Sintering is a primary
particulate manufacturing technology to provide densification and strength for
ceramics and many metals. A persistent problem in this manufacturing technology
has been to maintain the quality of the manufactured parts. This can be
attributed to the various sources of uncertainty present during the
manufacturing process. In this work, a two-particle phase-field model has been
analyzed which simulates microstructure evolution during the solid-state
sintering process. The sources of uncertainty have been considered as the two
input parameters surface diffusivity and inter-particle distance. The response
quantity of interest (QOI) has been selected as the size of the neck region
that develops between the two particles. Two different cases with equal and
unequal sized particles were studied. It was observed that the neck size
increased with increasing surface diffusivity and decreased with increasing
inter-particle distance irrespective of particle size. Sensitivity analysis
found that the inter-particle distance has more influence on variation in neck
size than that of surface diffusivity. The machine-learning algorithm Gaussian
Process Regression was used to create the surrogate model of the QOI. Bayesian
Optimization method was used to find optimal values of the input parameters.
For equal-sized particles, optimization using Probability of Improvement
provided optimal values of surface diffusivity and inter-particle distance as
23.8268 and 40.0001, respectively. The Expected Improvement as an acquisition
function gave optimal values 23.9874 and 40.7428, respectively. For unequal
sized particles, optimal design values from Probability of Improvement were
23.9700 and 33.3005 for surface diffusivity and inter-particle distance,
respectively, while those from Expected Improvement were 23.9893 and 33.9627.
The optimization results from the two different acquisition functions seemed to
be in good agreement with each other. The results also validated the fact that
surface diffusivity should be higher and inter-particle distance should be
lower for achieving larger neck size and better mechanical properties of the
material.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/13366775 |
| Date | 07 January 2021 |
| Creators | ARUNABHA BATABYAL (9761255) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/APPLYING_MACHINE_LEARNING_TO_OPTIMIZE_SINTERED_POWDER_MICROSTRUCTURES_FROM_PHASE_FIELD_MODELING/13366775 |
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