# US 2025/0367754 A1 · Workpiece monitoring and control for automated welding systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0367754 A1 - Document ID: US20250367754A1 - Status: Published application - Kind: A1 - Application number: 19/190,268 - Filed: 2025-04-25 - Published: 2025-12-04 - Earliest claimed: 2024-05-31 - Assignees: Norsk Titanium AS - Applicants: Norsk Titanium AS - Inventors: Reza Zamiri - Web page: https://patentgrep.com/patent/US20250367754A1 ## CPC - B23K 26/032: {using optical means} - G06T 7/0004: {Industrial image inspection} - B23K 26/08: Devices involving relative movement between laser beam and workpiece - B23K 31/02: relating to soldering or welding - B33Y 50/02: for controlling or regulating additive manufacturing processes - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30164: Workpiece; Machine component - B23K: SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - B33Y: ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING ## Abstract A computer-implemented method for monitoring a workpiece being manufactured using an automated welding system. These technologies can real-time monitor, read, or interrogate a workpiece or a substrate on which the workpiece is positioned, as the workpiece is moved past a directed energy source, or vice versa. The method can be used with an automated welding system for standoff distance monitoring and control, which can be responsive, dynamic, and in real-time. These technologies can use a feedback controller to responsively and dynamically control the standoff distance in real-time based on data from the standoff distance measurement system. ## Claims ### Claim 1 A computer-implemented method for monitoring a workpiece being manufactured by an automated welding system, the computer-implemented method comprising: causing an optical signal to be emitted from a signal emitter onto a surface of the workpiece, the optical signal indicating a section of the workpiece; obtaining data relating to the surface of the workpiece from a signal detector, the data including one or more reflections from the surface of the workpiece; inputting the data to a machine-learned model; receiving, from the machine-learned model, an identification of a reflection of the one or more reflections that corresponds to the optical signal indicating the section of the workpiece; and determining one or more geometric properties of the section based on the reflection. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the machine-learned model is an image segmentation model. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the machine-learned model is an instance segmentation model. ### Claim 4 (depends on 2) The computer-implemented method of claim 2, wherein the machine-learned model is a single-stage model. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the optical signal comprises a laser signal having a predefined geometric pattern. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein determining the indication of the reflection using the machine-learned model comprises receiving, from the machine-learned model, the data and an indication of a location of reflection within the data. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the machine-learned model is a first machine-learned model, and wherein the determining the one or more geometric properties of the section comprises determining the one or more geometric properties using a second machine-learned model. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein determining the one or more geometric properties of the section comprises: determining a physical position of each of a plurality of points on the surface of the section relative to a reference point. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, wherein determining the one or more geometric properties of the section further comprises: determining a feature of the section based on the physical positions of each of the plurality of points on the surface of the section. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein the feature comprises at least one of: a point from which a current standoff distance is to be determined; a surface defect; a feature of a weld groove; or a feature of a weld bead. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, wherein the feature comprises the point from which the current standoff distance is to be determined, and wherein the method further comprises: determining the current standoff distance as a distance between a welding tool of the automated welding system and the point; and providing the current standoff distance to a controller for operating a mover to achieve a predefined standoff distance between the welding tool and the point based on the current standoff distance, wherein the mover is configured to move at least one of the welding tool relative to the workpiece or the workpiece relative to the welding tool. ### Claim 12 A system comprising: a signal emitter; a signal detector; one or more processors; and non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising: causing an optical signal to be emitted from the signal emitter onto a surface of a workpiece, the optical signal indicating a section of the workpiece; obtaining data relating to the surface of the workpiece from the signal detector, the data including one or more reflections from the surface of the workpiece; inputting the data to a machine-learned model; receiving, from the machine-learned model, an identification of a reflection of the one or more reflections that corresponds to the optical signal indicating the section of the workpiece; and determining, based on the reflection, one or more geometric properties of the section. ### Claim 13 (depends on 12) The system of claim 12, wherein the machine-learned model is one of an image segmentation model, an instance segmentation model, or a single-stage model. ### Claim 14 (depends on 12) The system of claim 12, wherein the optical signal comprises a laser signal having a predefined geometric pattern. ### Claim 15 (depends on 12) The system of claim 12, wherein determining the one or more geometric properties of the section comprises: determining a physical position of each of a plurality of points on the surface of the section relative to a reference point. ### Claim 16 (depends on 15) The system of claim 15, wherein determining the one or more geometric properties of the section further comprises: determining a feature of the section based on the physical positions of each of the plurality of points on the surface of the section. ### Claim 17 A non-transitory computer-readable medium storing instructions, that when executed by one or more processors, cause the one or more processors to perform operations comprising: causing an optical signal to be emitted from a signal emitter onto a surface of a workpiece, the optical signal indicating a section of the workpiece; obtaining data relating to the surface of the workpiece from a signal detector, the data including one or more reflections from the surface of the workpiece; inputting the data to a machine-learned model; receiving, from the machine-learned model, an identification of a reflection of the one or more reflections that corresponds to the optical signal indicating the section of the workpiece; and determining, based on the reflection, one or more geometric properties of the section. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the machine-learned model is one of an image segmentation model, an instance segmentation model, or a single-stage model. ### Claim 19 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the machine-learned model is a first machine-learned model, and wherein the determining the one or more geometric properties of the section comprises determining the one or more geometric properties using a second machine-learned model. ### Claim 20 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein determining the one or more geometric properties of the section comprises: determining a physical position of each of a plurality of points on the surface of the section relative to a reference point.