Autodesk, Inc.

Joseph George Lambourne

Hooman Shayani

Nigel Jed Wesley Morris

Aditya Sanghi

Pradeep Kumar Jayaraman

Andreas Linas Bastian

Adrian Adam Thomas Butscher

Thomas Davies

Nandakumar Santhanam

Jingyang John Chen

Martin Cvetanov Marinov

Suguru Furuta

Marco Amagliani

Brian M Frank

Tristan Ward Barback

Nathan David Rogers

Michael Andrew Smell

Karl Darcy Daniel Willis

Daniele Grandi

Christopher Michael Wade

Peter Meltzer

Amir Hosein Khas Ahmadi

Paul Wilkinson

In various embodiments, a style comparison metric application generates a style comparison metric for pairs of different three dimensional (3D) computer-aided design (CAD) objects. In operation, the style comparison metric application executes a trained neural network any number of times to map 3D CAD objects to feature maps. Based on the feature maps, the style comparison metric application computes style signals. The style comparison metric application determines values for weights based on the style signals. The style comparison metric application generates the style comparison metric based on the weights and a parameterized style comparison metric.

Techniques for generating subjective style comparison metrics for B-reps of 3D CAD objects

In various embodiments, a parameter domain graph application generates UV-net representations of 3D CAD objects for machine learning models. In operation, the parameter domain graph application generates a graph based on a B-rep of a 3D CAD object. The parameter domain graph application discretizes a parameter domain of a parametric surface associated with the B-rep into a 2D grid. The parameter domain graph application computes at least one feature at a grid point included in the 2D grid based on the parametric surface to generate a 2D UV-grid. Based on the graph and the 2D UV-grid, the parameter domain graph application generates a UV-net representation of the 3D CAD object. Advantageously, generating UV-net representations of 3D CAD objects that are represented using B-reps enables the 3D CAD objects to be processed efficiently using neural networks.

Techniques for generating UV-net representations of 3D CAD objects for machine learning models

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using data format conversion (e.g., of output(s) from generative design processes) and user interface techniques that facilitate the production of 3D models of physical structures that are readily usable with 2.5-axis subtractive manufacturing, include: modifying smooth curves, which have been fit to contours representing discrete height layers of an object, to facilitate the 2.5-axis subtractive manufacturing; preparing an editable model of the object using a parametric feature history, which includes a sketch feature, to combine extruded versions of the smooth curves to form a 3D model of the object in a boundary representation format; reshaping a subset of the smooth curves responsive to user input with respect to the sketch feature; and replaying the parametric feature history to reconstruct the 3D model of the object, as changed by the user input.

Conversion of geometry to boundary representation with facilitated editing for computer aided design and 2.5-axis subtractive manufacturing

Systems and method relating to machining parts include a CNC system, and a computer including a processor and a computer-readable medium, wherein the computer-readable medium encodes instructions including receiving, at the computer program, output data from a CNC machine that receives instructions of a Numerical Control (NC) program at a computer of the CNC machine, the instructions causing the CNC machine to i) manufacture a part, and ii) output the output data, parsing, by the computer program, the output data before completion of the manufacturing of the part by the CNC machine in accordance with the instructions of the NC program, selecting, by the computer program and based on one or more predetermined parameters, a set of data from the parsed output data; and providing, by the computer program to a remote system, the set of data for processing to facilitate machining using the CNC machine.

Method and system for processing machine data before completion of machining

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London, United Kingdom

Joseph George Lambourne