In-Process Sensing Of Laser Powder Bed Fusion Additive Manufacturing

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In-Process Sensing of Laser Powder BedFusion Additive ManufacturingS. M. Kelly, P.C. Boulware, L. Cronley, G. Firestone, M.Jamshidinia, J. Marchal, T. Stempky, and C. ReichertPresenter: Yu-Ping Yang1A Workshop on Predictive Theoretical andComputational Approaches for Additive ManufacturingKeck Center, Room K-100500 Fifth St. NW Washington, DC

Acknowledgement:In Process Monitoring TeamJohn ZeigertAngela DaviesKyle ZhangWill LandShawn Kelly, PIMahdi Jamshidinia (AM)Jake Marchal (AM)Paul Boulware (Sensors)Connie Reichert (Sensors)Greg Firestone (Sensors)Lance Cronley (Design)Jaydeep KarandikarMasouhmeh AminzadehThomas KurfessJim CraigJim Williams2Mark ColaMatias Roybal

Outline3 Why in-process sensing of Laser Powder Bed Fusion(L-PBF) additive manufacturing is important How to develop in-process sensing technology Application of in-process sensing to monitor L-PBF How in-process sensing improves numerical modelprediction Sensing development status

Conventional ManufacturingTechniquesmeltformfinish Conventional material production steps are tightlymonitored and controlled to ensure quality. AM is Materials Creation directly into a functional part.

Why is In-Process MonitoringNeeded?1-inch L-PBF Cube5 miles of weld 5Each weld is an opportunity for a defectHours/days/weeks of build timePost process inspection can be difficult and costlyIn Process Sensing is necessary to move 3DP to AM

Approach to Process Sensing Without sensing: Rely on process development. Rely on Post-Process Inspection Incremental approach to material creation allows: Sensing of defects when they are created Access to difficult to inspect areas. Opportunities to cancel long builds. Sense first, control second.Monitor: KPP’s (Before, During, and After) Local Material/Process Interactions Global Material/Process Interactions6

Problem Statement andObjective Problem Statement: Laser Powder Bed Fusion (LPBF) systems do not possess the same level of qualitymonitoring that conventional manufacturing systemsemployObjectives: Evaluate and mature in process sensingtechniques on a L-PBF Sensor Test Bed to: Enable quality monitoring Process deviations Geometry, distortion, and bed flatness Metallurgical Pores/Lack of fusion/Cracking Create experimental measurements for validating numericalmodels of L-PBF7

Technical Approach Develop a L-PBF test bed It is difficult to install senses incommercial L-PBF machine Therefore, a L-PBF test bed wasdeveloped to allow for sensor evaluationwithout physical or software constraints Install local sensors Monitor the area near the point ofmaterial fusion Install global sensor Defect occurrence over entire bed Test sensors Produce thermal images Produce optical images8A Commercial L-PBF machine: EOS M280 with 400W laserfor L-PBF at EWI

Develop a L-PBF Test Bed1.2.9Design and fabricate test bedEvaluate the test bed

Design and Fabricate Test BedDesign 10FabricateEvaluateHARDWARE Checked positional axes to be within10um resolution Determined laser focus position,power calibration Completed build platform levelingCONTROLS All motor drives, solenoids, PCs,sensor COM, power, etc., integratedinto control cabinet 1 PC for sensor test control 1 PC for sensor data acquisition anddisplay

Production of Eight 5x10x10mmPrisms11

Equivalent Material EstablishedInconel 625 on EOS Machine12Inconel 625 on Sensor Test Bed

Open Architecture System 13Complete control overtoolpath generation;restricted to simple shapes.Control of laser power,travel speed, position ofbeamTriggering of sensors andtracking of X,Y position ofbeam (to track sensor data)Open access to the beamdelivery path

Local and Global SensorsIntegrate Sensors Into Sensor Test BedDevelop Defect-Generating Build MatrixEvaluate Sensors Across Build MatrixEnhance Sensor Quality Signals14

Defect Detection GoalsMetricGeometric DefectDetectionVolumetric Defects15Threshold Objective25 µm10 µm250 µm100 µmUnit of Measure50% of geometricdeviations of XX size50% of defects of XXsize

Sensors EmployedLocal Sensors 16Global SensorsPhotodetectorSpectrometerHigh Speed VideoTwo Color OpticalPyrometer High ResolutionImaging Laser Line Scan Global ThermalView process at point offusion; collect information atand surrounding the melt pool.FOV is the powder bed. Collectinformation before, during, andafter a layer is scanned.

Global17High Speed VideoVolumetric rtionDefect TypeProcessDeviationProcessLocal ObservationSensor MatrixDefect Generation UnderstandingThermal ImagingHigh Resolution ImagingXXXLaser Line ScannerXXXThermal ImagingPhotogrammetry (UNCC)XXProjection Moiré (UNCC)XXXXXXX

Local Techniques: High SpeedVideoObjective: Identify defect formation, melt pool characteristics; processunderstandingDetails: Bead on Plate; 40mm line; 1000FPS; laser 200W; speed: 200mm/s

Local Sensor: Thermal Imager Sensor installed on opticaltable and aligned with onaxis signalSensor details: 19Model: Stratonics, IRFrame rate: 1000 fpsExposure: 100 usFOV: 4.6 x 1.9 mmResolution: 6.8 um/pixelInvestigated melt poolbehavior over artificialdefective regionsInvestigated melt pool shapeand size with varyingparameters

Local Sensor: Thermal Imager Introduced a rectangularvolume of unfused powderto the build and observedmelt pool variation whenprocessing over this region Melt pool seems to be extremelystable when processing overmelted and re-solidified buildmaterial Melt pool distorts whenprocessing over artificial defectiveregionsDefective20

Local Sensor: Thermal Imager Melt pool width increases with energy density increases aremeasurable3.36J/mm22.78J/mm221

Local Sensor: Optical Imager 22Sensor is installed on opticaltable and aligned with on-axissignalSensor details: Model: IDT Vision, NX7-S2Frame rate: 1000 fpsExposure: 20 usFOV: 11.4 x 6.4 mmResolution: 5.9 um/pixelEarly images showed promisebut required higherillumination levelsHigh luminosity LED spotlights have been configuredand testedCurrently focal plane issuesare plaguing the resultsAnalysis software complete tomeasure melt pool size andshape

Global Sensor: Thermal Imager Camera is installed over thetop side viewing portSensor details: Model: Stratonics, ThermaVizFrame rate: 10 fpsExposure: 10 msFOV: 83.2 x 83.2 mmResolution: 130 um/pixelDirection of laser processprogression23

Global Sensor: Thermal ImagerTP 450 CLayer 1TP 228 CLayer 1024

Global Sensor: Thermal Imager 26Observed a difference in cooling when traversing the laserprogression parallel to gas flow versus normal to gas flow

Global Sensor: Optical Imager Camera is installed over thetop side viewing portSensor details: Model: PointGrey, Flea3 Resolution: 17.7 um/pixel FOV: 70x40 mm Images are taken after eachlayer is processedSoftware algorithms havebeen written to take keymeasurements on the buildlayerLimited analysis has beenperformed to date

Global Sensor: Laser Profiler Sensor is installed on the recoater armSensor details: Model: Keyence LJ-V7060 laser line scannerLine width: 15 mmResolution (width): 20 umResolution (height): 16 umLaser Scanned DataImage Scan

Sensing Helps NumericalModeling1.2.3.30Validate CFD modelValidate thermal modelValidate mechanical model

Sensing Helps Validate FluidFlow PredictionsJamshidinia et al. Journal of manufacturingscience and engineering, Vol. 135, Computational fluiddynamics (CFD) can be usedto predict the fluid flow inthe molten pool. Optical images can be usedto validate the CFDpredictions to improve thefundamental understandingof additive manufacturingprocess.

Sensing Helps ValidateTemperature PredictionThermal images can be usedto validate numerical thermalmodel predictions oftemperature.Numerical model predicted temperature distributionsThermal imagesScanning speed: (a) 100mm/s; (b) 300mm/s; (c) 500mm/s32Jamshidinia et al. Journal of manufacturingscience and engineering, Vol. 135,

Sensing Helps Validate Mechanical Model:Temperature, Stress, and DeformationLaser Scanned DataOut-of-plane deformation (mm)Temperature ( C)Principal Stress (MPa)

Sensing DevelopmentStatus1.2.3.34Local sensorsGlobal sensorsTechnical gaps

Local Sensor Progress to Date 35Currently collecting data at 10% of desire rate (onceevery 10 melt pools)Thermal: High resolution imaging of the melt pool;Currently operating in single-color mode due tosoftware issues.Visual: High speed video taken; balancingillumination and focus issues.Spectrometer: Slow response time of COT sensors;overall intensity dependencies; limited analysis ofline sensitivityPhotodetector: Could prove useful if spectral linescan be related to defects.

Global Sensor Progress to Date 36Collecting data every layer.Thermal: Promising results. Large embeddeddefects can clearly be seen; may be masked whenoverhangs are present.Visual: Machine vision promising; requiresalgorithm developmentLaser Line scanner: Similar to machine vision

Technical gaps 37Producing Known Defects and Evaluate All sensorsagainst these defects

Technical GapsBIG Challenge BIG Data throughput, processing/distillation,go/no-go, storage Global Imaging with 10MP camera: 9.6 GB Local sensing: measurement every beam width 80M data points38

Summary39 There is more to 3D Printing than the process Treat AM like any other manufacturing process. Quality Control and in process sensing will benecessary to move 3DP to AM. Developing a flexible sensor test bed for L-PBF andevaluating candidate sensor techniques for inprocess monitoring. Unique opportunity to inspect layer by layer

QuestionsYu-Ping Yang, Ph.D.Principal EngineerModeling and Simulationyyang@ewi.org614.688.525340

Each weld is an opportunity for a defect . Photodetector: Could prove useful if spectral lines can be related to defects. 35 : Global Sensor Progress to Date . Modeling and Simulation . yyang@ewi.org. 614.688.5253 . 40 . Title: Slide 1 Author: Shawn Kelly Keywords: 54523

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