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Predicting Mechanical Strength and Optimized Parameters in FDM-Printed Polylactic Acid Parts Via Artificial Neural Networks and Desirability Analysis Cover

Predicting Mechanical Strength and Optimized Parameters in FDM-Printed Polylactic Acid Parts Via Artificial Neural Networks and Desirability Analysis

Management Systems in Production Engineering
Volume 32 (2024): Issue 3 (August 2024)
By: Hind H. Abdulridha,  Tahseen F. Abbas and  Aqeel S. Bedan  
Open Access
|Sep 2024

Authors

Hind H. Abdulridha

Hind.H.Abdulridha@uotechnology.edu.iq

University of Technology

Tahseen F. Abbas

University of Technology

Aqeel S. Bedan

University of Technology
DOI: https://doi.org/10.2478/mspe-2024-0040 | Journal eISSN: 2450-5781 | Journal ISSN: 2299-0461
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Language: English
Page range: 428 - 437
Submitted on: Jan 1, 2024
Accepted on: Jul 1, 2024
Published on: Sep 5, 2024
Published by: STE Group sp. z.o.o.
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
FDM,
PLA,
process parameters,
ANOVA,
ANN
Related subjects:
Engineering,
Mechanical engineering,
Tools and methods,
Production technology,
Process engineering and industrial engineering,
Business and economics,
Business management,
Management accounting, financial controlling, cost calculation, investment

© 2024 Hind H. Abdulridha, Tahseen F. Abbas, Aqeel S. Bedan, published by STE Group sp. z.o.o.
This work is licensed under the Creative Commons Attribution 4.0 License.

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