Research Field Intelligent Joining System Technology

The research field Intelligent Joining System Technology was founded to bundle competences in the field of Artificial Intelligence across different topics and disciplines. With a focus on production applications in the field of joining technology and additive manufacturing, the research field focuses on the investigation of methods of machine learning in order to utilize the potential of progressive digitization for production technology applications.

Innovative leap for joining system technology

The overall goal of the research field is the realization of an intelligent joining system technology, which is characterized by the independent acquisition of suitable process variables, automated and robust recognition of correlations between process data and phenomena and the intelligent derivation of process interventions and recommendations for action.

Artificial intelligence as a central component for the processing and analysis of process data

The research focuses on methods of statistics and machine learning for the recognition of patterns and connections between the recorded data and process variables and effects. Based on these findings, measures are implemented to improve process results, process robustness and quality assurance.

The core competences of the research field:

• Sensor and measurement concepts for data acquisition
  To record meaningful process data, the selection and qualification of suitable measurement and sensor concepts is in the foreground in order to make the data usable in the most target-oriented way possible in the sense of targeted control and monitoring of process quality. Measurement data as well as data from digital images of the physical systems are used.

• Machine learning for robust processing of (large) data volumes
  The analysis of the recorded data is the central part of the research work, with the aim of an automated and robust evaluation. In addition to statistical methods, machine learning methods are used to identify correlations and correlate patterns in the measurement data with process characteristics. In addition to data preparation, the analysis methodology and the validation of the predictions are the focus of the investigations in order to qualify Artificial Intelligence for use in the production environment.

• Control and regulation of process and/or system technology
  Strategies for the control of plants and processes as well as for the automated adaptation of process control are developed in order to be able to use the knowledge gained from data evaluation. Expert systems for increasing process quality and stability are implemented with the aim of automatically adapting the system technology to any joining tasks.

The approach covering the entire process chain makes it easier and more efficient to set up the process for complex joining tasks and guarantees the desired process result. Networking along the entire process chain as well as across different processes makes expert systems for production engineering applications possible. Complex interdependencies can be recognized and used for future processes.

Further Information

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