DIGIMAP | Development of a Digital Twin for Advanced Manufacturing Processes

Development of a digital twin for the automated fiber placement process

Digitizing processes within the IoT landscape is transforming conventional methods by seamlessly integrating intelligent technologies. This facilitates the monitoring, analysis, and enhancement of business processes, allowing interconnected devices and systems to communicate and generate valuable data.

In the context of the DIGIMAP project, the objective is to establish a machine-oriented IoT infrastructure for the Automated Fiber Placement (AFP) Process, ensuring a seamless integration of the plant with the cloud infrastructure. Priority is given to developing a user-tailored IoT platform that enhances data transparency for the AFP process and enables the construction of a digital twin of the product using the gathered machine data.

Data-driven quality inspection and optimization in manufacturing processes.

The implementation and testing of these technologies in the real working environment of our project partner as part of the DIGIMAP project aims to develop an internal IoT data infrastructure for the automated fiber placement process . The implementation of a platform for IoT applications in the AFP sector creates the basis for precise monitoring and control of the manufacturing process. The use of machine data not only provides a better understanding of the AFP process, but also enables data-driven decision-making to continuously improve process quality.

System architecture in the production area: networking and data flow diagram (project DIGIMAP)
© Fraunhofer IGCV
System architecture in the production area: networking and data flow diagram (project DIGIMAP)

Focus on digitization: Efficient machine connectivity and data security using the example of Coriolis C1

The proficiency in the networking of production systems , demonstrated through the application of industrial communication protocols on the Coriolis C1 system, showcased a seamless integration into the internal IoT infrastructure , facilitating efficient data transmission and processing. The utilization of established industrial communication protocols ensured a robust and secure connection. Additionally, emphasis was placed on the clustering and pre-processing of machine data. Streamlining these procedures enhances the effectiveness of data utilization for digital twin creation. Intelligent pre-processing steps guarantee the extraction of pertinent information, making it readily available for subsequent analysis.

Data security is ensured through the implementation of the private on-premise infrastructure . This solution provides a highly secure environment for the storage and processing of sensitive machine data, ensuring maximum control over the data and minimizing potential security risks through local infrastructure components.

From data to knowledge: the crucial role of infrastructure in AI, digitalization, and quality management

The established platform for data acquisition not only forms the basis for efficient machine data processing but also opens up promising perspectives for the application of artificial intelligence (AI), advancing digitalization, and comprehensive quality management in the future.

A key element of our future projects is the introduction of an Automated Inspection System for digital inspection in the AFP (Automated Fiber Placement) process. This system uses advanced 3D scanners to create detailed scans of the manufactured parts in real-time. Currently, our team is focused on implementing machine learning methods that enable more precise detection and classification of manufacturing defects. This innovation increases the accuracy of our quality controls, allows for the immediate identification and correction of production flaws, enhances manufacturing efficiency, and leads to significant cost savings.

DIGIMAP: Simulation Qualitätssicherung
© Fraunhofer IGCV
Digitized process chains, from process planning to quality assurance

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