Abstract: The present disclosure provides techniques for processing a three-dimensional (3D) object file in a privacy-preserving manner. An example method includes obtaining an object file that comprises a specification of a 3D printable object, encrypting the object file using a public key to generate an encrypted object file, and sending, to a remote computing system, the encrypted object file and a request to process the encrypted object file to identify a characteristic of the 3D printable object. The method also includes receiving an encrypted result file from the remote computing system, wherein the encrypted result file comprises an encrypted Minkowski sum of the encrypted object file and an encrypted comparison file. The method also includes decrypting the encrypted result file using a private key corresponding with the public key to generate an unencrypted result file and processing the unencrypted result file to determine the characteristic of the 3D printable object.

Abstract: Set differences between an as-designed and an as-manufactured model are computed. Topological deviations between the as-designed model and the as-manufactured model are determined based under-deposition and over-deposition features of the set differences. Based on the discrepancies, an input to a manufacturing instrument is changed to reduce topological differences between the as-manufactured model and the as-designed model.

Abstract: A computer-implemented physics solver operates within a neural network framework. A neural network accepts a coordinate for each location within a design domain and outputs a local composition for each location. A model of the design domain is formed in a physics solver. The model includes discrete elements that encompass the design domain. A solution of the model provides a value of a design objective. For a plurality of iterations, the following is performed: the neural network determines current local compositions for the locations in the design domain corresponding to the discrete elements; the current local compositions are input into the discrete elements of the physics solver to obtain a current value of the design objective; and the current value of the design objective is used to find a loss gradient of a loss function. The loss gradient is used to update the neural network during the iterations.

Abstract: The present disclosure provides techniques for identifying regions of a three-dimensional (3D) printable object that are accessible by a tool. An example method includes receiving, from a remote computing device, an encrypted object file that includes a specification of a 3D printable object and receiving a request to process the encrypted object file to identify regions of the 3D printable object that are accessible by a tool. The method also includes obtaining a tool specification, computing a complement of the tool specification, and encrypting the complement of the tool specification to generate an encrypted comparison file. The method also includes computing an encrypted Minkowski sum of the encrypted object file and the encrypted comparison file to generate an encrypted result file that describes the regions of the 3D printable object that are accessible by the tool. The encrypted Minkowski sum is performed without decrypting the encrypted object file.

Abstract: The present disclosure provides techniques for processing a three-dimensional (3D) object file or object model in a privacy-preserving manner. An example method includes receiving, from a remote computing device, an encrypted object file comprising a specification of a 3D printable object and receiving a request to process the encrypted object file to identify a characteristic of the 3D printable object. The method also includes obtaining an encrypted comparison file and computing an encrypted Minkowski sum of the encrypted object file and the encrypted comparison file to generate an encrypted result file that comprises information about the characteristic. Computing the encrypted Minkowski sum is performed without decrypting the encrypted object file. The method also includes sending the encrypted result file to the remote computing device.

Abstract: The present disclosure provides techniques for finding non-printable features in a three-dimensional (3D) printable object. An example method includes receiving a first encrypted file and a request to process the first encrypted file to identify non-printable features of a 3D printable object, wherein the first encrypted file is an encrypted complement of an object file that comprises a specification of the 3D printable object. The method also includes computing a first encrypted Minkowski sum of the first encrypted file and an encrypted minimum feature file to generate an encrypted intermediate file and sending the encrypted intermediate file. The method also includes receiving a second encrypted file that comprises an encrypted complement of the encrypted intermediate file and computing a second encrypted Minkowski sum of the second encrypted file and the encrypted minimum feature file to generate an encrypted result file that describes non-printable features of the 3D printable object.

Abstract: In an example, a method for solving problems involving a Hierarchical Physical and/or Engineering System (HPES) includes: receiving, by a computing system, a declarative object-oriented definition of the HPES having one or more sub-systems and/or one or more components; receiving, by the computing system, a definition of a first problem to be solved, wherein the first problem comprises at least one of: an optimization problem or an inverse problem; processing, by the computing system, the declarative object-oriented definition of the HPES to generate a data structure representing relationships and interactions between the one or more subsystems and/or the one or more components of the HPES; transpiling, by the computing system, a first optimized code using the data structure, the declarative object-oriented definition and the definition of the first problem; and generating, by the computing system, a solution to the first problem by executing the first optimized code.

Abstract: This disclosure teaches techniques, devices, and systems for automatically modifying a 3-Dimensional (3D) object model. The apparatus comprising a memory and a processing device coupled to the memory. The processor and the memory are configured to obtain an input 3D object model comprising an input 3D mesh, analyze the input 3D mesh to identify unprintable overhangs that have an angle over an overhang angle limit and identify vertices associated with the unprintable overhangs as active vertices, and perform an optimization process to move the active vertices to eliminate the unprintable overhangs and generate a modified 3D mesh. The optimization process includes a gradient descent search algorithm performed on an objective function comprising a cost function configured to minimize a difference between the input 3D mesh and the modified 3D mesh subject to an overhang constraint related to the overhang angle limit.

Abstract: The present disclosure provides techniques for analyzing trapped unused materials during AM. For example, given a geometry or shape to be manufactured using one or more AM materials, a tool path for solidifying the one or more AM materials is generated to turn the geometry into a physical object based on one or more manufacturing parameters related to AM resolutions. A processing device may compute a simulated manufactured geometry (e.g., a realistic representation of the actual shape of the corresponding physical object to be produced) based on the tool path and the one or more manufacturing parameters. The processing device analyzes the simulated manufactured geometry for a portion of removable AM materials trapped inside the simulated manufactured geometry. The processing device then generates a report regarding the portion of the removable AM materials trapped inside the simulated manufactured geometry.

Abstract: This disclosure provides techniques for manufacturing parts using digital twin(s) to manage various aspects of the manufacturing system. An example method may include representing a manufacturing system using a digital twin. In some cases, representing the manufacturing system includes modeling, in the digital twin, digital representations that correspond to a number of physical components of the manufacturing system. The physical components include at least: a manufacturing material, a working environment for deposition of the manufacturing material (e.g., for additive manufacturing), a tool to manipulate the manufacturing material between at least two matter states in the working environment, and sensors measuring behaviors of the tool, the working environment, and the manufacturing material. The method may further include receiving a manufacturing production task.

Abstract: Techniques for modeling a droplet-based additive manufacturing process are disclosed. An example method includes obtaining training data, setting one or more hyperparameter values in a data-driven surrogate model architecture, and training, by a processing device, the surrogate model architecture on the training data to generate a trained surrogate model. The trained surrogate model is to be used in lieu of a physics-based model to make predictions about the results of an additive manufacturing process. The training data includes pairs of input data and output data, wherein the input data describes an initial state of a substrate and a molten droplet inside a moving subdomain prior to the molten droplet impacting the substrate and the output data describes a final state of the substrate inside that moving subdomain after the molten droplet has impacted the substrate and coalesced with previously deposited droplets making up the initial state of the substrate.

Abstract: The present disclosure provides techniques for processing a three-dimensional (3D) object file or object model in a privacy-preserving manner. An example method includes receiving, from a remote computing device, an encrypted object file comprising a specification of a 3D printable object and receiving a request to process the encrypted object file to identify a characteristic of the 3D printable object. The method also includes obtaining an encrypted comparison file and computing an encrypted Minkowski sum of the encrypted object file and the encrypted comparison file to generate an encrypted result file that comprises information about the characteristic. Computing the encrypted Minkowski sum is performed without decrypting the encrypted object file. The method also includes sending the encrypted result file to the remote computing device.

Abstract: The present disclosure provides techniques for processing a three-dimensional (3D) object file in a privacy-preserving manner. An example method includes obtaining an object file that comprises a specification of a 3D printable object, encrypting the object file using a public key to generate an encrypted object file, and sending, to a remote computing system, the encrypted object file and a request to process the encrypted object file to identify a characteristic of the 3D printable object. The method also includes receiving an encrypted result file from the remote computing system, wherein the encrypted result file comprises an encrypted Minkowski sum of the encrypted object file and an encrypted comparison file. The method also includes decrypting the encrypted result file using a private key corresponding with the public key to generate an unencrypted result file and processing the unencrypted result file to determine the characteristic of the 3D printable object.

Abstract: The present disclosure provides techniques for identifying regions of a three-dimensional (3D) printable object that are accessible by a tool. An example method includes receiving, from a remote computing device, an encrypted object file that includes a specification of a 3D printable object and receiving a request to process the encrypted object file to identify regions of the 3D printable object that are accessible by a tool. The method also includes obtaining a tool specification, computing a complement of the tool specification, and encrypting the complement of the tool specification to generate an encrypted comparison file. The method also includes computing an encrypted Minkowski sum of the encrypted object file and the encrypted comparison file to generate an encrypted result file that describes the regions of the 3D printable object that are accessible by the tool. The encrypted Minkowski sum is performed without decrypting the encrypted object file.

Abstract: The present disclosure provides techniques for finding non-printable features in a three-dimensional (3D) printable object. An example method includes receiving a first encrypted file and a request to process the first encrypted file to identify non-printable features of a 3D printable object, wherein the first encrypted file is an encrypted complement of an object file that comprises a specification of the 3D printable object. The method also includes computing a first encrypted Minkowski sum of the first encrypted file and an encrypted minimum feature file to generate an encrypted intermediate file and sending the encrypted intermediate file. The method also includes receiving a second encrypted file that comprises an encrypted complement of the encrypted intermediate file and computing a second encrypted Minkowski sum of the second encrypted file and the encrypted minimum feature file to generate an encrypted result file that describes non-printable features of the 3D printable object.

Abstract: A nondestructive method for detecting damage in parts and/or characterizing effective material properties may include: exposing a material to one or more nondestructive stimuli; measuring a response of the material to the stimuli; selecting at least one of a specific length scale or a specific time scale; and analyzing the measurement of the response with a scale-aware single- or multi-physics model to identify anomalies in the measurements as compared to an expected response of the material to the stimuli, wherein the scale-aware single- or multi-physics model is based on the at least one of the specific length scale or the specific time scale.

Abstract: A method includes receiving a representation of an initial design domain. The method also includes iteratively generating intermediate part designs by redistributing material within the initial design domain. The intermediate part designs each include a 3D part and a support volume. The method also includes calculating a measure of inaccessibility of the support volume of each intermediate part design by at least one subtractive tool assembly. At least one of the intermediate part designs is generated based at least partially upon the measure of inaccessibility of a previous one of the intermediate part designs.

Abstract: The present disclosure provides techniques for automatically modifying shape features related to overhangs such that support structures can be reduced or avoided. In an example, a shape modification processing device may obtain an initial shape and relevant production information (e.g., overhang angle threshold). The shape modification processing device may modify the shape to improve various aspects of production, including reduction or removal of the need for support materials or structures during additive manufacturing, with considerations that allow the modified portion (e.g., added features) be accessible for removal by subtractive manufacturing. For example, the shape modification processing device may modify a model to reduce or remove support structures needed during production. The added features may later be removed by subtractive manufacturing to restore any functional or desired shape.

Abstract: An innovative method to automatically place sensors and stimuli-sensitive active actuators to neutralize the effects of structural faults and design a smart fault-resilient system is described. To the best of our knowledge, the proposed framework is the first systematic integration of thermally activated shape memory polymer actuators with a sensor distribution framework targeted to bring a damaged structural system to its native state. The framework does not explicitly model the material constitutive model and hence can be applied to linear and nonlinear material behaviors. The approach enables the design of resilient smart structures that can be additively manufactured. The framework computes a matrix of relative importance for different sensor positions and uses that to optimally place actuators to reconfigure the system in presence of faults.

Abstract: A systematic framework to design active resilient structures that simultaneously optimizes the characteristics of the system to yield an optimal solution in terms of robustness to external disturbance, uncertainty in structural properties, and structural faults. The proposed approach will yield a preferred or optimal solution as opposed to designing individual components of the complete system. The framework provides the preferred or optimal location and precision of heterogeneous sensors and stimuli-sensitive active actuators and the control law guiding these active actuators to control the resilient structure. The framework provides a systematic approach to designing active resilient structures by choosing the location of smart actuators/sensors to mitigate the unwanted effects of uncertain structural properties.