Adaptive fabrication of material extrusion-AM process using machine learning algorithms for print process optimization

S. Sridhar (), K. Venkatesh, G. Revathy, M. Venkatesan and R. Venkatraman
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S. Sridhar: SASTRA Deemed University
K. Venkatesh: SASTRA Deemed University
G. Revathy: SASTRA Deemed University
M. Venkatesan: SASTRA Deemed University
R. Venkatraman: SASTRA Deemed University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 7, No 31, 5087-5111

Abstract: Abstract Material extrusion (ME) is an extensively used technique in additive manufacturing for making parts and prototypes. Optimizing the relationship between build time and material usage in ME is challenging but essential for improving production performance. This study aims to find the ideal parameter settings to minimize both time and material consumption. Machine learning (ML) models and a multidisciplinary evolution algorithm were used to predict and optimize the fused deposition process parameters. A definitive screening design (DSD) was conducted on 14 process parameters to identify those that significantly affect the output. Using the response surface method, a dataset of these influential parameters was created for the ML models. The prediction accuracy of the ML models was evaluated using regression metrics. It has been found that the random forest algorithm has a prediction accuracy greater than 90% over the other models after training and testing the dataset. To further optimize the process, the Non-dominated Sorting Genetic Algorithm was utilized to fine-tune the hyperparameters of the best-performing machine-learning model. The results showed that using the optimal parameter settings identified by NSGA-II, build time was reduced by 37% and material usage by 40% compared to standard print settings. A statistical two-way ANOVA test confirmed that these optimized settings significantly (p

Keywords: Material extrusion; Machine learning; Build time; Material consumption; Optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02495-z

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