +351 253 Published 510of761; +351 committee 253 604 741 Peer review the the fax: scientific of the 9th Conference on Learning Factories. 2351-9789 ©under 2019Tel.: Theresponsibility Authors. by Elsevier B.V. E-mail address: [email protected] Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2019 The Authors. Published by Elsevier B.V. Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. 10.1016/j.promfg.2019.03.032
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[1]. Such changes include usage of IoT (Internet of Things) devices, modern human-machine-interfaces, augmented reality as well as a consistent vertical and horizontal integration of different information and sensory systems [2]. These innovations heavily rely on the exchange of information between employees, machines and companies, which in the future will act as an integrated network in the sense of Industry 4.0 [3]. Industry 4.0 is not an off-the-shelf solution that can simply be purchased from a vendor. One of the main challenges in the implementation of Industry 4.0 is the interconnection of all systems from different operational areas. These areas are already well networked internally with individual software solutions for data modeling and management, modern machine controls and communication interfaces to the machine level. However, there is no concept of how these systems should share information for the formation of a holistic cyber-physical production system (CPPS) [4]. In order to use Industry 4.0 effectively, production processes have to be significantly changed. This presents a risk for small and medium-sized enterprises (SMEs), as these companies lack necessary resources like time, equipment and special knowledge [5]. To address these problems, the concept of Learning Factories appeared all over the world, gaining recognition in science, industry and society. In these collaborative learning environments, students and interested trainees from industry acquire methodological, social and personal competencies for future challenges in production. 2. TU Wien Pilot Factory Industry 4.0 The TU PF focuses on new concepts for multi-variant serial production in discrete manufacturing industry. On an available area of approximately 900 m2, the development of a 3D FDM Printer from design, mechanical part production and assembly till the shipment to the end customer is showcased. Companies benefit from a safe environment in which new technologies can be tested and experienced in a realistic industry like setting before they are transferred to their own production. In addition to a state-of-the-art production machine park, TU PF has a complete hybrid assembly infrastructure. The production and assembly areas are supplied and linked by a completely decentral controlled intralogistics system. In addition, a state-of-the-art communication network is available which enables the integration of different industrial systems using communication standards such as OPC UA (OPC Unified Architecture) and MTConnect (Manufacturing Technology Connect). Due to many different software systems as well as interfaces at all hierarchical levels, the implementation of this integrated communication is a very complex task. As a development framework for future standardized interfaces, the reference platform model Industrie 4.0 (RAMI4.0) was published by the German Platform Industrie 4.0. It provides standards for various levels of vertical integration, some of which are taken from the IEC62264 and 61512 and extended by “Connected World” and “Product” levels [6]. At TU PF, there are several software systems in use. The ERP / MES layer, in which the ERP (Enterprise Resource Planning) system accepts the customer orders, checks and transfers these orders into the MES (Manufacturing Execution System). These systems work closely with Product Lifecycle Management (PLM) and Computer Aided Design / Manufacturing (CAD / CAM) tools to provide the production resources with the information they need. A Tool Management System (TMS) is necessary to manage tool catalog data on the one hand and to enter it for the CAM process, but also to determine the net tool requirement for production and to generate setup sheets [7]. Furthermore, the “Product” level is addressed at the TU PF by RFID Tracking (Radio Frequency Identification). In addition to the product itself, the shop floor identifies production aids such as pallets, tools and containers via RFID at the individual processing stations. 2.1. ERP / PLM / MES The entry point into the information flow is the online product configurator, which allows customers to configure a product individually within a predefined logic of several options. The order (of a customized product) is then forwarded to the interface of the ERP system. If the product has many or continuous variants, the customers configuration choices are mapped onto product parameters which are forwarded to a parameterized CAD Model. There the individualized production documents are derived, referenced to PLM items and then forwarded to the ERP system via an interface. ERP generates the demand for manufacturing and purchase items as well as raw materials from order data and supplies the follow-up systems in production and assembly planning with required data. If the demand has been met, these systems report that production can start. At this point in the flow of information, sufficient data is
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available in the ERP to forward the complete production order to the MES and begin production planning. In the MES, the operations in the workplan are assigned to specific manufacturing resources and released for production. 2.2. Manufacturing A production order released into the MES must contain all data necessary for processing a part on machining tools. These are among others the raw material or semi-finished part, the clamping devices, the NC program and the tool list with the respective operating times of the single processing step. In order to get to the tool net requirement list that is actually necessary for the setup, it is important to compare the remaining tool life on the system. The creation of the net requirement and the general procedure for tool management within the TU PF were already described in [7, 8]. Each released production order is split into the single operations from the workplan. Each operation is a generic task that is offered by the control of the manufacturing cell and is concretized on multiple layers within the cell controller and finally mapped to an elementary function or data item on a resource. All processes necessary for manufacturing and handling are orchestrated by the cell controller [9–11] on the machine level through OPC UA. It is particularly suitable as a communication protocol in production because it is used for controlling processes below the MES layer between the control and management level [12]. OPC UA offers the advantage that systems and plant components can be structured and described using a stored information model [13], providing specific variables and methods of all components for clients, such as cell control. For this purpose, handling robots and machine tools each have OPC UA servers with special information models for machine tools and industrial robots [14]. 2.3. Assembly As soon as all parts required for a product variant are available in the warehouse, MES generates a corresponding assembly order and forwards it to the Cyber-Physical Assembly System of the TU PF. The product is mounted on mobile, height-adjustable assembly tables. These are composed of autonomous, automated guided vehicle systems (AGV) and a specially developed device, which automatically enables all stations required for the current product variant to be used, thus enabling a flexible, non-cycle-bound material flow [15]. High complexity of assembly processes in combination with the demand to be able to competitively produce smallest lot sizes precludes complete mechanical automation of assembly [16]. Due to the required flexibility in this volatile working environment, shop floor workers conceptually remain in the center of the assembly system. [17]. Since paper-based work instructions reach their economic and technical limit, employees are supported by digital assistance systems in assembly [18, 19]. A force- and power-limited lightweight robot supports the employee in the assembly of the 3D printer-head. This type of work task sharing between human and robot, known as human-robot collaboration (HRC), is considered one of the key trends in the fourth industrial revolution [20]. The robot serves as a “third hand” to support the worker during handling, assembly and fixing of various parts. The employee has both hands free and does not have to lift, hold or manipulate heavy components. The lightweight robot is controlled by the worker guidance system and adapts to the assembly worker in terms of ergonomically optimal working height [21]. In the last step of the assembly process, the 3D printer is inspected for visually recognizable quality defects and then supplied with electrical energy. After passing a simple test print, the product is packaged and shipped to the customer. 3. Pilot, Demonstration and Learning Factory The term Learning Factory was first mentioned in 1994 by Nation Science Foundation in USA and has been described by different authors [22]. According to Hambach et al., Learning Factories can serve on the one hand as complex learning environments to enable high-quality competence development and on the other hand as models or replicas of value-added processes and factories to enable informal and formal learning in semi-real production environments [23]. In contrast to the term “Learning Factory”, to the authors’ best knowledge the terms “Pilot Factory” and “Demonstration Factory” are quite undefined words in literature. A Pilot Factory is a facility that enables the research, development and evaluation of prototype artefacts in a realistic environment without disturbing or interrupting existing production processes. The focus is on testing new concepts and generating new knowledge. On the other hand, a Demonstration Factory enables the communication of already tested concepts and state-of-the-art
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knowledge to interested parties as well as the general public. Distances, fears and reservations are to be reduced and the knowledge recipients are to be given the opportunity to apply and experience new concepts themselves. 3.1. Pilot Factory: Development of Prototypes TU PF enables companies to jointly develop, implement and evaluate prototypes and train employees in a protected environment, without disrupting real production processes. Prototypes can be physical products, production processes or new software solutions. As a result, even SMEs that do not usually have the necessary capacities, equipment and know-how, can develop and test new prototypes without disturbing their own production. A specially developed process model guarantees a structured approach as well as an efficient project organization in a defined cost and time frame: An in-depth analysis of the company’s current production processes enables the selection of suitable use cases and an assessment of the potential for improvement. In the joint planning, construction and design of the prototype, the specific requirements of the company are taken into account in order to prevent subsequent discrepancies. TU PF has a large network of external experts for special questions, for example to create a safety concept required for certification. After testing and approval by the stakeholders, the company’s employees can be trained directly on the prototype in the Pilot Factory. Occurring feedback from employees can be used to further mature the prototype. Finally, the developed and tested prototype can be integrated into the real production process of the company. 3.2. Demonstration Factory: Demonstrating Industry 4.0 While the pilot approach deals with the application of prototypes in a production environment, the demonstration aspect is one step ahead. A Demonstration Factory offers proper dissemination that allows not only specialists, but also for the general public to understand the basic concepts behind the developments and thereby supports the acceptance for new technologies. TU PF therefore offers guided tours by research assistants and students. The different aspects of Industry 4.0 in terms of connectivity, automation and human-machine collaboration are exhibited and explained. Visitors get a visual insight into the challenges and opportunities, enabled through the connectivity of shop floor resources, through dashboards and displays providing information about the current activities. Many different interest groups are visiting the TU PF and it is already pursuing an educational mission for its visitors to draw attention to current developments in future generations of engineers and students. 3.3. Learning Factory: Teaching Knowledge TU PF offers the ideal opportunity to experience theoretically learned subject matter in reality. On the one hand, new practice oriented technical insights are possible, which otherwise remain only theoretical in heterogeneously networked systems. On the other hand, the students’ knowledge is deepened in practical application, by dealing with practical problems and developing solutions for them. Teaching is interwoven with the TU PF in three different ways. For TU Wien students, the practical part of the course “Integrative Product Creation” takes place at TU PF. In this subject, they need to revise a given sub-optimal product, considering the need to meet various requirements. After the development cycles have been iterated and completed, the students create CNC codes, assembly instructions and cost structures with respect to the integrated production environment of the TU PF. For trainees, TU Wien offers an innovation course on digital transformation in product development and production, called “DigiTrans 4.0” for short, where practical exercises are partly offered in the TU PF. The aim of the course is to teach business professionals and decision makers the gradual implementation of Industry 4.0 topics, from a technology IT integration point of view. The constitution of the consortium and the multidisciplinary exchange, result in gains in (i) the joint development of new fields of application in the field of Industry 4.0, (ii) the addressing of business-related topics in the field of Industry 4.0, and (iii) the implementation of future-oriented technology fields, such as cyberphysical production systems (CPPS) or an Industrial Internet of Things (IIoT). Last but not least, the aim is to establish sustainable cooperation between the scientific and business partners as well as between the participating companies.
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3.4. Interrelationship between Pilot, Demonstration and Learning Factory The TU PF is a Pilot, Demonstration and Learning Factory, aiming not only on teaching knowledge, but to provide students, companies and general public a fundamental insight into Industry 4.0 possibilities, applications and associated challenges through exemplary implementation of a digitized production environment and subsequent research, education and workshops. Figure 1 shows the fundamental model of TU PF as a collaboration platform of different technology users, technology providers, scientists, students (academic) and trainees (non-academic).
Fig. 1. Model of TU PF as an interrelationship between Pilot, Demonstration and Learning Factory.
The combination of two types of factories enables several advantages and synergy effects: a) P-L (scientists meet students and trainees): The combination of Pilot and Learning Factory makes it possible for students and trainees to develop new models, prototypes or software artefacts during their bachelor or master thesis. This motivates them to trigger a higher interest in fundamental and applied research topics, e.g. innovation technology as well as modern approaches of lean management. Students and trainees have to collaborate with scientists to ensure knowledge transfer to industrial oriented use cases. b) P-D (scientists meet technology users and providers): By bringing Pilot and Demonstration Factory together, new developed prototypes are demonstrated to a broad range of technology users by technology providers to transfer practical knowledge and spark innovative ideas for future collaboration. c) D-L (technology users and providers meet students and trainees): In this scenario, Demonstration and Learning Factory are combined, which connects students with technology users and providers. This helps students and trainees to get a better insight into practical problems from industry and promotes the knowledge transfer between technology users and students. 4. Summary and Outlook The concept of Learning Factories has a considerable influence on society and economy. The elaborated TU PF research, demonstration and teaching approaches pursued here promote the competitiveness of the production in Austria in the area of discrete manufacturing. TU PF helps particular SMEs to take a step in this direction by providing them with knowledge transfer, sandboxing and role models. In many instances, there was already positive feedback, achievable learning achievements and a keen interest of the public and the private sector. In DigiTrans 4.0, we instantiated an education controlling system based on the model of the 4-level evaluation model according to Kirkpatrick [24]. The learning success of each module in the course was continuously evaluated, both off-the job and on-the-job. For this purpose, we asked the participants’ state of knowledge before and after holding a module for the respective Industry 4.0 topic. The outcome of this transfer-monitoring shows that 50,17% of the participants have acquired not only a concrete understanding of Industry 4.0 topics but have generated new knowledge [25]. Thus, they are able to initiate digitization processes in their own companies and to adopt future-proof corporate strategies. The implementation of the project made it transparent which functions are particularly valuable and which new challenges have to be considered.
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Acknowledgements The TU PF has been partly funded by the public through the Austrian Research Promotion Agency (FFG) with funds of the Federal Ministry for Transport, Innovation and Technology under grant number 852105. The course “Digitrans 4.0” is supported as part of the program “Innovationslehrgänge” by the Austrian Research Promotion Agency with funds of the Federal Ministry for Digital and Economic Affairs under grant number 854157. References [1] U. Dombrowski, T. Wagner, and C. Riechel, “Concept for a cyber physical assembly system”, 2013 IEEE International Symposium on Assembly and Manufacturing (ISAM), pp. 293–296, 2013. [2] L. Monostori, “Cyber-physical production systems: roots, expectations and R&D challenges”, Procedia CIRP, vol. 17, pp. 9–13, 2014. [3] M. Brettel, N. Friederichsen, M. Keller, and M. Rosenberg, “How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective”, International Journal of Mechanical, Industrial Science and Engineering, vol. 8, no. 1, pp. 37–44, 2014. [4] J. Musil, A. Musil, D. Weyns, and S. Biffl, “An architecture framework for collective intelligence systems”, in Software Architecture (WICSA), 2015 12th Working IEEE/IFIP Conference on, pp. 21–30, IEEE, 2015. [5] D. Gerhard, “Product lifecycle management challenges of CPPS”, in Handbuch Produktentwicklung (L. A. Biffl, S. and D. Gerhard, eds.), Springer, 2017. [6] P. Adolphs, and U. Epple, “Statusreport: Referenzarchitekturmodell Industrie 4.0”, VDI/VDE-Gesellschaft April, pp. 1–32, 2015. [7] S. Mansour, T. Trautner, and F. Pauker, “Integrated tool lifecycle”, 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, July 2018, Gulf Naples, Italy. In press., 2018. [8] T. Trautner, F. Pauker, and B. Kittl, “Advanced MTConnect Asset Management (AMAM)”, IN-TECH, no. September, pp. 6–9, 2016. [9] “Forschungsprojekt OPC4Factory”, https://www.ift.at/forschungsbereiche/forschungsprojekte/opc4factory, Accessed: 2018-10-31. [10] I. Ayatollahi, B. Kittl, F. Pauker, and M. Hackhofer, “Prototype OPC UA Server for Remote Control of Machine Tools”, IN-TECH 2013 Proceedings of International Conference on Innovative Technologies, 2013. [11] F. Pauker, I. Ayatollahi, and B. Kittl, “Service Orchestration for Flexible Manufacturing Systems using Sequential Functional Charts and OPC UA”, International Conference on Innovative Technologies, pp. 9–12, 2015. [12] M. Schleipen, S.-S. Gilani, T. Bischoff, and J. Pfrommer, “OPC UA & Industrie 4.0 - enabling technology with high diversity and variability”, 49th Conference on Manufacturing Systems, 2016. [13] W. Mahnke, S. H. Leitner, and M. Damm, “OPC unified architecture”, Springer-Verlag Berlin Heidelberg, 2009. [14] F. Pauker, I. Ayatollahi, and B. Kittl, “OPC UA for machine tending industrial robots - Prototypic development of an OPC UA server for ABB industrial robots”, in Proceedings of the second International Conference on Advances in Mechanical and Robotics Engineering 2014, pp. 79– 83, Oct 2014. [15] 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. [16] O. Korn, A. Schmidt, and T. Hörz, “Assistive systems in production environments: Exploring motion recognition and gamification”, 2012. [17] A. Claeys, S. Hoedt, N. Soete, H. van Landeghem, and J. Cottyn, “Framework for evaluating cognitive support in mixed model assembly systems”, IFAC Papers On Line, vol. 48, no. 3, pp. 924–929, 2015. [18] P. Hold, F. Ranz, and W. Sihn, “Konzeption eines MTM-basierten Bewertungsmodells für digitalen Assistenzbedarf in der cyber-physischen Montage”, 2016. [19] J. Franke, and F. Risch, “Effiziente Erstellung, Distribution und Rückmeldung von Werkerinformationen in der Montage”, 2009. [20] A. Markis, and F. Ranz, “Sicherheit in der Mensch-Roboter Kollaboration: Teil 1”, 2016. [21] A. Markis, and F. Ranz, “Safety & Security in der Mensch-Roboter-Kollaboration: Teil 3, Einfluss der IT-Security”, 2018. [22] J. Enke, J. Kaiser, and J. Metternich, “Die Lernfabrik als Export-Erfolg”, ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 112, no. 10, pp. 644–647, 2017. [23] E. Abele, J. Metternich, M. Tisch, G. Chryssolouris, W. Sihn, H. ElMaraghy, V. Hummel, and F. Ranz, “Learning factories for research, education, and training”, Procedia CIRP, vol. 32, pp. 1–6, 2015. [24] Evaluation training programs: The four levels. San Francisco: Berrett-Koehler, 1994. [25] J. Seidel. „Transferkompetenz und Transfer: Theoretische und empirische Untersuchung zu den Wirksamkeitsbedingungen betrieblicher Weiterbildung“, Empirische Pädagogik, Edition 1, Juni 2012.
ScienceDirect ScienceDirect
Available Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Procedia Manufacturing 00 (2019) 000–000 Procedia Manufacturing 00 (2019) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Manufacturing 31 (2019) 200–205 Procedia Manufacturing 00 (2017) 000–000
9th Conference on Learning Factories 2019 9th Conference on Learning Factories 2019
www.elsevier.com/locate/procedia
TU Wien Pilot Factory Industry 4.0 TU Wien Pilot Factory Industry 4.0
a 2017, MESIC Manufacturing Society a,International Conference Junea, Martin Henniga,Engineering Gerhard Reisinger *, Thomas Trautner , Philipp Holda,2017, Detlef28-30 Gerhard a a,Vigo (Pontevedra),bSpain a a 2017, Martin Hennig , Gerhard Reisinger Alexandra *, ThomasMazak Trautner , Philipp Hold , Detlef Gerharda, Alexandra Mazakb Wien Pilot Factory Industry 4.0, Seestadtstraße 27, 1220 Vienna, Austria Costing models TU for optimization inVienna, Industry 4.0: Trade-off TU capacity Wien, Favoritenstrasse 9-11, 1040 Austria TU Wien Pilot CDL-MINT, Factory Industry 4.0, Seestadtstraße 27, 1220 Vienna, Austria TU Wien, CDL-MINT, Favoritenstrasse 9-11, 1040 Vienna, Austria between used capacity and operational efficiency a a
b b
Abstract A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb Abstract a Driven by present megatrends like digitization demographic Learning Universityand of Minho, 4800-058change, Guimarães, PortugalFactories all over the world are gaining b Unochapecó, 89809-000 Chapecó, Brazil Factories recognition in science, industry like and society. In these learning environments, students from Driven by present megatrends digitization and collaborative demographic change,SC, Learning all and overinterested the worldtrainees are gaining industry acquire methodological, social and personal competencies learning for future challenges instudents production. TU Wien trainees Pilot Factory recognition in science, industry and society. In these collaborative environments, and interested from Industry 4.0 (in short TU PF) is a Pilot, and Learning for Factory, to provide companies a fundamental insight industry acquire methodological, socialDemonstration and personal competencies futureaiming challenges in production. TU Wien Pilot Factory into Industry 4.0short techniques, and associated challenges implementation of aa digitized production Industry 4.0 (in TU PF) applications is a Pilot, Demonstration and Learningthrough Factory,exemplary aiming to provide companies fundamental insight Abstract environment as techniques, well as subsequent research, workshops and presentations. Thereby a focus is set information and into Industry 4.0 applications and associated challenges through exemplary implementation of a on digitized production communication (ICT) particularly, to realize the of customer-specific products lot sizes.and In environment as technologies well as subsequent research, workshops andproduction presentations. Thereby a focus is set inonsmall information Under the concept of "Industry 4.0", production processes will be pushed tobutbealsoincreasingly interconnected, contrast to other Learning Factories, TU PF emphasizes not onlyproduction on teaching on the demonstration of novel communication technologies (ICT) particularly, to realize the ofknowledge, customer-specific products in small lot sizes. In information based onwell a real time basis necessarily, much more efficient. In this context, production concepts as as the development, implementation and of prototypes industryoptimization partners. This contrast to other Learning Factories, TU PFand, emphasizes not only on evaluation teaching knowledge, buttogether also on with thecapacity demonstration of novel goes beyond traditional of capacity maximization, contributing also for organization’s profitability and value. paper givesconcepts anthe insight current status of TU PF and its consistent information flow from engineering to production. production asinto wellthe asaim the development, implementation and evaluation of prototypes together with industry partners. This Indeed, lean management and continuous approaches suggest capacity optimization instead of paper gives an insight into the current status of TU improvement PF and its consistent information flow from engineering to production. © 2019 The Authors. Published by Elsevier B.V. maximization. The study of capacity optimization and costing models is an important research topic that deserves © 2019 The Authors. Published by B.V. Peer review theboth responsibility of the and scientific committee of the 9th Conference on Learning Factories. © 2019 Theunder Authors. Published by Elsevier Elsevier B.V. contributions from the practical theoretical perspectives. This paper presents and discusses a mathematical Peer review under the responsibility of the scientific committee of the th9th Conference on Learning Factories. Peer review under the responsibility of the scientific committee of themodels 9 Conference on Learning Factories. model for capacity management based on different costing (ABC and TDABC). A generic model has been Keywords: Pilot Factory, Demonstration Factory, Learning Factory, Continuous Information Flow, Industry 4.0, Digitization, Digital Factory developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s Keywords: Pilot Factory, Demonstration Factory, Learning Factory, Continuous Information Flow, Industry 4.0, Digitization, Digital Factory value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency. 1. Introduction
© 2017 The Authors. Published by Elsevier B.V. 1. Introduction Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference Present mega trends like digitization and demographic change present enormous challenges for established 2017.
Present mega trendsin like digitization and In demographic change present enormous challenges for established production companies high-wage countries. order to obtain flexible production processes, companies have to production companies in high-wage countries. In order to obtain flexible production processes, companies have to make fundamental changes to their business processes and achieve the ability to create disruptive technical innovations Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency make fundamental changes to their business processes and achieve the ability to create disruptive technical innovations
1. Introduction * The Corresponding author. Tel.: +43 888 616 36. information for companies and their management of extreme importance cost of idle capacity is 676 a fundamental E-mail address:author. [email protected] * Corresponding Tel.: +43 676 888 616 36. in modern production systems. In general, it is defined as unused capacity or production potential and can be measured E-mail address: [email protected] in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity 2351-9789 © 2019 The Authors. Published by Elsevier B.V. * Paulo Afonso. +351 253 Published 510of761; +351 committee 253 604 741 Peer review the the fax: scientific of the 9th Conference on Learning Factories. 2351-9789 ©under 2019Tel.: Theresponsibility Authors. by Elsevier B.V. E-mail address: [email protected] Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2019 The Authors. Published by Elsevier B.V. Peer review under the responsibility of the scientific committee of the 9th Conference on Learning Factories. 10.1016/j.promfg.2019.03.032
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[1]. Such changes include usage of IoT (Internet of Things) devices, modern human-machine-interfaces, augmented reality as well as a consistent vertical and horizontal integration of different information and sensory systems [2]. These innovations heavily rely on the exchange of information between employees, machines and companies, which in the future will act as an integrated network in the sense of Industry 4.0 [3]. Industry 4.0 is not an off-the-shelf solution that can simply be purchased from a vendor. One of the main challenges in the implementation of Industry 4.0 is the interconnection of all systems from different operational areas. These areas are already well networked internally with individual software solutions for data modeling and management, modern machine controls and communication interfaces to the machine level. However, there is no concept of how these systems should share information for the formation of a holistic cyber-physical production system (CPPS) [4]. In order to use Industry 4.0 effectively, production processes have to be significantly changed. This presents a risk for small and medium-sized enterprises (SMEs), as these companies lack necessary resources like time, equipment and special knowledge [5]. To address these problems, the concept of Learning Factories appeared all over the world, gaining recognition in science, industry and society. In these collaborative learning environments, students and interested trainees from industry acquire methodological, social and personal competencies for future challenges in production. 2. TU Wien Pilot Factory Industry 4.0 The TU PF focuses on new concepts for multi-variant serial production in discrete manufacturing industry. On an available area of approximately 900 m2, the development of a 3D FDM Printer from design, mechanical part production and assembly till the shipment to the end customer is showcased. Companies benefit from a safe environment in which new technologies can be tested and experienced in a realistic industry like setting before they are transferred to their own production. In addition to a state-of-the-art production machine park, TU PF has a complete hybrid assembly infrastructure. The production and assembly areas are supplied and linked by a completely decentral controlled intralogistics system. In addition, a state-of-the-art communication network is available which enables the integration of different industrial systems using communication standards such as OPC UA (OPC Unified Architecture) and MTConnect (Manufacturing Technology Connect). Due to many different software systems as well as interfaces at all hierarchical levels, the implementation of this integrated communication is a very complex task. As a development framework for future standardized interfaces, the reference platform model Industrie 4.0 (RAMI4.0) was published by the German Platform Industrie 4.0. It provides standards for various levels of vertical integration, some of which are taken from the IEC62264 and 61512 and extended by “Connected World” and “Product” levels [6]. At TU PF, there are several software systems in use. The ERP / MES layer, in which the ERP (Enterprise Resource Planning) system accepts the customer orders, checks and transfers these orders into the MES (Manufacturing Execution System). These systems work closely with Product Lifecycle Management (PLM) and Computer Aided Design / Manufacturing (CAD / CAM) tools to provide the production resources with the information they need. A Tool Management System (TMS) is necessary to manage tool catalog data on the one hand and to enter it for the CAM process, but also to determine the net tool requirement for production and to generate setup sheets [7]. Furthermore, the “Product” level is addressed at the TU PF by RFID Tracking (Radio Frequency Identification). In addition to the product itself, the shop floor identifies production aids such as pallets, tools and containers via RFID at the individual processing stations. 2.1. ERP / PLM / MES The entry point into the information flow is the online product configurator, which allows customers to configure a product individually within a predefined logic of several options. The order (of a customized product) is then forwarded to the interface of the ERP system. If the product has many or continuous variants, the customers configuration choices are mapped onto product parameters which are forwarded to a parameterized CAD Model. There the individualized production documents are derived, referenced to PLM items and then forwarded to the ERP system via an interface. ERP generates the demand for manufacturing and purchase items as well as raw materials from order data and supplies the follow-up systems in production and assembly planning with required data. If the demand has been met, these systems report that production can start. At this point in the flow of information, sufficient data is
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available in the ERP to forward the complete production order to the MES and begin production planning. In the MES, the operations in the workplan are assigned to specific manufacturing resources and released for production. 2.2. Manufacturing A production order released into the MES must contain all data necessary for processing a part on machining tools. These are among others the raw material or semi-finished part, the clamping devices, the NC program and the tool list with the respective operating times of the single processing step. In order to get to the tool net requirement list that is actually necessary for the setup, it is important to compare the remaining tool life on the system. The creation of the net requirement and the general procedure for tool management within the TU PF were already described in [7, 8]. Each released production order is split into the single operations from the workplan. Each operation is a generic task that is offered by the control of the manufacturing cell and is concretized on multiple layers within the cell controller and finally mapped to an elementary function or data item on a resource. All processes necessary for manufacturing and handling are orchestrated by the cell controller [9–11] on the machine level through OPC UA. It is particularly suitable as a communication protocol in production because it is used for controlling processes below the MES layer between the control and management level [12]. OPC UA offers the advantage that systems and plant components can be structured and described using a stored information model [13], providing specific variables and methods of all components for clients, such as cell control. For this purpose, handling robots and machine tools each have OPC UA servers with special information models for machine tools and industrial robots [14]. 2.3. Assembly As soon as all parts required for a product variant are available in the warehouse, MES generates a corresponding assembly order and forwards it to the Cyber-Physical Assembly System of the TU PF. The product is mounted on mobile, height-adjustable assembly tables. These are composed of autonomous, automated guided vehicle systems (AGV) and a specially developed device, which automatically enables all stations required for the current product variant to be used, thus enabling a flexible, non-cycle-bound material flow [15]. High complexity of assembly processes in combination with the demand to be able to competitively produce smallest lot sizes precludes complete mechanical automation of assembly [16]. Due to the required flexibility in this volatile working environment, shop floor workers conceptually remain in the center of the assembly system. [17]. Since paper-based work instructions reach their economic and technical limit, employees are supported by digital assistance systems in assembly [18, 19]. A force- and power-limited lightweight robot supports the employee in the assembly of the 3D printer-head. This type of work task sharing between human and robot, known as human-robot collaboration (HRC), is considered one of the key trends in the fourth industrial revolution [20]. The robot serves as a “third hand” to support the worker during handling, assembly and fixing of various parts. The employee has both hands free and does not have to lift, hold or manipulate heavy components. The lightweight robot is controlled by the worker guidance system and adapts to the assembly worker in terms of ergonomically optimal working height [21]. In the last step of the assembly process, the 3D printer is inspected for visually recognizable quality defects and then supplied with electrical energy. After passing a simple test print, the product is packaged and shipped to the customer. 3. Pilot, Demonstration and Learning Factory The term Learning Factory was first mentioned in 1994 by Nation Science Foundation in USA and has been described by different authors [22]. According to Hambach et al., Learning Factories can serve on the one hand as complex learning environments to enable high-quality competence development and on the other hand as models or replicas of value-added processes and factories to enable informal and formal learning in semi-real production environments [23]. In contrast to the term “Learning Factory”, to the authors’ best knowledge the terms “Pilot Factory” and “Demonstration Factory” are quite undefined words in literature. A Pilot Factory is a facility that enables the research, development and evaluation of prototype artefacts in a realistic environment without disturbing or interrupting existing production processes. The focus is on testing new concepts and generating new knowledge. On the other hand, a Demonstration Factory enables the communication of already tested concepts and state-of-the-art
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knowledge to interested parties as well as the general public. Distances, fears and reservations are to be reduced and the knowledge recipients are to be given the opportunity to apply and experience new concepts themselves. 3.1. Pilot Factory: Development of Prototypes TU PF enables companies to jointly develop, implement and evaluate prototypes and train employees in a protected environment, without disrupting real production processes. Prototypes can be physical products, production processes or new software solutions. As a result, even SMEs that do not usually have the necessary capacities, equipment and know-how, can develop and test new prototypes without disturbing their own production. A specially developed process model guarantees a structured approach as well as an efficient project organization in a defined cost and time frame: An in-depth analysis of the company’s current production processes enables the selection of suitable use cases and an assessment of the potential for improvement. In the joint planning, construction and design of the prototype, the specific requirements of the company are taken into account in order to prevent subsequent discrepancies. TU PF has a large network of external experts for special questions, for example to create a safety concept required for certification. After testing and approval by the stakeholders, the company’s employees can be trained directly on the prototype in the Pilot Factory. Occurring feedback from employees can be used to further mature the prototype. Finally, the developed and tested prototype can be integrated into the real production process of the company. 3.2. Demonstration Factory: Demonstrating Industry 4.0 While the pilot approach deals with the application of prototypes in a production environment, the demonstration aspect is one step ahead. A Demonstration Factory offers proper dissemination that allows not only specialists, but also for the general public to understand the basic concepts behind the developments and thereby supports the acceptance for new technologies. TU PF therefore offers guided tours by research assistants and students. The different aspects of Industry 4.0 in terms of connectivity, automation and human-machine collaboration are exhibited and explained. Visitors get a visual insight into the challenges and opportunities, enabled through the connectivity of shop floor resources, through dashboards and displays providing information about the current activities. Many different interest groups are visiting the TU PF and it is already pursuing an educational mission for its visitors to draw attention to current developments in future generations of engineers and students. 3.3. Learning Factory: Teaching Knowledge TU PF offers the ideal opportunity to experience theoretically learned subject matter in reality. On the one hand, new practice oriented technical insights are possible, which otherwise remain only theoretical in heterogeneously networked systems. On the other hand, the students’ knowledge is deepened in practical application, by dealing with practical problems and developing solutions for them. Teaching is interwoven with the TU PF in three different ways. For TU Wien students, the practical part of the course “Integrative Product Creation” takes place at TU PF. In this subject, they need to revise a given sub-optimal product, considering the need to meet various requirements. After the development cycles have been iterated and completed, the students create CNC codes, assembly instructions and cost structures with respect to the integrated production environment of the TU PF. For trainees, TU Wien offers an innovation course on digital transformation in product development and production, called “DigiTrans 4.0” for short, where practical exercises are partly offered in the TU PF. The aim of the course is to teach business professionals and decision makers the gradual implementation of Industry 4.0 topics, from a technology IT integration point of view. The constitution of the consortium and the multidisciplinary exchange, result in gains in (i) the joint development of new fields of application in the field of Industry 4.0, (ii) the addressing of business-related topics in the field of Industry 4.0, and (iii) the implementation of future-oriented technology fields, such as cyberphysical production systems (CPPS) or an Industrial Internet of Things (IIoT). Last but not least, the aim is to establish sustainable cooperation between the scientific and business partners as well as between the participating companies.
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3.4. Interrelationship between Pilot, Demonstration and Learning Factory The TU PF is a Pilot, Demonstration and Learning Factory, aiming not only on teaching knowledge, but to provide students, companies and general public a fundamental insight into Industry 4.0 possibilities, applications and associated challenges through exemplary implementation of a digitized production environment and subsequent research, education and workshops. Figure 1 shows the fundamental model of TU PF as a collaboration platform of different technology users, technology providers, scientists, students (academic) and trainees (non-academic).
Fig. 1. Model of TU PF as an interrelationship between Pilot, Demonstration and Learning Factory.
The combination of two types of factories enables several advantages and synergy effects: a) P-L (scientists meet students and trainees): The combination of Pilot and Learning Factory makes it possible for students and trainees to develop new models, prototypes or software artefacts during their bachelor or master thesis. This motivates them to trigger a higher interest in fundamental and applied research topics, e.g. innovation technology as well as modern approaches of lean management. Students and trainees have to collaborate with scientists to ensure knowledge transfer to industrial oriented use cases. b) P-D (scientists meet technology users and providers): By bringing Pilot and Demonstration Factory together, new developed prototypes are demonstrated to a broad range of technology users by technology providers to transfer practical knowledge and spark innovative ideas for future collaboration. c) D-L (technology users and providers meet students and trainees): In this scenario, Demonstration and Learning Factory are combined, which connects students with technology users and providers. This helps students and trainees to get a better insight into practical problems from industry and promotes the knowledge transfer between technology users and students. 4. Summary and Outlook The concept of Learning Factories has a considerable influence on society and economy. The elaborated TU PF research, demonstration and teaching approaches pursued here promote the competitiveness of the production in Austria in the area of discrete manufacturing. TU PF helps particular SMEs to take a step in this direction by providing them with knowledge transfer, sandboxing and role models. In many instances, there was already positive feedback, achievable learning achievements and a keen interest of the public and the private sector. In DigiTrans 4.0, we instantiated an education controlling system based on the model of the 4-level evaluation model according to Kirkpatrick [24]. The learning success of each module in the course was continuously evaluated, both off-the job and on-the-job. For this purpose, we asked the participants’ state of knowledge before and after holding a module for the respective Industry 4.0 topic. The outcome of this transfer-monitoring shows that 50,17% of the participants have acquired not only a concrete understanding of Industry 4.0 topics but have generated new knowledge [25]. Thus, they are able to initiate digitization processes in their own companies and to adopt future-proof corporate strategies. The implementation of the project made it transparent which functions are particularly valuable and which new challenges have to be considered.
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Acknowledgements The TU PF has been partly funded by the public through the Austrian Research Promotion Agency (FFG) with funds of the Federal Ministry for Transport, Innovation and Technology under grant number 852105. The course “Digitrans 4.0” is supported as part of the program “Innovationslehrgänge” by the Austrian Research Promotion Agency with funds of the Federal Ministry for Digital and Economic Affairs under grant number 854157. References [1] U. Dombrowski, T. Wagner, and C. Riechel, “Concept for a cyber physical assembly system”, 2013 IEEE International Symposium on Assembly and Manufacturing (ISAM), pp. 293–296, 2013. [2] L. Monostori, “Cyber-physical production systems: roots, expectations and R&D challenges”, Procedia CIRP, vol. 17, pp. 9–13, 2014. [3] M. Brettel, N. Friederichsen, M. Keller, and M. Rosenberg, “How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective”, International Journal of Mechanical, Industrial Science and Engineering, vol. 8, no. 1, pp. 37–44, 2014. [4] J. Musil, A. Musil, D. Weyns, and S. Biffl, “An architecture framework for collective intelligence systems”, in Software Architecture (WICSA), 2015 12th Working IEEE/IFIP Conference on, pp. 21–30, IEEE, 2015. [5] D. Gerhard, “Product lifecycle management challenges of CPPS”, in Handbuch Produktentwicklung (L. A. Biffl, S. and D. Gerhard, eds.), Springer, 2017. [6] P. Adolphs, and U. Epple, “Statusreport: Referenzarchitekturmodell Industrie 4.0”, VDI/VDE-Gesellschaft April, pp. 1–32, 2015. [7] S. Mansour, T. Trautner, and F. Pauker, “Integrated tool lifecycle”, 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, July 2018, Gulf Naples, Italy. In press., 2018. [8] T. Trautner, F. Pauker, and B. Kittl, “Advanced MTConnect Asset Management (AMAM)”, IN-TECH, no. September, pp. 6–9, 2016. [9] “Forschungsprojekt OPC4Factory”, https://www.ift.at/forschungsbereiche/forschungsprojekte/opc4factory, Accessed: 2018-10-31. [10] I. Ayatollahi, B. Kittl, F. Pauker, and M. Hackhofer, “Prototype OPC UA Server for Remote Control of Machine Tools”, IN-TECH 2013 Proceedings of International Conference on Innovative Technologies, 2013. [11] F. Pauker, I. Ayatollahi, and B. Kittl, “Service Orchestration for Flexible Manufacturing Systems using Sequential Functional Charts and OPC UA”, International Conference on Innovative Technologies, pp. 9–12, 2015. [12] M. Schleipen, S.-S. Gilani, T. Bischoff, and J. Pfrommer, “OPC UA & Industrie 4.0 - enabling technology with high diversity and variability”, 49th Conference on Manufacturing Systems, 2016. [13] W. Mahnke, S. H. Leitner, and M. Damm, “OPC unified architecture”, Springer-Verlag Berlin Heidelberg, 2009. [14] F. Pauker, I. Ayatollahi, and B. Kittl, “OPC UA for machine tending industrial robots - Prototypic development of an OPC UA server for ABB industrial robots”, in Proceedings of the second International Conference on Advances in Mechanical and Robotics Engineering 2014, pp. 79– 83, Oct 2014. [15] 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. [16] O. Korn, A. Schmidt, and T. Hörz, “Assistive systems in production environments: Exploring motion recognition and gamification”, 2012. [17] A. Claeys, S. Hoedt, N. Soete, H. van Landeghem, and J. Cottyn, “Framework for evaluating cognitive support in mixed model assembly systems”, IFAC Papers On Line, vol. 48, no. 3, pp. 924–929, 2015. [18] P. Hold, F. Ranz, and W. Sihn, “Konzeption eines MTM-basierten Bewertungsmodells für digitalen Assistenzbedarf in der cyber-physischen Montage”, 2016. [19] J. Franke, and F. Risch, “Effiziente Erstellung, Distribution und Rückmeldung von Werkerinformationen in der Montage”, 2009. [20] A. Markis, and F. Ranz, “Sicherheit in der Mensch-Roboter Kollaboration: Teil 1”, 2016. [21] A. Markis, and F. Ranz, “Safety & Security in der Mensch-Roboter-Kollaboration: Teil 3, Einfluss der IT-Security”, 2018. [22] J. Enke, J. Kaiser, and J. Metternich, “Die Lernfabrik als Export-Erfolg”, ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 112, no. 10, pp. 644–647, 2017. [23] E. Abele, J. Metternich, M. Tisch, G. Chryssolouris, W. Sihn, H. ElMaraghy, V. Hummel, and F. Ranz, “Learning factories for research, education, and training”, Procedia CIRP, vol. 32, pp. 1–6, 2015. [24] Evaluation training programs: The four levels. San Francisco: Berrett-Koehler, 1994. [25] J. Seidel. „Transferkompetenz und Transfer: Theoretische und empirische Untersuchung zu den Wirksamkeitsbedingungen betrieblicher Weiterbildung“, Empirische Pädagogik, Edition 1, Juni 2012.