Methods and apparatus for two-dimensional and three-dimensional scanning path visualization are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to identify at least one melt pool dimension using a beam parameter setting, the at least one melt pool dimension identified from a plurality of melt pool dimensions obtained by varying the beam parameter setting, identify a response surface model based on the plurality of melt pool dimensions to determine an effect of variation in the beam parameter setting on the at least one melt pool dimension, output a three-dimensional model of a scanning path for an additive manufacturing process using the response surface model, and adjust the beam parameter setting based on the three-dimensional model to identify a second beam parameter setting.

Techniques are disclosed for fabricating multi-part assemblies. In particular, by forming release layers between features such as bearings or gear teeth, complex mechanical assemblies can be fabricated in a single additive manufacturing process.

An additive manufacturing system includes an additive manufacturing unit configured to shape an object including a plurality of layers, a measurement unit configured to measure a state of each of the plurality of layers, and a control unit. The control unit includes a storage unit configured to store reference information based on internal defect information indicating a defect existing inside a sample object shaped by the additive manufacturing unit and including the plurality of layers, based on an electromagnetic wave which has passed through the sample object, and sample measurement information indicating a measurement result of the plurality of layers of the sample object measured by the measurement unit, and an estimation unit configured to estimate whether a defect occurs inside the object, based on measurement information indicating a measurement result of the plurality of layers of the object measured by the measurement unit and the reference information.

An additive manufacturing system includes an additive manufacturing unit configured to shape an object including a plurality of layers, a measurement unit configured to measure a state of each of the plurality of layers, and a control unit. The control unit includes a storage unit configured to store reference information based on internal defect information indicating a defect existing inside a sample object shaped by the additive manufacturing unit and including the plurality of layers, based on an electromagnetic wave which has passed through the sample object, and sample measurement information indicating a measurement result of the plurality of layers of the sample object measured by the measurement unit, and an estimation unit configured to estimate whether a defect occurs inside the object, based on measurement information indicating a measurement result of the plurality of layers of the object measured by the measurement unit and the reference information.

Systems, apparatus, computer-readable medium, and associated methods to monitor, analyze, and adjust at least one of 1) additive machine health and configuration or 2) build health and configuration are disclosed. An example apparatus includes an analytics processor, separate from and in a trusted relationship with an additive manufacturing machine building a part, to process, based on a trigger, data from monitoring of the additive manufacturing machine and the build of the part, the analytics processor including a hybrid model fusing additive process physics and data science to process the data to identify an abnormality in at least one of the build or the additive manufacturing machine and to adjust a configuration of the additive manufacturing machine during the build to address the abnormality.

Systems, apparatus, computer-readable medium, and associated methods to monitor, analyze, and adjust at least one of 1) additive machine health and configuration or 2) build health and configuration are disclosed. An example apparatus includes an analytics processor, separate from and in a trusted relationship with an additive manufacturing machine building a part, to process, based on a trigger, data from monitoring of the additive manufacturing machine and the build of the part, the analytics processor including a hybrid model fusing additive process physics and data science to process the data to identify an abnormality in at least one of the build or the additive manufacturing machine and to adjust a configuration of the additive manufacturing machine during the build to address the abnormality.

This disclosure describes various methods and apparatus for calibration of temperature sensors in additive manufacturing systems. A method for calibration of temperature sensors can include selecting a first wavelength and a second wavelength spaced apart from the first wavelength; measuring an amount of energy radiated from a black body source at the first wavelength; measuring an amount of energy radiated from the black body source at the second wavelength; generating a relationship between a ratio of the amount of energy radiated at the first wavelength to the amount of energy radiated at the second wavelength; and determining, using the relationship, variations in a temperature of a build plane of an additive manufacturing system based upon a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength.

This disclosure describes various methods and apparatus for calibration of temperature sensors in additive manufacturing systems. A method for calibration of temperature sensors can include selecting a first wavelength and a second wavelength spaced apart from the first wavelength; measuring an amount of energy radiated from a black body source at the first wavelength; measuring an amount of energy radiated from the black body source at the second wavelength; generating a relationship between a ratio of the amount of energy radiated at the first wavelength to the amount of energy radiated at the second wavelength; and determining, using the relationship, variations in a temperature of a build plane of an additive manufacturing system based upon a ratio of energy radiated at the first wavelength to energy radiated at the second wavelength.

Certain aspects of the present disclosure provide a method for optimizing process parameters for additive manufacturing, including: determining a change to at least one process parameter of a plurality of process parameters while additively manufacturing a first part using an additive manufacturing apparatus according to a build file comprising machine code defining the plurality of process parameters; modifying the build file based on the determined change to the at least one process parameter to generate a modified build file; additively manufacturing a second part using the additive manufacturing apparatus according to the modified build file, wherein: additively manufacturing the first part is performed in a closed-loop control mode, and additively manufacturing the second part is performed in an open-loop control mode.

Certain aspects of the present disclosure provide a method for optimizing process parameters for additive manufacturing, including: determining a change to at least one process parameter of a plurality of process parameters while additively manufacturing a first part using an additive manufacturing apparatus according to a build file comprising machine code defining the plurality of process parameters; modifying the build file based on the determined change to the at least one process parameter to generate a modified build file; additively manufacturing a second part using the additive manufacturing apparatus according to the modified build file, wherein: additively manufacturing the first part is performed in a closed-loop control mode, and additively manufacturing the second part is performed in an open-loop control mode.