Wear behavior of tool flank in the side milling of Ti6Al4V: An analytical model and experimental validation

Due to the poor machinability of Ti6Al4V material, the cutting tool can easily suffer flank wear during the process of high-speed side milling, which reduces the tool life as well as the surface integrity of workpiece. Further, an effective method for predicting the flank wear of end mill during side milling of Ti6Al4V is lacking in the existing literature, which makes it difficult to improve the productivity of the overall process. To this end, in this study, a flank wear prediction model is constructed based on three main mechanisms: abrasive wear, adhesive wear, and diffusive wear. Subsequently, a normal stress model and temperature field model of wear land on the flank of end mill are established. Finally, these two models are incorporated in the flank wear model to obtain the variation rate of wear land width, which is regarded as a criterion to evaluate the reliability of the proposed flank wear prediction model of side mill. The prediction results are found to be in excellent agreement with the experimental results, which verifies the high prediction accuracy of the proposed model. Overall, this model can serve as a useful theoretical basis for the rational selection of tool geometry and cutting parameters.

Experimental Analysis and Prediction Model of Milling-Induced Residual Stress of Aeronautical Aluminum Alloys

Aeronautical thin-walled frame workpieces are usually obtained by milling aluminum alloy plates. The residual stress within the workpiece has a significant influence on the deformation due to the relatively low rigidity of the workpiece. To accurately predict the milling-induced residual stress, this paper describes an orthogonal experiment for milling 7075 aluminum alloy plates. The milling-induced residual stress at different surface depths of the workpiece, without initial stress, is obtained. The influence of the milling parameters on the residual stress is revealed. The parameters include milling speed, feed per tooth, milling width, and cutting depth. The experimental results show that the residual stress depth in the workpiece surface is within 0.12 mm, and the residual stress depth of the end milling is slightly greater than that of the side milling. The calculation models of residual stress and milling parameters for two milling methods are formulated based on regression analysis, and the sensitivity coefficients of parameters to residual stress are calculated. The residual stress prediction model for milling 7075 aluminum alloy plates is proposed based on a back-propagation neural network and genetic algorithm. The findings suggest that the proposed model has a high accuracy, and the prediction error is between 0–14 MPa. It provides basic data for machining deformation prediction of aluminum alloy thin-walled workpieces, which has significant application potential.

Study on Design, Manufacture and Cutting Performance of Circular-arc Milling Cutters for Machining Titanium Alloy

Titanium alloy is widely used for manufacturing structural parts of high-end equipment due to its excellent mechanical properties, despite difficulty in being machined. Nowadays, titanium alloy parts are mostly machined by ball-end milling cutters (BEMC), but the cutting edge structure of the BEMC limits the improvement in machining efficiency and surface quality of the parts. In this paper, a circular-arc milling cutter (CAMC) with large-curvature cutting edge was proposed; the differential geometry method was used for establishing the geometric model for the contour surface of the CAMC and the mathematical model for the spiral cutting edge line; the conversion matrix between grinding wheel and workpiece coordinates was introduced to derive the equation of grinding wheel trajectory when the rake face of the CAMC was ground; the self-designed CAMC was ground and tested in accuracy. The comparative research was conducted experimentally on the side milling of titanium alloy TC4 with the CAMC and BEMC, and consequently the variation laws of milling forces, wear morphology and machined surface quality were obtained about the two types of milling cutters. The results indicated that the CAMC can effectively reduce the main milling force and keep the milling process stable. Moreover, the CAMC was worn slower and produced better surface quality than the BEMC.

Using tool wear to increase process stability when milling Al7075 and AISI 4140+QT

One of the main limits of productivity during cutting processes is the occurrence of regenerative chatter. Due to these self-excited vibrations, the load capacity of the machine components, the tool as well as the machine performance cannot be fully utilized. There are several methods to stabilize the milling process. One is the use of increased process damping, which results from the contact of the tool’s flank face and the workpiece. The flank wear land naturally increases the contact between tool and workpiece. However, this effect has not been used to increase productivity in milling processes. This paper investigates with experiments and numerical simulations how tool wear affects process stability in milling of aluminum and steel. Therefore slot milling and side milling tests were carried out with tools of various states of flank wear. It could be shown that increasing flank wear allows to raise the depth of cut ap up to 300% in machining aluminum and perform the machining process with a higher productivity.

Modelling of surface roughness and studying of optimal machining position in side milling

In this work, a theoretical analysis of surface generation numerical model is presented to predict the surface roughness achieved by side milling operations with cylindrical tools. This work is focused on the trajectory of tools with two teeth by influencing of tool errors such as radial runouts, as well as straightness with dynamic effects. A computational system was developed to simulate roughness topography in contour milling with cylindrical tool. Finally, the PSO (particle swarm optimization) algorithm is employed to find the optimal machining position for the best surface roughness. Experimental data is satisfied with the the novel pretiction model for the tooth’s trajectory, and the the final prediction accuracy is high enough, i.e. that the prediction surface roughness. Low prediction surface roughness error (1.37 ~ 15.04%) and position error (0.95 ~ 1.25 mm) indicate effectiveness of the model built in this work. The novel model may be used to determine the variation in surface roughness.