Expertise

The relevance and added value of artificial intelligence (AI) and machine learning (ML) have increased significantly in recent years. Extensive potential has emerged, particularly in the area of production. However, the high complexity of ML and the lack of evidence of its added value often mean that particularly small and medium-sized enterprises (SMEs) do not engage further with its introduction and use. For this reason, a holistic guide has been developed that accompanies manufacturing SMEs from the identification of suitable use cases and maturity level analysis through to the implementation of measures and continuous improvement processes, providing the required concepts.

Machine learning, implementation strategy, guide, production, maturity level

Process monitoring and the resulting increase in quality through AI are attracting increasing attention in large parts of the manufacturing industry. The possibilities of inline process monitoring of cross-wedge rolling are being investigated as part of the research of the Collaborative Research Center 1153. The aim is to develop a monitoring system that enables inline process control in order to compensate process deviations that occur during the forming process. Therefore, an algorithm is developed that can detect and classify process deviations within a few seconds and while the process is still running. An AI-based image recognition algorithm was applied as part of this research work. The process data was collected as part of a sensitivity study of the process parameters. A parameter study was used to determine optimized hyperparameters for AI modeling that enable a high prediction accuracy. The challenge of the necessary speed of the prediction was tested and validated. The evaluation of the algorithm including the generation of a picture requires 270 ms on average and is therefore fast enough to be used as preparation for process control. The investigations revealed a possibility for data augmentation that significantly increases the predictive accuracy of the models. Leave-One-Out Cross-Validation (LOOCV) was used to conclude the overall performance of the model.

Cross-wedge rolling, Hybrid components, Process monitoring, AI-based image recognition

Artificial intelligence (AI) can be used to reliably recognise errors, reduce defective parts and increase component quality. In the 'AutoPress' project, researchers have developed a system of sensors and AI that recognises 95 to 98 per cent of all process deviations.

Artificial intelligence, AI, process monitoring, error detection