The present disclosure relates to computer-implemented methods for tuning parameters associated with powder bed fusion processes of additive manufacturing, such as laser sintering. Disclosed herein are methods for determining scanning strategies on the basis of information about the build material, additive manufacturing apparatus, and desired or intended features of the part.

An additive manufacturing system includes an additive manufacturing (AM) device, a first sensor device, and a compute device. The AM device is configured to form a bulk component in a layer-by-layer manner, by at least iteratively depositing a first layer of raw material onto a working surface in a deposition chamber, consolidating the initial layer into an initial additive portion of the bulk component, then forming subsequent additive portions of the bulk component by depositing and consolidating a subsequent plurality of layers of the raw material onto the first additive portion. The first sensor device is configured to measure an actual composition of at least one first byproduct portion formed upon consolidation of one of the first or subsequent layers of raw material in the deposition chamber. The compute device includes a processor and a memory, and is communicatively coupled to the additive manufacturing device and first sensor device. The additive manufacturing device and compute device provide an in situ sensor analysis of the component while in a formation state during a build process by comparing an actual composition of the at least one first byproduct portion to an expected composition range stored in the memory.

According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a part parameter dictionary module comprising a processor, geometry data for a plurality of geometric structures forming a plurality of parts, wherein the parts are manufactured with an additive manufacturing machine; determining, using the processor of the part parameter dictionary module, a feature set for each geometric structure; generating, using the processor of the part parameter dictionary module, one of a coupon and a coupon set for the feature set; generating an optimized parameter set for each coupon, using the processor of the part parameter dictionary module, via execution of an iterative learning control process for each coupon; mapping, using the processor of the part parameter dictionary module, one or more parameters of the optimized parameter set to one or more features of the feature set; and generating a dictionary of optimized scan parameter sets to fabricate geometric structures with a material used in additive manufacturing. Numerous other aspects are provided.

Process control parameters are predicted to fabricate an object using deposition. An input design geometry is provided for the object. A training data set includes past post-build physical inspection data for a plurality of objects that comprise at least one object that is different from the object to be physically fabricated; and training data generated through a repetitive process of randomly choosing values for each of multiple process control parameters and scoring adjustments to the multiple process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments. A machine learning algorithm is trained using the provided training data set and a predicted optimal set of the multiple process control parameters is generated for initiating and performing the deposition process to fabricate the object.

Disclosed are an integrated inspection system for a 3D printing process using a thermal image and a laser ultrasound wave and a 3D printing system having the inspection system. The inspection system includes a thermal imaging camera for creating a thermal image of a molten pool formed in a printing object when a base material supplied to the printing object is melted by a laser beam irradiated from a 3D printing laser source, a laser ultrasonic device for receiving a laser ultrasonic wave included in the laser beam reflected from the printing object, and a control unit for estimating a physical property of the printing object and detecting a defect of the printing object based on the thermal image created by the thermal imaging camera and the laser ultrasound wave received by the laser ultrasonic device. The thermal imaging camera and the laser ultrasonic device are disposed coaxially with the 3D printing laser source.

A procedure for calibration of at least one scanning system of a laser sinter or laser melt facility can be carried out in a short time, can take place automatically, and thereby can be carried out before each individual construction process. The procedure may include generation of at least one line pattern through at least one scanning system on a surface at the level of a construction field.

A method and an apparatus for additive manufacturing pertaining to high efficiency, energy beam patterning and beam steering to effectively and efficiently utilize the source energy. In one embodiment recycling and reuse of unwanted light includes a source of multiple light patterns produced by one or more light valves, with at least one of the multiple light patterns being formed from rejected patterned light. An image relay is used to direct the multiple light patterns, and a beam routing system receives the multiple light patterns and respectively directs them toward defined areas on a powder bed.

A method of fabricating process-equivalent test specimens to an additively manufactured component includes generating a processing history model of a component, dividing the component into regions based on input data variations in processing history, wherein each region is characterized by an identified range of input data, determining additive manufacturing processing parameters needed to additively manufacture one or more test specimens that each mimic the processing history in one of the regions, and fabricating the one or more test specimens using the processing parameters determined.

A system for diagnosing an additive manufacturing device is provided. The system includes one or more processors, one or more non-transitory memory modules communicatively coupled to the one or more processors and storing machine-readable instructions. The machine-readable instructions, when executed, cause the one or more processors to: determine parameters associated with at least one subsystem of the additive manufacturing device, the parameters being related to a build generated by the additive manufacturing device; compare the parameters with threshold values; and determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values.

A method of optimizing an additive manufacturing (AM) process includes receiving at least one design parameter of the AM process, receiving information relating to uncertainty in at least one other parameter of the AM process, performing uncertainty quantification in the optimization processor based on the at least one design parameters and uncertainty information to identify a shape error in an object being produced, updating the at least one design parameter of the AM process and utilizing the updated at least one design parameter in the AM process. A system for optimizing an AM process includes a design processor to produce at least one design parameter for an object to be manufactured, and an optimization processor to receive the at least one design parameter and uncertainty information to identify a shape error in the object to be manufactured and update the design parameters based on the shape error, prior or during the manufacturing process.