Multi-objective optimization of machining parameters in micro-milling LF 21 based on the AHP-entropy weight method

Micro thin-walled structure of aluminum alloy LF 21 has a strong ability to reflect electromagnetic waves and is widely used in waveguide radar antenna. Micro-milling technology is a potentially effective technique for machining micro thin-walled structure of LF 21. However, LF 21 has the characteristics of low strength and hardness and high plasticity and is prone to plastic flow under cutting force, resulting in extrusion, accumulation, and other defects and easy to produce burrs. Then the surface roughness and surface residual stress are difficult to guarantee. To ensure the machining quality of micro-milling thin-walled LF 21, a 3D simulation model of micro-milling LF 21 process is established based on Abaqus to output residual stress. The validity of the residual stress output is verified by experimental results. Four-factor and three-level orthogonal simulation experiments are conducted, and a prediction model of residual stress is established based on the simulation results. Based on the response surface method (RSM), four-factor and five-level center composite design (CCD) experiments are conducted to establish a surface roughness prediction model and a top burr size prediction model. The validity of the built prediction models is verified by experiments. Multi-objective optimization (the minimum surface roughness, the minimum top burr size, and the maximum surface compressive residual stress) is transformed into single-objective optimization by using the analytic hierarchy process (AHP)-entropy weight method. Genetic algorithm (GA) is used to solve the above optimization problem, and the optimal machining parameters of micro-milling LF 21 are achieved. The optimal micro-milling parameters are as follows: the spindle speed n is 62,000 r/min, the axial depth of cut a p is 70 μm, the radial depth of cut a e is 65 μm, and the feed per tooth f z is 0.95 μm/z.