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Machine Learning in Additive Manufacturing: A Review

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Abstract

In this review article, the latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression, classification, and clustering. The performance of various ML algorithms in these types of AM tasks are compared and evaluated. Finally, several future research directions are suggested.

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Abbreviations

Abbreviation :

Meaning

3D:

Three dimensional

AE:

Acoustic emission

AI:

Artificial intelligence

AM:

Additive manufacturing

BJ:

Binder jetting

BoW:

Bag of words

BP:

Backpropagation

CNN:

Convolutional neural network

CT:

Computed tomography

CV:

Cross-validation

DA:

Discriminant analysis

DBN:

Deep belief network

DED:

Direct energy deposition

DT:

Decision tree

FFF:

Fused filament fabrication

FN:

False negative

FP:

False positive

GP:

Gaussian process

KNN:

k-Nearest neighbors

LOOCV:

Leave-one-out cross-validation

L-PBF:

Laser powder bed fusion

ME:

Material extrusion

MJ:

Material jetting

ML:

Machine learning

NN:

Neural network

PBF:

Powder bed fusion

PCA:

Principal component analysis

PSP:

Process-structure–property

RF:

Random forest

RMSE:

Root mean square error

RT:

Regression tree

SL:

Sheet lamination

SOM:

Self-organizing map

SVM:

Support vector machine

TN:

True negative

TP:

True positive

UQ:

Uncertainty quantification

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Acknowledgement

The work is conducted under CCDC Army Research Laboratory Cooperative Research and Development Agreement 19-013-001. This work is partially supported by “Human Resources Program in Energy Technology (No. 20194030202450)” and “Power Generation & Electricity Delivery Grant (No. 20193310100030)” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), Republic of Korea.

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Authors and Affiliations

  1. Department of Mechanical and Energy Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA

    Lingbin Meng & Jing Zhang

  2. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA

    Brandon McWilliams

  3. Praxair Surface Technologies, Indianapolis, IN, 46222, USA

    William Jarosinski

  4. Department of Materials Science and Engineering, Changwon National University, Changwon, 51140, Republic of Korea

    Hye-Yeong Park, Yeon-Gil Jung & Jehyun Lee

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  1. Lingbin Meng
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Correspondence to Jing Zhang.

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Meng, L., McWilliams, B., Jarosinski, W. et al. Machine Learning in Additive Manufacturing: A Review. JOM 72, 2363–2377 (2020). https://doi.org/10.1007/s11837-020-04155-y

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