ArticleScientific reports2025
Multi objective optimization of FDM 3D printing parameters set via design of experiments and machine learning algorithms.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Multi-Objective Optimization of FDM Dimensional Accuracy for PLA/TPU Blends Based on Entropy Weight-TOPSIS and Orthogonal Array Design.Polymers · 2026Article
- HMQ-ES-Stack-GBR: A Hybrid Ensemble Learning Model for Mechanical and Physical Quality Prediction in FDM 3D Printing.Micromachines · 2026Article
- Article
- Parameter Optimisation in 3D Extrusion Printing of Polyhydroxybutyrate Using Design of Experiment Methodology.Journal of functional biomaterials · 2026Article
- Development and validation of material selection method for cost-effective lab-scale 3D-printed platforms in biotechnology.Frontiers in bioengineering and biotechnology · 2026Article
- Development of an efficient approach to boost fused deposition modeling (FDM) printing of felodipine-HPMC tablets for enhanced physical stability.International journal of pharmaceutics: X · 2025Article
- Article
- Multi-Objective Machine Learning Optimization of Cylindrical TPMS Lattices for Bone Implants.Biomimetics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The choice of the optimal printing setup for Fused Deposition Modeling (FDM) 3D-printing technology is challenging due to complex interactions between process parameters and mechanical properties. This especially affects engineering applications where the maximum performance is required. To address this challenge, this study explores the influence of main controllable printing parameters including layer thickness, extrusion temperature, printing speed and deposition patterns, on the mechanical properties of FDM-printed ABS specimens using the Design-of-Experiments (DoE) approach by a
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Registered trials
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