Work

Interpretable machine learning analysis of printing parameter effects on mechanical behavior of FDM-printed polylactic acid

Year: 2026

Type: article

Abstract: Significant attention was given to the use of machine learning (ML) in fused deposition modeling (FDM) to optimize printing processes. In this study, an ML-based framework was developed for establishing interpretable quantitative relationships between printing parameters and the mechanical properties of polylactic acid (PLA). A polynomial ElasticNet model was employed to derive closed-form predictive equations with a strong predictive performance (R2 $$\approx$$ 0.87 for ultimate tensile strength and R2 $$\approx$$ 0.89 for elastic modulus). The layer height was identified as the dominant parameter. The proposed approach provides continuous, interpretable relationships to enable efficient process optimization in FDM.

Source: MRS Communications

DOI: 10.1557/s43579-026-01016-8

Authors: Mohammad Hossein Golbabaei, Amin Kuhzadmohammadi, Zachary Porter, Hunter Garner, Ning Zhang

Institution: Baylor University

Language: English

Topic: Additive Manufacturing and 3D Printing Technologies

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,390

Funders: National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation