Aluminum alloy materials are very difficult to break in the processing process; chip accumulation leads to poor processing stability. In this paper, a coated composite micro–textured tool is presented. The influence of coated composite micro–textured tool on chip shape and cutting force under the same cutting parameters is studied. Firstly, the combination of cutting force and cutting temperature is optimized based on cutting test. Secondly, combined with the finite element simulation technology, three kinds of composite micro–texture tools are tested to determine the same cutting parameters, tools and workpiece materials, and optimize the composite micro–texture combination from the angle of chip shape, cutting temperature and cutting force. Finally, on the basis of optimizing the microstructure combination, the optimization of the cutting performance of aluminum alloy by coating material is studied. It was found that the chip was more easily broken in the composite microstructure when two tools were used. The cutting force decreased by 25% and the cutting temperature decreased by 9.09% compared with the non–micro–texture tools. The cutting temperature and cutting force decreased by 3% and 4.99% compared with those of uncoated composite microstructure.

To improve the cutting performance when cutting hard alloys and achieve reasonable optimization of micro-texture parameters, this paper proposes a sinusoidal micro-textured tool, with four parameters set as micro-textured spacing, period, width, and amplitude. Establish three-dimensional models of non-micro-textured and micro-textured milling tools and simulate the milling process using finite element simulation. Study the effects of milling force and milling temperature on tool performance. Prepare micro-textured milling tools for orthogonal experiments, analyse the effects of milling force and the surface roughness of a titanium alloy workpiece, and study the impact of different micro-textured parameters on milling tool performance. Obtain the parameters that have the greatest impact on milling tool performance, and then use genetic algorithm to optimize three sets of parameter combinations. Use the optimized parameters to prepare milling tools for comparative experiments, and then determine the optimal parameter combination. The research results indicate that micro-textured tools can effectively improve the milling performance of the tool, and the spacing between sinusoidal micro-textures has the greatest effect on improving the milling performance of the tool. When the period of sinusoidal micro-texture is 2.87 and the amplitude is 25.13 μm, width is 79.89 μm, and spacing is 134.54 μm, the milling performance of the tool is optimal.