Experimental Investigation and Machine Learning-Based Prediction of Cutting Force, Vibration, and Bending Moment During Cortical Bone Milling
Abstract
Cortical bone machining is considered to be difficult as the tissue has an anisotropic and heterogeneous microstructure based on the osteon that greatly affects cutting force, vibration, and tool loading. To address this research gap, the present study focused on dry milling experiments on pig cortical bone using a ball-nose end mill. The spindle speed (SS) ranged from 3200 to 4000 r/min and the feed rate (f) was from 0.12 to 0.18 mm/rev. The cutting force, vibration, and bending moment were measured simultaneously. The results showed that the three responses increased with both the SS and f. The highest cutting force of 78 N, vibration of 0.0389 g, and bending moment of 5.4414 Nm were measured at the highest feed–speed combination. In addition to the machinability study, three machine learning (ML) models (ridge regression, Extra Trees Regressor, and Support Vector Regression) were compared to predict the measured responses. Ridge regression was the best model to predict the cutting force and vibration, which means that the responses are mostly controlled by a near-linear relationship with the machining parameters. On the other hand, the best model to predict the bending moment (Bm) was Extra Trees Regressor, with an R2 value of 0.913, which means that the responses were mainly controlled by a non-linear interaction between SS and f. This result is further supported by Taylor diagram analysis.