The trail „3D Material Analytics“ demonstrates the use of modern cloud and big data technologies in the field of material testing in industrial manufacturing. For an effective and high-performance quality assurance, large amounts of sensor data must be recorded during ongoing production, product quality characteristics have to be analyzed in real time and the processed test results have to be made available to production monitoring and control. This requires IoT solutions that master the interaction of modern measurement technology on robot-based industrial test systems with highly available (cloud-based) IT systems. Using the example of process-oriented quality assurance of carbon fiber reinforced plastics (CFRP), such a solution is presented as part of the trail.
Dr. Beatrice Beyer, SURAGUS GmbH
- Special relevance to representatives of the following industries: Lightweight construction in automotive, aerospace, thin films in display, touch screen, battery electrodes
- Primarily addressed user groups: Production planner, factory planner, development manager, process engineer, quality manager, automation technician
- Also interesting for: IT managers interested in designing, setting up and operating data-intensive, high-availability applications
Partner from Research, Industry:
|SURAGUS GmbH||Quality assurance systems and systems for process control of inline processes||Richard Kupke|
|DevBoost GmbH||Measurement data management, design of modern software architectures, development and expansion of cloud infrastructures, system integration, software development in industrial environments||Dr. Tobias Nestler|
|HIGHTEX Verstärkungsstrukturen GmbH||Tailored or stress-resistant reinforcing structures for very light and highly rigid and strong fiber composite components||Dr. Dirk Feltin|
|Fraunhoger Institute for Ceramic Technologies and Systems IKTS||Measurement technology in the field of material characterization and error testing||Prof. Dr. Henning Heuer|
|Technische Universität Dresden, Institute of Textile Machinery and High Performance Material Technology||Simulation-based development of polymer, mineral and metallic fiber-based high-tech materials and processes||Dr. Thomas Gereke|
|Technische Universität Dresden, Institute of Software and Multimedia Technology, Chair of Software Technology||Cyber-Physical Systems, Robot Control, Data Modeling, Modern Software Architectures, Cloud Computing||Prof. Dr. Uwe Aßmann|
Remark: Some of the results presented within the „3D Material Analytics“ trail are funded by the European Regional Development (SAB-Project “3D-FAST”).
Those interested in the trail can expect an introduction to innovative, eddy-current-based measuring methods for non-destructive material testing of poorly conductive materials. Particularly for carbon fiber materials, thin films (Graphene, TCO, CNT) and conductive surface layers, the potential testing at the early stage of the value chain promises to improve quality assurance processes and reduce production costs.
Production and factory planners as well as process engineers and QM managers who work with the above-mentioned materials have the opportunity to enter into dialogue with experts in the measurement process.
By means of modern IoT solutions, manual processes have become more transparent, good / bad decisions automatable, data for process checks and control more quickly available and comparable across different facilities. Security-relevant test results as well as additional data can be archived (on local servers in the factory as well as centrally in protected data centers) and, for example, can be kept immanently and highly available for emerging customer inquiries.
The trail illustrates by means of a demonstrator concrete applications in the mass production testing of safety-relevant material of the automotive and aerospace industry for CFRP bodywork. This is particularly interesting for representatives of the supply industry, which guarantee flawless materials and require the data to secure the recourse claims of the OEMs.
The demonstrators illustrate the potential that arises in the aggregation and real-time evaluation of the resulting measurement data for running production operations. Use cases of the visitors as well as the feasibility of individual software applications can be discussed. IT leaders can gain insight into the technologies used to process mass data and discuss with software experts how to integrate such solutions into their existing IT infrastructures.
Teaser Trail, whose duration and format are individually tailored to the interested visitors. For example, lectures and / or demonstrations of the trail can be carried out within the framework of existing event formats or local expert group meetings, as well as individual on-site field surveys at Fraunhofer IKTS.
Using the example of process-oriented quality assurance of carbon fiber reinforced plastics (CFRP), the interaction of a software platform based on current cloud technologies with modern, flexible testing systems for complex 3D structures from the automotive and aerospace sectors will be demonstrated. These can be, for example, the CFRP window frames of the A350 or large, endless carbon rolls that will later be used in the light fuselage of the aircraft.
Hereby, the internal structure of a component is scanned by a robot-guided eddy current sensor (Figure 1). The raw generated data is transmitted to the software platform. The large amounts of measurement data are being stored and archived distributed and redundantly. From this raw data, pre-configured algorithms (based on recipes) can be used to automatically analyze (volumetric as well as superficial) quality properties of the component and material (see Figure 2).
The analysis results are stored in the software platform and can now be reused in many ways. For example, the worker on the test bench can receive initial feedback on the test result (see Figure 3) or the production line can be controlled or optimized.
The data can also be transferred to central production, monitoring and analysis systems or visualized using web-based dashboards (see Figure 4). Using for instance analytics to detect trends in the data, creeping errors can be detected and direct process feedback can be generated across company locations. If required, historical data can be repeatedly evaluated using reconfigured pattern recognition.
Opportunities for Projects and Funding Instruments:
- Proof-of-concept development and the installation of pilot plants
- Individual customer projects
- Research projects (SAB, BMBF, ZIM, BMWI, H2020)
- Conception and development of individual software systems in industrial manufacturing
You are interested in this Trail? Contact us!