A Method for Evaluating the Error of Free Curve Profile Degree Based on Hybrid Evolutionary Algorithm

Abstract

The invention discloses a free curve profile error evaluation method based on a hybrid evolutionary algorithm, which is a data processing method for free curve profile error evaluation based on a least square method, a non-uniform rational B-spline interpolation function and a multi-dimensional hybrid evolutionary algorithm. According to the invention, a hybrid evolutionary algorithm of a particleswarm algorithm with parallel adaptive weights and a niche genetic algorithm based on DC is adopted, the adaptive adjustment of an actually measured coordinate system and a theoretical coordinate system is realized according to the criterion of the least square method, and the position error is eliminated from the profile error. The method does not require a preset initial value, the influence ofthe preset initial value on the final position error result is avoided, the convergence speed of the optimization algorithm is accelerated, the ability of the optimization algorithm in local optimization is improved, local optimum is avoided during global search, and the position error is eliminated from the profile error result, so as to ensure the accuracy of the evaluation line profile error.

Description

technical field [0001] The invention relates to a method for evaluating error of free curve contour degree based on hybrid evolutionary algorithm. Background technique [0002] In the machinery manufacturing industry, the surface curve profile of many parts plays a very important role, such as: involute, ellipse, parabola and cycloid profile are widely used in engineering, usually such curves can be expressed by standard curve equations ; In addition, there are many complex free-form surfaces, such as steam turbine blades, radar antennas and cams, etc. The error measurement of surfaces is generally characterized by measuring a series of cross-sectional curve profiles. Therefore, the free curve profile measurement of the blade section becomes an important content of the line profile measurement. Profile degree is the most widely used item in the national and international standards for shape and position tolerance of parts, but it is difficult to measure and evaluate. With ...

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Problems solved by technology

The first two methods need to preset the initial value of (tx, ty, θ). The initial value has a great influence on the final optimization result, and the optimized result is very inaccurate. Secondly, it takes a lot of time to select the initial value

Both the genetic algorithm and the particle swarm optimization algorithm belong to the evolutionary algorithm. The two randomly produce the initial solution, realize the optimal solution search in the complex space, and update the iterative results according to the fitness. The particle swarm optimization algorithm converges faster, while the genetic algorithm is compared with Particle swarm optimization algorithm greatly enriches the diversity of understanding space and has good global search ability, but both basic genetic algorithm and particle swarm optimization are easy to fall into the local optimal solution in the iterative process, and the local search ability is not strong

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