Machine Learning-Assisted Prediction of Fatigue Behaviour in Fiber-Reinforced Composites Manufactured via Material Extrusion - Research paper

Aug 5, 2026

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Material extrusion (MEX) enables polymeric composite components for structural and load-bearing applications, yet cyclic loading can trigger fatigue damage and cause catastrophic failure at lower stress levels. Predicting fatigue life in MEX-manufactured fiber-reinforced composites is challenging because material and printing-induced anisotropy complicate analytical modeling. This study applies machine learning to learn patterns from data and estimate fatigue life of composites produced by the MEX process, leveraging training on labeled relationships to support accurate prediction for new cases.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Material extrusion (MEX) and applications

Fatigue behavior and influencing parameters

Machine learning overview

Supervised learning

Unsupervised learning

Reinforcement learning

Machine Learning-Assisted Prediction of Fatigue Behaviour in Fiber-Reinforced Composites Manufactured via Material Extrusion Mithila Rajeshirke*, Suhas Alkunte*, Orkhan Huseynov* and Ismail Fidan‡ *Department of Mechanical Engineering, Tennessee Tech University, Cookeville, TN 38505, USA ‡Department of Manufacturing and Engineering Technology, Tennessee Tech University, Cookeville, TN 38505, USA Abstract The recent advancements in material extrusion (MEX) have expanded the potential use of polymeric and composite structures in a wide range of structural and load-bearing applications. However, cyclic loads can induce fatigue, resulting in the development of structural damage and potentially leading to catastrophic failure at lower stress levels compared to normal mechanical loading. Therefore, it is crucial to thoroughly investigate and understand the fatigue behavior of composite parts manufactured using MEX. Predicting the fatigue life of polymeric composite components poses a significant challenge due to the complex nature of the materials involved. In this research, the aim is to utilize Machine Learning (ML) techniques to predict the fatigue life of fiber-reinforced composites produced through the MEX process. ML focuses on developing models that can learn from data, recognize underlying patterns within the data, and use those patterns to make accurate predictions or decisions. Keywords: Material Extrusion, Fatigue Prediction, Composites, Machine Learning. 250 Introduction MEX is a well-received additive manufacturing (AM) technique used to create 3D objects by extruding semi-molten thermoplastic materials from a heated nozzle or nozzles onto a platform. The filaments used for the process are usually made of polymeric materials such as epoxy, nylon, polycarbonate (PC), polyester, acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), and polyamide (PA) [1]–[3] . MEX has several advantages including the ability to produce complex parts, low cost, minimal material wastage, design flexibility, customization of products for individual consumers, and production of small lots of parts [4]–[8] . Its affordability and accessibility make it popular for both hobbyists and professionals [9],[10] . It offers a wide range of compatible polymeric materials and composites, allowing for different mechanical properties and aesthetic finishes [11]–[14] . MEX finds applications in rapid prototyping, manufacturing tools, and jigs, functional end-use parts, architectural models, customized consumer products, medical and dental applications, and aerospace and automotive parts [15]–[19] . Investigation of the fatigue behavior of composite materials manufactured by MEX has become important due to their applications in the structural field. Fatigue testing encompasses a range of loading conditions, such as tension, compression, torsion, bending, or their combinations which are used to assess the performance and durability of materials under cyclic loading [20],[21] . Various parameters affecting the fatigue life of any fiber-reinforced composites include fiber material, matrix material, volume or weight percentage of fiber in the matrix, fiber type and length, as well as 3D Printing (3DP) parameters such as infill pattern, build orientation, infill density, layer height, printing speed, printing plane, nozzle diameter, bed temperature, [etc. as](etc. as) shown in figure 1. Anisotropy induced due to these 3DP parameters makes it very difficult to analytically predict the fatigue behavior of composites. For predicting fatigue life, several researchers have focused on various statistical methodologies. Kakiuchi et al. studied the fatigue strengths ofthe AM Ti-6Al- 4V at room temperature and at elevated temperature evaluated by Murakami’s model [22] . In this paper, ML is employed as a statistical method. Bao et al. provided a fatigue life prediction method for SLM (selective laser melting) processed Ti-6Al-4 V parts usi

FAQs

Why is fatigue behavior critical for MEX-manufactured fiber-reinforced composites?

Cyclic loads can induce fatigue damage, leading to structural degradation and potential catastrophic failure at lower stress levels than expected under normal loading.

What makes analytical prediction of fatigue life difficult for these composites?

Fatigue response is affected by many parameters, and MEX-related 3D printing conditions create anisotropy, making the behavior complex to model analytically.

How does the machine learning approach support fatigue life prediction in this research?

Machine learning models learn patterns from training data and then use the learned relationships to predict fatigue life for new, unseen composite cases produced via MEX.

Aug 5, 2026

Upload by Eliana