Embodiments relate to in-situ process monitoring of a part being made via additive manufacturing. The process can involve capturing computed tomography (CT) scans of a post-built part. A neural network (NN) can be used during the build of a new part to process multi-modal sensor data. Spatial and temporal registration techniques can be used to align the data to x,y,z coordinates on the build plate. During the build of the part, the multi-modal sensor data can be superimposed on the build plate. Machine learning can be used to train the NN to correlate the sensor data to a defect label or a non-defect label by looking to certain patterns in the sensor data at the x,y,z location to identify a defect in the CT scan at x,y,z. The NN can then be used to predict where defects are or will occur during an actual build of a part.

Embodiments relate to in-situ process monitoring of a part being made via additive manufacturing. The process can involve capturing computed tomography (CT) scans of a post-built part. A neural network (NN) can be used during the build of a new part to process multi-modal sensor data. Spatial and temporal registration techniques can be used to align the data to x,y,z coordinates on the build plate. During the build of the part, the multi-modal sensor data can be superimposed on the build plate. Machine learning can be used to train the NN to correlate the sensor data to a defect label or a non-defect label by looking to certain patterns in the sensor data at the x,y,z location to identify a defect in the CT scan at x,y,z. The NN can then be used to predict where defects are or will occur during an actual build of a part.

An inspection system for in situ evaluation of an additive manufacturing (AM) build part is provided. The inspection system comprises a build plane induction coil sensor configured and positionable so that during construction of the build part, the sensor's magnetization and sensor coils surround at least the last-produced layer of the AM build part in the build plane. The inspection system further comprises an energization circuit and a central processing system. The central processing system comprises a communication processor configured for sending command signals to the energization circuit and receiving impedance data from the build plane induction coil sensor, and energization controller configured for determining energization commands for transmission to the energization circuit, and an induction data analyzer configured for processing build part impedance data using complex impedance plane analysis and for identifying anomalies in the AM build part.

Systems and methods determine a layer of a part to be manufactured within a build module a 3D printing system. Within the layer, lasing tasks to be performed for manufacturing the layer of the part. Constraints are determined that represent one or more limitations associated with at least one of an order or a timing in which the lasing tasks are performed. Based at least in part on the constraints a directed acyclic graph (DAG) is generated that is associated with the at least one of the order or the timing in which the lasing tasks are performed.

Systems and methods determine a layer of a part to be manufactured within a build module a 3D printing system. Within the layer, lasing tasks to be performed for manufacturing the layer of the part. Constraints are determined that represent one or more limitations associated with at least one of an order or a timing in which the lasing tasks are performed. Based at least in part on the constraints a directed acyclic graph (DAG) is generated that is associated with the at least one of the order or the timing in which the lasing tasks are performed.

Systems and methods for simulating a melt pool characteristic for selective laser melting additive manufacturing. The system includes a selective laser melting apparatus and an electronic controller configured to obtain a surface geometry of a previous layer of a component being manufactured using the selective laser melting apparatus, simulate an addition of a powder layer having a desired powder layer thickness to the component based upon the surface geometry of the previous layer, determine a melt pool characteristic based upon geometric information of the simulated powder layer and the desired powder layer thickness, determine an adjustment to the simulated powder layer based upon the melt pool characteristic, and actuate the selective laser melting apparatus based upon the simulated powder layer and the determined adjustment.

A method operates a three-dimensional (3D) metal object manufacturing system to compensate for errors that occur during object formation. In the method, thermal image data and dimensional image data of a metal object being formed by the 3D metal object manufacturing system is generated prior to completion of the metal object. Thermal conditions are identified from these data and compared to predetermined ranges corresponding to the identified thermal conditions to identify one or more errors. For identified errors outside a corresponding predetermined difference range, the method performs an error compensation technique. The error compensation includes modification of a surface data model, modification of machine-ready instructions, or operation of a subtractive device.

A method operates a three-dimensional (3D) metal object manufacturing system to compensate for errors that occur during object formation. In the method, thermal image data and dimensional image data of a metal object being formed by the 3D metal object manufacturing system is generated prior to completion of the metal object. Thermal conditions are identified from these data and compared to predetermined ranges corresponding to the identified thermal conditions to identify one or more errors. For identified errors outside a corresponding predetermined difference range, the method performs an error compensation technique. The error compensation includes modification of a surface data model, modification of machine-ready instructions, or operation of a subtractive device.

A system, is disclosed having a polymer-based additive manufacturing subsystem, a metallic-based additive manufacturing subsystem, an exchanger to place at least one of the polymer-based additive manufacturing subsystem and the metallic-based additive manufacturing subsystem into a position to provide a manufacturing process, a build area where a part is created with the polymer-based additive manufacturing subsystem and the metallic-based additive manufacturing subsystem, and an environmental control unit to collect debris produced during operation of the polymer-based additive manufacturing subsystem and the metallic-based additive manufacturing subsystem. Another system and method are also disclosed.

A method is disclosed. The method involves establishing a number of times that a batch of build material has been processed as part of one or more additive manufacturing processes without forming part of a three-dimensional object formed during the one or more additive manufacturing processes. The method also involves determining, based on the established number of times, build parameters to be applied in respect of an additive manufacturing a batch of build material has process to be performed using the batch of build material to generate a three-been processed as part of one or dimensional object.