Surface roughness prediction by combining static and dynamic features in cylindrical traverse grinding

• Published in: International journal of advanced manufacturing technology Vol. 75; no. 5-8; pp. 1245 - 1252
• Main Author: Guo, Jianliang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2014; Springer Nature B.V
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Cylindrical grinding; Engineering; Grinding; Industrial and Production Engineering; Kernel functions; Machine learning; Mathematical models; Mechanical Engineering; Media Management; Original Article; Parameters; Predictions; Random vibration; Surface roughness; Traverse grinding; Workpieces; Surface roughness; Grinding; Relevance vector machine; Prediction
• DOI: 10.1007/s00170-014-6189-5
• ISSN: 0268-3768

With a given grinding system, surface roughness of the ground workpiece depends mainly on the settable grinding parameters and is also inevitably influenced by some random factors during grinding. Based on the relevance vector machine, which can effectively avoid over-fitting and can also present a fast prediction, a model is proposed for predicting the surface roughness of ground parts. In the model, the grinding parameters are considered as the static features, and some features of the random vibration signal work as the dynamic features. The static and dynamic features compose a feature vector together, which is used as the input variable of the model to predict the surface roughness. A series of experiments were carried out to validate the model and the results show that for the particular grinding system and conditions in this work, when the width parameter of kernel function is set to 100 and all features are normalized on 100, both the predicted surface roughness and its variation trend are close to the measured values. It can be inferred that the model generates a precise prediction only when the width parameter and normalization parameter match the given grinding system and conditions.