Force and temperature modelling of bone milling using artificial neural networks. (February 2018)

• Record Type: Journal Article
• Authors: Al-Abdullah, Kais I. Abdul-lateef; Abdi, Hamid; Lim, Chee Peng; Yassin, Wisam A.
• Is Part Of: Measurement. Volume 116(2018)
• Page Start: 25
• Page End: 37
• Publication Date: 2018-02
• Subjects: Bone milling -- Milling force -- Milling temperature -- Artificial neural networks modelling; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.10.051
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Modelling bone milling forces and temperature as a function of milling parameters. Using ANNs to estimate bone milling forces and temperature based on experimental data. Estimating temperature elevation at the cutting tool and the fresh-milled bone surface. Exposing the correlation of milling forces and temperature with milling parameters. Abstract: The force and temperature of bone milling depends on a large number of parameters pertaining to the bone tissue and cutting tools. In the current literature, there is a lack of information on bone milling for cancellous tissues. In this paper, we use the artificial neural network (ANN) methodology to develop appropriate force and temperature models based on real experimental measurement data of bone milling on artificial tissues with cancellous properties. The models estimate the milling force and temperature as a function of feed rate and spindle speed. Two temperature models are considered, the bur temperature and the fresh-milled bone surface temperature. A full factorial design of experiment (DOE) is used to collect the necessary data for developing and validating the models. A very good agreement between the estimated and the experimental milling forces and temperature is found. The established models are useful for real-time bone milling optimization and control.