Publications by authors named "Valarmathi Prahasam"
Micromachines (Basel)
December 2022
Article Synopsis
- 3D printing is being widely adopted across industries due to its advantages like precision and reduced fabrication time, but it faces errors like stringing and overheating.
- This study explores using machine learning to optimize 3D printing parameters, incorporating factors like material type and temperature, and uses four network architectures (CNN, ResNet152, MobileNet, Inception V3) for this purpose.
- The Inception V3 model achieved the highest accuracy at 97%, effectively predicting parameters and detecting errors, thus preventing material waste in manufacturing.
