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Neuro-fuzzy modelling methods for relative density prediction of stainless steel 316L metal parts produced by additive manufacturing technique

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Abstract

Two machine learning (ML) methods, adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN) have been implemented to predict the relative density (RD) of stainless steel 316L parts which are produced in additive manufacturing (AM) machines. The objective of this paper was to create ML models adapted for AM technique to verify the generalized model that predicts RD with the least error. Some important process parameters in AM such as scanning speed, laser power, hatch distance and layer thickness were picked as input and RD was set as output. Effects of the input parameters on RD were discussed and they were represented in the form of surface plots. It has been found that ANN method’s convergence rate was better than that of ANFIS method, which confirms that usage of neural networks is a better choice than the usage of fuzzy reasoning in modelling AM technique.

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Abbreviations

AM :

Additive manufacturing

ANN :

Artificial neural network

ANFIS :

Adaptive neuro fuzzy inference system

E :

Energy density

EBM :

Electron beam melting

HD :

Hatch distance

MIMO :

Multi input multi output

MISO :

Multi input single output

ML :

Machine learning

LP :

Laser power

LT :

Layer thickness

SISO :

Single input single output

SLM :

Selective laser melting

SS :

Scanning speed

RD :

Relative density

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Acknowledgments

This publication is a part of Doctoral Dissertation. The authors would like to thank the Additive Manufacturing Technologies Application and Research Center (EKTAM) team, Gazi University, Ankara, Turkey for their technical support.

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Authors and Affiliations

  1. Graduate School of Science and Engineering, Hacettepe University, Ankara, 06800, Turkey

    Can Barış Toprak & C. U. Dogruer

  2. Additive Manufacturing Technologies Application and Research Center (EKTAM), Gazi University, Ankara, 06560, Turkey

    Can Barış Toprak

Authors
  1. Can Barış Toprak
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  2. C. U. Dogruer
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Corresponding author

Correspondence to Can Barış Toprak.

Additional information

Can Barış Toprak received his B.Sc. from School of Mechatronics, Kocaeli University, Turkey and M.Sc. from School of Automation, Beijing Institute of Technology, Beijing China. Currently, he is a Ph.D. student at Mechanical Engineering, Hacettepe University, Ankara Turkey. He is also a Lecturer at Mechanical Engineering connected with Additive Manufacturing Technologies Research Center (EKTAM), Gazi University, Ankara, Turkey. His research interests include Additive Manufacturing, Powder Bed Fusion Technologies and Artificial Intelligence.

C.U. Dogruer received his B.Sc. from Mechanical Engineering Department, Middle East Technical University, in 1996 and M.Sc. from the same department in 1999 as well as his Ph.D. from the same department, in 2009. Currently, he is an Associate Professor at Mechanical Engineering at Hacettepe University. His academic interests are system dynamics and control, model predictive control.

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Toprak, C.B., Dogruer, C.U. Neuro-fuzzy modelling methods for relative density prediction of stainless steel 316L metal parts produced by additive manufacturing technique. J Mech Sci Technol 37, 107–118 (2023). https://doi.org/10.1007/s12206-022-1211-6

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  • DOI: https://doi.org/10.1007/s12206-022-1211-6

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