Abstract: The invention relates to method and system for automatic identification of a primary manufacturing process (PMP) from a three-dimensional (3D) model of a product. The method includes generating a plurality of images corresponding to a plurality of views of the product based on the 3D model of the product; determining a plurality of confidence score vectors, based on the plurality of images, using a first Artificial Neural Network (ANN) model; determining an aggregate confidence score vector, representing a pre-defined PMP category with maximum frequency, based on the plurality of confidence score vectors; extracting a set of manufacturing parameters associated with the product, based on the 3D model of the product; and identifying the PMP based on the aggregate confidence score vector and the set of manufacturing parameters, using a second ANN model.

Abstract: The invention relates to method and system for extracting and classifying manufacturing features from a three-dimensional (3D) model of a product. The method includes generating graph corresponding to product based on 3D model of product. The graph includes nodes corresponding to faces of the product and links corresponding to edges of product. The graph generation includes determining adjacency attribute matrix from the 3D model. The method further includes assigning scores to each of links; determining a cumulative score for each of links; extracting sub-graphs from graph by discarding one or more links from links; extracting node parameters and edge parameters from 3D model of product; determining node feature vector based on node parameters and edge feature vector based on edge parameters; and determining a type of manufacturing feature based on corresponding node feature vector and edge feature vector using a Graph Neural Network (GNN) model.

Abstract: The invention relates to method and system for automatic identification of a primary manufacturing process (PMP) from a three-dimensional (3D) model of a product. The method includes generating a plurality of images corresponding to a plurality of views of the product based on the 3D model of the product; determining a plurality of confidence score vectors, based on the plurality of images, using a first Artificial Neural Network (ANN) model; determining an aggregate confidence score vector, representing a pre-defined PMP category with maximum frequency, based on the plurality of confidence score vectors; extracting a set of manufacturing parameters associated with the product, based on the 3D model of the product; and identifying the PMP based on the aggregate confidence score vector and the set of manufacturing parameters, using a second ANN model.

Abstract: A method of automatic manufacturability evaluation of plastic models comprises generation of a likely pulling direction, recognition of common features on plastic parts, and then applying manufacturability rules The manufacturability rules can be specified and customized through user specified rule parameters and depend upon the geometric parameters of the recognized features. A system comprises a user interface for selection and customization of DFX (Design for ‘X’) rules for evaluation of a design. The system includes a user interface integrated with a CAD system for receiving the CAD data and displaying the results to the user. Geometry analysis engines are integrated into the system, for extracting the various features and corresponding parameters required as input to the manufacturability rules. The system further involves extensible interfaces for rules and analysis engines which allows users to write their own customized rules and engines and integrate these into the CAD based DFX evaluation system.

Abstract: A method of automatic manufacturability evaluation of plastic models comprises generation of a likely pulling direction, recognition of common features on plastic parts, and then applying manufacturability rules The manufacturability rules can be specified and customized through user specified rule parameters and depend upon the geometric parameters of the recognized features. A system comprises a user interface for selection and customization of DFX (Design for ‘X’) rules for evaluation of a design. The system includes a user interface integrated with a CAD system for receiving the CAD data and displaying the results to the user. Geometry analysis engines are integrated into the system, for extracting the various features and corresponding parameters required as input to the manufacturability rules. The system further involves extensible interfaces for rules and analysis engines which allows users to write their own customized rules and engines and integrate these into the CAD based DFX evaluation system.

Abstract: A method of automatic manufacturability evaluation of plastic models comprises generation of a likely pulling direction, recognition of common features on plastic parts, and then applying manufacturability rules The manufacturability rules can be specified and customized through user specified rule parameters and depend upon the geometric parameters of the recognized features. A system comprises a user interface for selection and customization of DFX (Design for ‘X’) rules for evaluation of a design. The system includes a user interface integrated with a CAD system for receiving the CAD data and displaying the results to the user. Geometry analysis engines are integrated into the system, for extracting the various features and corresponding parameters required as input to the manufacturability rules. The system further involves extensible interfaces for rules and analysis engines which allows users to write their own customized rules and engines and integrate these into the CAD based DFX evaluation system.