Magnesium-lithium alloy surface treatment process parameter optimization method

Abstract

The invention discloses a magnesium-lithium alloy surface treatment process parameter optimization method. The method comprises the steps of: initializing a BP algorithm model, performing input and output mapping training on the BP network model, initializing a simulated annealing algorithm and variables, iterating the simulated annealing algorithm, calling the BP algorithm to solve and decode a target function to obtain an actual process parameter output value of a BP algorithm calculation result, updating a local optimal solution in current iteration, and judging to accept a new solution after annealing is finished; comparing the local optimal solution of the current annealing temperature with the currently accumulated global optimal solution; and outputting a globally optimal solution as a final optimal process parameter combination scheme after an iteration termination condition is met, and obtaining a process parameter optimization scheme value output of the magnesium-lithium alloy surface treatment. According to the method, intelligent learning training optimization can be carried out on magnesium-lithium alloy surface treatment process parameters, and the quality of magnesium-lithium alloy surface treatment is improved.

Description

technical field [0001] The invention relates to the technical field of metal surface treatment technology, in particular to a method for optimizing the parameters of a magnesium-lithium alloy surface treatment process. Background technique [0002] With the extensive application of magnesium-lithium alloys as ultra-light alloys in the manufacture of weight-reducing structural parts, the corrosion resistance of the alloy surface cannot adapt to some high and low temperature, humidity, salt spray and other working conditions, and surface micro-arc oxidation It has become an important surface treatment technology to increase the surface treatment of the ceramic-like layer to improve its strength and corrosion resistance. However, the selection of process parameters of micro-arc oxidation has a decisive impact on the surface quality. Once the process parameters are not suitable, quality problems such as poor film density, easy to fall off, and poor mechanical properties will occ...

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Problems solved by technology

However, the selection of process parameters of micro-arc oxidation has a decisive impact on the surface quality. Once the process parameters are not suitable, quality problems such as poor film density, easy to fall off, and poor mechanical properties will occur, which restrict this surface treatment technology. development bottleneck

At present, the micro-arc oxidation treatment on the surface of magnesium-lithium alloy mainly relies on the experience of technicians, and a basically qualified surface treatment process is obtained by manually adjusting a large number of experimental parameters of the combination of process parameters. This repeated experiment will inevitably lead to various Waste of resources, even many applications can not find a successful process solution and give up the surface treatment process

Such problems have seriously plagued many application units, leading to the contradiction between the current strong market demand for magnesium-lithium alloys and the unstable quality of the surface film layer

[0003] For the selection of multi-process parameters in micro-arc oxidation on the surface of magnesium-lithium alloys, there are not only multiple input process parameters, but also multiple performance parameters to evaluate the quality of the film layer, and it is difficult to describe the corresponding input and output parameters with an explicit function. The optimization of this kind of problem belongs to nonlinear, multivariate, multi-objective, discrete and difficult problems

Therefore, a single intelligent optimization for multi-parameter process optimization is not applicable, nor is an intelligent algorithm with a determined objective function

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