Surface roughness prediction in turning processes for grey cast iron: A hybrid machine learning approach integrating infrared thermography

Aguado, Sergio (Universidad de Zaragoza) ; Pueo, Marcos (Universidad de Zaragoza) ; Acero, Raquel (Universidad de Zaragoza) ; Majarena, Ana Cristina (Universidad de Zaragoza) ; Santolaria, Jorge (Universidad de Zaragoza)

Resumen: Workpiece surface quality is a critical control parameter in machining processes, influencing functional performance, dimensional precision, and wear resistance. However, accurately predicting surface roughness is complex, often limited by the computational demands of traditional high-precision methods and the reliance of existing models solely on cutting parameters, hindering real-time monitoring. This research introduces a hybrid Artificial Neural Network (ANN) methodology specifically developed for predicting surface roughness of grey cast iron GG-25 workpieces machined in turning processes, a material previously unstudied in this context. The methodology integrates real-time infrared thermal measurements from multiple defined regions of interest (ROIs) within the tool-workpiece contact zone, along with cutting parameters.
Experimental results demonstrated that feed rate (f) is the most significant cutting parameter (effect = 0.43) affecting surface quality, followed by its combination with cutting speed (Vc) (effect = −0.25) and cutting speed (effect = 0.18). Correlation and non-linear regression analyses revealed complex, often exponential relationships between temperature and surface roughness, showing temperature an upward trend as machining progressed. The developed ANN achieves a correlation coefficient (R) value of 0.99 both when predicting the roughness arithmetic mean deviation (Ra) parameter in training conditions and when using data from experiments not used in training (validations data). Moreover, the model reaches a correlation coefficient value of 0.85 (test data) under cutting conditions different from those used in experiments, demonstrating robustness, significantly outperforming Support Vector Regression (SVR). This model represents a highly potential tool for real-time online inspection.

Idioma: Inglés

DOI: 10.1016/j.engappai.2025.112180

Año: 2025

Publicado en: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 161 (2025), 112180 [16 pp.]
ISSN: 0952-1976

Financiación: info:eu-repo/grantAgreement/ES/DGA/T56-17R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/PID2021-125530OB-I00

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Expresión Gráfica en Ing. (Dpto. Ingeniería Diseño Fabri.)
Área (Departamento): Área Ing. Procesos Fabricación (Dpto. Ingeniería Diseño Fabri.)