Abstract
Deformation of thin-walled titanium alloys can occur during the milling process due to the cutting force and chatter vibration, which can influence the precision of the finished parts. In this research, a new milling method without auxiliary support for machining of thin-walled parts was proposed. A large cutting depth and layered milling technology were used during rough machining, with a different machining allowance for each subsequent remaining layer. In the finishing stage, the surface of the previous layer needed to be dressed before processing the next layer. A TiAlSiN-coated, cemented carbide milling cutter was used to machine titanium alloy thin-walled parts, which are characterized by continuous multilayers of unequal thickness. The processing path was simulated using HyperMILL software, and the machining accuracy was detected by the 3D optical scanner. The measurement results indicated that the surface contour accuracy of the parts was ±0.21 mm, within a range of ±0.30 mm. The machining efficiency was increased by 40%, while guaranteeing machining accuracy.
Highlights
Titanium alloys have various applications in the aerospace field due to their high strength, high-temperature resistance, corrosion resistance, and low-temperature brittleness [1]
We propose a new processing technology based on a typical thin-walled titanium alloy part
The machining efficiency was increased by 40% using this method, while ensuring the processing quality
Summary
Titanium alloys have various applications in the aerospace field due to their high strength, high-temperature resistance, corrosion resistance, and low-temperature brittleness [1]. The method of low melting point alloy auxiliary support was used to balance the axial force in the cutting process to improve the cutting rigidity of the thin-walled parts. The feasibility of this method was verified by machining a 15-mm-thick beryllium bronze workpiece [2]. Campa et al predicted the stability problems during machining by establishing a three-dimensional dynamic model of the stability convex corners of low-rigidity parts, and provided guidance for parameter optimization and tool selection. Thepsonthi et al established a model of machining process parameters and surface quality based on the statistical multi-objective particle swarm optimization method to determine the optimal cutting process parameters during the milling process. The machining efficiency was increased by 40% using this method, while ensuring the processing quality
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