UNCERTAINTY QUANTIFICATION OR PREDICTIVE DEFECT MODEL FOR MULTI-LASER POWDER BED FUSION ADDITIVE MANUFACTURING

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.

BACKGROUND

The present disclosure relates generally to additive manufacturing, and more specifically to a process for predicting flaw formation by taking into account the uncertainty that can be described by local variation in powder bed layer thickness in single and multi-laser additive manufacturing operations.

Additive manufacturing is a process that is utilized to create components by applying sequential material layers, with each layer being applied to the previous material layer. As a result of the iterative, trial and error, construction process, multiple different parameters affect whether an end product created using the additive manufacturing process includes flaws or is within acceptable tolerances of a given part. Typically, components created using an additive manufacturing process are designed iteratively, by adjusting one or more parameters each iteration and examining the results to determine if the results have the required quality.

Multi-laser additive manufacturing (AM) technology is a promising process to increase allowable part size and rate of production. However, multiple lasers in additive systems could add further complications and challenges to material quality. There is no known tool to predict defect formation and dependency to process parameters for multi-laser applications. It is known how to predict defect type, density and location at the part level under a single laser operation. An example can be the teaching in U.S. Pat. No. 10,252,512 which is incorporated by reference herein.

What is not well known is prediction of the effect of spatter, which can deposit large (>60 μm) particles on the powder bed. These spatter particles locally increase the thickness of the powder bed and may induce lack of fusion defects due to partial melting when irradiated by the laser. Multi-laser additive manufacturing, owing to multiple lasers producing spatter at a given time as well as large build plates that are more likely have spatter land on them, is particularly susceptible to spatter. As the number of lasers acting simultaneously increases, the likelihood of multi-laser interaction goes up.

What is needed is a process for accounting for uncertainty that can be described by local variation in powder bed layer thickness influencing types of defects in components produced by multi-laser powder bed fusion additive manufacturing (PBFAM).

SUMMARY

In accordance with the present disclosure, there is provided a system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for predicting defects in powder bed fusion additive manufacturing process for a part, the set of instructions comprising: an instruction to execute computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber; an instruction to assign 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; an instruction to execute computational fluid dynamics post processing for spatter particle tracking; an instruction to predict a spatter particle landing pattern; an instruction to feed the spatter particle landing pattern prediction into a defect model; an instruction to produce a layer thickness map, the layer thickness map configured to demonstrate a location of locally thicker layers on the part; and an instruction to predict defect location and density to accumulate lack-of-fusion risk as a function of part placement, orientation, and scan strategy.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the computational fluid dynamics modeling of the gas flow predicts a flow field inside the chamber.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the spatter particle includes a vector having velocity and direction influenced by the gas flow and laser/melt pool/powder bed dynamics.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the tracking of the spatter particle includes tracking the spatter particle within the chamber as the spatter particle travels into an un-melted powder of the particle bed.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the gas flow influences the spatter particle and a plume formed within the chamber, wherein the gas flow entrains the spatter particle and influences a trajectory of the spatter particle.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include an accumulation of spatter particles are formed into a representative spatter particle landing pattern.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the system for additive manufacturing further comprising an instruction to integrate spatter risk by controlling at least one laser to move the melt pool/spatter pattern to a location that reduces formation of defects.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the system for additive manufacturing further comprising an instruction to include a representation of spatter accumulation by local thickness variation in the defect model.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the system for additive manufacturing further comprising an instruction to provide local variation zones to the defect model through boundary polygons for each layer.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the system for additive manufacturing further comprising an instruction to include a nominal additive manufacturing build parameter as an input to the defect model.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include a local increase of layer thickness is responsive to a lack of fusion in the powder bed.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the local increase of layer thickness is responsive to at least one of a spatter particle landing on the powder bed and a damaged recoater blade.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the spatter particle landing pattern is configured representative of various scan angle directions relative to the gas flow, wherein the scan angle is selected from the group consisting of 0 degrees, −15 degrees, −30 degrees, −45degrees, and −60 degrees.

In accordance with the present disclosure, there is provided a process for uncertainty quantification for a predictive defect model for multi-laser additive manufacturing of a part comprising 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.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the process further comprising integrating a spatter risk by controlling at least one laser to move the melt pool/spatter pattern to a location that reduces formation of defects.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the process further comprising including a representation of spatter accumulation by local thickness variation in the defect model.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the process further comprising providing local variation zones into the defect model through boundary polygons for each layer.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the process further comprising including a nominal additive manufacturing build parameter as an input to the defect model.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include the gas flow influences the spatter particle and a plume formed within the chamber, wherein the gas flow entrains the spatter particle and influence a trajectory of the spatter particle.

A further embodiment of any of the foregoing embodiments may additionally and/or alternatively include a local increase of layer thickness is responsive to a lack of fusion in the powder bed; wherein the local increase of layer thickness is responsive to at least one of a spatter particle landing on the powder bed and a damaged recoater blade.

Other details of the process are set forth in the following detailed description and the accompanying drawings wherein like reference numerals depict like elements.

DETAILED DESCRIPTION

Referring now toFIG.1, schematically illustrates an additive manufacturing machine10, such as a laser powder bed fusion additive manufacturing (PBFAM) machine. In alternate examples, the powder bed fusion machine can be an electron beam powder bed fusion machine. The exemplary additive manufacturing machine10includes a manufacturing chamber12with a platform14upon which a part16(alternatively referred to as a work piece) is additively manufactured. A controller18is connected to the chamber12and controls the additive manufacturing process according to any known additive manufacturing control system.

Included within the controller18is a processor20that receives and interprets input operations to define a sequence of the additive manufacturing. As utilized herein “operations” refers to instructions specifying operational conditions for one or more step in an additive manufacturing process. The controller18can, in some examples, include user interface devices such as a keyboard and view screen. In alternative examples, the controller18can include a wireless or wired communication apparatus for communicating with a remote user input device such as a PC.

Also included in the controller18is a memory22. In some examples, the controller18receives a desired additive manufacturing operation, or sequence of operations, and evaluates the entered operation(s) to determine if the resultant part16will be free of flaws. For the purposes of the instant disclosure, free of flaws, or flaw free, refers to a part16or workpiece with no flaws causing the part or workpiece to fall outside of predefined flaw tolerance. By way of example, the predefined tolerances can include an amount of unmelt, a surface roughness, or any other measurable property of the part16. By way of example, factors impacting the output parameters can include material properties, environmental conditions, laser power, laser speed, or any other factors. While described and illustrated herein as a component of a laser powder bed fusion additive manufacturing machine, the software configuration and operations can, in some examples, be embodied as a distinct software program independent of the additive manufacturing machine, or included within any other type of additive manufacturing machine.

A build strategy is parsed and/or specifically prescribed scan vectors are used to create stripe and hatch definitions in each layer of the build. The additive build is simulated layer-by-layer. The output is a map in build parameter space (e.g. laser power, laser speed, layer thickness, etc.). The map is partitioned into different regions reflecting whether flaws are present: lack of fusion, keyholing, the flaw-free “good” zone, etc. A process map is optionally location-specific and dependent upon geometry. If the entirety of a part is in the “good” zone of the process map, it is predicted to be flaw-free.

By using the defined process map, a technician can generate a part16, or design a sequence of operations to generate a part16, without requiring substantial empirical prototyping to be performed. This, in turn, allows the part to be designed faster, and with less expense, due to the substantially reduced number of physical iterations performed.

Referring also toFIG.2, the part16is shown as being created by a single laser and multiple lasers. The part16shown on the left side ofFIG.2is laid down by use of a single laser24. The whole part16is assigned to the single laser24. The part16shown to the right side ofFIG.2is assigned to multiple lasers24. The multi-laser fusion is configured to increase the rate at which the part16can be built. The single laser fusion can have a different set of heat flux, interlayer dwell time, underlying temperature than the multi-laser fusion configuration.

With multi-laser fusion processes the part16can be divided into multiple regions26, such as laser1region, laser2region and laser3region, as shown. Each region26can be processed by the different lasers24. So, each region may have a different set of heat flux, underlying temperature, and the like.

InFIG.2, the multi-laser arrangement can include laser interface28along the common boundaries of the regions26. It is possible to create a laser interaction zone30near these interfaces28. The lasers24can create conditions that cause interaction between the adjoining lasers24. (Spatter/plume) laser interaction zone30is not constrained to the region26boundaries.

Referring also toFIG.3, showing spatter incorporation32. Spatter generation is due to laser activity. A single laser can also cause spatter. The laser interaction zone30is caused by the two lasers operating simultaneous and contemporaneously adjacent along the laser interface28. Each laser24can create spatter that interacts with the other laser24. The laser spatter interaction can result in increased occurrence of lack of fusion defects34, and a layer thickness disparity36, as seen inFIG.3. The laser interaction zone30can include conditions that negatively impact one or more of the contemporaneous lasers24that results in deviation from normal laser application, intensity, location and the like. Spatter or other unwanted particle contamination can influence the quality of the build.

A particle or powder38can be shielded by spatter and fail to melt. The un-melted powder38can cause layer disparity36. Also partially melted spatter particles40can stand proud of the weld track surface41and create the layer disparity36, shown as a local increase in layer thickness.

Referring also toFIG.4, a multi-step process to predict effect of spatter on LPBF defects is shown. The process50can include a step52which includes computational fluid dynamics (CFD) modeling of a gas flow42(argon and the like) in the additive manufacturing machine10manufacturing chamber12.FIG.5shows an exemplary additive manufacturing machine10manufacturing chamber12. The gas flow42shown as flow arrows44. The computational fluid dynamics (CFD) modeling of the gas flow42in the manufacturing chamber12can result in a prediction of the flow field inside the chamber12as depicted. The prediction of the gas flow42direction as shown by arrows44in the chamber12provides a basis for the estimation of spatter flight46as described in more detail below.

The next step54includes assigning spatter particle46size S, velocity V and direction D relative to a melt pool48, shown inFIG.6. The melt pool48is shown on solidified material56. The particle46can include a vector58having velocity V and direction D as influenced by the gas flow42and laser/melt pool/powder bed dynamics. The particle46can be seen as ejected from the powder bed and being carried in the gas flow42as spatter particle60.

The next step62includes computational fluid dynamics (CFD) post processing for particle tracking, as also shown inFIG.7,FIG.8, andFIG.9. The post-processing for particle tracking can help to track the spatter particle(s)46as it travels into the un-melted powder particle bed64. The spatter particle46can have various diameter sizes S. A plume66is shown formed proximate the melt pool48a result of the laser activity. The plume66can include vaporized composition of laser interaction byproducts. The spatter particle46and plume66are shown being influenced by the gas flow42, as determined in step52. The spatter particle46will have an initial velocity V, as determined in step54. The gas flow42can entrain the spatter particle46and influence the trajectory68.

FIG.8shows a plan view of spatter particles60flowing over a build plate70. The spatter particles60have a vector direction58. The stripe direction72is indicated. The spatter particles of varying diameter S, are assigned a velocity V and direction68.

FIG.9shows a spatter particle60landing site prediction diagram74. The diagram74illustrates the pattern of landing sites for spatter particle60. The stripe direction72and vectors58are shown relative to a spatter particle60landing site76. The diagram74is relative to a −30 degree scan angle relative to gas flow42. The Fluent or CFX particle tracking can be based on computational fluid dynamics gas flow inside the chamber12. The spatter prediction can be fed into a defect model and used to accumulate lack-of-fusion risk as a function of part placement, orientation, and scan strategy. The laser24heats the metal so quickly that some of the material is boiled. This boiled metal is the plume and generates a driving pressure to eject spatter60. The initial conditions of the spatter60travel is dictated by the conditions of the melting and boiling. The trajectory of the spatter depends on these initial conditions and the gas flow42.

The process50can include the next step78, which entails the accumulation of spatter particles60in a representative pattern80. Representative patterns80are shown taken from various scan angles inFIG.10. There can be various scan angle directions relative to the gas flow42, such as 0 degrees, −15 degrees, −30 degrees, −45degrees, and −60 degrees. The representative patterns80around the melt pool48can be “lobes” of spatter accumulation location and size predicted for the varied scan directions.

The process50can include step82integration of spatter risk by moving the melt pool/spatter pattern. FIG.11shows the additive manufacturing build plate70with predicted spatter particle60landing sites76on the part16. The spatter particle60can have a diameter w=w(x, y). Spatter risk can be used to determine the optimal locations for parts16that reduces the risk of defect formation.FIG.11would be generated based on an input build layout, then converted intoFIG.12to assess the risk of spatter60landing to create defects (by using the defect model with locally varied powder thickness), resulting inFIG.13.FIG.13could be used as input to modify the locations of parts or critical regions within a part relative to each other to mitigate any spatter risk. In this embodiment, this would be an iterative process to optimize the resultant build layout.

Step84can include the representation of spatter accumulation by local thickness variation in a defect model. TheFIG.12shows a layer thickness map86. The layer thickness map86demonstrates the location of locally thicker layers88with the thickness t=t(x, y). The remainder of the locations on the part can have a nominal layer thickness90.

FIG.13shows the predicted defect location and density. Step84includes a prediction of defect location and defect density d=d(x, y). The predicted lack-of-fusion defect regions92are shown. The local layer thickness94is an input. Another input can include a nominal additive manufacturing build parameter96. Local increase of layer thickness94can result in a lack of fusion in the powder bed. The layer thickness94can locally increase for different reasons, such as, landing spatters and damaged recoater blade. The focus of the disclosure is to evaluate the effect of layer thickness variation on defect formation. Two sources of thickness variation are landing spatters and recoater error. Local variation zones are inputs to the defect model through boundary polygons for each layer. Landing spatters is one way to get local thickness variation, though the framework can also accept other boundary polygons for other layer thickness variation if available.

An alternative to computational fluid dynamics can include a What-if spatter location and size study as shown inFIG.4at step98. In this case the user evaluates the risk of defect formation by assuming different thickness variation scenarios.

A technical advantage of the disclosed process can include the prediction of local variation of layer thickness, which can detect the lack of fusion or keyholing.

Another technical advantage of the disclosed process can include prediction of the lack of fusion from spatter from the same layer or prior layers in the build landing on the powder bed.

Another technical advantage of the process can include prediction of the lack of fusion or keyholing from recoater error.

Another technical advantage of the process can include applications to both single-laser and multi-laser powder bed fusion additive manufacturing.

Another technical advantage of the process can include providing a higher quality single-laser and multi-laser powder bed fusion additive manufacturing.

Another technical advantage of the process can include optimized laser path planning to maximize laser on-time while minimizing laser interaction and therefore defect production. This results in faster powder bed fusion additive manufacturing processing.

Another technical advantage of the process can include helping engineers and designers understand and develop multi-laser powder bed fusion additive manufacturing processes to increase rate of production and build large size parts.

Another technical advantage of the process can include minimizing the costly and time consuming trial and error practices which are currently used for qualifying additive manufacturing parts.

Another technical advantage of the process can include information obtained from this predictive model can be utilized to additively manufacture high quality parts which in turn minimizes post-build operations in the production process chain.

Anther technical advantage of the process can include a means to optimize the placement and/or orientation of parts within the build volume to minimize defects in multi-laser systems.

There has been provided a process. While the process has been described in the context of specific embodiments thereof, other unforeseen alternatives, modifications, and variations may become apparent to those skilled in the art having read the foregoing description. Accordingly, it is intended to embrace those alternatives, modifications, and variations which fall within the broad scope of the appended claims.