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Computational AI models in VAT photopolymerization: a review, current trends, open issues, and future opportunities

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Abstract

Artificial intelligence has played a potential role in present technological advancements. In terms of additive manufacturing or 3D printing techniques, computational AI models and algorithms such as artificial neural network, genetic algorithms, evolutionary algorithms, conventional machine learning techniques like decision tree, Naïve Bayes, K nearest neighbours, support vector machine, and ensemble methods including random forest, etc., has shown incredible results in the past few years. The applications of artificial intelligence in manufacturing are rapidly influencing most of the factors such as process optimization, material property prediction, determining the probability of product failure, real-time monitoring of processes, secure remote customer interactions, feature automation, material tuning, design feature recommendation, precise analysis, quality control/enhancement, or dynamic system modelling. Recent research in the field of VAT photopolymerization indicates that the creation of complex, versatile material systems with adaptable mechanical, chemical, and optical properties via the high-resolution processes includes a variety of 3D printing technologies, like stereolithography, digital illumination processing, and continuous liquid interface production. It has a compelling future in the last industrial revolution, Industry 4.0. This review compiles the evolution, current trends, open issues, and future computational AI models in 3D-printing VAT photopolymerization. Possibilities, prospects, and projects are well discussed to understand the significance of this technology.

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Abbreviations

AI:

Artificial intelligence

ACO:

Ant colony optimization

AM:

Additive manufacturing

ANN:

Artificial neural network

API:

Application programme interface

BN:

Bayesian network

BP:

Backpropagation

CAD:

Computer-aided design

CART:

Classification and regression trees

CDLP/CLIP:

Continuous digital light processing/continuous liquid light processing

CFD:

Computational fluid dynamics

CLIP:

Continuous liquid interface production

CNN:

Convolutional neural network

CS:

Cyber security

DAG:

Directed acyclic graph

DL:

Deep learning

DLP:

Digital light processing

DNN:

Deep neural network

DT:

Decision tree

EA:

Evolutionary algorithm

EM:

Expectation maximization

FDM:

Fused deposition modelling

FEA:

Finite element analysis

FRE:

Freeform reversible embedding

GA:

Genetic algorithm

GelMA:

Gelatin methacrylate

GP:

Gaussian processes

HML:

Hierarchical machine learning

IoT:

Internet of things

KNN:

K nearest neighbours

LR:

Logistic regression

MAE:

Mean absolute error

ML:

Machine learning

MLP:

Multilayer perceptron

MSE:

Mean square error

NB:

Naïve bayes

NLP:

Natural language processing

NN:

Neural network

OLS:

Ordinary least squares

PLA:

Polylactic acid

PSO:

Particle swarm optimization

PSO:

Particle swarm optimization

QDA:

Quadratic discriminant analysis

R:

Maximum error

R2:

Coefficient of determination

RE:

Relative error

RF:

Random forest

RMSE:

Root mean square error

RR:

Ridge regression

RVFL:

Random vector functional link

SA:

Simulated annealing

SDG:

Shape deviation generator

SHD:

Structural heart disease

SLA:

Stereolithography

SLA:

Stereolithography

SLS:

Sensitive laser sintering

SME:

Small to medium enterprise

SVM:

Support vector machine

SVR:

Support vector regression

UV:

Ultra violet

VP:

VAT photopolymerization

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Authors and Affiliations

  1. School of Information Technology & Engineering (SITE), Vellore Institute of Technology, Vellore, India

    Isha Sachdeva

  2. Department of Manufacturing, School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Vellore, India

    Sivasubramani Ramesh, Utkarsh Chadha & Senthil Kumaran Selvaraj

  3. School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, India

    Hruditha Punugoti

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  1. Isha Sachdeva
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  2. Sivasubramani Ramesh
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  3. Utkarsh Chadha
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  5. Senthil Kumaran Selvaraj
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Sachdeva, I., Ramesh, S., Chadha, U. et al. Computational AI models in VAT photopolymerization: a review, current trends, open issues, and future opportunities. Neural Comput & Applic 34, 17207–17229 (2022). https://doi.org/10.1007/s00521-022-07694-4

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