Introducing 

Applied Optimization (AO)

Metals 

Challenge

Digital Thread 

data analytics 

to discover

flaws and 

trends

2022

Phase II Award 

$50,000

2017

2

nd Prize 

Part-Scale Process 

Modeling

$40,000

AMP2

2

nd Prize 

 Solidification Microstructure 

 Modeling

AMP 2018 2

3

rd Place 

Melt Pool Modeling

AMP 2020 2

3

rd Prize 

Scan Path Generation

2021

$11,600

AMP2

ALSAM

Our Awards in AM Challenges for

Modeling, Scan Path Generation,

and Digital Thread Analytics

Challenges: (1) Limited interoperability for the data to report results 

of process simulation, and to demonstrate maturity for the models, 

(2) Scan path data is not available on most commercial systems due 

to IP requirements, and (3) High-temperature material properties

Developer of Additive Manufacturing Parameter Predictor (AMP2) software and Open Sensor Interface (OSI)

Copyright 2023, Applied Optimization, Inc. 
Applied Optimization, Inc., SBIR Data Rights, 2023 2

250x Reduction in Defects using 

Track-by-Track Feature-Specific Parameters (FSP)

1

Detrimental 

to Fatigue Life

Standard 

Default 

Parameters:

6881 Defects

Feature

Specific

Parameters:

27 Defects

1

Porosity 

Reduced 

250x

2 2

3 3

1. Near-surface porosity

2. Core porosity

3. Max pore size

Reduced Max 

Defect Size 

50%

Defects Further

Away From

Skin

Porosity: 0.1% Porosity: 0.0002%

Parameter Prediction

by AMP2software

Copyright 2023, Applied Optimization, Inc. 
3

With FFC

0.089% Porosity

Standard Default Parameters

0.4724% Porosity

3

1. Near-surface porosity

2. Core porosity

3. Max pore size

4. Critical regions

2

4

1

Detrimental to 

Fatigue Life

1

Nearly 

eliminated

2

Significantly 

improved

3

Improved

4

Significantly 

improved

Porosity 

Reduced by: 

81%

Feed-Forward Control (FFC) of Laser Powder Bed Fusion

Layer-by-Layer Process Control (Its methods drew upon the understanding gained using process simulation)

V-22 Link

Copyright 2023, Applied Optimization, Inc. 
4

This data was collected in April 2023 for ~400 layers. Such data can be collected anywhere on the build plane. 

We had a low SNR (~1 to 3), we are modifying the optics design to significantly increase the SNR

MWIR Spectrometer Data (SM32Pro)

1.5 – 5 mm waveband

FOV: 1x4 mm2, SNR ~1-3

Melt pool Data (Our 1st Experiment)

MWIR Thermal Imager Data (FLIR)

500x500 pixels, 3-5 micron waveband

70 mm resolution, Pixel resolution: 39 mm

Adjacent to Melt pool

Data is registered with

Scan Path Track Vectors

Boresight Location

Track-by-Track High-Resolution In-Situ Sensing Data for the Entire Build 

to Validate FSP predicted by Process Simulations

Parameter Prediction

by AMP2software

Copyright 2023, Applied Optimization, Inc. 
5

US Patent Pending: 

17/202,945

XY Galvo 

Scan Mirror

Sensor

Viewport

Thermal 

Imager

Spectrometer

3

rd Party

Sensors

Fine Resolution 

‘Anywhere’

Field of View 

20 x 20 mm2

Open Sensor Interface (OSI)

Build 

Plane

LPBR Machine Viewing Window

Window Mount

Track-by-Track In-Situ Sensing to: 

(1) Validate FSP, and (2) Control the LPBF Process

Open Sensor 

Interface (OSI)

Challenges: (1) Data management, interoperability 

and reusability for the cross-correlation data from 

in-situ sensing and physics-based models, (2) 

To navigate IP issues for commercial systems 

Copyright 2023, Applied Optimization, Inc. 
6

Difficulties to Transition Products

• We do not need it

• AM part quality is good enough

• Need improvement for critical parts

• Who is going to pay for it

• Need to prove mechanical properties

• Demonstration of maturity metrics for the models

• Repeatability and reliability

• Need material and scan path data

• People challenge at customer facilities

• Consistent availability of a trained person

• CMMC Level 2 compliance

• High cost + Lack of people 

Copyright 2023, Applied Optimization, Inc. 
7

• Limited interoperability and reusability of data to report 

• Process simulation results

• Part-specific predictions

• Feature specific parameters

• Cross-correlation with in-situ sensing

• Schema for a Technology Data Package

• Evidence of higher TRL 

• Fulfilment of IP requirements for proprietary systems, e.g., 

• Unavailability of scan path 

• Neutral interface for data exchange

• Reduce work-in-progress costs

• High-temperature material properties data

• We can generate properties using thermodynamic modeling

• Experimental data is very limited, particularly for liquid phase

• Insufficient metadata in the historical project records to

• Validate and mature models

Data Needs and Challenges

For enhancing the value proposition for Additive Manufacturing

Copyright 2023, Applied Optimization, Inc.