Active learning for prediction of tensile properties for material extrusion additive manufacturing

Machine learning techniques were used to predict tensile properties of material extrusion-based additively manufactured parts made with Technomelt PA 6910, a hot melt adhesive. An adaptive data generation technique, specifically an active learning process based on the Gaussian process regression alg...

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Vydáno v:Scientific reports Ročník 13; číslo 1; s. 11460 - 14
Hlavní autoři: Nasrin, Tahamina, Pourali, Masoumeh, Pourkamali-Anaraki, Farhad, Peterson, Amy M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 15.07.2023
Nature Publishing Group
Nature Portfolio
Témata:
639/166/988
639/301/923
639/705/117
Active learning
Additive manufacturing
Algorithms
Cluster Analysis
Data Collection
Humanities and Social Sciences
Learning algorithms
Linear Models
Machine Learning
multidisciplinary
Predictions
Regression analysis
Science
Science (multidisciplinary)
Tensile properties
ISSN:2045-2322, 2045-2322
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Shrnutí:Machine learning techniques were used to predict tensile properties of material extrusion-based additively manufactured parts made with Technomelt PA 6910, a hot melt adhesive. An adaptive data generation technique, specifically an active learning process based on the Gaussian process regression algorithm, was employed to enable prediction with limited training data. After three rounds of data collection, machine learning models based on linear regression, ridge regression, Gaussian process regression, and K-nearest neighbors were tasked with predicting properties for the test dataset, which consisted of parts fabricated with five processing parameters chosen using a random number generator. Overall, linear regression and ridge regression successfully predicted output parameters, with < 10% error for 56% of predictions. K-nearest neighbors performed worse than linear regression and ridge regression, with < 10% error for 32% of predictions and 10–20% error for 60% of predictions. While Gaussian process regression performed with the lowest accuracy (< 10% error for 32% of prediction cases and 10–20% error for 40% of predictions), it benefited most from the adaptive data generation technique. This work demonstrates that machine learning models using adaptive data generation techniques can efficiently predict properties of additively manufactured structures with limited training data.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-38527-6