Amirkabir University of Technology Amirkabir Journal of Mechanical Engineering 2008-6032 53 Issue 2 (Special Issue) 2021 04 21 Process modeling of force behavior in the automatic bovine cortical bone milling process using adaptive neuro-fuzzy inference system Process modeling of force behavior in the automatic bovine cortical bone milling process using adaptive neuro-fuzzy inference system 1287 1306 3824 10.22060/mej.2020.16766.6436 FA Vahid Tahmasbi صنعتی اراک-مهندسی مکانیک Amir Hossein Rabiee mechanical engineering, arak university of technology Mahdi Safari Department of Mechanical Engineering/Arak University of Technology Journal Article 2019 07 16 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. 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. https://mej.aut.ac.ir/article_3824_d7f82a5cfa88ca6ed8b2027760231036.pdf