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Aluminum alloy LF 21 has a strong ability to reflect electromagnetic waves. LF 21 waveguide slit array structure is widely used in waveguide radar antenna. The stiffness of the slit array structure is relatively weak. So, the structure is prone to deformation under the cutting force in the conventional milling process. Micro-milling technology can realize high-precision machining of mesoscale parts/structures and is a potential effective machining technology for the waveguide slit array structure. However, the diameter of the micro-milling cutter is small, and the feed per tooth is comparable to the arc radius of the cutting edge, so the micro-milling cutter is prone to wear. In addition, the effects of elastic recovery of material, the minimum cutting thickness and friction of cutting dead zone on micro-milling force cannot be ignored. A simulation model of micro-milling aluminum alloy LF 21 processes based on DEFORM 3D is built by combining the theory of cutting and the technology of process simulation. Prediction of tool wear is achieved. The quantitative relationship between the arc radius of the cutting edge and tool wear is clarified for the first time. The authors built an improved cutting force model in micro-milling LF 21 considering tool wear and cutter runout with the minimum cutting thickness as the boundary. The validity of the built micro-milling force model is verified by experiments.

The surface quality of the sidewall in waveguide antennae is important, especially surface roughness, which directly affects the electrical performance of the slotted waveguide antenna. Micro-milling is a potentially effective processing technique for the antenna. However, surface roughness has been difficult to guarantee within a reasonable accuracy range. In this study, orthogonal experiments of micro-milling LF21 waveguide slits were conducted. The results of the range analysis mainly sorted the factors that affected the surface roughness and also helped to determine how surface roughness could be kept at a minimum. The surface roughness was predicted by using the group method of data handling (GMDH). The importance of the applied GMDH was that it continuously adjusted the network structure according to the potential relationship between cutting parameters and the corresponding surface roughness, which helped determine the model most optimally fitted to the experimental data. This research can be used as a reference for selecting cutting parameters in micro-milling LF21.