Metals characterization using deep learning
image analysis

Introduction to MIPAR✓

Started in materials science✓

Invented at research university✓

Developed by actual users✓

Spun-out in 2017✓100s of schools and companies✓

In over 40 countries

What is possible with Image Analysis for Metals Characterization?

✓ Phase Analysis✓ Grain Size Analysis✓ Porosity Analysis✓ Inclusions Analysis✓ Layer Thickness Analysis

Copper alloy grain size measurement
following ASTM E-112 standard.

✓ Particle Analysis✓ Defect Analysis✓ Many more...

Segments rounder particles from acicularfeatures in [α+β]-processed Ti-alloy
microstructures.

Segments ferrite from surrounding
martensite in martensitic stainless steel weld
cross-sections.

Analysis Challenges
Challenges:- Image analysis expertise- Operator bias
- Long analysis time
- Low accuracy and reproducibility- Difficult, complicated images- Noisy microstructures- Poor sample prep (scratches)- Ambiguous feature boundaries- Wide variety of contrast conditions- Features of similar grey values- Poor contrast

Deep learning addresses all these challenges, it
improves image analysis, increases accuracy andthroughput, without any image analysis expertise.

MIPAR Image Analysis Software✓Power: Solve problems others can’t, thanks to powerful Recipes✓

Speed: Solve problems faster with intuitive design and batch processing✓

Versatility: Solve problems across many application areas✓

Objectivity: Reduce subjectivity in analysis using the Optimization Engine✓

Expertise: Call on experts to rapidly deliver custom solutions

Grain Analysis - Twin n e d Gra in s in Bra ss
Original Image

Deep Learning Applied

Automated measurement of complex grains despite poor contrast
Replace manual tracing
Deep learning enables highly complex classification

Final Segmentation

Grain Analysis – Be ta gra in s in Tita n iu m

Original Image

Deep Learning Applied

Final Segmentation

Accurately detects beta grains despite very faint contrast
Grain mean size and distribution measurements possible

Grain Analysis – Co p p e r Allo yOriginal✓ Measurements meet ASTM-E112

grain size guidelines

Identified Grains

Automated,unbiasedresults✓

Grain Size Measurements

Phase Analysis – La ve s in In co n e lPhase Analysis – Pa rticle s in Nicke l-b a se Su p e ra llo yOriginalParticle detection

Area fraction can be measured
T.M. Smith, P. Bonacuse, J. Sosa, M. Kulis, L. Evans, A quantifiable and automatedvolume fraction characterization technique for secondary and tertiary γ′
precipitates in Ni-based superalloys, Materials Characterization, Volume 140,
2018, Pages 86-94

Tough to distinguish particles segmented
Fully automated workflow

Phase Analysis – Alp h a a n d Be ta in Tita n iu m

Morphology Classification - Glo b u la r Alp h a
Original Image

Deep Learning Applied

Accurately and automatically measure globular alpha fraction
Replace heavy amounts of manual correction
Globular percentage and size measurements possible

Final Segmentation

Morphology Classification – Co lo n y vs. Ba ske twe a ve
Original Image

Deep Learning Applied

Automated classification of morphologies based on image texture
Replace tedious point-counting and user bias
Deep learning enables highly complex classification

Final Segmentation

Steel Inclusions
Original

Identified and Classified✓

Measurements meet ASTM-A247 graphite
characterization in steel guidelines✓

Tailored recipes to internal standards

Nodularity Measurement
Non-nodular Graphite
Nodular Graphite

Defects Analysis – Ad d itive Ma n u fa ctu rin gOriginal✓

Calibrate recipe to match existing
techniques✓

Facilitates workflow integration

Identified and Classified

PoresCracks

Crack + Pore Measurement

Layer Thickness Analysis – Gra in -Ba n d Id e n tifica tio nOriginal

Grain-Band Identification

Thickness Measurement

Powder particles – Size a n d Sh a p e Me a su re m e n ts

Powder particles – Sa te llite An a lysis
Original

Particle detection

Diameter of particles can be measured
Fully automated satellite detectionPorosity Analysis – Ad d itive Ma n u fa ctu re d Pa rts

Metals Applications
Additive Manufacturing

Titanium ResearchLearn More

Website: mipar.us

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