Experimental study on the 3D-printed plastic parts and predicting the mechanical properties using artificial neural networks

This paper presents an investigation of the capabilities of artificial neural networks (ANN) in predicting some mechanical properties of Ferrite-Martensite dual-phase steels applicable for different industries like auto-making. Using ANNs instead of different destructive and non-destructive tests to determine the material properties, reduces costs and reduces the need for special testing facilities. Networks were trained with use of a back propagation (BP) error algorithm. In order to provide data for training the ANNs, mechanical properties, inter-critical annealing temperature and information about the microstructures of many specimens were examined. After the ANNs were trained, the four parameters of yield stress, ultimate tensile stress, total elongation and the work hardening exponent were simulated. Finally a comparison of the predicted and experimental values indicates that the results obtained from the given input data reveal a good ability of the well-trained ANN to predict the described mechanical properties.

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Prediction of the mechanical properties of hot-rolled CMn steels using artificial neural networks

Journal of Materials Processing Technology ◽

10.1016/0924-0136(95)02089-6 ◽

1996 ◽

Vol 57 (3-4) ◽

pp. 332-336 ◽

Cited By ~ 22

Author(s):

Z.Y. Liu ◽

W.-D. Wang ◽

W. Gao

Keyword(s):

Mechanical Properties ◽

Neural Networks ◽

Artificial Neural Networks ◽

Artificial Neural ◽

Hot Rolled

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Artificial Neural Networks for the Prediction of Mechanical Properties of Soils

10.1007/978-3-030-90321-3_62 ◽

2021 ◽

pp. 758-779

Author(s):

Lusdali Castillo Delgado ◽

Daniel Enrique Porta Maldonado ◽

Juan J. Soria ◽

Leopoldo Choque Flores

Keyword(s):

Mechanical Properties ◽

Neural Networks ◽

Artificial Neural Networks ◽

Artificial Neural ◽

Prediction Of Mechanical Properties

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Classifying Degraded Three-Dimensionally Printed Polylactic Acid Specimens Using Artificial Neural Networks based on Fourier Transform Infrared Spectroscopy

Applied Sciences ◽

10.3390/app9132772 ◽

2019 ◽

Vol 9 (13) ◽

pp. 2772

Author(s):

Sung-Uk Zhang

Keyword(s):

Neural Networks ◽

Fourier Transform ◽

Artificial Neural Networks ◽

Thermal Degradation ◽

Polylactic Acid ◽

Fourier Transform Infrared ◽

Ann Model ◽

Flexible Material ◽

3D Printed ◽

Artificial Neural

Fused filament fabrication (FFF) is commonly employed in multiple domains to realize inexpensive and flexible material extrusion systems with thermoplastic materials. Among the several types of thermoplastic materials, polylactic acid (PLA), an environment-friendly bio-plastic, is commonly used for FFF for the sake of the safety of the manufacturing process. However, thermal degradation of three-dimensionally (3D)-printed PLA products is inevitable, and it is one of the failure mechanisms of thermoplastic products. The present study focuses on the thermal degradation of 3D-printed PLA specimens. A classification methodology using artificial neural networks (ANNs) based on Fourier transform infrared (FTIR) and was developed. Under the given experimental conditions, the ANN model could classify four levels of thermal degradation. Among the FTIR spectra recorded from 650 cm−1 to 4000 cm−1, the ANN model could suggest the best wavenumber ranges for classification.

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