Machine Learning-Assisted Pattern Recognition Algorithms for Estimating Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid Specimens - Study Overview

Aug 5, 2026

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Supervised machine learning classification is applied to estimate the Ultimate Tensile Strength (UTS) of polylactic acid (PLA) specimens fabricated via the fused deposition modeling (FDM) process. Thirty-one PLA specimens are produced, using infill percentage, layer height, print speed, and extrusion temperature as input parameters. Four algorithms are evaluated: logistic classification, gradient boosting classification, decision tree, and K-nearest neighbor. Decision tree and KNN reach an F1 score of 0.71, while KNN achieves the strongest AUC (0.79), indicating better separation of UTS classes. Results support KNN as a favorable classifier for additive-manufacturing prediction models.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Relationship between structural integrity and UTS in FDM PLA

Study objective and classification approach

Machine Learning-Assisted Pattern Recognition Algorithms for Estimating Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid Specimens Akshansh Mishra 1, *, Vijaykumar S Jatti2 1School of Industrial and Information Engineering, Politecnico Di Milano, Milan, Italy 2Department of Mechanical Engineering, Symbiosis Institute of Technology, Pune, India Corresponding Author Mail id: [akshansh.mishra@mail.polimi.it](akshansh.mishra@mail.polimi.it) Abstract: In this study, we investigate the application of supervised machine learning algorithms for estimating the Ultimate Tensile Strength (UTS) of Polylactic Acid (PLA) specimens fabricated using the Fused Deposition Modeling (FDM) process. A total of 31 PLA specimens were prepared, with Infill Percentage, Layer Height, Print Speed, and Extrusion Temperature serving as input parameters. The primary objective was to assess the accuracy and effectiveness of four distinct supervised classification algorithms, namely Logistic Classification, Gradient Boosting Classification, Decision Tree, and K-Nearest Neighbor, in predicting the UTS of the specimens. The results revealed that while the Decision Tree and K-Nearest Neighbor algorithms both achieved an F1 score of 0.71, the KNN algorithm exhibited a higher Area Under the Curve (AUC) score of 0.79, outperforming the other algorithms. This demonstrates the superior ability of the KNN algorithm in differentiating between the two classes of ultimate tensile strength within the dataset, rendering it the most favorable choice for classification in the context of this research. This study represents the first attempt to estimate the UTS of PLA specimens using machine learning-based classification algorithms, and the findings offer valuable insights into the potential of these techniques in improving the performance and accuracy of predictive models in the domain of additive manufacturing. Keywords: Additive Manufacturing; Machine Learning; Fused Deposition Modeling; Classification Algorithms 1. Introduction In recent years, Artificial Intelligence (AI) has emerged as a transformative force across various industries, revolutionizing processes and driving innovation. The manufacturing and health sector is no exception, as it has experienced significant benefits from the integration of AI-driven technologies [1-4] . Among the most significant benefits of AI in manufacturing is its capacity to enhance efficiency and productivity. AI-enabled systems can process enormous volumes of data in real-time, allowing manufacturers to detect patterns and trends that can be harnessed for process improvement. Machine learning algorithms, a branch ofAI, can evolve and refine over time, making manufacturing systems increasingly adept at forecasting equipment malfunctions and reducing downtime [5-9] . In a study conducted by Du et al. [10], the researchers examined the conditions leading to void formation in friction stir welded joints, as these voids negatively impact the mechanical properties of the joints. To investigate this phenomenon, the authors employed a decision tree and a Bayesian neural network. They analyzed three types of input datasets, including unprocessed welding parameters and computed variables derived from both analytical and numerical models of friction stir welding. In a study conducted by Roman Hartl et al. [11] ., the authors investigated the application of Artificial Neural Networks (ANNs) in analyzing process data from friction stir welding to predict the quality of the resulting weld surface. Artificial Intelligence is gaining interest in additive manufacturing industries also like other industries. Du et al. [12] demonstrated that employing a synergistic approach that combines physics-informed machine learning, mechanistic modeling, and experimental data can mitigate the prevalence of common defects in additive manufacturing. By scrutinizing experimental data on defect formation for widely used alloys, w

FAQs

What is the goal of the study?

To evaluate supervised machine learning classification algorithms for predicting the Ultimate Tensile Strength (UTS) of FDM-manufactured PLA specimens.

Which input parameters are used to predict UTS?

Infill percentage, layer height, print speed, and extrusion temperature are used as input parameters.

Which algorithm performs best in the reported results?

K-nearest neighbor (KNN) performs best, achieving an AUC of 0.79, while decision tree and KNN both reach an F1 score of 0.71.

Aug 5, 2026

Upload by Patrick