Fraunhofer Institute for Manufacturing Technology and Advanced Materials (Fraunhofer IFAM)
Canvas Category Consultancy : Research : National
Assembly Line
Advancements in Predicting the Fatigue Lifetime of Structural Adhesive Joints
While physics-based models offer the highest accuracy for analyzing these joints, they require meticulous parameter calibration for every new adhesive. For example, consider a fatigue test on a structural adhesive joint with 10 million cycles at a frequency of 10 Hz. These tests are demanding and time-consuming, taking over 10 days to complete. Adding to the challenge is the need for numerous data points to construct a comprehensive fatigue design curve, a fundamental aspect of structural analysis. Given the need to optimize both efficiency and accuracy, engineers and researchers need and pursue innovative solutions.
One path to solution is the integration of Artificial Intelligence (AI) and Machine Learning (ML) into materials science. Recognized for its ability to address complex problems through learning from existing knowledge, AI provides a promising avenue for structural modeling by generating mathematical expressions that capture the interplay of various parameters. We expect that this rationale also applies to the structural modelling of the fatigue behavior of structural adhesive joints, which is the subject of our ongoing research.
This showcase exemplifies our commitment to revolutionizing materials selection and fatigue life prediction for adhesive joints. Leveraging the Citrine Platform [2], we seamlessly apply machine learning methods to integrate experimental datasets with physics-based modeling (based on stress concentration factors). This innovative approach not only significantly elevates the precision of fatigue predictions but also enables the precise selection of optimal adhesives for bonded structures, factoring in various material and geometrical properties, as well as usage conditions.
How Paint Robots Reduce Rework
There are few wild beasts more fearsome and concerning to the everyday finishing engineer than the dread three R’s: Rework, Rejections and RMAs.
In finishing, particularly when it comes to spray processes, achieving the kind of consistency and quality customers expect requires a high degree of both reliability and precision. Experienced painters and operators – or elaborate automation systems – can be engineered to provide high output, but over time many parts will seep through the cracks and simply not get the attention they require.
Automating Carbon-Fiber Composite Fuselage Assembly
“During the last 10 years, increased commercial aircraft production rates have led to more interest in automating assembly processes,” Brieskorn points out. “To reduce process times and cost, automation is becoming more appealing to engineers.
“However, the main challenge is that large aircraft parts come with relatively high geometry deviations, so robots need sensor guidance,” says Brieskorn. “Strict requirements and tight tolerances in the final structures are also challenging for standard automation systems.”