An additive manufacturing system may include an energy source, an optical system to modify and direct an energy beam from the energy source toward a component to form a melt pool, and a material delivery device to deliver material to the melt pool. The optical system may form an annular energy beam, direct the annular energy beam toward the component, receive at least a portion of thermal emissions produced by the annular energy beam and the melt pool, and direct the portion of the thermal emissions toward an imaging device, which may be used to control the energy source.

This disclosure describes an additive manufacturing method that includes monitoring a temperature of a portion of a build plane during an additive manufacturing operation using a temperature sensor as a heat source passes through the portion of the build plane; detecting a peak temperature associated with one or more passes of the heat source through the portion of the build plane; determining a threshold temperature by reducing the peak temperature by a predetermined amount; identifying a time interval during which the monitored temperature exceeds the threshold temperature; identifying, using the time interval, a change in manufacturing conditions likely to result in a manufacturing defect; and changing a process parameter of the heat source in response to the change in manufacturing conditions.

According to one embodiment, a monitoring system includes a collection device and a processing device. The collection device collects information of a solidified portion that is solidified in additive manufacturing. The additive manufacturing forms a plurality of layers by repeatedly melting and solidifying a metal powder. The processing device generates quality data of an existence or absence of a defect of the solidified portion by using the information to determine the existence or absence of the defect.

In an example, an apparatus includes an image processing system, a print engine, and a vision system. The image processing system generates electronic signals based on a model of an object to be fabricated using an additive manufacturing process. The print engine performs the additive manufacturing process in a plurality of passes based on the electronic signals. The vision system acquires a plurality of thermal images of the plurality of passes and assigns individual passes to individual images based on data acquired during a build of a calibration object by the additive manufacturing process. The print engine may further include a material coater to spread a powder coating material, a plurality of fluid ejection devices to eject a fusing agent, and an emitter to emit energy to fuse the fusing agent and the powder coating material into a layer of the object to be fabricated.

In an example, an apparatus includes an image processing system, a print engine, and a vision system. The image processing system generates electronic signals based on a model of an object to be fabricated using an additive manufacturing process. The print engine performs the additive manufacturing process in a plurality of passes based on the electronic signals. The vision system acquires a plurality of thermal images of the plurality of passes and assigns individual passes to individual images based on data acquired during a build of a calibration object by the additive manufacturing process. The print engine may further include a material coater to spread a powder coating material, a plurality of fluid ejection devices to eject a fusing agent, and an emitter to emit energy to fuse the fusing agent and the powder coating material into a layer of the object to be fabricated.

A method for producing a support structure in the additive manufacturing of a component, includes: a) providing a geometry for the component having a region to be supported, b) providing a support structure for the region of the component, c) defining an irradiation pattern for an irradiation of layers of a raw material for the support structure, wherein surface vectors for an irradiation for a structure of the component extend into a region of the support structure, wherein common surface vectors are defined for the component and for the support structure, and d) selective irradiation of layers of the raw material for the component and the provided support structure according to the defined irradiation pattern.

A method for producing a support structure in the additive manufacturing of a component, includes: a) providing a geometry for the component having a region to be supported, b) providing a support structure for the region of the component, c) defining an irradiation pattern for an irradiation of layers of a raw material for the support structure, wherein surface vectors for an irradiation for a structure of the component extend into a region of the support structure, wherein common surface vectors are defined for the component and for the support structure, and d) selective irradiation of layers of the raw material for the component and the provided support structure according to the defined irradiation pattern.

A method of feedback controlling a 3D printing process in real time, and a system therefor are disclosed. The method includes collecting big data, generated through 3D printing experiments, related to process variables of 3D printing, measurement signals, and 3D printing quality of the 3D printing object; building an artificial neural network model by performing machine-learning based on the collected big data; evaluating whether or not a 3D printing quality of the 3D printing object is abnormal in real time based on an actual measurement signal of the 3D printing object and the artificial neural network model; and feedback controlling printing quality of the 3D printing object in real time based on the evaluation result of whether or not the 3D printing quality of the 3D printing object is abnormal.

A method for training a machine learning engine for modeling of a physical system includes receiving process data representing measurements of a physical system. The method includes applying a transform to values of the at least two variables of the process data to generate a dimensionless parameter having a parameter value corresponding to each measurement of the physical system for the at least two variables. The method includes training the machine learning engine using a set of generated training data including the non-dimensionalized parameter, to output a prediction of a value of a physical effect of the physical system for values of the variables that are not included in the process data. The method includes controlling an additive manufacturing process for the material by setting the at least one physical property to the value of the at least one process variable during fabrication of a part.

An electron beam melting machine and a method of operation are provided which maintains constant energy absorption within a build layer by adjusting an incident energy level to compensate for energy not absorbed by the additive powder. This unabsorbed energy is detected in the form of electron emissions, which include secondary electrons, backscattered electrons, and/or electrons which are transmitted through the build platform.