Patents by Inventor Svyatoslav Korneev
Svyatoslav Korneev has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11958112Abstract: A three-dimensional (3D) printer includes a nozzle and a camera configured to capture a real image or a real video of a liquid metal while the liquid metal is positioned at least partially within the nozzle. The 3D printer also includes a computing system configured to perform operations. The operations include generating a model of the liquid metal positioned at least partially within the nozzle. The operations also include generating a simulated image or a simulated video of the liquid metal positioned at least partially within the nozzle based at least partially upon the model. The operations also include generating a labeled dataset that comprises the simulated image or the simulated video and a first set of parameters. The operations also include reconstructing the liquid metal in the real image or the real video based at least partially upon the labeled dataset.Type: GrantFiled: June 24, 2021Date of Patent: April 16, 2024Assignee: XEROX CORPORATIONInventors: Robert R. Price, Raja Bala, Svyatoslav Korneev, Christoforos Somarakis, Matthew Shreve, Adrian Lew, Palghat Ramesh
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Patent number: 11919241Abstract: A method is disclosed for designing a nozzle for jetting printing material in a printing system including selecting a surface tension and viscosity of a printing material at a jetting temperature, selecting a drop volume of the printing material, and constructing a constricted axisymmetric dissipative section of the nozzle, which may include defining a length of the constricted axisymmetric dissipative section and defining a cross-sectional area of the constricted axisymmetric dissipative section.Type: GrantFiled: February 25, 2021Date of Patent: March 5, 2024Assignee: XEROX CORPORATIONInventors: Christoforos Somarakis, Svyatoslav Korneev, Saigopal Nelaturi, Adrian Lew
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Publication number: 20240017325Abstract: 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.Type: ApplicationFiled: July 13, 2022Publication date: January 18, 2024Inventors: Søren Taverniers, Morad Behandish, Svyatoslav Korneev
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Publication number: 20230410278Abstract: Image processing techniques for determining print quality for a 3D printer are disclosed. An example method includes obtaining an image of material jetted from a nozzle of the 3D printer. The method also includes binarizing the image to distinguish background features from foreground features contained in the image. The method also includes determining, by a processing device, a jetting quality based on the binarized image.Type: ApplicationFiled: June 15, 2022Publication date: December 21, 2023Inventors: Christoforos Somarakis, Robert R. Price, Svyatoslav Korneev, Matt Patterson, Adrian Lew, Brendan Mcnamara, Eli Wilson
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Publication number: 20230410277Abstract: Techniques for determining print quality for a 3D printer are disclosed. An example method includes obtaining an image of a stream of material jetted from a nozzle of the 3D printer, and binarizing the image to distinguish background features from foreground features contained in the image. The method also includes identifying elements of jetted material in the foreground features, and computing statistical data characterizing the identified elements. The method also includes generating a quality score of jetting quality based on the statistical data and controlling the 3D printer based on the quality score. The quality score indicates a degree to which the elements of jetted material form droplets of a same size, shape, alignment, and jetting frequency.Type: ApplicationFiled: June 15, 2022Publication date: December 21, 2023Inventors: Christoforos Somarakis, Robert R. Price, Svyatoslav Korneev, Matt Patterson, Adrian Lew, Brendan Mcnamara, Eli Wilson
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Publication number: 20230356302Abstract: Techniques for calibrating a high fidelity (HF) model of molten droplet coalescence are disclosed. An example method includes selecting initial HF parameter values for the HF model. The method also includes iteratively refining the HF parameter values until the HF model converges with experimental data. At each iteration, the HF parameter values are applied to the HF model and a plurality of simulations are run using the HF model to generate the simulated numerical data. For each simulation, a Reduced Order Model (ROM) is fitted to the simulated numerical data to generate ROM parameter values for ROM parameters of the ROM. Correlations between the ROM parameters and the HF parameters are identified to narrow the search space to be searched in a next iteration.Type: ApplicationFiled: May 5, 2022Publication date: November 9, 2023Inventors: Giovanna Bucci, Svyatoslav Korneev, Adrian Lew, Morad Behandish
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Patent number: 11741273Abstract: A geometry of a substrate surface is received at a neural network. The neural network is trained using one or more training sets. Each training set comprises a different type of substrate geometry and a collection of manufacturing process parameters. The substrate is configured to receive at least one liquid droplet. A shape of the at least one droplet after it has been deposited on the substrate is determined based on the received geometry. An output representing the determined shape of the at least one droplet is produced.Type: GrantFiled: June 11, 2020Date of Patent: August 29, 2023Assignee: Palo Alto Research Center IncorporatedInventors: Svyatoslav Korneev, Vaidyanathan Thiagarajan, Saigopal Nelaturi
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Patent number: 11571740Abstract: A computer representation of a printable product part and a plan for the printable product part to be deposited using an additive manufacturing process are received. The printable product part comprises an accumulation of material deposited by the additive manufacturing process. The plan comprises a tool-path representation of the printable product part and process parameters. A plurality of as-printed shapes of the printable product part are determined after it has been deposited according to the plan. Geometric differences between any of the plurality of as-printed shapes with the computer representation of the product part are determined.Type: GrantFiled: March 17, 2020Date of Patent: February 7, 2023Assignee: Palo Alto Research Center IncorporatedInventors: Svyatoslav Korneev, Vaidyanathan Thiagarajan, Saigopal Nelaturi, Ziyan Wang
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Publication number: 20220414380Abstract: A method includes defining a model for a liquid while the liquid is positioned at least partially within a nozzle of a printer. The method also includes synthesizing video frames of the liquid using the model to produce synthetic video frames. The method also includes generating a labeled dataset that includes the synthetic video frames and corresponding model values. The method also includes receiving real video frames of the liquid while the liquid is positioned at least partially within the nozzle of the printer. The method also includes generating an inverse mapping from the real video frames to predicted model values using the labeled dataset. The method also includes reconstructing the liquid in the real video frames based at least partially upon the predicted model values.Type: ApplicationFiled: June 24, 2021Publication date: December 29, 2022Applicants: PALO ALTO RESEARCH CENTER INCORPORATED, XEROX CORPORATIONInventors: Robert R. Price, Raja Bala, Svyatoslav Korneev, Christoforos Somarakis, Matthew Shreve, Adrian Lew, Palghat Ramesh
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Publication number: 20220410273Abstract: A three-dimensional (3D) printer includes a nozzle and a camera configured to capture a real image or a real video of a liquid metal while the liquid metal is positioned at least partially within the nozzle. The 3D printer also includes a computing system configured to perform operations. The operations include generating a model of the liquid metal positioned at least partially within the nozzle. The operations also include generating a simulated image or a simulated video of the liquid metal positioned at least partially within the nozzle based at least partially upon the model. The operations also include generating a labeled dataset that comprises the simulated image or the simulated video and a first set of parameters. The operations also include reconstructing the liquid metal in the real image or the real video based at least partially upon the labeled dataset.Type: ApplicationFiled: June 24, 2021Publication date: December 29, 2022Applicants: PALO ALTO RESEARCH CENTER INCORPORATED, XEROX CORPORATIONInventors: Robert R. Price, Raja Bala, Svyatoslav Korneev, Christoforos Somarakis, Matthew Shreve, Adrian Lew, Palghat Ramesh
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Patent number: 11524463Abstract: A plurality of scanned prints of a product part and a scan-path are received. A shape of a minimum printable feature of the product part is determined by analyzing the respective prints in a scan-path representation. A manufacturing error of the minimum printable feature is determined based on the analysis. A manufacturing error of a shape of the part is determined based on the determined manufacturing error of the minimum printable feature. An estimated manufactured shape of the part is produced based on the determined manufacturing error of the part.Type: GrantFiled: June 11, 2020Date of Patent: December 13, 2022Assignee: Palo Alto Research Center IncorporatedInventors: Svyatoslav Korneev, Vaidyanathan Thiagarajan, Saigopal Nelaturi
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Patent number: 11461511Abstract: A method for producing a design includes receiving a set of design constraints. A spatial field is created based on the design constraints. The spatial field is represented with a linear combination of one or more bases. A number of the one or more bases is less than a number of elements in the spatial field. Respective weights are optimized for each of the one or more bases. A design is produced based on the spatial field and the weights.Type: GrantFiled: March 13, 2020Date of Patent: October 4, 2022Assignee: Palo Alto Research Center IncorporatedInventors: Nurcan Gecer Ulu, Svyatoslav Korneev, Saigopal Nelaturi, Erva Ulu
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Publication number: 20220266513Abstract: A drop-on-demand (DOD) printer is disclosed having an ejector which may include a nozzle, the nozzle including a tank in communication with a source of printing material, a constricted dissipative section in communication with the tank, which may include an elongated internal channel, and a shaping tip in communication with the constricted dissipative section which can include an exit orifice. The (DOD) printer also includes a power source configured to supply one or more pulses of power to the ejector, which causes one or more drops of the printing material to be jetted out of the nozzle. The DOD printer may include a constricted dissipative section configured to obstruct fluid flow that is cylindrical or axisymmetric and having a diameter less than a diameter of the tank and less than a diameter of the shaping tip. The DOD printer may include a nozzle or an array of nozzles.Type: ApplicationFiled: February 25, 2021Publication date: August 25, 2022Applicant: PALO ALTO RESEARCH CENTER INCORPORATEDInventors: Christoforos Somarakis, Svyatoslav Korneev, Saigopal Nelaturi, Adrian Lew
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Publication number: 20220266512Abstract: A nozzle for a printing system is disclosed. The nozzle includes a tank in communication with a source of printing material. The nozzle also includes a constricted dissipative section in communication with the tank, which may include an elongated internal channel. The nozzle may also include a shaping tip in communication with the constricted dissipative section may include an exit orifice. The constricted dissipative section may be axisymmetric and may include at least three internal channels not in communication with one another. Also disclosed is an array of nozzles for a printing system including a plurality of nozzles, with each nozzle including a tank in communication with a source of printing material, a constricted dissipative section in communication with the tank and configured to obstruct fluid flow and having an elongated internal channel, and a shaping tip in communication with the constricted dissipative section may include an exit orifice.Type: ApplicationFiled: February 25, 2021Publication date: August 25, 2022Applicant: PALO ALTO RESEARCH CENTER INCORPORATEDInventors: Christoforos Somarakis, Svyatoslav Korneev, Saigopal Nelaturi, Adrian Lew
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Publication number: 20210387418Abstract: A plurality of scanned prints of a product part and a scan-path are received. A shape of a minimum printable feature of the product part is determined by analyzing the respective prints in a scan-path representation. A manufacturing error of the minimum printable feature is determined based on the analysis. A manufacturing error of a shape of the part is determined based on the determined manufacturing error of the minimum printable feature. An estimated manufactured shape of the part is produced based on the determined manufacturing error of the part.Type: ApplicationFiled: June 11, 2020Publication date: December 16, 2021Inventors: Svyatoslav Korneev, Vaidyanathan Thiagarajan, Saigopal Nelaturi
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Publication number: 20210390224Abstract: A geometry of a substrate surface is received at a neural network. The neural network is trained using one or more training sets. Each training set comprises a different type of substrate geometry and a collection of manufacturing process parameters. The substrate is configured to receive at least one liquid droplet. A shape of the at least one droplet after it has been deposited on the substrate is determined based on the received geometry. An output representing the determined shape of the at least one droplet is produced.Type: ApplicationFiled: June 11, 2020Publication date: December 16, 2021Inventors: Svyatoslav Korneev, Vaidyanathan Thiagarajan, Saigopal Nelaturi
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Publication number: 20210291261Abstract: A computer representation of a printable product part and a plan for the printable product part to be deposited using an additive manufacturing process are received. The printable product part comprises an accumulation of material deposited by the additive manufacturing process. The plan comprises a tool-path representation of the printable product part and process parameters. A plurality of as-printed shapes of the printable product part are determined after it has been deposited according to the plan. Geometric differences between any of the plurality of as-printed shapes with the computer representation of the product part are determined.Type: ApplicationFiled: March 17, 2020Publication date: September 23, 2021Inventors: Svyatoslav Korneev, Vaidyanathan Thiagarajan, Saigopal Nelaturi, Ziyan Wang
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Publication number: 20210286910Abstract: A method for producing a design includes receiving a set of design constraints. A spatial field is created based on the design constraints. The spatial field is represented with a linear combination of one or more bases. A number of the one or more bases is less than a number of elements in the spatial field. Respective weights are optimized for each of the one or more bases. A design is produced based on the spatial field and the weights.Type: ApplicationFiled: March 13, 2020Publication date: September 16, 2021Inventors: Nurcan Gecer Ulu, Svyatoslav Korneev, Saigopal Nelaturi, Erva Ulu