Challenges and Requirements for the Application of Industry 4.0: A Special Insight with the Usage of Cyber-Physical System

2.1 Key Enablers for the Application

Industrie 4.0 was first advanced as a forward-look project in the high-tech strategy of the German government. Then after, under the high demands of flexibility and responsiveness, and because of the intelligent capability of current technologies, Industrie 4.0 has further been trended as the pursuits of industrial environment. Based on the experiences of Industrie 4.0 Working Group, three key enables have been gathered for the implementation of Industrie 4.0 in companies. They are Internet of Thing (IoT), Cyber-Physical System and Smart Factory [7]. In general, these three are not independent, but overlapping with each other.

Thus, via the interconnection of smart objects, Smart Factory enables companies with flexibility, efficiency and effectiveness. Besides, with the combining and interactions of these three enablers, the applications of Industrie 4.0 would be enhanced continuously. Under the scenarios of Industrie 4.0, companies are supposed to achieve the manufacture of “even one-off items” with profit [13]. The advantage of allowing “last minute changes” also enables the production of companies with more flexibility [13]. In addition, with the end-to-end transparency over the manufacturing process, Industrie 4.0 is also potential to facilitate optimized decision-making [13]. In general, there is no doubt about the possible benefits of Industrie 4.0. However, despite its attractiveness, still big challenges are existed for the realization of this strategic scenario. Further question arise on: ‘how to implement Industry 4.0’? To deal with this, a derivative question that should be clearly understood is: ‘which perspectives should be taken into account, so as to help to realize Industrie 4.0 with more detail’. Thus, in the following stages, tasks of the study will be moved from the macro understanding to the implementation aspect of the object.

2.2 Reference Architecture of Industrie 4.0

Considering as a revolution, the concept Industrie 4.0 is criticized to be “lack of knowledge of the details” [14], especially when it comes to the detail applications of this industrial strategy. With this in mind, and in order to support to execute Industrie 4.0 in companies, a reference architecture has been established as in Figure 1.

As illustrated in Figure 1, four major perspectives have been emphasized for the execution of Industrie 4.0. They are issues related to the manufacturing process, devices, software and engineering. With the employment of IoT technologies, all possible processing and transport functions of the manufacturing process would be mapped and recorded as in the virtual information system with real time. Moreover, devices within the manufacturing system would be considered as ‘Things’ or ‘objects’. These include (smart) automation devices, field devices, fieldbuses, programmable logic controllers, operating devices, mobile devices, servers, workstations, web access devices and so on. Relative embedded technologies will also be used to merge these physical devices with virtual systems. And the conditions of the physical devices would be recorded automatically to the information system. The software perspective emphasizes on the software realization for the interfaces and integration among items from the physical-, cyber- and automation-levels. Possible existing software includes business management software, production management software, control and regulation software and so on. And the application of these software not only brings with interactions among “Things”, but also enables users to achieve planning, organizing, coordination and controlling of “Things”. The perspective of engineering in the manufacturing system is more related on the Production Lifecycle Management. With the using of data derived from the manufacturing process, analyze to plan the necessary resources in terms of both machinery and human resource. And the tasks of resource allocation include issues related to production design and development, production planning, production engineering, production, and services.

In general, these four perspectives work together as in a reference architecture, which helps to detail the application of Industrie 4.0 into the implementation level. And the former two focus on the mapping and merging between real and virtual systems, while the late highlight on the fusion and application based on the integration of these two systems. Thus, considering the usage of IoT technologies as the foundation, further challenge for the implementation of Industrie 4.0 lies on the issue “how to realize fusion for the autonomous decision”. Here, the application of Cyber-Physical Systems was suggested as one of the most efficient channels [3].

2.3 Structure of Cyber-Physical System

Cyber-Physical Production System refers to “physical and virtual, local and global, horizontally and vertically networked systems, dynamic system boundaries, partial or complete autonomy, active real-time control, cooperation and comprehensive cooperation between human and system” [14]. It comprises smart machines, warehousing systems and production facilities that have been developed digitally and feature end-to-end Information Communication Technology-based integration, from inbound logistics to production, marketing, outbound logistics and service. Based on the Final report of the Industrie 4.0 Working Group [7], general structure of Cyber-Physical System has been gathered as in Figure 2.

As illustrated in Figure 2, the basic logic for the establishment of Cyber-Physical System is the connecting of real and virtual production. It is developed based on the establishment of virtual system with real time information (shown as in the left side of Figure 2), and goes beyond the scope of IoT. In others words, Cyber-Physical System is the sublimation for the application of IoT. General focus for the usage of Cyber-Physical System lies not only on the gathering of real time data from the physical environment to the digital system, but more on the structure analysis of data sources for (partly) autonomous and self-organized processes. Thus, the back-loops of virtual information system to the operation and controlling of physical processes is considered as the spirit of Cyber-Physical Systems. And with the establishment of Cyber-Physical Systems, Smart Factory is enabled to be possible in companies. Here, Smart Factory “constitutes - key features of Industrie 4.0” [7]. And under the Smart Factories, “in addition to condition monitoring and fault diagnosis, components and systems are able to gain self-awareness and self-productiveness, which will provide management with more insight on the status of the factory. Furthermore, peer-to-peer comparison and fusion of health information from various components provides a precise health prediction in component and system levels and force factory management to trigger required maintenance at the best possible time to reach just-in time maintenance and gain near zero downtime” [15]. Taken together, it is of great importance for the realization of Industrie 4.0 in companies. And in order to provide an in-depth insight on the possible solutions on the application of Industrie 4.0, experiences with applied cases will be worked out as in the following stages.

3.1 Self-sustaining Intelligent Sensor Nets for Production

In a production system, sensor technique plays an important role for the operation of IoT and Cyber-Physical Systems. A sensor is “a mechanical device sensitive to light, temperature, radiation level, or the like, that transmits a signal to a measuring or control instrument” [16]. It is a useful device that converts information from physical world into data in cyber system [17]. However, concerning the application of sensor, general problem arises on ‘how to interlink these information islands efficiently’. With this in mind, the project ‘Self-sustaining intelligent sensor nets for production (AiS)’ has been carried out in the Technische Universität Chemnitz, Germany. In this project, together with another three universities, we dealt with the issues on ‘how to design the sensor nets’, ‘how to realize sensor communication’ and ‘how to achieve self-sustaining of the intelligent sensor nets’. General aim of this project is to integrate a sensor nets into the production environment, and realize energy efficient and reliable sensor communication. All these are based on the utilization of Experimental and Digital Factory (EDF) in the Technische Universität Chemnitz. Here, EDF is well known as a representation of a “complete mini-factory” with all the essential processing and logistic components. The overall goal of EDF is to implement symbiosis between virtual and real factory [18]. As the laboratory environment within EDF represents current state of technology in reality, it enables the application of innovative technologies and/or concepts within the companies with a reality-inspired means. In general, basic logic for the designing of the intelligent sensor nets is worked out as in Figure 3.

From Figure 3, we see the basic foundation for the establishment of sensor nets is to gather and transfer the real time information from the manufacturing system to computers; and with the network algorithms in cyber systems, the improved instructions will be transferred back to the physical system, so as to realize better controlling of the process. In this applied project, acoustic sensors have been used for the abrasion detection of the logistic devices. Based on the monitoring of the real conditions, optimal maintenance periods would also been obtained and forwarded to the physical system via sensor nets. Another innovative design of this system is ‘self-sustaining’. That is, to provide reliable ambient energy supply for singular sensor knots and realize wireless energy transmission to moving objects. Taking as a whole, results of this project not only help to in-depth the theory on ‘how to construct the sensor nets’, but also work as the bridge to combine sensor theory to the intelligent application in industries. In fact, this well-validated intelligent sensor nets have already been used successfully in a famous automobile company in Saxony, Germany.

3.2 Virtual Space for the Controlling of Double-arm Robot

In a Cyber-Physical System, smart machines work together for the realization of common targets. And the coordination and controlling of these machines is one of the keys to bring about ‘smart’. In this work, the controlling of the doubt-arm robot has been taken as an example (seen as in Figure 4). This is based on the results of an applied project carried out for one of the biggest automobile company in Germany. In this case, double-arm robot is programmed for the material distribution in the assembly line, and the target of the arrangement here is: 1) self-recognize of the roller providing line. That is, these over 20 kg material boxes are transferred by the roller line to the double-armed robot (seen in Figure 5). And when there are only two boxes left, the sensor located by the roller line can recognize this and will give the information to stop the further picking up activities of the robot. 2) Self-control of the movement. For the distribution of the material boxes to different rows, the double-arm robot can move like a human being. Its arms will not move with the shortest path, but with the continuous way. As a result, the general time needed for the material distribution would be the shortest. Moreover, with the help of sensors surrounding the shelf (marked as green), robot can “see” if the shelf is full or not. When it is full, robot can stop the distribution activities itself. 3) Safety control of the operating space. This is based on the utilization of the so-called technique ‘safety-eye’ (shown as in Figure 6). From Figure 6, we can see a virtual space has been defined as in red. And the function of the virtual space is to monitor the environment and keep safe during the operation of the robot. In other words, when these is someone or other machines (e.g., automated guided vehicle) disturb to the red virtual space as shown in Figure 4, the system can recognize the risk and the movement of the robot will be stopped. At the same time, a special sound will be heard to remind the operator. When the risk is removed, the operator can reset the system and the movement of the robot will be start again. In general, the application of sensors and the setup of the virtual space would be efficient to bring ‘smart’ to the operation of the double-arm robot. And the principle of this function can also be used in other industrial environment.

3.3 Synchronous Production through Semi-autonomous Planning and Human-centered Decision Support

One of the biggest challenges for the application of Cyber-Physical systems, is the “real-time” link of physical production and digital factory. Besides, with a view to different information technology systems existing in companies, how to merge all these relevant data from different systems to the implementation of Cyber-Physical systems arises as another problem. With these in mind, a synchronous production model has been developed based on the carry out of the project “synchronous production through semi-autonomous planning and human-centered decision support (SOPHIE)” (seen as in Figure 7).

As shown in Figure 7, three levels of objects have been constructed for the implementation of synchronous production. They are physical level (including real factory, employees, visualization and interaction devices, etc.), cyber level (including agent-service system, digital factory, simulation applications and so on) and automation level. Via the description and modeling of objects within the production system, digital factory would be mapped to the cyber level. In addition, automation level with the existing but developed information systems, plays as a foundation and mediated role for the application of Cyber-Physical systems. Here, within the automation level, all the involved systems would be gathered and sorted as in a pyramid manner. Therefore, with the help of these structured information systems, dynamics of the physical objects can be reflected real time to the automation systems. Moreover, the changes of data within the automation systems will automatically be adapted to the digital systems. In the digital system, the status of planned and actual process can be compared via the usage of virtual technologies. Safeguard operational decisions will also be provided based on the valid and continuously update simulation model. Taken together, with the development of the synchronous production model, the real-time link between physical production and the digital factory becomes possible, and the general logic of this model can also be transferred to different industries.

3.4 Resource-cockpit for Socio-Cyber-Physical Systems

Considering the scopes of general value-added chain, the applications of cyber-physical systems can also be extent to the maintenance issue, as the scheduling of maintenance activities plays an important role for the availability and reliability of production facilities [11]. Maintenance is defined as “the combination of all technical, administrative and managerial actions during the life cycle of an item intended to retain it in, or restore it to, a state in which it can perform the required function” [21]. For the planning of maintenance, information from different sections of the production system should be taken into account. They are not only concerning to the real-time monitoring conditions of machines, but also to the scheduling of staffs, availability of other resources and so on. With existing but developed IoT technologies (e.g., sensor, RFID), facilities are equipped to be smart [10]. However, considering the dynamics of all those involved objects, challenges for the realization of smart maintenance lies on the on-time integration of all relative information. It is not just “marginal” involving of decentralized data in the digital system [11], but unifying and structuring of these real-time information for the general arrangement. With this in mind, a project called “Resource-cockpit for Socio-Cyber-Physical Systems (S-CPS)” has been funded by the German Federal Ministry of Education and Research (BMBF). And the target of the project is to realize flexible integration of heterogeneous master and dynamic data. Moreover, with the combination of “socio” perspective, interaction and collaboration between human and smart objects would be focused. As a result, mobile support for internal and external maintenance personnel would be realized, so as to improve efficiency and flexibility for the on-site and off-site maintenance [11].

Based on the systematic modeling and optimization of maintenance processes, all kinds of information related to the product and production resources would be merged as in a resource cockpit (seen as in Figure 8). As the output, the cockpit will create lists of tasks, where different roles of users will be adapted [11]. Contents of the lists include not only detail tasks, but also necessary resources required, resource availability, competence required, machines states and relative schedule. Thus, with the usage of resource cockpit, efficiency and efficacy would be enhanced considering the management of the maintenance activities. General concept of this project can also be adapted and further extent to the coordination and controlling of other stages of value-added chain.