Industry-Oriented Method for Dynamic Force Identification in Peripheral Milling Based on FSC-LSQR Using Acceleration Signals

Online measurement of milling force play a vital role in enabling machining process monitoring and control. In practice, the milling force is difficult to be measured directly with the dynamometer. This paper develops a novel method for milling force identification called least square QR-factorization with fast stopping criterion (FSC-LSQR) method, and the queue buffer structure (QBS) is employed for the online identification of milling force using acceleration signals. The convolution integral of milling force and acceleration signals is discretized, which turns the problem of milling force identification into a linear discrete ill-posed problem. The FSC-LSQR algorithm is adopted for milling force identification because of its high efficiency and accuracy, which handles the linear discrete ill-posed problem effectively. The online identification of milling force can be realized using the acceleration signal enqueue and the milling force dequeue operations of the QBS. Finally, the effectiveness of the method is verified by experiments. The experimental results show that the FSC-LSQR algorithm running time is within \((0.05s)\) and the calculation error is less than \((10\%)\). The proposed method can make the sampling frequency of the milling force reach 10240Hz by employing QBS, which satisfy the industry requirements of milling force measurement.

Prediction of Nonlinear Micro-Milling Force with a Novel Minimum Uncut Chip Thickness Model

The minimum uncut chip thickness (MUCT), dividing the cutting zone into the shear region and the ploughing region, has a strong nonlinear effect on the cutting force of micro-milling. Determining the MUCT value is fundamental in order to predict the micro-milling force. In this study, based on the assumption that the normal shear force and the normal ploughing force are equivalent at the MUCT point, a novel analytical MUCT model considering the comprehensive effect of shear stress, friction angle, ploughing coefficient and cutting-edge radius is constructed to determine the MUCT. Nonlinear piecewise cutting force coefficient functions with the novel MUCT as the break point are constructed to represent the distribution of the shear/ploughing force under the effect of the minimum uncut chip thickness. By integrating the cutting force coefficient function, the nonlinear micro-milling force is predicted. Theoretical analysis shows that the nonlinear cutting force coefficient function embedded with the novel MUCT is absolutely integrable, making the micro-milling force model more stable and accurate than the conventional models. Moreover, by considering different factors in the MUCT model, the proposed micro-milling force model is more flexible than the traditional models. Micro-milling experiments under different cutting conditions have verified the efficiency and improvement of the proposed micro-milling force model.

Stability Analysis and Structure Optimization of Unequal-Pitch End Mills

The damping performance of unequal tooth milling cutters is controlled by the pitch parameters. How to improve the vibration damping and dynamic balance of milling cutters needs to be further studied. This paper analyzes the pitch angle through the stability of the lobe diagram and the spectral characteristics, and unequal-pitch end mills with asymmetric structure were determined to have better cutting stability. Due to the principle error of the asymmetrical tool, dynamic balance accuracy is poor. The dynamic balance of the tool is analyzed, and the centroid model of the tool is established. In order to improve the dynamic balance accuracy of tools, the parameters of the groove shape are analyzed and optimized, and balance accuracy is improved. Through modal and milling-force analysis, the relative vibration displacement and cutting force of the optimized tool were reduced by 17% and 10%, respectively, which determined that such tools have better dynamic performance. Here, unequal tooth end mills could reduce vibration and had higher accuracy in dynamic balance by adjusting the parameters of the pitch angles and chip pockets, so that the tool could have higher cutting stability.

Prediction of micro milling force and surface roughness considering size-dependent vibration of micro-end mill

When the characteristic structure size of the component is at the micron level, the internal crystal grains, grain boundaries and pore defects of the component material with the same size at the micron level cannot be ignored, so the micro-sized component will show different physical properties from the macro-sized component, which is called size effect. Since the tool diameter of micro-end mill is in the micron level, the micro-end mill will also show a significant size effect phenomenon. In addition, in micro milling process, because the surface roughness that affects the performance and service life of micro parts is mainly influenced by the vibration of micro-end mill, in order to enhance the machined surface quality, it is crucial to research the formation mechanism of surface topography in micro milling process. In this paper, a comprehensive method is proposed to predict micro-end mill vibration, micro milling force and surface roughness. At first, a size-dependent dynamic model of micro-end mill is presented based on the strain gradient elasticity theory (SGET). Secondly, considering the feedback of micro-end mill vibration, the micro milling force model is presented and solved through iterative method. Then the machined surface topography is simulated through the actual cutting edge trajectory considering the micro-end mill size-dependent vibration and material elastic recovery. The results show that the vibration of the micro-end mill will increase the micro milling force and surface roughness. In order to verify the accuracy and efficiency of the presented method, experiments are performed, and it is found that the experimental results are consistent with the predicted results.

Milling Force Coefficients-based Tool Wear Monitoring for Variable Parameters Milling

Tool wear is an important factor that affects the aeronautical structural parts' quality and machining accuracy in the milling process. It is essential to monitor the tool wear in titanium alloy machining. The traditional tool wear features such as root mean square (RMS), kurtosis, and wavelet packet energy spectrum are related to not only the tool wear status but also to the milling parameters, thus monitoring the tool wear status only under fixed milling parameters. This paper proposes a new method of online monitoring of tool wear using milling force coefficients. The instantaneous cutting force model is used to extract the milling force coefficients which are independent of milling parameters. The principal component analysis (PCA) algorithm is used to fuse the milling force coefficients. Furthermore, support vector machine (SVM) model is used to monitor tool wear states. Experiments with different machining parameters were conducted to verify the effectiveness of this method used for tool wear monitoring. The results show that compared to traditional features, the milling force coefficients are not dependent on the milling parameters, and using milling force coefficients can effectively monitor the transition point of cutters from normal wear to severe wear (tool failure).