Can a Black‐Box AI Replace Costly DMA Testing?—A Case Study on Prediction and Optimization of Dynamic Mechanical Properties of 3D Printed Acrylonitrile Butadiene Styrene

Can a Black‐Box AI Replace Costly DMA Testing?—A Case Study on Prediction and Optimization of Dynamic Mechanical Properties of 3D Printed Acrylonitrile Butadiene Styrene

The complex and non‐linear nature of material properties evolution during 3D printing continues to make experimental optimization of Fused Deposition Modeling (FDM) costly, thus entailing the development of mathematical predictive models. This paper proposes a two‐stage methodology based on coupling...

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Bibliographic Details
Main Authors: Milani, A.S (Author), Vahed, R. (Author), Zareie Rajani, H.R (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
3-D printing
3D-printing
ABS resins
acrylonitrile butadiene styrene
Acrylonitrile-butadiene-styrene
additive manufacturing
artificial neural networks
Black boxes
Butadiene
Case-studies
Complex networks
Deposition
Digital storage
dynamic mechanical properties
Dynamic mechanical property
Experimental optimization
Fused Deposition Modeling
Heuristic methods
Neural networks
Non linear
Optimisations
Particle Swarm Optimization
Particle swarm optimization (PSO)
Process parameters
Styrene
Viscoelasticity
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