Találati lista | Tudóstér

001-es BibID:	BIBFORM123230
Első szerző:	Kharate, Namrata
Cím:	Explainable AI Techniques for Comprehensive Analysis of the Relationship between Process Parameters and Material Properties in FDM-Based 3D-Printed Biocomposites / Namrata Kharate, Prashant Anerao, Atul Kulkarni, Masuk Abdullah
Dátum:	2024
ISSN:	2504-4494
Megjegyzések:	This study investigates the complex relationships between process parameters and material properties in FDM-based 3D-printed biocomposites using explainable AI techniques. We examine the effects of key parameters, including biochar content (BC), layer thickness (LT), raster angle (RA), infill pattern (IP), and infill density (ID), on the tensile, flexural, and impact strengths of FDM-printed pure PLA and biochar-reinforced PLA composites. Mechanical testing was used to measure the ultimate tensile strength (UTS), flexural strength (FS), and impact strength (IS) of the 3D-printed samples. The extreme gradient boosting (XGB) algorithm was used to build a predictive model based on the data collected from mechanical testing. Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Partial Dependence Plot (PDP) techniques were implemented to understand the effects of the interactions of key parameters on mechanical properties such as UTS, FS, and IS. Prediction by XGB was accurate for UTS, FS, and IS, with R-squared values of 0.96, 0.95, and 0.85, respectively. The explanation showed that infill density has the most significant influence on UTS and FS, with SHAP values of +2.75 and +5.8, respectively. BC has the most significant influence on IS, with a SHAP value of +2.69. PDP reveals that using 0.3 mm LT and 30? RAenhances mechanical properties. This study contributes to the field of the application of artificial intelligence in additive manufacturing. A novel approach is presented in which machine learning and XAI techniques such as SHAP, LIME, and PDP are combined and used not only for optimization but also to provide valuable insights about the interaction of the process parameters with mechanical properties.
Tárgyszavak:	Műszaki tudományok Anyagtudományok és technológiák idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk 3D printing fused deposition modelling biocomposite explainable artificial intelligence (XAI) machine learning
Megjelenés:	Journal of Manufacturing and Materials Processing. - 8 : 4 (2024), p. 1-15. -
További szerzők:	Anerao, Prashant Kulkarni, Atul Masuk, Abdullah (1997-) (mechatronics)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: