Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods

Year 2025, Volume: 4 Issue: 1, 206 - 225, 18.02.2025

Ahmet Burak Tatar

https://doi.org/10.62520/fujece.1604379

Abstract

This study examines how printing parameters affect the roughness, tensile strength, and elongation of 3D-printed parts used in various applications. Machine learning-based regression models were employed to optimize product quality. The open-source "3D Printer Material Requirement" dataset obtained from the Kaggle platform was utilized to predict product quality. This dataset includes input parameters such as layer height, wall thickness, infill density, infill pattern, nozzle temperature, bed temperature, print speed, printing material (PLA and ABS), and fan speed. These parameters were analyzed for their impact on the product's roughness, load resistance, and elongation under tensile force. Based on these evaluations, product quality was estimated according to its intended use. Parameters such as layer height, wall thickness, infill density, infill pattern, nozzle temperature, bed temperature, print speed, printing material, and fan speed were identified as key factors influencing output performance. Within this framework, prediction models including Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gaussian Process Regression (GPR), and Multi-Layer Perceptron (MLP) were developed, and their performances were assessed using metrics such as accuracy (R²), error rates (RMSE, MSE, MAE), and computational time. Among these methods, GPR demonstrated the highest prediction accuracy for elongation, tensile strength, and roughness, with respective values of 0.98, 0.9, and 1. The findings indicate that machine learning applications are effective tools for quality prediction and optimization in the production processes of 3D printers. Furthermore, this study provides a novel perspective on quality control and design optimization in 3D printing processes.

Keywords

3D printing, Machine learning, Regression, Printing parameters

Ethical Statement

There is no conflict of interest with any person/institution in the prepared article.

Makine Öğrenmesi Tabanlı Regresyon Metotları ile Üç Boyutlu (3B) Baskı Parça Kalitesinin Tahmini

Abstract

Bu çalışmada farklı alanlarda kullanılacak ürünlerin üç boyutlu yazıcılarda imal edilirken baskı parametreleri dikkate alınarak kullanım amacına göre parçanın pürüzlülük oranı, yüke dayanımı ve çekme kuvvetine göre uzama gerilmesi verileri değerlendirilmiş ve ürün kalitesi makine öğrenmesi regresyon metotları ile optimize edilmiştir. Ürün kalitesinin tahmini için Kaggle platformundan elde edilen “3D Printer Material Requirement” açık kaynak veri seti kullanılmıştır. Bu veri setinde sisteme girdi olarak verilen; katman yüksekliği, duvar kalınlığı, dolgu yoğunluğu, dolgu deseni, nozul sıcaklığı, tabla (yatak) sıcaklığı, baskı hızı, baskı malzemesi (PLA ve ABS) ve fan hızı parametrelerine göre baskı sonucu ürünün pürüzlülüğü, yüke dayanım gücü ve çekme kuvvetlerinin etkisiyle ürünün uzama gerilmesi değerleri incelenmiştir. Bu değerler doğrultusunda da ürünün kullanım amacına göre kalitesi tahmin edilmeye çalışılmıştır. Katman yüksekliği, duvar kalınlığı, dolgu yoğunluğu, dolgu deseni, nozul sıcaklığı, yatak sıcaklığı, baskı hızı, baskı malzemesi ve fan hızı gibi parametreler, çıktı performansını etkileyen temel faktörler olarak kullanılmıştır. Bu çerçevede, Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gaussian Process Regression (GPR), Multi-Layer Perceptron (MLP) tahmin modelleri geliştirilmiş ve model performansları, doğruluk (R²), hata oranları (RMSE, MSE, MAE) ve işlem süresi gibi metrikler açısından değerlendirilmiştir. Bu yöntemler içerisinde GPR ile uzanım, gerilim mukavemeti ve pürüzlülük açısından en başarılı tahmin oranları sırasıyla 0,98, 0,9 ve 1 olarak elde edilmiştir. Elde edilen bulgular, 3B yazıcıların üretim süreçlerinde kalite tahmini ve optimizasyonu için makine öğrenmesi uygulamalarının etkili bir araç olduğunu göstermektedir. Ayrıca bu çalışma, 3B baskı süreçlerinde kalite kontrolü ve tasarım optimizasyonuna yeni bir perspektif sunmaktadır.

Keywords

3D baskı, Makine öğrenmesi, Regresyon, Baskı parametreleri

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