Abstract: A system includes an apparatus for building a component by additive manufacturing, a camera, and a computing device. When the computing device receives an image of the build plane captured by the camera, the image includes a fiducial marker positioned on the build plane. The computing device then rectifies the image to generate a distortion-corrected image, and identifies the coordinates of the fiducial marker in the distortion-corrected image. The computing device then compares identified coordinates of the at least one fiducial marker from the distortion-corrected image with corresponding nominal coordinates of the at least one fiducial marker in a coordinate system of the apparatus, and generates a coordinate transfer function based on a result of the comparison. The computing device then applies the coordinate transfer function to the apparatus to calibrate a position of the energy beam on the build plane.

Abstract: A system includes an apparatus for building a component by additive manufacturing using an energy beam, at least one first camera having a first field of view of the build plane, at least one second camera having a second field of view of the build plane, and a computing device. When the computing device receives a first image and a second image of the build plane captured by the at least one first camera and second camera, the images include a fiducial marker positioned on the build plane within an overlap region. Coordinates of the fiducial marker in the first image within the overlap region and within the second image are then identified. The first image and the second image are combined to form a merged image based on the identified coordinates of the fiducial markers in the first and second images.

Abstract: A system includes an apparatus for building a component by additive manufacturing, a camera, and a computing device. When the computing device receives an image of the build plane captured by the camera, the image includes a fiducial marker positioned on the build plane, the computing device identifies the coordinates of the fiducial marker in the image in a coordinate system of the camera. The computing device then identifies corresponding coordinates of the fiducial marker in the coordinate system of the apparatus, and generates an image transfer function that converts the coordinates in the camera system to coordinates of the apparatus. The image transfer function is then applied to the image to generate a distortion-corrected image.

Abstract: A computer program product comprising a non-transitory computer-readable medium storing instructions, that when executed by a computer processor, cause the computer processor to perform obtain a model defining a geometry of a component for manufacturing using an additive manufacturing system, discretize the geometry of the component into a plurality of voxels, each voxel representing a volumetric portion of the component, identify voxels of the plurality of voxels having a value of stress greater than a threshold value, determine regions of the component contributing to the identified voxels having the value of stress greater than the threshold value through a build process simulation configured to iteratively simulate modifications to voxels of the component so that the value of stress of the identified voxels is reduced, modify the geometry of the regions of the component, and store an updated model of the component incorporating the modifications to the geometry.

Abstract: An example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. The controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.

Abstract: An example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. The controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.

Abstract: An example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. The controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.

Abstract: A device and procedure for checking the health of a pressure transducer in situ is provided. The procedure includes measuring a fixed change in pressure above ambient pressure and a fixed change in pressure below ambient pressure. This is done by first sealing an enclosed volume around the transducer with a valve. A piston inside the sealed volume is then driven forward, compressing the enclosed gas, thereby increasing the pressure. A fixed pressure below ambient pressure is obtained by opening the valve, driving the piston forward, sealing the valve, and then retracting the piston. The output of the pressure transducer is recorded for both the overpressuring and the underpressuring. By comparing this data with data taken during a preoperative calibration, the health of the transducer is determined from the linearity, the hysteresis, and the repeatability of its output. The further addition of a thermometer allows constant offset error in the transducer output to be determined.

Abstract: A multi-sensor transducer and processing method allow insitu monitoring of the senor accuracy and transducer ‘health’. In one embodiment, the transducer has multiple sensors to provide corresponding output signals in response to a stimulus, such as pressure. A processor applies individual weight factors to reach of the output signals and provide a single transducer output that reduces the contribution from inaccurate sensors. The weight factors can be updated and stored. The processor can use the weight factors to provide a ‘health’ of the transducer based upon the number of accurate versus in-accurate sensors in the transducer.