Abstract: A method includes identifying at least one region-of-interest (ROI) of an article that is to be additively manufactured by powder bed fusion, determining a thermal profile of the ROI, where the thermal profile includes at least a maximum temperature and a cooling rate, determining a geometry of an insulation layer upon which at least one process-equivalent test specimen (PETS) is to be additively manufactured by powder bed fusion such that a thermal profile of the at least one PETS replicates the thermal profile of the at least one ROI, and fabricating the at least one PETS in accordance with the thermal profile of the PETS by using the determined geometry of the insulation layer such that the at least one PETS and the at least one ROI are metallurgically equivalent.

Abstract: A method for location-specific probabilistic prediction of formation of a defect in powder bed fusion additive manufacturing of an article includes determining first statistical distributions of multiple process parameters selected from laser power, scan speed, laser spot size, and powder layer thickness and density, based on the first statistical distributions, for locations across the article, determining second statistical distributions of a threshold temperature (Tthresh) for formation of the defect at each of the locations, determining a cumulative temperature thermal history (T0) at each of the locations across the article, for each of the locations across the article, determining a probability of formation of the defect based upon a probability of Tthresh versus T0, and from the probability of formation of the defect at each of the locations, generating an article integrity map.

Abstract: The present disclosure provides improved additive manufacturing methods and systems. More particularly, the present disclosure provides advantageous additive manufacturing methods and systems for the production of hierarchical design optimized components (e.g., composite or composite-like materials). The present disclosure provides a methodology to produce hierarchical design optimized additively manufactured parts/materials that include an inhomogeneous structure with variable local mechanical properties across the entire volume. Hierarchical inhomogeneous structure/composite materials can be produced through a laser powder bed fusion (LPBF) process. A novel LPBF method can be used to obtain location-specific properties through in-situ controlling of the local cooling rate during the additive manufacturing process.

Abstract: A process for an external optimization framework utilizing a defect model for multi-laser additive manufacturing of a part including determining a scalar metric for the part; employing the scalar metric in the defect model; providing at least one output from the defect model to the external optimization framework; and optimizing the powder bed fusion additive manufacturing process for the part with the external optimization framework.

Abstract: An analysis tool for multi-laser additive manufacturing including a build file module; a preprocessor in operative communication with the build file module; a prime module in operative communication with the preprocessor; and a defect code module in operative communication with the prime module.

Abstract: A process for a laser plume interaction for a predictive defect model for multi-laser additive manufacturing of a part including executing computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber; approximating a laser plume relative to a melt pool on a powder bed disposed on a build plate within the manufacturing chamber; executing a space-time analysis to identify a laser plume interaction; creating a plume interaction zone map; feeding the plume interaction zone map prediction into a multi-laser defect model; and predicting defect location and density to accumulate lack-of-fusion risk as a function of part placement, orientation, and scan strategy.

Abstract: This disclosure is directed to a method for modeling additive manufacturing of a part, including a number of steps. The steps include constructing a model for estimating output of a simulated additive manufacturing process, followed by entering process operating parameters for an additive manufacturing system into the model to produce an output. The output is compared to acceptance criteria to determine whether the output is acceptable or unacceptable. Next regions of operating parameters that support production of the part with acceptable quality characteristics are determined based upon the output. Regions of operating parameters that support production of the part with acceptable quality characteristics are added to a process map for additive manufacturing the part. The steps are repeated for different operating parameters until the process map is complete.

Abstract: A process for uncertainty quantification for a predictive defect model for multi-laser additive manufacturing of a part including executing computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber; assigning a spatter particle size, velocity and direction relative to a melt pool on a powder bed disposed on a build plate within the manufacturing chamber; executing computational fluid dynamics post processing for spatter particle tracking; predicting a spatter particle landing pattern; feeding the spatter particle landing pattern prediction into a defect model; producing a layer thickness map, the layer thickness map configured to demonstrate a location of locally thicker layers on the part; and predicting defect location and density to accumulate lack-of-fusion risk as a function of part placement, orientation, and scan strategy.

Abstract: The present disclosure provides assemblies, systems and methods for a single-step process for selective heat treatment of metals. More particularly, the present disclosure provides assemblies, systems and methods for a single-step process for selective heat treatment of metals using multiple heating sources. A hybrid modeling-test approach can be used in the design process to improve or optimize the process parameters to achieve location specific and improved/optimal microstructure and residual stress to enhance the part performance. It is also noted that performing the selective heat treatment in a single step can reduce the cycle time significantly. Moreover, large thermal gradients can be avoided in the part as different volumes of the part are heated to their desired temperature simultaneously.

Abstract: The present disclosure provides assemblies, systems and methods for a single-step process for selective heat treatment of metals. More particularly, the present disclosure provides assemblies, systems and methods for a single-step process for selective heat treatment of metals using multiple induction heating coils. A hybrid modeling-test approach can be used in the design process to improve or optimize the process parameters to achieve location specific and improved/optimal microstructure and residual stress to enhance the part performance. It is also noted that performing the selective heat treatment in a single step can reduce the cycle time significantly. Moreover, large thermal gradients can be avoided in the part as different volumes of the part are heated to their desired temperature simultaneously.

Abstract: The present disclosure provides assemblies, systems and methods for a single-step process for selective heat treatment of metals. More particularly, the present disclosure provides assemblies, systems and methods for a single-step process for selective heat treatment of metals using a coil-in-furnace configuration. A hybrid modeling-test approach can be used in the design process to improve or optimize the process parameters to achieve location specific and improved/optimal microstructure and residual stress to enhance the part performance. It is also noted that performing the selective heat treatment in a single step can reduce the cycle time significantly. Moreover, large thermal gradients can be avoided in the part as different volumes of the part are heated to their desired temperature simultaneously.

Abstract: A method of evaluating an additive manufacturing process includes receiving a set of additive manufacturing parameters and an additive manufacturing part design at an analysis module, receiving a set of random values at the analysis module, determining a probability distribution of stochastic flaws within a resultant additively manufactured article using at least one multidimensional space physics model, and categorizing the additive manufacturing part design as defect free when the probability distribution is below a predefined threshold. Each value in the set of random values corresponds to a distinct variable in a set of variables. Each variable in the set of variables at least partially defines at least one of an uncontrolled additive manufacturing parameter and an uncontrollable additive manufacturing parameter.

Abstract: A method of evaluating an additive manufacturing process includes receiving a set of additive manufacturing parameters and an additive manufacturing part design at an analysis module, receiving a set of random values at the analysis module, determining a probability distribution of stochastic flaws within a resultant additively manufactured article using at least one multidimensional space physics model, and categorizing the additive manufacturing part design as defect free when the probability distribution is below a predefined threshold. Each value in the set of random values corresponds to a distinct variable in a set of variables. Each variable in the set of variables at least partially defines at least one of an uncontrolled additive manufacturing parameter and an uncontrollable additive manufacturing parameter.