Application fields

Fields of Application in Phase I

The construction of the Pilotfabrik Industrie 4.0 takes place in a staged plan, i. through a step-by-step development of application fields in the sense of application scenarios in the pilot factory with which specific contents (research, training, know-how transfer, demonstration) can be mapped. These application fields determine the investment requirements and also the necessary personnel resources. The application fields listed below describe the content in phase I or initial phase. In terms of content, these are derived from discussions with industry partners that were conducted in advance or from the requirements addressed here. The further development of Pilotfabrik Industrie 4.0 in subsequent further phases can not yet be named at the present time, but will be based on initial collaborations in Pilotfabrik Industrie 4.0 or be conceptualized on the basis of these.

Field of Application A: Reconfigurable, Adaptive Production System / Manufacturing Cell Including Tool And Workpiece Handling

Situation:

The producing companies in Austria are facing a change. In global competition, providers from low-wage countries are gradually catching up and offering increasingly competitive products. Both consumer products and capital goods are therefore designed more and more according to customer requirements and are therefore subject to unpredictable fluctuations in demand (high mix – low volume). As a result, production and logistics processes have to respond comprehensively dynamically.

With today’s status quo in production with centrally controlled processes, this is hardly possible. The coming requirements can only be met with innovative production systems, so-called Cyber-Physical Production Systems (CPPPS), which are controlled by means of Cyber-Physical Systems (CPS). These have different sensors for the perception of their environment and actuators with which products, machines and systems can be optimized themselves and adapted to changing orders and operating conditions.

Objective:

With this field of application, for the subtractive (CNC machining) and additive technologies (build-up welding and FDM 3D printing), it will be shown how the complete job control is implemented in software across all levels. Changes in the order lead to (re-) configuration / adaptation of the production facilities. In order to realize this vision, the following development goals are sought:

  • Creation of Cyber-Physical Production Systems (CPPS) through the use of CPS.
  • Complete virtualization of production
    • Simulation of the manufacturing process
    • Virtual Commissioning
  • Development of self-X functionalities for manufacturing cells
  • Consistent uniform networking from the sensor level to the MES level
  • Development of a flexible control architecture for the orchestration of manufacturing cells
  • Use of “smart assets” in production

Implementation:

To demonstrate the goals, state-of-the-art manufacturing cells will be set up and gradually converted into cyber-physical production systems.

anwendungsfelda1

With the help of OPC Unified Architecture a uniform communication and description of the existing entities in the production is to be realized. OPC UA is a communication and data modeling standard for the exchange of information in industrial automation over IP networks. The so-called semantic interfaces based on OPC UA servers integrated in the respective components provide all necessary information and operations through the components of the manufacturing cells (for example loading doors, chucks, tools, NC programs) to higher-level systems. This is achieved model-based in a unified and abstracted manner, whereby the new interface concept leads to a change in the (re-) configuration workflow, namely parameterization instead of programming.

anwendungsfelda2

The (re-) configuration of production is supported by the complete virtual mapping of production in suitable software systems. Manufacturing stations, workpieces or workpiece carriers and transport units can all be virtually represented. Manufacturing stations have knowledge of the processing operations, capacities and utilization of a local station and are able to autonomously control and monitor them. Workpieces and devices (“Smart Assets”) have knowledge of necessary processing steps for the workpiece in the context of the manufacturing process and their possible manufacturing sequences.

In addition, a flexible control architecture for entities of a production (machines, robots, workpiece carriers, transport system, …) will be developed. Different approaches (serviants, agents, SOA, …) will be investigated, providing a decentralized solution for the production process planning (sequence planning) and the control of the execution.

Field of Application B: Cyber-Physical Mounting Systems

Situation:

In future work systems, the design of collaboration between people and machines will become increasingly important and complex. This is due to the fact that machines are increasingly equipped with intelligence – the ability to perceive, learn, and make decisions about their environment (including humans). Machines and their various forms, e.g. Robots become an active part of the work system. The relationship between man and machine is supported by a unidirectional relationship, where man acts as a “machine operator and leader” to complement a bidirectional relationship where the machine communicates, manages and actively manages, at least in some areas.

Assembly systems are characterized in the production system especially by a high proportion of human labor. The effective and efficient design Cooperation between man and machine is a special challenge and opportunity here. The networking of humans, machines with other resources and systems allows the consistent use of data to optimize the overall system assembly in terms of flexibility, quality, security, costs. The close cooperation between machines and people poses challenges for the planning and design of such work systems. In particular, these are the following challenges and problem areas:

  • Planning, engineering of collaborative man-machine work systems with regard to the optimal flow of work, material and information
  • Ergonomic and safe design of collaborative man-machine work systems
  • Economic optimization of collaborative man-machine work systems
  • IT integration of the mounting system into the production planning and control system

Objective:

  • Build and run of an exemplary cyber-physical assembly line
  • Demonstration of different scenarios of human-machine / robot collaboration in assembly
  • Demonstration of different concepts of visual, adaptive and real-time data-based worker guidance (assistance systems) in assembly
  • Demonstration of Virtual Reality / Augmented Reality Concepts in Assembly Planning
  • Demonstration of consistent real-time data driven planning and control of assembly processes

Implementation:

The mounting system is designed as a cyber-physical system. Assembly of the variant-rich product with batch size 1 takes place along several connected assembly stations within an assembly line. The assembly station is designed according to ergonomic and especially age- and age-appropriate requirements. As a result, performance-reducing and harmful influences on humans are minimized. The mechanic is supported in coping with complex work contents by visual and adaptive worker guidance and assistance systems (including via virtual / augmented reality using “wearable devices” – directly in the field of vision, “hands-free”) throughout. In view of the physical, cognitive and psychological demands arising from the work task, a collaborative human robot supports humans. This sensitive and collaborative robot assistance system provides context-dependent and situational support to employees. This ensures ergonomic, age-appropriate and productive workmanship.

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Figure 1: Mounting system

The workpiece itself is mounted on driverless transport systems with intelligent workpiece carriers. The driverless transport systems are controlled as required via the indoor location system. From this aspect, the assembly cell receives configurable the workpiece. Intelligent tightening systems can autonomously adjust to the required torque based on real-time information. Set-up operations can be kept as low as possible in this way.

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Figure 2: Mounting system with human-robot cooperation

Field of Application C: Adaptive Logistics Systems

Situation:

In the factory of the future, physical material flows continue to take place between the manufacturing and assembly stations. The material flows must be continuously adapted to changes in the production process against the background of a production largely focused on the customer order (lot size 1). Throughout the material flow from goods receipt to goods issue, there are different requirements for the transport, storage and picking systems.

Transport and storage systems must be able to adapt to changing conditions at short notice, with little effort and at low cost. With regard to changing demands on production or assembly due to a strong trend in the direction of “lot size 1”, the requirements for employees in the warehouse or picking change very much.

Objective:

  • Demonstration of intelligent storage systems, including intelligent warehouse management systems through the use of intelligent control software.
  • Demonstration of innovative picking technologies using augmented reality to illustrate human-machine interaction and advantages over conventional (receipt-based) picking methods. This is intended to illustrate improvements in quality and efficiency as well as facilitation for the employee in everyday working life.
  • Demonstration of intelligent transport units or material containers in order to let the control of intralogistics start from them. This is to be demonstrated by means of identification solutions and real-time feedback by means of ongoing inventory (eg via optical methods). Solutions to pinpoint material inventories through dedicated apps are designed to support this scenario.
  • Demonstration of autonomous and driverless free-navigating transport systems that adapt to changing physical conditions (such as obstacles) and changing processes.

Implementation:

The objectives described above are realized through the construction of an adaptive logistics system. In order to ensure the interface between the production and assembly areas for intralogistics, an intelligently controlled, lane-bound automated guided vehicle transport system as well as automated transfer systems and unloading devices are provided with regard to material supply. Intelligent warehouse / container management monitors the current filling level of containers in the production areas or in the logistics warehouse via built-in sensors or cameras in real time and derives reorders via permanent data transmission. Unnecessary logistical activities are thus eliminated and capital-holding stocks are kept low. In order to reduce order picking errors and enable human-machine interaction to increase the picking quality, the warehouse uses the innovative pick-by-vision concept using augmented reality. The information recording of the employee takes place here directly “hands-free” situation-adapted via the eye, which allows an efficient information collection. Other currently existing solutions such as pick-by-scan, pick-by-light and pick-by-voice support the order-picking process with an intelligent warehouse management system, and the resulting increase in efficiency and quality improvements.

Figure 3: Schematic representation of the field of application based on Fraunhofer Austria, 2014:

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Field of Application D: IT Integration And Digital Twin

Situation:

The initial situation is that a number of production facilities, logistics components and assembly systems are available in the pilot factory, which individually cover or support a specific functionality in the production system. These are initially not networked or integrated at the IT and automation level. In addition, in the industrial environment, there is often a digital “gap” between product development, work preparation, and production, i.e., a virtualization gap. The consistency of the data processing is not realized to the extent that is given as a prerequisite for Industry 4.0. This is primarily due to lack of standardization or different semantics of the software systems involved and functional bottlenecks, especially when using software from different manufacturers. Similarly, the “operational behavior” in the operation of the production system is often not available 1: 1 in the form of a digital representation. The as-planned and as-built status of product and production systems are well covered by models, but both worlds are poorly integrated and the operating phase with the constant changes is not shown.

Objective:

The aim of application field D is first of all the development and implementation of the overall IT architecture from the concept for the overall system Pilotfabrik and in particular taking into account all fields of application or demonstration scenarios. In addition to CAx tools for various engineering tasks, PLM is used for product data and lifecycle management, ERP especially for order processing, materials management as well as production planning and control, MES for production fine planning and operations including data feedback via BDE / MDE. This results in both data model requirements and interface definition between the application software systems CAx-PLM, PLM-ERP, ERP-MES, MES-BDE / MDE including the required customizing of the software applications for integrated process chains. In the pilot factory, an integrated representation of the real production system in the virtual world is to be implemented in order to test changes and adjustments (simulation, optimization) and to be able to trace data back into product and system development.

Implementation:

After implementation of the individual software systems, the step-by-step integration takes place in the following stages:

  • Realization of the continuous process chain from PLM to ERP and MES up to the machine for the example product 3D-printer
  • Modeling of the complete system Pilot factory consisting of product and production system components as “Digital Twin”
  • Development and implementation of a concept for the provision of information for assistance systems and employee management
  • Development and implementation of methods for the return of real-time production data for production process optimization and data analytics functions

In the following, the essential software components are shown to ensure the required information supply and software control of the pilot factory.

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