US 11,815,876 B2
Method and system for automatic identification of primary manufacturing process from three-dimensional model of product
Dhiraj Suvarna, Bengaluru (IN); and Christine Zuzart, Pune (IN)
Assigned to HCL Technologies Limited, New Delhi (IN)
Filed by HCL Technologies Limited, New Delhi (IN)
Filed on Aug. 2, 2021, as Appl. No. 17/391,839.
Prior Publication US 2022/0299974 A1, Sep. 22, 2022
Int. Cl. G05B 13/02 (2006.01); G06N 3/045 (2023.01); G05B 19/4097 (2006.01)
CPC G05B 19/4097 (2013.01) [G05B 13/027 (2013.01); G06N 3/045 (2023.01); G05B 2219/32335 (2013.01); G05B 2219/35134 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for automatic identification of a primary manufacturing process (PMP) from a three-dimensional (3D) model of a product, the method comprising:
generating, by a PMP identification device, a plurality of images corresponding to a plurality of views of the product based on the 3D model of the product, wherein the 3D model is rotated at a predefined step angle along an axis of rotation to obtain the plurality of views of the product;
determining, by the PMP identification device, a plurality of confidence score vectors, based on the plurality of images, using a first Artificial Neural Network (ANN) model, wherein the first ANN model extracts a plurality of visual features of the product from the plurality of images to capture a complexity of the product, and wherein each of the plurality of confidence score vector correspond to a plurality of pre-defined PMP categories;
determining, by the PMP identification device, an aggregate confidence score vector, representing a pre-defined PMP category with maximum frequency, based on the plurality of confidence score vectors;
extracting, by the PMP identification device, a set of manufacturing parameters associated with the product, based on the 3D model of the product, wherein the set of manufacturing parameters comprises at least one of a first set of parameters with categorical values and a second set of parameters with numerical values; and
identifying, by the PMP identification device, the PMP based on the aggregate confidence score vector and the set of manufacturing parameters, using a second ANN model, wherein the second ANN model captures non-linear dependencies of identification of the PMP.