Milled Surface Generation Model for Chip Thickness Detection in Peripheral Milling

AbstractPrediction of forces between tool and workpiece is essential in order to optimize machining and preserve process stability. In the last decades different predictive approaches have been developed: mainly mechanistic and numerical models. Mechanistic models could be applied to a wide range of cutter geometry and workpiece combination, even if a specific tuning, depending on material and application, is always needed. Numerical models could take in account many operative conditions than analytical ones, and allow predicting other parameters like stress, strain rate, temperature distribution, etc., but the computational time required is often unacceptable. The paper presents an innovative hybrid numerical-analytical approach for uncut chip cross-sectional area calculation in 2.5 axis end milling operations. The proposed model uses a mechanistic cutting force model to couple tool and workpiece finite element (FE) models: FE time domain simulations provide to predict effective paths of tool teeth relative to the workpiece, taking into account the dynamics of the entire system; while an appropriate algorithm, developed in Matlab®, allows to achieve a more realistic uncut chip area, from which it is possible to calculate the cutting forces. This approach provides an accurate representation of the machined surface. Simulation is compared with experimental results

Speed-varying Machine Tool Dynamics Identification Through Chatter Detection and Receptance Coupling

AbstractTool-tip Frequency Response Function (FRF) represents one of the essential inputs to predict chatter vibration and compute the Stability Lobe Diagram (SLD). Tool-tip FRFs are generally obtained for the stationary (non-rotating) condition. However, high speeds influence spindle dynamics, leading to a reduced accuracy of the SLD prediction. This paper presents a comprehensive method to identify speed-varying tool-tip FRFs and improve chatter prediction. First, FRFs for a screening tool is identified by a novel technique based on a dedicated experimental test and analytical stability solution. Then, a tailored receptance coupling technique is used to predict speed-varying tool-tip FRFs of any other tool. Proposed method was experimentally validated: chatter prediction accuracy was demonstrated through chatter tests