US 11,741,273 B2
Fabricated shape estimation for droplet based additive manufacturing
Svyatoslav Korneev, Stanford, CA (US); Vaidyanathan Thiagarajan, Palo Alto, CA (US); and Saigopal Nelaturi, Mountain View, CA (US)
Assigned to Palo Alto Research Center Incorporated, Palo Alto, CA (US)
Filed by Palo Alto Research Center Incorporated, Palo Alto, CA (US)
Filed on Jun. 11, 2020, as Appl. No. 16/898,994.
Prior Publication US 2021/0390224 A1, Dec. 16, 2021
Int. Cl. G06F 30/17 (2020.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06F 119/18 (2020.01)
CPC G06F 30/17 (2020.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06F 2119/18 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
inputting a geometry of a substrate surface and manufacturing process parameters to a neural network, the neural network comprising a combination of a linear layer, non-linear activation functions, and a convolution layer, the neural network trained using one or more training sets, each training set comprising a different type of substrate geometry, and a collection of the manufacturing process parameters, the substrate surface configured to receive at least one droplet;
determining a shape of the at least one droplet after it has been deposited on the substrate surface based on an output of the neural network; and
producing a simulation output representing the determined shape of the at least one droplet.