Keywords

1 Introduction and Motivation

1.1 Motivation and Goals

Due to the continuous emergence of new products and the acceleration of product complexity, enterprises are required to quickly obtain production system solutions and to smoothly put them into production according to market demands. At the same time, production systems have to be able to change with the product design through rapid reconstruction and generate the corresponding technical scheme of intelligent features [3, 6]. Currently, there are a large number of non-standard equipment software interfaces and communication protocols, resulting in long build times and low reliability of production systems, which significantly delays the market entry of new products. Established companies, especially those that integrate and operate production systems, often know the characteristics of Industry 4.0 and are therefore able to identify or define the relevant features of the systems and products, but new companies usually lack this knowledge and capability. Therefore, the market for reconfigurable intelligent manufacturing system features, or in other words, the demand for “turnkey production systems” is growing rapidly, compared with traditional manufacturing [9]. A common marketplace for manufacturers, system integrators, service providers, agile and rapidly evolving requirements, especially considering configuration, reconfiguration and monitoring of production systems as a combination of integrated services, is a viable and efficient way for all stakeholders in the process of creating production systems [1].

Manufacturing companies are facing big changes in the current industrial environment - the dynamic market demands, increase in personalization, pursuit of high-quality products, flexible production batches, shorter product life cycle, and so on, which force manufacturing companies to realize high flexibility, driving the transition of the traditional production system to the next generation of manufacturing systems [4, 5]. The flexible production model oriented to Industry 4.0 can respond quickly in the continuously changing market, shorten the period from the establishment of product development and production system to the commissioning, and flexibly schedule the system operation process to improve production efficiency (especially on the shopfloor). The realization of flexible production is inseparable from the fast matching, connecting, debugging, and operating of functional components in the production system. So, Plug and Play (PnP) is one of the key features of future manufacturing. To realize PnP equipment and software, it is necessary to integrate and encapsulate all kinds of hardware and software in standard modules and connect them with unified interfaces and data formats [2]. In addition, real-time, efficient, and stable communication means are needed to guarantee the rapid operation and interaction of the equipment, especially for the reconfigurable PnP modular equipment, which sets a higher demand on the strong compatibility and rapid configuration of communication.

1.2 Research Goal

The purpose of a turnkey engineering platform is to quickly obtain production system solutions according to product design requirements. Production systems need to be reconfigurable and to include certain smart elements or “intelligence”. At present, there are many non-standard equipment and communication protocols, the planning and construction process of a production system takes a long time, is highly cost intensive with often poor reliability, resulting in serious delays in the overall production process. The aim of the project was to study the reconfigurable intelligent architectures and digital twin models of production systems, to build a reconfigurable intelligent manufacturing system for an Industry 4.0 platform. Through the research and development of enabling technologies such as system configuration and model drive, the project quickly generated a turnkey engineering production system solution, and simulated and evaluated its business model and trial operation status by reorganizing the production system of the platform.

The main goal of the project is to build a software-supported and model-driven factory automation platform through a use case driven construction of a connected reconfigurable intelligent production system. The key scientific problems to be addressed are threefold:

  1. 1.

    Development of a reference architecture and instantiation enabling tools for turnkey reconfigurable intelligent production systems;

  2. 2.

    The construction of digital twins for reconfigurable intelligent production systems heterogeneous integration and reconfiguration methods;

  3. 3.

    Implementation, validation and demonstration of the digital twin-based reconfigurable intelligent production system.

The key contribution here, is the development of methods for configuring the turnkey production system, the definition of interfaces, the development of the configuration logic, the creation of the digital module models and the conception of embedded systems to make components smart, i.e. network-capable. Furthermore, methods for the continuous simulation of turnkey production systems are developed. This can also include procedures and the adaptation of simulation methods to simulate the value streams through the plant. In addition, control modules (for operation on the hardware level) and for controlling the overall system (e.g. material flow control, Manufacturing Execution System (MES) etc.) are to be integrated. Thus the factory automation platform has the potential to reshape how work tangent or related to the integrated production planning process, e.g. product development, consulting services, production planning, integration, procurement, plug & play, build, quality management, production monitoring as well as other accompanying services are being executed.

1.3 Structure

In the Industry 4.0 factory automation platform, participating companies are not only involved as suppliers of materials and users of the platform, but also establish themselves as system integrators on the platform and take on coordinating tasks in the medium term. Chinese industrial companies, in particular as suppliers of subsystems for turnkey production systems, but also German companies are to be integrated via the platform. The framework of this integrated Industry 4.0 factory automation platform offers the opportunity to address further research questions, in particular regarding open, modular German-Chinese research. For example, questions from the field of logistics, production processes, digital process chain or network architectures. It also enables the expansion of this platform to include other research centers and research partners as required.

The architecture of the reconfigurable intelligent production system is shown in Fig. 1. Key technologies include the Industrial Internet of Things, common interfaces and standards for machine tools, configuration of communications, and digital twin models. Specifically, the research objectives of the physical layer and cyber layer of the project include the following:

Physical layer

  • According to the requirements of the turnkey production system, the granularity, interfaces and modular attributes of functional units of reconfigurable intelligent production systems have to be defined for its whole life cycle.

  • Through the unit configuration function, the reconfigurable production system reference architecture for personalized customization is build.

  • On this basis, an enabling tool for system construction, reconstruction and quick verification is developed.

Cyber layer

  • Establish the digital twin model of the modular units of the intelligent production system, define the communication logic of the digital twin models of each functional module, establish the system configuration method;

  • Determine the configuration logic of physical systems and digital twin models to validate the configuration process; Establish the digital twin model of the intelligent production system driven by real-time data of the physical system.

Fig. 1.
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Project architecture (own illustration)

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2 Digital Enablers

The following chapter presents a selection of methods and digital enablers primarily integrated in the digital process chain for configuring adequate production systems.

2.1 Agile Development

Using agile development techniques, the project team created a mock-up of the platform to communicate its value proposition and possible application scenarios. Using a web-based interface the mock-up was then transferred into a minimum viable product (MVP) through which user studies could be done and single applications and services could be integrated and tested early on.

2.2 Digital Module Models

To plan entire production systems and automatically configure them according to particular product designs, the computer internal representation of the individual elements, which compose the production systems, has to be modeled in a way that enables attribution of components, tools, machines, as well as production systems as well as their categorical dependencies and interfaces to procedural requirements. Furthermore, procedural requirements should also be automatically derived from material composition, geometry, product manufacturing information. When optimizing for cost and efficiency, the ultimate processing recommendations are influenced by the set of information about already existing tools and machinery. Therefore, a digital module model description was generated mostly derived from current digital twin description models in accordance with RAMI 4.0 and the asset administration shell to represent assets and their relationships with each other in a common (model based) systems engineering approach.

2.3 Feature Recognition/Process Identification

Using known techniques from CAD-CAM tools, product geometries in the standard exchange format STEP, including product manufacturing information (PMI) were used as product description. We use algorithms to analyze the geometries, to extract features that can then be translated into potential processing steps.

2.4 Machine Tool Selection

Having detected and selected potential processing steps for the production of the product to be build, now machines and tools have to be selected corresponding to the individual processing steps. This is multidimensional optimization problem where time, costs, maximum number of pieces for manufacturing, energy consumption, raw materials, design adjustment feedback loops, etc. have to be taken into consideration. We therefore considered only three possible optimization criteria, that mostly can be translated into a function of costs and time, resulting in a limited amount of potential machine configurations to be validated.

2.5 Layout Generation, Validation and Optimization

After generating the production system configuration, we combine processing information, processing order, machining attribution, machine geometries (CAD-Data) together with the workshop floor plan to automatically create visual layout propositions, 3D-process simulations of the production processes as well as logistics processes to enable a fast and interactive production system planning as well as a validation process via the platform. Figures 2, 3 and 4 show the different development stages of this process including various use cases. In Fig. 2 we can see a layout proposition for a production system. Using a general representation for material source and sink. The red line represents Space around the modular units required for maintenance and other work on the machine. The blue turquoise line represents the material flows and can be adjusted. All machines can present key performance indicators and are flexibly adjustable in their positioning and orientation.

Fig. 2.
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Automated layout generation (own illustration)

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In Fig. 3 we use a modular layout of production components of fixed sized modules and production components, where small production elements stand atop of modular table sized units. The handling of work pieces is performed here by a robot unit (UR5). Whereas the green outlined boxes represent buffering stations.

Figure 4 shows an integrated view of the web-site mock-up where a potential customer can visualize his or her production system configuration and adjust it interactively. After the setup of the production system, the platform can then be used for live updates and monitoring purposes.

Fig. 3.
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Production system configuration simulation of a process workflow on a web-based platform prototype (own illustration)

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Fig. 4.
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Automated layout generation on a modular demonstrator (own illustration)

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3 Application Scenarios

3.1 Plug-and-Play Application - Communication Technology for Digital Twins

The 5G technology enables high growth potentials in the manufacturing sector [7], providing readily available rich data for processing machine information, sensor feedback and twinning capabilities for big volumes of data. When trying to achieve a quick set-up or the reconfiguration of a turnkey ready production system of functional units, the further development of the Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS) are part of the main issues that need to be resolved [8]. Promoting and advancing the development potential of future manufacturing industries will therefore be enabled by the use of 5G technology to realize the agile real-time transmission of large amounts of data, combined with a standardized modular design to achieve plug-and-play capability of functional units and the use of IIOT and CPS. In order to create application scenarios for the quick setup and operation of production systems, the Advanced Manufacturing Technology Center (AMTC) at Tongji University (Shanghai, China) carried out a self-optimizing factory design and implementation of 5G-based turnkey applications. Additionally, at the AMTC, use cases were created for research and enhanced students’ awareness of 5G and plug and play in the industrial sector.

For demonstration purposes, a single standard modular unit and a collection of modular sub-units were developed on the basis of modularized digital twin components. Here, the German and the Chinese partners used different but similar modular technical components (the Chinese partners used iSESOL-modules whereas the German partners utilized a similar demonstration line at KIT with functionally similar component and modules). The standard module unit demonstrates the design of the Plug and Play module. The collection of modular components, which are potentially not standardized, provides users the chance to test out small-scale 5G-based turnkey production system applications, where manufacturing- as well as assembly steps are being integrated into the processes.

Fig. 5.
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Structure of the standard modular unit [10]

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Figure 5 depicts the structural makeup of the Chinese standard modular units. The module integrates lifting wheels, a 5G module, control systems, supply & clamping interfaces, fast change fixtures for devices like robot arms, tiny machines, etc. These components make up the majority of the modules. The modular units are connected via a control panel and an industrial cloud, using the 5G module. Data i.e. equipment operation data, sensor data, control instructions, operational procedures, etc. are all included in the communication. The operation of the entire unit is managed by the control system. Supply & clamping interfaces are in charge of positioning and clamping between modular parts as well as connecting power and gas sources. Devices are quickly installed and replaced using quick change fittings. The primary parts may be readily taken apart or put back together since they are removable. After encapsulation, the 5G module may also become a Plug and Play device and be swiftly put together to create a 5G- PnP modular unit.

Fig. 6.
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Simplified exemplary setup (own illustration)

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In order to fulfill the PnP between modules and for training reasons, the set of modular units can be properly modified and simplified as an example of PnP application. As seen in Fig. 6, modular parts in the present design comprise storage components, robots, small milling machines, assembly tables, buffer units, and visual identification systems and process flow indicators. For various products, different alternative layouts may be created. For instance, if certain components need to be printed and put together. To monitor and assess the operation status and product quality of the manufacturing line, digital twins are created for the major units, thus generating a bidirectional feedback loop with the physical system and the digital representation. A master control system is included, and manages the overall scheduling of the modular units in accordance with the production process. Figure 7 shows such a setup where we connected a virtual hard-ware environment with the virtual system.

Fig. 7.
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Set of modular units connected to a virtual hardware-setup (own illustration)

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3.2 Artificial Intelligence Technology in Machining: Chatter Identification Tools for Intelligent Manufacturing

In this section, we describe an exemplary scenario how machine learning methods can be used to intelligently solve manufacturing issues in such a connected environment.

In the aerospace, automotive, mobile phone, and almost all other sectors, cutting force, vibration, and stiffness ultimately lead to workpiece deformation, machining mistakes, tool wear, and other undesired material behaviors. Chatter is one of the trickiest issues in real-world machining. For metal cutting, the spindle-tool-workpiece production system frequently generates chatter, a rather intense regeneration/self-excited vibration that has a number of detrimental impacts on machine tools and workpieces. Chatter leaves ripple patterns on the finished surface in addition to lowering machining efficiency and surface quality. Several signals produced during the cutting process, including spindle signals, displacement, acceleration, sound, picture data, cutting force, encoder, and current from the machine's computerized numerical control (CNC) controller, can be used to identify this issue. If one or more acceptable signals are chosen, these signals can be used to indicate chatter. As a consequence, it is possible to keep an eye on the state of the machine and take action to optimize the cutting settings and produce a high-quality surface.

The surface topography of the workpiece, which is connected to machine vibration, may be immediately reflected in image data. To track the state of machine tools that mill, a concept based on the hybrid analysis of multiple signals (cutting force, acceleration, and picture signals) is created. As the machining object, a thin-walled component made of the aluminum alloy AA-7075T6 is used. Then, a streamlined chatter detection index vector is created by mapping the feature values of multi-sensor signals. An enhanced multi-support vector machine (SVM) classification model is trained using this vector to display chatter. The following phases make up the suggested chatter detection approach for intelligent manufacturing, which is depicted in Fig. 8. The following steps are involved: I Signal selection and data acquisition; ii) Signal pre-processing and calculation; iii) Construction of the detection index vector; iv) Building an improved SVM multi-classification model; v) Training the model to achieve chatter identification; vii) Establishing a case study and a database for intelligent manufacturing.

Fig. 8.
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The process of chatter detection toll (own illustration)

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Cutting depth increases cause a significant shift in machine vibration. In order to assure axial cutting depth, the thin-walled component remains installed at an angle as illustrated in Fig. 9, progressively rising to a maximum of 10 mm. In this manner, three different vibration types may be concurrently monitored in one parameter when the cutter feeds in the x direction. The cutting force from the dynamometer's y direction is chosen as the effective signals because the workpiece's primary vibration direction, caused by the horizontal x-cutting route, is in this direction. Similarly, the axial direction of the cutter is the y direction, and the acceleration sensor's z direction is perpendicular to the surface since it is attached to the side of the thin-walled portion. Compared to the other directions, the acceleration signals in the z direction can more accurately indicate the workpiece's vibration status. The process parameters are chosen after taking into account a number of elements. The spindle speed ranges from 2000 rpm to 3500 rpm in 500 rpm increments, while the feed rates range from 0.1 m/min to 0.25 m/min. The milling width ranges from 0.05 mm to 0.2 mm. This collection of settings may ensure that three milling conditions would be created in trials after a total of 64 milling operations.

Fig. 9.
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Experimental scene layout (own illustration)

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4 Socio-technical Aspects and Common Marketplace

In the following chapter we want to highlight some of the socio-technical challenges faced during the project. We first want to describe the Sino-German collaboration aspects, discuss interests of the different stakeholders and give an outlook on future growth and different stakeholder integration models.

4.1 International Collaboration

The Sino-German collaboration included industry and academic partners from both China and Germany. After establishing tandem-pairings between the respective institutes of the academic partners, academic exchange and sub-project developments were well coordinated and closely integrated. Through exchange programs, language barriers were reduced and collaboration on scientific challenges were expanded. Yet still, the open exchange of data between the two countries to achieve a common platform had its challenges. Finding a common server infrastructure situated in China and Germany, establishing trust between the partners and enabling the exchange of the required data, working in compliance with the then newly formed Chinese Cybersecurity Law and considering all stated as well as internal interests of the individual partners brought challenges that had to be overcome during the course of the project.

4.2 Socio-economic Potential and Challenges of the Different Stakeholders

Industry partners from both countries offer high-tech products and services. They have the combined potential to validate many platform aspects and offer selective digital services. Having generally superior products regarding efficiency and cost effectiveness automatically open new sales channels through the platform since the selection process would always suggest the superior product according to customer needs. This leads to challenges when different companies offer similar components, where the differentiation is only represented in smaller but potentially significant details. Those details should be adequately represented in the selection process but are sometimes impractical or difficult to assess e.g. previous experiences with the product, availability and quality of services like maintenance etc.

As with any platform solution, platform growth and quality is generated and therefore highly dependent on both growth of the demand as well as the supply side. Demand side has to create a sufficient pull effect to create enough traction for the supply side to increase their offerings. One of the biggest challenges of the platform is to enable the collaboration between different suppliers, but also making them comparable with little effort. Oftentimes comparable information is not readily available or needs to be homogenized therefore posing hurdles to platform entry. In our risk analysis we analyzed, how potential upsides of products might be overstated, while downsides of products might also be described in less detail or not be stated at all.

Companies would have big interests in offering services on the platform that would accompany sales processes in the form of product service systems. We therefore integrate easily accessible application programming interfaces (APIs) to connect existing services into the platform. E.g. one of them being the planning and configuration of delta robots according to product material, piece number and weight as well as a robotic gripper selection dependent on the product geometries and weight. Furthermore, we connected preexisting shopfloor monitoring platforms with their virtual representation on the platform, to have an integrated view for the customer, on the one side handling the planning phase of the production system but also the execution phase, for which further integrated planning and reconfiguration scenarios can easily be validated virtually.

5 Capacity Building

This project showcases a trend that’s currently happening in all of industry. Connecting suppliers, integrating different services for products into platforms generates new and different sales channels, accelerates decision processes and helps reducing the time to market of new products. As a byproduct new jobs are being generated due to new market needs always adjusting to customer needs over time. New online sales channels offer opportunities to higher capacity utilization of manufacturing companies, thus enabling lower price offerings, optimizing workloads over all connected companies. Customers of the platform services who want to set up production for new products have fast, cost transparent and, in comparison likely inexpensive opportunities to set up their own production facilities or reconfigure their preexisting facilities. This creates opportunities for new and relevant jobs in the meantime. The platform itself offers a lot of potential for new work, but also connects existing domains of work. Due to its open framework, partners wanting to offer services related to machine tools, automation, consulting, AI and other micro services, data analytics, maintenance, marketing, etc. are all welcomed and increase platform value and traction with each new contribution. As a result, creating a new marketplace of services and products related to customized production system configurations and reconfigurations.

In the new digital era, 5G-based Plug and Play hardware modules not only give full use of advanced technologies, but also meet the needs and trends of the manufacturing industry`s current development. However, the use of new technologies is also changing the associated requirements for professional skills, which poses a challenge for talent development. In order to provide application scenarios for the rapid establishment and operation of production systems, and effectively cultivate the knowledge and skills of young talents and professionals in IIOT, CPS, AI in the field of advanced manufacturing, as well as provide resources for the retraining and upskilling of employees, it is necessary to create an industry-oriented education and research environment, i.e. to establish student-oriented intelligent manufacturing centers. Therefore, a 5G-based turnkey manufacturing facility will be established in China at AMTC and in Germany at KIT for relevant research and education purposes, so that students can personally experience the practical application of 5G, turnkey systems combined with artificial intelligence methods, learn relevant knowledge and independently design and build modular production lines, gain technical experience, train analytical thinking and innovation abilities, and fully convert the learned knowledge into skills. We now shortly describe the design and progress of the case implementation. Currently, the intelligent factory design is being prepared at AMTC. Construction of modular units has begun, with initial results in application development of 5G modules and software for production line configuration and layout. For example, the 5G module is applied for remote access to a Siemens PLC, which can be used to perform both program download, maintenance and troubleshooting. Then, the modules are installed in the modular units and the master control system for real-time communication between these units to support the transfer of operational data, processing programs and operating instructions, and to solve the docking and coordination problems between units. These application scenarios and tools can provide a foundation for building a complete plant. By learning and using 5G modules and software systems, and comparing the construction and efficiency of this generated solution with the traditional manually developed solutions, students can more intuitively appreciate the advantages and application prospects of 5G and plug and play, and master the relevant knowledge and skills.

6 Summary

In this article we present a Sino-German approach for a factory automation platform in the context of Industry 4.0 and the Internet of Things. We describe technical details of the platform, while also discussing the effects a platform like this will have on the human workforce.

A prototype of a technical solution of a factory automation platform has been implemented during the course of this Sino-German project including basic framework concepts as well as exemplary services for product feature extraction, processing information recommendation, machine tool configuration selection, simulation and validation services, as well as after sales monitoring services.

Through open accessibility, the platform generates new business opportunities for many different stakeholders, enabling new sales channels for production system and component manufacturers, new as well as established companies with new product ideas. Services surrounding and accompanying the product development and production system configuration and -reconfiguration processes as well as monitoring and prognostic elements are manifold. Thus, the factory automation platform creates new digital opportunities for engineering service providers, production system integrators, consulting businesses e.g. in the field of sustainability and circular economy, simulation experts, automation and artificial intelligence experts, creating new and exciting jobs, shifting expertise to the digital realm while collecting data and knowledge for new and more integrated business opportunities.