Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures by generating prismatic CAD models using machine learning, include: obtaining an input embedding that encodes a representation of a target two-dimensional (2D) shape; processing the input embedding using a 2D decoder of a 2D autoencoder to obtain a decoded representation of the target 2D shape; determining a fitted 2D parametric sketch model for the input embedding, including: finding a 2D parametric sketch model for the input embedding using a search in an embedding space of the 2D autoencoder and a database of sketch models associated with the 2D autoencoder, and fitting the 2D parametric sketch model to the decoded representation of the target 2D shape; and using the fitted 2D parametric sketch model in a computer modeling program.

Abstract: 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.

Abstract: In various embodiments, an inference application performs tasks associated with 3D CAD objects that are represented using B-reps. A UV-net representation of a 3D CAD object that is represented using a B-rep includes a set of 2D UV-grids and a graph. In operation, the inference application maps the set of 2D UV-grids to a set of node feature vectors via a trained neural network. Based on the node feature vectors and the graph, the inference application computes a final result via a trained graph neural network. Advantageously, the UV-net representation of the 3D CAD object enabled the trained neural network and the trained graph neural network to efficiently process the 3D CAD object.

Abstract: In various embodiments, a training application trains machine learning models to perform tasks associated with 3D CAD objects that are represented using B-reps. In operation, the training application computes a preliminary result via a machine learning model based on a representation of a 3D CAD object that includes a graph and multiple 2D UV-grids. Based on the preliminary result, the training application performs one or more operations to determine that the machine learning model has not been trained to perform a first task. The training application updates at least one parameter of a graph neural network included in the machine learning model based on the preliminary result to generate a modified machine learning model. The training application performs one or more operations to determine that the modified machine learning model has been trained to perform the first task.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures include: obtaining a B-Rep model including parametric surfaces and associated coedges; generating feature matrices from the coedges associated with the parametric surfaces; using the feature matrices in a convolution layer of a convolutional neural network of a machine learning algorithm to recognize a collection of faces or edges in the B-Rep model, including concatenating results of transforming the feature matrices using a topological walk matrix that represents topological information for the parametric surfaces based on adjacency relationships among the coedges (e.g.

Abstract: In various embodiments, a style comparison application compares geometric styles of different three dimensional (3D) computer-aided design (CAD) objects. In operation, the style comparison application executes a trained neural network one or more times to map 3D CAD objects to feature map sets. The style comparison application computes a first set of style signals based on a first feature set included in the feature map sets. The style comparison application computes a second set of style signals based on a second feature set included in the feature map sets. Based on the first set of style signals and the second set of style signals, the style comparison application determines a value for a style comparison metric. The value for the style comparison metric quantifies a similarity or a dissimilarity in geometric style between a first 3D CAD object and a second 3D CAD object.

Abstract: In various embodiments, a style comparison application generates visualization(s) of geometric style gradient(s). The style comparison application generates a first set of style signals based on a first 3D CAD object and generates a second set of style signals based on a second 3D CAD object. Based on the first and second sets of style signals, the style comparison application computes a different partial derivative of a style comparison metric for each position included in a set of positions associated with the first 3D CAD object to generate a geometric style gradient. The style comparison application generates a graphical element based on at least one of the direction or the magnitude of a vector in the geometric style gradient and positions the graphical element relative to a graphical representation of the first 3D CAD object within a graphical user interface to generate a visualization of the geometric style gradient.

Abstract: 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.

Abstract: 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.

Abstract: 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.