About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Nanoindentation Load-Displacement Analysis Using a Genetic Algorithm |
Author(s) |
Abe C. Burleigh, Andy Lau, Jeff Terry |
On-Site Speaker (Planned) |
Abe C. Burleigh |
Abstract Scope |
An automated tool for analysis of nanoindentation load-displacement curves using a Genetic Algorithm (GA) with the Oliver Pharr method is presented. Some materials, such as polycrystalline isotropic graphites, are difficult to fit using least squares methods. At the indentation depths required for reproducible results in these graphites the material cannot recover significantly during unloading. This results in hard to fit sharply-peaked unload curves that result in overestimation of the parameter describing indenter tip geometry. GA, a robust metaheuristic method, automatically processes batches of nanoindentation data with minimal user input while producing physically meaningful parameters. The GA begins with a population of temporary solutions; from these we find a fitness value for each solution, and select the best. These are mixed with random solutions to crossover producing the next generation. Following this a mutation operator is applied to existing solutions by random perturbations, and finally the optimal solution is selected. |
Proceedings Inclusion? |
Undecided |