Physics-informed neural network
Assembly Line
Physics-Aware AI Is The Key To Next Gen IC Design
AI on the inside is a fundamentally different approach favored by Ansys where the core simulation and analysis algorithms are modified to operate with new AI understanding and new AI guidance. This serves to make the core engines run faster and give better results in terms of speed, capacity, and accuracy-vs-time efficiency. AI-inside achieves these benefits for all users and without any changes to the use model for the customer. A good example of AI-inside is the thermal simulation engine in RedHawk-SC Electrothermal for 3DIC analysis. Thermal simulation requires the creation of a finite element mesh as a first step. The finer the mesh the more accurate the result, but the simulation also takes longer. Ansys’ thermal engine is able to build an adaptive mesh that is fine only where it needs to be, around thermal hotspots, and is coarser elsewhere where a fine mesh is unnecessary. The problem with this approach is how to know ahead of time where these hotspots are located? AI offers a perfect solution for this because it can very quickly estimate a rough temperature distribution that is good enough to guide the adaptive mesh builder. The benefit is that it makes thermal simulation much faster without sacrificing any appreciable accuracy. This sort of enhancement under-the-hood is sometimes called bottom-up AI and it improves the fundamental operation of the tool any time thermal simulation is done in whatever context.
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.