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Use of Modeling and Data to Design 

and Control AM Processes

David Furrer

Pratt & Whitney

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NIST Workshop

Empowering Small and Medium Size Enterprises Through Effective Additive Manufacturing Data Management

June 6 – 8, 2023

Gaithersburg, MD
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Materials & Product Engineering

• Mechanical Properties → fn (chemistry and structure)

• Structure → fn (chemistry and processing)

• Processing → fn (component geometry)

Design

Mtls Mfg

Materials, Manufacturing Methods 

and Component Design are 

Strongly Coupled

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Industry 4.0

Industry 4.0 is a true technology revolution and not a buzz-word term

Mechanization and 

Powered Equipment

Mass Production and 

Assembly Line 

Methods

Automation, 

Computerization and 

Robotics

Smart Factories, 

Simulation, Digital Data, 

Adaptive Controls, AI/ML 

Methods

Framework for digital engineering, 

manufacturing, big-data, modeling 

and simulation, communication and 

component optimization .... 

Including qualification and certification

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Model-Based Engineering 

• Current industry-wide effort focused on solid models with 

further incorporation of component-specific requirements

• Model-based material and process modeling emerging as a 

means to link materials and process information with 

component geometric design optimization

© 2023 Raytheon Technologies Corporation, Pratt & Whitney division

D. U. Furrer, D. M. Dimiduk, J. D. Cotton, C. H. Ward, “Making the Case for a 

Model-Based Definition of Engineering Materials”, Integrated Materials and 

Manufacturing Innovation (2017) 6:249–263, DOI 10.1007/s40192-017-0102-7 

(http://link.springer.com/article/10.1007/s40192-017-0102-7).

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Certification and Qualification

• What does “Good” look like?

• What is the pragmatic “Engineering Design Intent”?

• Can we define an AM part sufficiently to establish an 

efficient, robust Testing Plan?

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Integrated Materials & Process Modeling

Use of models to link design, producibility & component performance

Parametric model includes local 

structure and properties

Model-based 

Mt’l definition

Model-based 

Mfg process 

definition

Model-based 

component 

definition

Component Design Component Manufacture

Utilization of Modeling to Predict Component Capabilities 

and Proactively Mitigate Producibility Risks Path-dependent properties

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Predicted Material and 

Component Properties

Materials Modeling

Design for Variation

Identification and Assessment of 

Material and Process Variability

Development and Application 

of Physics-Based Models

Probabilistic Property & Performance Predictions

Material 

Definition

Mfg

Process

Definition

Component Modeling &

Prediction

Lifing

Analysis

Life-Cycle

Cost

Analysis

Mechanical 

Design 

Holistic Design

Optimization

Material and manufacturing process modeling enables design for variation

© 2023 Raytheon Technologies Corporation, Pratt & Whitney division

D. Furrer and J. Schirra, “The 

Development of the ICME SupplyChain: Route to ICME 

Implementation and Sustainment”, 

JOM, volume 63, pages 42–48 (2011). 

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Model-Informed Process Controls and Product Testing

• Modeling methods are guiding process control requirements

• Prediction of component location-specific attributes provide 

insight relative to test locations that are most sensitive to 

processing

– Smart testing to minimize tests 

and maximize value

Engineered process controls and test location selection provides for efficient processes

© 2023 Raytheon Technologies Corporation, Pratt & Whitney division This page does not contain any export regulated technical data.

Test to confirm component capabilities 

versus model prediction

Continuous learning about material and 

process with Bayesian updating approach
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AM Certification and Qualification 

• Process defects

• Microstructure control

• Chemistry control

• Resultant property scatter

• Part-to-part/Batch-to-batch/ 

Machine-to-machine 

variability

• Powder handling and re-use

• Geometry control

• Surface finish

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The Devil is in the Details

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Integrated physics-based simulation of AM processes to predict part level distortion, 

defects, microstructure and establish correlation to performance (fatigue)

Quantitative Process Relationships 

(fast acting modeling) Predicted 

Properties

In-situ sensing, 

monitoring and machine 

learning

Fast convection inside the pool 

caused by Marangoni effect is 

taken into account

Heat conduction outside the pool

l

v

xmelt

Zmelt

Melt pool dynamics Bulk thermal history & 

Defects map

3D geometry representation

Fatigue life prediction

Process optimization

Laser Powder Bed Fusion Modeling Framework

Validated High Fidelity Physics 

Based Modeling 

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Model Integration with AM Process Design 

Materials properties are manufacturing process path dependent

Process definition development

• Physics-based reasoning

• Materials and process design space redefined

• Defining process window based on build parameters, 

part geometry and process monitoring

Product data

• Specimen testing

• Component testing

• Process 

equivalent test 

specimens (PETS)

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Model capabilities and features

Calculation of process map. Visualization of defect free/rich areas in P(laser 

power) – V (scanning rate) cross-section of multi-parameter space

Calculation of 2D and 3D defect maps from first-principles with minimal and 

universal calibration 

Calculation of 3D defect map for simple geometry takes ~ 7 s, for complicated 

geometry takes ~ 100 seconds on 4-core desktop

Analytical model-based approach are computationally efficient

6 s 75 s

Physics-based fast acting tool for defects prediction

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AM IN718 component microstructure

AM Material Microstructure Analysis and Control 

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Machine Learning Providing New Understanding

CLASSIFICATION

ML Tools and Methods can be applied directly to manufacturing data as well as component properties.

Data: 

Microstructure dataset can be collected with variation in 

manufacturing pedigree 

Machine Learning models can be used to provide principal 

component analysis (PCA)

Predictive models can also be developed to guide testing and 

process control understanding

Model Results:

Source: P&W 

Immediate applications for:

• Visual similarity assessment / lookup

• Outlier detection

• Quality control

• Process development

Models are fast -- analyze 100’s of images / second

Microstructure data can be used to predict properties and classify materials

© 2023 Raytheon Technologies Corporation, Pratt & Whitney division This page does not contain any export regulated technical data.
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Automated Data Capture and Analytics 

• Industry 3.0 provided manufacturing automation and 

computerization

• Industry 4.0 provides simulation, automated capture of 

sensor data which enables real-time automated process 

monitoring and controls

– Linkage of process data capture, data analysis and modeling

Industrial processes generate large amounts to data that produce digital thread elements

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Digital Data Management

Zero Cost for Data Capture • Zero Data Loss • Data Availability for Analytics

Industry 4.0 requires a robust digital data infrastructure

© 2023 Raytheon Technologies Corporation, Pratt & Whitney division

• Material and process pedigree capture

• Performance correlation to 

processing

• Model-based data capture and 

visualization activities

• Models used as Repository of 

Corporate Knowledge

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Automated Process Control – Monitoring and Trending

• Integrated sensors and component testing provides means for 

adaptive process controls

• Data capture and analysis designed to trigger proactive intervention 

of issues

Individual and Trend Alerts

Automated 

capture and 

analysis of 

supplier 

generated 

data

Industry 4.0 requires a robust digital data infrastructure

© 2023 Raytheon Technologies Corporation, Pratt & Whitney division

Quality Control and Monitoring Data Capture and Creation of Knowledge

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Opportunities for the Community

• Standards for Digital Certificate of Conformance Data

– ASM Materials Data Management and Analytics Committee

– Workshop on Industry Standard for Digital Certificates of Conformance

– Potential Hierarchical Requirements Structure

• Overarching Framework defining Digital Data Requirements and Linkage to Subordinate Specs (NEW)

• Material, Process and Testing-Specific Specs Define Data Types and Associated Meta-Data (Enhancements 

of Current Specs)

• Model-Based Material and Process Definitions

– Development, Validation and Deployment of Physics-Based Models

• Microstructure, Properties, Defects, Residual Stresses, Distortion

– NASA STRI being Launched for Model-Guided Rapid Qualification of AM Components

• Tony Rollet (CMU) and Somnath Ghosh (JHU)

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Conclusions and Take-Away

• Integration of modeling, sensors and data analytics are providing significant 

benefits in the era of Industry-4.0 and AM

• Models can guide process window development and control requirements

• Smart testing can provide faster and more informed product validation and 

certification

• Data management through data analytics and modeling tools provides for a 

more complete means of knowledge capture

© 2023 Raytheon Technologies Corporation, Pratt & Whitney division This page does not contain any export regulated technical data.