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Procedia CIRP 57 (2016) 2 - 7
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49th CIRP Conference on Manufacturing Systems (CIRP-CMS 2016)
Industrie 4.0 - From the perspective of applied research
Reimund Neugebauer, Sophie Hippmann*, Miriam Leis, Martin Landherr
Fraunhofer-Gesellschaft, Hansastr. 27 c, 80686Munich, Germany Corresponding author. Tel.: +49 (0)89 12051054; fax: +49 89 1205-771054. E-mail address: sophie.hippmann@zv.fraunhofer.de
Abstract
Industrie 4.0 is the German description for the 4th industrial revolution. While in Germany "Industrie 4.0" aims at putting the strong German manufacturing industry in a position of future readiness through integrated digitization, for the ICT-dominant USA, "Smart Manufacturing" is ought to revive the country's re-industrialization. Fraunhofer, a major European Research and Technology Organization (RTO), has a strong focus on Industrie 4.0 technologies throughout the whole production value chain. Together with research partners from universities, Fraunhofer supports the German and European industry to benefit from the new possibilities enabled through Industrie 4.0 developments. With several thousand experts and researchers Fraunhofer works to realize the development of the smart factory, e.g. in areas like production planning, manufacturing technologies ranging from deep-drawing to laser applications, as well as Internet of Things (IoT) applications and services, supply chain management or efficient buildings. Key enablers for harnessing the benefits of digitization for Industrie 4.0 are widely accepted standards, extreme low latency in digital communication as well as safety and security for data analytics and data exchange. To foster a straightforward communication of the challenges and developments described under the term Industrie 4.0 and corresponding technologies, an Industrie 4.0 description model, the "Fraunhofer layer model", was developed. This paper describes the concept of Industrie 4.0, the technological challenges and the extensive "Fraunhofer layer model" together with its bottom-up genesis based on Fraunhofer technologies.
©2016PublishedbyElsevierB.V. Thisisanopenaccess 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 49th CIRP Conference on Manufacturing Systems Keywords: Production; Production planning; Adaptive manufacturing; Manufacturing process; Manufacturing system; Industry 4.0
1. The concept oflndustrie 4.0
Industrie 4.0 stands for the next step in industrial production, with the central objective of fulfilling individual customer needs. Therefore, it affects all areas from order management, research and development, manufacturing, commissioning, delivery up to the utilization and the recycling of produced goods. The fundament for new opportunities is the availability of relevant information everywhere and at any time. To enable this, all involved resources like humans, objects and systems have to be integrated as dynamic, self-organized, real-time and autonomously optimized value-adding systems. [1]
The term "Industrie 4.0" was created to emphasize the enormous opportunities of digitizing and integrating all instances of the value-adding system. These opportunities are estimated to be as important as the substitution of muscle power by steam power or the division of labor into smaller
units that can be easily standardized and powered by electricity. These two milestones on the way to modern production are identified to be the origin of the first two industrial revolutions (Fig. 1). The third industrial revolution was then based on the further automation of production processes by the use ofelectronics and IT.
4. Industrial Revolution based on Cyber-physical Systems
3. Industrial Revolution Electronics and IT for further automation of production
2. Industrial Revolution Mass production based on division of labor powered by electrical energy
1. Industrial Revolution Mechanical production facilities powered by water and steam
End 18. Early 20. Early 70s 20. Today
Century Century Century
Fig. 1. Historical background of the 4th industrial revolution [2]
2212-8271 © 2016 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 49th CIRP Conference on Manufacturing Systems doi: 10.1016/j .procir.2016.11.002
Fernet of Things
Fig. 2. The smart factory surrounded by the internet ofthings and services [2]
The digitization and real-time-oriented integration of all elements in a value-adding system as the key of the 4th industrial revolution is still continuing. That is the reason why the 4th industrial revolution is often described more as an evolution than a revolution since there will be many steps on the way to the vision of Industrie 4.0. [3]
1.1. The internet of things and cyber-physical systems
The Internet of Things (IoT) represents the network architecture for a massive digitization of various areas of life (Fig. 2) [4].Technical solutions that are connected via the IoT can be identified as cyber-physical systems (CPS). In the manufacturing area, these instances are called cyber-physical production systems (CPPS). These systems are aiming at closing the gap between the physical and the digital domain. Thus, we need not only the bare infrastructure but also smart solutions for the
• interaction between physical systems and humans (human-machine-interaction),
• reflection of physical objects in the information world (digital shadow of production as the real-time model of value-adding systems),
• transaction in terms of software services,
• interoperation that is enabled by interacting, cloud-based platforms,
• prescription to handle big data with the goal of retrieving new and unexpected information and
• communication using the concepts and solutions coming from the network architecture of IoT. (Fig. 3)
Infrastructure (physical, digital)
Cyber-Physical Production System
Product Life Cycle (valuable = personalized + sustainable)
Interaction
Physical Systems (act, sense, communicate)! Human Beings (decide, create, communicate)
Rofloi-tinn
Digital Shadow (Real time Model of Everything)
Transaction
Software Service (Machine Skills, Apps for humans, Platform services)
InteroDeration
Cloud-based Platforms (Private, Community, Public)
Prescription
Analytics (Big Data/ machine learning)
1 rnmmnniratinn
Internet of Everything (Human beings, services, things)
Fig. 3. Functionalities ofCPS as the elements oflndustrie 4.0 [41
1.2. Closing the gap between the physical and the digital domain
Researchers from the Fraunhofer Institute for Manufacturing Engineering and Automation IPA identified a comprehensive model of the actual production system as the fundamental requirement for real-time-oriented manufacturing operation and optimization. This can be achieved by the massive consideration of cyber-physical systems in production. The representations of physical objects in the information world are called the "Digital Shadow of Production". One topic, to be addressed, is the data acquisition and the automated retrieval of situation-based information to be able to react to changing conditions and dynamic bottlenecks in near real-time. Addressing this task, the "Smart System Optimization" is developed (Fig. 4).
With the help of smart cameras, a non-invasive process observation becomes possible. The recurring behavior and variances of the processes are recognized based on special features that are taught to the system in advance. Afterwards a data-fusion of all detected features of all involved cameras takes place in real-time. The detected patterns are compared to existing ones or a new pattern of significant states is created. Using this system, a quantitative process analysis is possible in an automated way. The created information is then used to support the decisions of the manufacturing staff. The next step in the respective research and development tries to link the retrieved information with automated reaction in the physical world to further close the gap between the physical and the digital world in real-time in the field of factory operation and optimization. [5]
2. Models for describing Industrie 4.0
Since it is such a potentially revolutionary topic for the future of industrial production, several proposals have been developed for the description and definition of Industrie 4.0. Some descriptions are extensions of the classic division of industrial production into shop floor, office floor and peripheral entities like utilities, storage and transport facilities [6]. Although this model is familiar and thus easy to communicate, it may miss some essential points of Industrie 4.0 where data is the common denominator causing a growing permeability between the different functional hierarchies.
Fig. 4. The scientist Felix Mülleruses the "Smart System Optimization" for the detection of errors and their propagation throughout (semi-)automated production systems (Source: Fraunhofer IPA, Foto: Rainer Bez)
Classic functions of management and control are more and more directly integrated into the manufacturing process and the tasks of shop-floor workers increasingly shift towards classic office-floor activities like control, management and supervising. Peripheral systems such as logistics and resources are closely coupled to the manufacturing processes in Industrie 4.0 instead of being passive providers.
Other descriptions put their main focus on organizational and technical possibilities that can be achieved through Industrie 4.0, often to depict the new chances and opportunities. Such documents are often aimed at political and public audiences and depict technologies on a rather aggregate level. [7]
Another prominent model, the "Reference Architecture Model Industrie 4.0", or short RAMI4.0 developed by VDI/VDE, ZVEI and BITKOM [8], addresses the fundamental challenge of widely-accepted standards. The motivation for RAMI4.0 has been the "grouping of highly diverse aspects into a common model" [8] whereas the common definition of standards is the main objective. The description of RAMI4.0 is "cubic". It is comprised of life cycle & value stream chains on the x-axis, hierarchy levels on the y-axis, describing classifications of functional assignments of Industrie 4.0, and several layers on the z-axis for the Industry 4.0 component description. [8]
Although the RAMI4.0 is a very comprehensive framework, it is not too feasible for common communication as its main development objective has been ICT-standardization based on existing technical standards of the International Electrotechnical Commission (IEC).
Therefore it has been considered useful to develop an Industrie 4.0 description model by starting bottom-up with individual technology themes. Since Fraunhofer has a strong focus on Industrie 4.0 technologies, we took the Fraunhofer thematic portfolio as basis. The following sections will provide a description of the model Fraunhofer has developed.
3. The "Fraunhofer Industrie 4.0 layer model"
The "Fraunhofer Industrie 4.0 layer model" (in the following referred to as "Fraunhofer layer model"), has been developed to depict and structure the major themes related to Industrie 4.0 based on the Fraunhofer R&D landscape (Fig. 5). The input data of R&D topics with descriptive texts for the thematic lists has been extracted from research databases and reports [9] as well as internal documents and internal expert interviews. More than 100 Industrie 4.0 related topics have been identified as a result.
In the next step, these themes have been arranged along concentric layers divided into a) production-related themes at the core of Industrie 4.0; b) information and communication technologies (ICT) as enablers for the realization of Industrie 4.0; and c) themes around the enterprise transformation realized through Industrie 4.0.
The results and thematic assignments were then fine-tuned and validated in several expert workshops. Although the "Fraunhofer layer model" has been specifically developed with the landscape of Fraunhofer R&D in mind, due to its quite comprehensive character and bottom-up creation with
Fig. 5. Structure ofthe whole "Fraunhofer Industrie 4.0 layer model" with the outer, middle and inner core layer.
top-down validation, the model can also serve as reference for a general, tangible description of Industrie 4.0.
The following paragraphs will provide a short description of the concentric layers with a special emphasis on the production-related core layer.
3.1. Description of the outer layer related to enterprise transformation
The outer or most peripheral layer comprises Industrie 4.0 R&D themes which relate to the enterprise transformation. Their subdivisions include human resource management, business models, business planning and cases, as well as transformations and change management. This layer with its subdivisions reflects the technologies and processes that are resulting from the Industrie 4.0 revolution, e.g. through the interaction with customers or the possibility of IT-driven customized production. It also affects human resource management as customization requires a larger degree of flexibility. New forms of data-driven production necessitate different skill sets for employees. As a whole Industrie 4.0 requires changes in business planning and organizational structuring, but also opens up new business models, products, services and pricing models.
3.2. Description of the layer describing ICT-enabling technologies
ICT is a major enabler and driver for Industrie 4.0. Progress in hardware and software technologies has now reached a point where computing power, data storage capacities, data transmission volume and speed and miniaturization of integrated circuits and sensor systems are advanced and cheap enough for data-driven production, the essence of Industrie 4.0.
Rising computing power and data storage technologies allow for an overall coordination of all entities involved in the whole manufacturing value chain, which also includes machine-to-machine communication, human-machine-interaction, data analysis and visualization as well as machine learning.
Also resilient data transmission stands as a central topic for Industrie 4.0. One aspect relates to the requirement for extreme low latency digital communication in order to translate sensor and analytics data into real-time control signals for actuators and machinery for process optimization, flexible production and predictive maintenance.
The other crucial aspect relates to ICT safety and security concerning data analytics and data exchange in data-driven production. The high dependency of Industrie 4.0 on digital, data-driven control and digital connections to peripheral systems like electricity suppliers, logistics systems, external ICT systems etc., leads to new vulnerabilities as malicious activities do not necessarily require physical intrusion. As the Stux-net worm demonstrated, malware, ICT and data manipulation can lead to serious consequences in data-driven production and Industrie 4.0, e.g. production standstill, direct product manipulation or illegal tapping of internal data.
This makes ICT security a key enabler and cornerstone for successful Industrie 4.0. In order to achieve secure and trusted exchange of data for generating data-driven added value, the Fraunhofer-Gesellschaft has initiated the Industrial Data Space© (IDS) as a "Network of Trusted Data" (Fig. 6). Its goal is to enable the secure exchange and processing of combined data originating from different sources, while maintaining the sovereignty of the respective owners over their data. The concept of the IDS represents an alternative architecture to both centralized data management concepts (e.g. so-called "data lakes") and decentralized data networks, which generally have no common rules.
Fig. 6. Key elements of the "Industrial Data Space" [101
Therefore a core element of the IDS is the "Network of Trusted Data", where the data owners retain the sovereignty over their data and determine the terms and conditions for the use of their data. This allows for a different way of data exchange and data linkage in a decentralized manner as all participating entities are linked via an IDS-Connector. Connected entities that provide and receive data can be enterprises, objects like machines, cars, appliances etc., public data bases (e.g. weather stations, traffic management systems etc.) and humans, that are certified, trusted and adhere to a common set of rules. The IDS is based on several technologies to ensure the secure and trusted data exchange. These include the "linked data" approach, i.e. the realization of a semantic data web; hardware-based security like Hardware Security Modules (HSM), Trusted Platform Modules (TPM) or Smartcard Chips for enabling secure key storage, secure boot processes and third party integrity verification; strategic data management as well as solutions for standardization and usage control. [10]
3.3. Description of the core layer: production
At the core of the "Fraunhofer layer model" stands the central layer comprising the core production-related themes. This layer is also the most complex as it is divided into five concentric sub-layers (labeled 1 to 5) as well as 10 segments. The segments describe 10 core-technology categories directly related to the production process within the value chain of Industrie 4.0. The core technologies are: I) engineering (e.g. digital and virtual engineering, embedded software, smart materials); II) manufacturing technologies and organization (e.g. self-directed manufacturing, responsive and adaptive manufacturing, additive manufacturing); III) machines (e.g. intelligent sensors and actuators, remote and predictive maintenance, plug and produce integration and configuration); IV) smart capabilities (e.g. machine learning, multi-agent control, self-localization); V) robotics and human-robot-collaboration (e.g. mobile robots, soft robotics, data and sensor fusion); VI) production planning and control (e.g. intelligent production planning and control, manufacturing execution system integration, standardized interfaces); VII) logistics (e.g. autonomous logistics systems, track and trace concepts, geo-fencing ); VIII) work organization (e.g. flexible work organization, social business process management, competence management); IX) workplace design and assistance (e.g. human-machine interfacing, knowledge augmentation through Virtual/Augmented Reality, mobile devices, wearables, assistance and decision support systems); X) resource and energy efficiency (e.g. emissionneutral production, recycling management, urban manufacturing).
Additionally to the segments described above the core production layer is divided into five sub-layers defined as functional areas [11], which are crossing over all segments.
1) Data collection and processing
• through sensors, RFID, storage and transmission etc.
2) Assistance systems
• through Augmented Reality, human-machine-interfacing, decision support etc.
Machine Learning for flexible Manufacturing Processes
Fig. 7. Example ofthe central "production layer" ofthe model with the concentric sub-layers (1-5) and the 10 segments (I - X) with selected technology samples described in the next section.
3) Networks and integration
• through connected devices, cyber-physical (production) systems etc.
4) Decentralization, service-orientation and flexibility
• leading to new business models, flexibility and modular as well as scalable systems.
5) Self-organization and autonomy
• through control loops, self-configuration and optimization, machine learning etc.
3.4. Fraunhofer-R&D-Examples
In this chapter we provide three examples from Fraunhofer Industrie 4.0-related R&D focusing on the core production-related layer in the segments IV) smart capabilities, X) resource and energy efficiency and VII) logistics.
3.4.1. Machine Learning for flexible manufacturing processes A possible digitization-driven, future step for realizing efficient and flexible manufacturing processes in the Industrie 4.0 context are machines and production systems that have the capabilities for self- and process optimization. Self-optimizing systems possess a degree of "cognitive abilities" that enable machines to adapt to changes and optimize parameters and strategies in real time. Sensor systems and Machine Learning methods help machines and robots to learn new tasks (e.g. how to best manipulate objects with different geometries) and to identify further optimization potentials. Processes (e.g. deep drawing process) are characterized by their condition (e.g. stress distribution on a component) and are affected by actuating variables (e.g. blank holder force) and unknown disturbance variables (e.g. friction). However, the condition is often not directly measurable and can only be made accessible though observable parameters like expansions or strains at selected locations ofthe component or tool.
Since a process regulation based on a complete description of all conditions is unfeasible and far too complex, machine learning techniques like dimensionality reduction and the
integration of specific process knowledge are used to extract the relevant characteristic features required for optimization tasks. Through data enrichment, so-called "black-box" models are transformed into "grey box" models, which include additional and more detailed process knowledge and parametric descriptions. The description of relationships, condition characteristics, control variables and disturbances together with the goal of process optimization leads to new control rules for manufacturing processes achieved through machine learning. This can be expanded towards the whole process chain to support self-optimization and smart capabilities.
3.4.2. Full transparency for control of energy-aware factory operation
Researchers from Fraunhofer Institute for Machine Tools and Forming Technology IWU are working at the vision of an energy adaptive factory in means of using more volatile energy sources, without disturbing the production process. Typically, efficiency improvements in production facilities are reached by introducing more efficient equipment, reducing or recycling waste, increasing staff awareness or changing the production organization. While the former are state of the art in many companies, the latter still holds considerable potential especially for improving the flexibilization of energy requirements in a factory.
Within the "E3-Forschungsfabrik" the basis for an energy aware production planning and control (PPC) is given by having full transparency on all processes and flows. In practice, it means that material flow information as well as energy data from machines, equipment and building infrastructure TBS are acquired, analyzed, stored and linked within the "LinkedFactory" software solution. Thereby, the "LinkedFactory" is part of the Industry 4.0-Stack, a modular solution framework, containing components for machine connectivity, data storage, visualization and analytics, completed by localization and identification of smart objects in factories.
While the volatility in energy consumption stems from imbalances in the production process, flexibilities arise from temporal or material buffers. These can be used to match the immediate demand of the production equipment with the available capacities in the supply level and possibly the infrastructure level. Furthermore, the knowledge on the actual demand for processing on certain equipment may be used to follow shutdown strategies which aim to lower the overall energy consumption without influencing the material flow.
Fig. 8. Rica Hartenstein in front ofthe "Energy Dashboard" - a energy oriented visualisation surface from the LinkedFactory
3.4.3. Smart system logistics
Beside the improvement of manufacturing and production processes the efficient transport of material and goods along the value-chain is essential. Hence, logistics is one ofthe most obvious fields of application. Within the last years researchers from Fraunhofer Institute for Material Flow and Logistics IML developed several solutions. Cellular Transport Systems (CTS) (c.f. Fig. 9 left side) are a combination ofarack shuttle, which is able to operate an automatic bin warehouse, and a fully functional AGV, which is able to move without guidelines or other fixed marks. This system allows to minimize the permanently installed conveyor technology and maximize the flexibility at the same time.
Fig. 9 Examples of decentral controlled automated guided vehicles as a networked working swarm for flexible transport tasks.
Centralized control and management systems are a repressive alternative, due to their missing flexibility and the big amount of vehicles which needed to be controlled. The coordination ofthe CTS - the "Vehicle Swarm" - is executed without a central control unit. A multi agent system is used -decentralized and following the Internet of Things principle. This is the first vehicle swarm in an industrial scale controlled on basis of meta-heuristics like pheromone-algorithm (c.f. [12]).
As a consequent enhancement of cellular transport systems, the latest concept enables an additional degree of freedom. The so called Bin:Go (illustrated on the right side of Fig. 9.) is a drone-based transport system for light-weight goods. Facing the main drawbacks of drones - high energy consumption while flying and limited operation in a human-machine environment due to safety issues - Bin:Go is mainly rolling and only flying if necessary. Its cage-like structure is also protecting human beings. Descending from e.g. rack bay will be implemented by rail-equipped spirals that enable gravity-driven transport as well as parallel charging ofthe batteries via conductor rails. Hence, Bin:Go could be a competitive solution compared to existing technologies.
4. Outlook
Industrie 4.0 allows for efficient, smart and on-demand industrial manufacturing, also enabling the production of individualized and customized goods at reasonable costs. These abilities are seen as a unique competitive advantage for already highly developed high-wage countries.
In order to grasp and communicate those concept changes together with the underlying technologies, new possibilities and challenges (e.g. in regard to digital/cyber security), a tangible description model is helping. Fraunhofer has
developed its "Fraunhofer layer model" for this purpose, part as portfolio-inventory and part as structuring model. In the future as technology advances, modifications may be made to the model.
As computers are becoming faster, machines smarter, sensors smaller and ubiquitous, data storage cheaper and data transmissions faster and securer, all entities that can add value to a product will be able to communicate with and learn from each other. The factory ofthe future will be much more like a smart, learning and interacting organism than a static array of machines, predefined processes and strict division oflabor.
5. Acknowledgement
The authors want to acknowledge the support of the Fraunhofer IPA, Prof. Thomas Bauernhansl in Stuttgart and Fraunhofer IML, Prof. Michael ten Hompel in Dortmund, and Fraunhofer IWU, Prof. Putz in Chemnitz as well as the support of Hans-Georg Schnauffer, Armin Ritter and Michael Fritz from Fraunhofer headquarters who developed the model bottom up in fruitful discussions with experts and researchers.
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