Predicting Linear Dimensional Accuracy of Material Extrusion Parts in Dependence of Process Parameters Using Neural Networks Optimized by an Evolutionary Algorithm-Reference-Cited by-同舟云学术

Predicting Linear Dimensional Accuracy of Material Extrusion Parts in Dependence of Process Parameters Using Neural Networks Optimized by an Evolutionary Algorithm

Published:2024-03-19 Issue: Volume: Page:
ISSN:2329-7662
Container-title:3D Printing and Additive Manufacturing
language:en
Short-container-title:3D Printing and Additive Manufacturing

Author:

Schmidt Carsten¹^ORCID,Funk Jonas¹,Griesbaum Rainer¹,Sehrt Jan T.²,Finsterwalder Florian¹

Affiliation:

1. Institute of Applied Research, Karlsruhe University of Applied Sciences, Karlsruhe, Germany.

2. Department of Hybrid Additive Manufacturing, Institute Product and Service Engineering, Ruhr University Bochum, Bochum, Germany.

Publisher

Mary Ann Liebert Inc

Link

https://www.liebertpub.com/doi/pdf/10.1089/3dp.2023.0192

Reference18 articles.

1. A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics

2. Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction

3. Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method

4. Experimental investigation for dynamic stiffness and dimensional accuracy of FDM manufactured part using IV-Optimal response surface design

5. A Study on Dimensional Accuracy of Fused Deposition Modeling (FDM) Processed Parts using Fuzzy Logic

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1. Model performance evaluation of build time using geometric shape complexity and process parameters in material extrusion;Additive Manufacturing;2024-07

2. Ensuring Part Quality for Material Extrusion by Developing a Methodology for Use-Case-Specific Parameter Set Determination Using Machine Learning Models;Journal of Manufacturing and Materials Processing;2024-03-02