David Vedvick


Solid Freeform 2023 Notes

From August 14-16, 2023 I attended an academic conference on 3D printing research, below are my notes.

Technion Additive Manufacturing Center

  • Current geometric design pardigm ha sbeen with us for over a decade
  • V-rep operations:
    • Filleting and rouding
    • Mixed symbolic/numeric computations over multivariate (and V-rep) splines
  • https://csaws.cs.technion.ac.il/~gershon/irit

In-Situ Process Monitoring in AM

Michael J. Heiden

Purpose, Motivation

  • Failure mode analysis (ie spike in oxygen)
  • Reduce coupon inspection
  • Runs in parallel with the build process

Detection Methods and Processes

  • Optical IR
  • Use the Peregrine machine learning model from Oakridge
  • Use multi-modal data (thermal imaging/thermography, high speed cameras, spectroscopy, acoustic monitoring)

Using the Data to Create Useful 3D Digital Twins

  • Show discrepancies between what is supposed to be printed and the actual

Future Targets

  • Real time monitoring
  • Closed lop control for defect correction (this is what we do!)

Machine Learning Applied to Process Monitoring for Laser Hot Wire Directed Energy Deposition

Carnegie Mellon University

Comparison with Laser Powder Bed Fusion

  • Under acceptable conditions, melt pools are not stable
  • Monitoring of rare unwanted events such as spatter requires significant ML traning (1k-10k hand-labeled images)

Laser Hot Wire Additive Manufacturing

  • Well behaved melt pools under normal conditions
  • Can monitor entire buildds in real time
  • Approach: detect unsteady behavior of the melt pool, don't attempt to learn the appearnce of irregular events


  • Many flaws exist across multiple frames, so a time dependent analysis is beneficial
  • Long Short-Term Memory networks are able to learn time dependencies in video data
  • Use previous frames to predict future frames (about 10-20 frames of past history)
  • Capture a regularity scorePredicition: within 5-10 years we will see fully sensed and automated DED systems

Known Flaw Types

  • Stable Deposition
  • Melt pool oscillation
  • Arcing
  • Wire stubbing
  • Wire dripping

Summary and Conclusions

  • ML model detects unsteady behavior, which is indicative of process instability (unlike LBPF)
  • Prediction is that DED systems will be deployed with fully automatic quality control in next 5-10 years

Area Printing

  • Area printing is stamping on the powderbed
  • Seurat is the company that developed this technology

Rethinking Additive Manufacturing

  • DfAM transcends disciplinary boundaries
  • Traditional manufacturing informs our design sensibilities
  • Evaluate design using consensual design techniques
  • DfAM increases as experience increases
  • Restrictive DfAM: "Can we print it"
  • Opportunistic DfAM: "Should we print it"
  • Unlearning requires designers to identify good DfAM solutions
  • VR can help with DfAM
  • Use VR for simulating a printer in test?
    • Probably not super beneficial for our needs

Co-deisgn of 3D Printing

  • You cannot monitor your way out of trouble: need all the insights possible from in-situ and operando monitoring.
  • In aircraft manufacturing: "Anything that allows you to gain efficiency is worth the manufacturing cost"
  • Diffusion Modeling is not sufficient for AM Heat Exchangers
    • Use heat exchangers used with solar energy?
    • Alternate technology: diffusion bonding
  • Improved heat exchangers reduces defects?

Physical Validation of Job Placement Optimization in Cooperative 3D Printing

  • Traditional vs Cooperative 3D printing
    • Traditional: gantry systems
    • Limited in scalability and flexibility
  • Partitioning Strategy: Chunking
    • First chunk into layers (Z-Chunking)
    • XY-Chunking: chunk adjacent chunks at an angle to facilitate bonding of chunks
  • Use grid search algorithm (A* Path Planning) to plan robot movement
    • If there are two robots that are going to take a path at the same time, apply a conflict avoidance algorithm
  • When a robot moves to a new location, it requires manual calibration

Time Optimal Path Planning for Heterogenous Robots in Swarm Manufacturing

  • Continuous spatial profile with arbitrary robot gemoetry that includes orienatation
  • Dynamic environment with arbitrary number of obstacles
  • Time-optimal solution for multiple agents
  • Projecting Dynamic Obstacles
    • Use "safe intervals" to reduce number of computations
    • Based on safe intervals, split the configurations based on what has more than one safe interval
  • Limitations:
    • Does not make real-time adjustments
    • Truly centralized optimal planning scales exponentially with the number of agents
  • Open questions:
    • How does shape discretizaiton impact solution quality?
    • What is the upper number of agents before it is too computationally expensive?

Layer Wise Prediction of Microstructural Evolution

  • Predict grain size and melt-pool depth using thermal model features
  • As structure grows, cooling rate decreases
  • Prediction using physics-guided machine learning (SVM models)
    • Predict Meltpool Depth
      • Not inuitively explained by process parameters
    • Thermal Feature Correlate to Grain Size
      • Grain size predicted with 90% F-Score
  • Cooling rate also decreases with heat build-up

Post Superficial Temperature Monitoring During Additive Manufacturing

  • CNN goal is to produce predicted temperature of build
  • Best results come from combining a machine learning model, in situ monitoring, and sensors
  • Normal Data + Wall Simulation as inputs gives best ML performance on experimental data
  • In Situ Sensors + ML models as a "Digital Twin" of how the part will be produced

Optimal Control of Wire DED Systems

Framework for Physics Guided Machine Learning

  • Process variations are high in AM
  • PSP: Process-structure-property

PSP Linkages for In-situ Data

  • Predictive Melt-pool Modeling
    • Multi-layer model produced best results
  • Predictive Pore-structure Model
  • Predictive Surface-height Model

AI Driven In-Situ Monitoring

Intrinsic Keyhole Oscillation

Keyhole: a puncture in the powderbed that leads to an unexpected cavity ("keyhole pore") in the powderbed

  • Combine X-Ray imaging and Thermal Imaging
  • Intrinsic Keyhole Oscillation
    • Surface tension and coil pressure
    • Liquid flow on the outside of the meltpool increases keyhole size
    • Lower than 30KHz
  • Perturbative Keyhole Oscillation
    • Greater than 40KHz

Gradient-weighted Class Activation Mapping (Grad CAM)

  • Predict where keyholes will form using an ML model, avoiding further X-Ray synchotron experiments
  • An input to the trained model is a simulation of the build

PRISM: Process Parameter Optimization for Selective Manufacturing

  • Goldilocks zone:
    • Not enough: lack of fusion
    • Too much: keyhole formulation
  • Use machine learning to determine proper process parameters!
  • Goal is to achieve high density, assume density is a surrogate for:
    • CTE (Coefficient of thermal expansion)
    • Elastic modulus
    • Strength
    • Toughness
  • Data will be published and open source
  • ML Driven Design of Experiment
    • Latin hyper cube sample generation
    • Predict Printability using tree algorithm (bounds determined by expert)
    • Predict density using tree algorithms (XGBoost)
  • Select candidates: diversity (determined by L2 distance), quality, and random sleection
  • Used FLAML to automate experimentation
  • Would probably need to re-train per material type

FAIR Knowledge Management System for Additive Manufacturing

  • AMMD.nist.gov, AMBench2022.nist.gov
  • ASTM F42 - working group developing data models to represent AM

Unconventional Data Sources for Market Intelligence

  • Aachen Center for AM
  • Market intelligence uses external data over internal data (patent databases, social media, etc.)
  • Databases used for research: Amadeus, LinkedIn, Scopus

Digital Twins

  • Part qualification is manual and slow
  • Can digital twins help understand the process to manufacturing property map?
  • Direct Ink Write
  • Using robotic automation to generate large amounts of parts and data

Executable for Avant Garde Laser Exposure (EAGLE)

  • Common command interfaces
  • Ability to support any input file
  • Plugin support
  • Process Buckets
  • Limited interference with machine
  • Common Build Data Storage
    • SQL Database on machine or network
  • Print Process Flow:
    • Pre-Process
    • Layer Process:
      • Verify -> Scan -> Characterize -> Recoat
    • Post-Process
  • EAGLE is written in Rust and Javascript using Tauri
    • Designed to support print files > 100GB

Direction not intention determines destination

In-Situ Monitoring Using Neutron Rays

  • Neutrons have high penetration in metals
  • Different contrast from X-Rays
  • Phase evolution
  • In-Situ Laser Metal Deposition
    • Completmentary to lab and synchotron experiments
  • In-Beamline Strain Measurement
  • Neutrons are non-destructive, tri-axial, and sub-surface
  • Additive has a much wider distribution of gamma prime and gamma double prime sizes

X-Ray Compute Tomagrphy

  • Determine powder size because that's always going to be stuck on the surface
  • Could minimum powder size (and laser amplitude?) define minimum defects size?


SCT and MPM Data

  • Lack of fusion and keyhole are two major issues (laser power)
  • Rnadom forest prediction works with decent acccuracy
  • U-net model?
  • Some defects will heal automatically, and don't need to be treated
  • Non-balanced gain control introduces saturation in the photodiodes
  • OEM gives a graphical representation of the data, not a tabular or formatted version
  • Photodiodes operate at >11MHz
  • Sensor captures images at 400um resolution

Intelligent Process Control

  • Using FPGA to provide faster response times
  • Goal is to catch a defect within the transient state (10-20us), they've (potentially) achieved nanosecond response times

High precision measurement of Melt Pools

  • FPS of 40,000
  • Resolution: 896 x 176 pixels

End-to-End AI Models for Error Detection

  • Current issue - solutions are not general
  • Matta (Cambridge company), Grey-1 AI Copilot for AM
  • Multi-head residual attention convolutional neural network:
    • Develops its own masks to determine where to pay attention

Note posted on Saturday, September 23, 2023 6:19 PM CDT - link