Process modeling of force behavior in the automatic bovine cortical bone milling process using adaptive neuro-fuzzy inference system (ANFIS)

In this article, an adaptive neuro-fuzzy inference system is utilized to model the effect of important parameters in the cortical bone milling process including the rotational speed, feed rate, depth of cut and tool diameter to predict the cutting forces. To model the process force behavior, experimental tests are conducted on the fresh cow femur. Next, the results of performed experiments are used to train and test the inference system. In this model, the most influential parameters of automatic cortical bone milling process including the rotational speed, feed rate, tool diameter and depth of cut are taken as the input parameters, while the cutting forces in the feed direction, normal to the feed direction and normal to the bone surface as well as the resultant force are considered as the output. To this aim, the adaptive neuro-fuzzy inference system relies on 75% of the trained laboratory data and the remaining 25% to test the model validation. The accuracy of the obtained model is investigated using different diagrams and numerous statistical criteria. The results indicate that the adaptive neuro-fuzzy network has shown a successful performance in predicting the cutting forces of cortical bone milling process.