Metaheuristic Approaches for Modeling and Optimization of FDM Process

Ahmad Aminzadeh; Mohammad Aberoumand; Davood Rahmatabadi; Mahmoud Moradi

doi:10.1007/978-3-030-68024-4_25

Metaheuristic Approaches for Modeling and Optimization of FDM Process

Ahmad Aminzadeh, Mohammad Aberoumand, Davood Rahmatabadi, Mahmoud Moradi^*

^*Corresponding author for this work

Research output: Contribution to Book/Report › Chapter › peer-review

Abstract

According to the fact that the FDM process has several input parameters that should be optimized toward a zero-defect manufacturing production. In this chapter, at first statistical analysis and mathematical methods used in the design of the experiment and optimizing printing parameters, which are used in FDM printing parameters are defined, and then they are reviewed, discussed and categorized. In this regard, the application of different methods such as statistical modelling, design of experiments (DOEs), Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Hybrid approaches are discussed. In the next section, using the results of researches in this field, they are reviewed on a case-by-case basis and the optimal printing parameters with different conditions, materials and goals are introduced in FDM 3D printing.

Original language	English
Title of host publication	Fused Deposition Modeling Based 3D Printing
Publisher	Springer
Pages	483-504
ISBN (Print)	978-3-030-68024-4, 978-3-030-68023-7
DOIs	https://doi.org/10.1007/978-3-030-68024-4_25
Publication status	Published - 22 Apr 2021
Externally published	Yes

Keywords

fused deposition modelling
additive manufacturing
metaheuristic approaches
optimization

Access to Document

10.1007/978-3-030-68024-4_25

Cite this

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abstract = "According to the fact that the FDM process has several input parameters that should be optimized toward a zero-defect manufacturing production. In this chapter, at first statistical analysis and mathematical methods used in the design of the experiment and optimizing printing parameters, which are used in FDM printing parameters are defined, and then they are reviewed, discussed and categorized. In this regard, the application of different methods such as statistical modelling, design of experiments (DOEs), Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Hybrid approaches are discussed. In the next section, using the results of researches in this field, they are reviewed on a case-by-case basis and the optimal printing parameters with different conditions, materials and goals are introduced in FDM 3D printing.",

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AU - Aminzadeh, Ahmad

AU - Aberoumand, Mohammad

AU - Rahmatabadi, Davood

AU - Moradi, Mahmoud

PY - 2021/4/22

Y1 - 2021/4/22

N2 - According to the fact that the FDM process has several input parameters that should be optimized toward a zero-defect manufacturing production. In this chapter, at first statistical analysis and mathematical methods used in the design of the experiment and optimizing printing parameters, which are used in FDM printing parameters are defined, and then they are reviewed, discussed and categorized. In this regard, the application of different methods such as statistical modelling, design of experiments (DOEs), Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Hybrid approaches are discussed. In the next section, using the results of researches in this field, they are reviewed on a case-by-case basis and the optimal printing parameters with different conditions, materials and goals are introduced in FDM 3D printing.

AB - According to the fact that the FDM process has several input parameters that should be optimized toward a zero-defect manufacturing production. In this chapter, at first statistical analysis and mathematical methods used in the design of the experiment and optimizing printing parameters, which are used in FDM printing parameters are defined, and then they are reviewed, discussed and categorized. In this regard, the application of different methods such as statistical modelling, design of experiments (DOEs), Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Hybrid approaches are discussed. In the next section, using the results of researches in this field, they are reviewed on a case-by-case basis and the optimal printing parameters with different conditions, materials and goals are introduced in FDM 3D printing.

KW - fused deposition modelling

KW - additive manufacturing

KW - metaheuristic approaches

KW - optimization

UR - https://pureportal.coventry.ac.uk/en/publications/metaheuristic-approaches-for-modeling-and-optimization-of-fdm-process(61b809c0-1d14-4135-b70f-281f992e1127).html

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M3 - Chapter

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SP - 483

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BT - Fused Deposition Modeling Based 3D Printing

PB - Springer

ER -

Metaheuristic Approaches for Modeling and Optimization of FDM Process

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Keywords

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