Microstructural Material Modeling for Faster Property Prediction
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Solution Overview
Problem
The development of new materials processes, particularly for safety-critical applications, is costly and time-consuming due to extensive experimentation and validation, requiring trail-and-error methodologies.
Innovation Solution
A method involving microstructural modeling, simulation, and virtual characterisation to predict mechanical properties of materials undergoing transformations, using crystal plasticity and multi-phase field models, with validation steps to adjust the model based on real-world comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial-and-error experimentation is used to develop new materials processes, then reliable material properties can be obtained, but the development time and costs increase significantly
Solution Approach 1:
The patent applies preliminary action by creating a microstructural model before actual experimentation to predict material behavior under various processing conditions. The digital twin is established in advance to simulate microstructural evolution and mechanical properties, allowing researchers to pre-screen processing parameters and avoid unnecessary physical experiments, thereby reducing development time while maintaining reliability
Solution Approach 2:
The patent creates a digital copy (digital twin) of the material's microstructure that replicates its physical properties and behavior. This virtual model is used to simulate microstructural transformations and predict mechanical properties without requiring physical experimentation for every scenario, significantly reducing the time and resources needed for materials development while maintaining accuracy through validation against experimental data
2Manufacturing precision
If extensive experimentation is conducted to optimize process parameters, then optimal material properties are achieved, but laboratory costs and resource consumption increase
Solution Approach 1:
The microstructural model is used to pre-optimize processing parameters by simulating microstructural evolution under different conditions before actual experimentation. This allows the identification of promising parameter combinations in silico, reducing the number of physical experiments needed to achieve optimal material properties and thereby conserving laboratory resources and energy
Solution Approach 2:
The digital twin serves as a virtual testbed for optimizing process parameters through simulation rather than exhaustive physical experimentation. By copying the material's behavior in the virtual environment, researchers can identify optimal processing conditions with far fewer physical trials, significantly reducing laboratory resource consumption while maintaining manufacturing precision
3Reliability
If validation experiments are performed after achieving desirable parameters, then process reliability is confirmed, but lead time and costs are further increased
Solution Approach 1:
The microstructural model performs preliminary validation by comparing simulated microstructural evolution and mechanical properties against experimental data before full-scale production. This pre-validation step identifies potential issues early in the development process, reducing the need for extensive post-development validation experiments and thereby maintaining reliability while improving development speed
Solution Approach 2:
The patent implements a feedback mechanism where experimental validation results are fed back into the microstructural model to refine and improve its predictive accuracy. This continuous improvement loop allows the model to become increasingly reliable over time, reducing the extent of validation experiments needed as the model matures, thus balancing reliability confirmation with productivity enhancement
Data Source
AI summary
A method of predicting a mechanical property of a material subjected to a transformation process is disclosed including a modelling step, wherein a microstructural model of the material is created, a simulation step, wherein the microstructural model of the material is virtually subjected to a transformation process (such as a heat treatment process), a generation step, wherein at least one micro-scale model configured for predicting at least one mechanical property of the material is generated, and a virtual mechanical characterisation, wherein at least one mechanical property of the material is predicted. Advantageously, by implementing this method it has been found that lead times incurred when developing new material transformation processes can be reduced.