Model Analysis of Feedstock Behavior in Fused Filament Fabrication: Enabling Rapid Materials Screening

Jake Fallon, Eric Gilmer, Darren Miller, Dr. Michael J. Bortner

Outline

• Introduction of additive manufacturing (AM)

• Motivation

• Flow phenomena discussion

• Flow modeling

• Sensitivity analysis

• Empirical model validation

• Results & conclusions

2

What is additive manufacturing (AM)?

• ASTM 52900-15 Definition of additive manufacturing

• “Process of joining materials to make parts from 3D model

data, usually layer upon layer, as opposed to subtractive

manufacturing and formative manufacturing methodologies.” 1

• 7 Families of additive manufacturing

• vat photo polymerization, powder bed fusion, binder jetting,

material jetting, sheet lamination, material extrusion, direct

energy deposition

31ASTM ISO/ASTM52900-15 Standard Terminology for Additive Manufacturing –General Principles – Terminology, ASTM International, West Conshohocken, PA, 2015,

How does material extrusion based AM work?

1. Polymer feedstock is forced into hot end

2. Polymer is heated to a flow inducing

temperature

3. Forced out of small orifice

4. Selectively placed on build platform to

form one layer

5. Multiple layers a stacked to form a final

3D geometry

4

Heat Sink

Temperature

Sensor

Heater

Element

Nozzle

Current limitations of material extrusion AM

• Limited material selection

• PLA, ABS, Thermoplastic polyurethanes,

Filled Nylon, Polycarbonate

• Time consuming and material intensive

testing process for new materials

• No prediction method for predicting how

well a material will extrude

5

We need a method for efficiently innovating AM materials!

• Hypothesis: If we can model known failure

modes of extrusion based AM, then we can

predict the extrudability of new/novel/other

materials

• Result: A model which can predict a material

ability to extrude without ever forming the

material into a filament

6

STRATI – Local Motors

Worlds 1st 3D printed electric car

Shelby Cobra - ORNL

Concept to car in 6 weeks

Current failure modes in extrusion based AM

7

1. 2. 3.

1. Diametric Tolerance

• Processing issue

2. Annular backflow

• Unsolved

3. Filament buckling

• Solved by N. Venkataraman

Approach for modeling annular backflow

8

• Molten polymer rises above molten-to-solid

transition region

• Solidifies and prevents movement of solid

filament

• Model velocity profile with Cauchy’s

equations of motion

• Area under the curve (net flow magnitude)

determines backflow behavior

Approach for modeling annular backflow

9

Nozzle

Wall

Boundary

Velocity

= 0 mm/s

Filament

Boundary

Normalized Net Flow

Magnitude

No Backflow: < 0.5

Transition: 0.5 – 0.75

Backflow: > 0.75

Approach for modeling annular backflow

Nozzle

Wall

Boundary

Velocity

= 0 mm/s

Filament

Boundary

10

• Dimensionless number to correspond to normalized net magnitude of flow predict backflow

• Flow Identification Number (FIN)

Finding the flow identification number (FIN)

11

FIN=Δ𝑃/𝐿

𝜂∗𝑣∗ 𝜋 𝐷𝐵

2 − 𝐷𝐹2

Where…

• Δ𝑃/𝐿: Pressure gradient inside liquefier

• 𝜂: Viscosity (directly measured)

• 𝑣: Filament feed rate

• 𝐷𝐵2: Diameter of liquefier

• 𝐷𝐹2: Diameter of filament

No Backflow

<153

Transition

153-185

Backflow

>185

FIN Equation

𝐹𝐼𝑁 =Δ𝑃/𝐿

𝜂 ∗ 𝑣∗ 𝜋 𝐷𝐵

2 − 𝐷𝐹2

Power Law Equation𝜂 ሶ𝛾 = 𝑚 ሶ𝛾𝑛−1

Sensitivity Analysis – Power Law

12

Nominal Case

Consistency index (m) 20,000 ± 10,000 Pa.sn

Non-Newtownian Index (n) 0.3 ± 0.1

Feed Rate (v) 5 ± 4 mm/s

Filament Diameter (Df) 1.75 ± 0.1 mm

Sensitivity Analysis – Power Law

13

Results

• Consistency Index (m) and Feed

Rate (v) do not significantly impact

net flow magnitude

• Power Law Index (n) and Filament

Diameter (Df) do significantly impact

the net flow magnitude

Materials for testing the screening process

14

• Acrylonitrile butadiene styrene (ABS)

• Commonly used AM material

• Low density polyethylene (LDPE)

• Has been used in FFF previously1

• Sodium sulfonated polyethylene glycol

(NaSPEG)2

• Material in which backflow was originally seen

1J. Novakova-Marcincinova, L Novak-Marcincin, J Barna, J. Torok, IEEE Int. Conf. Intell. Eng. Syst. (2012) 73–76.2A.M. Pekkanen, C. Zawaski, A. Stevenson, R. Dickerman, A.R. Whittington, C.B. Williams, T.E. Long

Results of testing the screening process

15

Material Feed Rate FIN Value

ABS

5 mm/s

150

LDPE 156

NaSPEG 204

No Backflow

<153

Transition

153-185

Backflow

>185

NaSPEGBackflow predicted

Summary

• Dimensionless number for quick screening analysis of backflow potential in new

materials

• Screening process proven to accurately predict extrudability and failure of various materials

• Filament feed rate had minimal effect on propensity to backflow

• Filament diameter and shear thinning behavior had greatest effect on

propensity to backflow

• Proof of importance of onset of, and degree of, shear thinning on extrudability

16

Acknowledgements

Dr. Michael Bortner

• Eric Gilmer

• Kathleen Chan

• Cailean Pritchard

• David Anderegg

• Darren Miller

• Ben Kolb

• Jacob Rendall

• Kelsey Niehoff

• Sam Oxley

• Samantha Stutz

• Alexandra Marnot

• Jim Owens

Dr. Christopher Williams

• Camden Chatham

• Callie Zawaski

Dr. Timothy Long

• Allison Pekkanen

Dr. Richey Davis

Questions

Questions?