Predictive modeling of additively manufactured carbon fiber-PLA mechanical components via ML
Yükleniyor...
Dosyalar
Tarih
2025-01-01
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Purpose: The study aims to predict and optimize two critical parameters, surface roughness and energy consumption, in additive manufacturing (AM) processes using carbon fiber-reinforced polylactic acid (PLA) material. These parameters are essential for enhancing the efficiency and quality of AM-produced components. Design/methodology/approach: A mechanical connector was fabricated using the AM process, employing the Box–Behnken experimental design method with four input parameters: layer thickness (LT) (150–200–300 µm), infill density (ID) (40%–80%–100%), nozzle temperature (NT) (200–210–220 °C) and printing speed (PS) (40–80–120 mm/s). Predictive models were developed using four machine learning (ML) algorithms: Gaussian process regression (GPR), extreme gradient boosting (XGBoost), artificial neural network (ANN) and random forest regression (RFR). Model performance was evaluated using mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE) and R-squared (R2). Additionally, ANOVA was conducted to identify the most influential parameters on surface roughness and energy consumption. Findings: The RFR model demonstrated superior accuracy with low error values and high R2 scores in estimating both surface roughness and energy consumption. ANOVA results indicated that LT (43.96%) and PS (40.01%) were the most significant factors affecting surface roughness, while LT (50.45%) and ID (27.58%) significantly influenced energy consumption. Originality/value: This study underscores the effectiveness of ML algorithms and statistical analysis in modeling and optimizing AM processes. The findings provide valuable insights into improving the efficiency and quality of 3D-printed components, particularly through the integration of carbon fiber-reinforced materials and advanced predictive modeling techniques.
Açıklama
Anahtar Kelimeler
Additive manufacturing, Box–Behnken design, Machine learning, Optimization, Prediction
Kaynak
Multidiscipline Modeling in Materials and Structures
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
