Tolerance optimization distribution method based on adaptive decision operator whale optimization algorithm

Abstract

The invention relates to the technical field of mechanical design, in particular to a tolerance optimization distribution method based on a self-adaptive decision operator whale optimization algorithm, and aims to realize a multi-objective optimization function of planetary gear tolerance design. The tolerance manufacturing cost and the mass loss cost of a tooth thickness reduction amount error, an eccentric error, an axis flatness error, an axis position degree error and a bearing radial clearance error are taken as objective functions, and the tolerance value upper and lower limits and transmission error precision requirements of the error terms are taken as constraint conditions, so that a tolerance multi-objective optimization model of the planetary gear is established; and introducing a whale algorithm under an adaptive decision operator as an optimization algorithm, and solving the multi-objective optimization model to realize planetary gear tolerance optimization distribution of the error term. Experiments prove that the manufacturing cost model of each tolerance machining geometric feature is formulated, the accuracy of the target function is improved, and the global optimization efficiency and reliability of the multi-target coupling tolerance system are improved.

Claims

1. A tolerance optimization allocation method based on the adaptive decision operator whale optimization algorithm, characterized in that: The following steps are involved: Step 1: Construct a manufacturing cost model with a correlation relationship between tolerance processing cost function and different processing characteristics of error terms; Step 2: Using the squared quality loss method, a quality loss cost model is constructed to quantify the economic impact of quality fluctuations on multiple parties. Step 3: Establish a tolerance total cost model consisting of a manufacturing cost model and a quality loss model; Step 4: Set the constraints of the optimization model based on the company's economic processing capabilities and the planetary gear transmission accuracy requirements; Step 5: Solve the tolerance total cost model based on the whale optimization algorithm under the adaptive decision operator; Step 6: Based on the solution of the tolerance total cost model, formulate a planetary gear tolerance optimization allocation plan to complete the planetary gear tolerance optimization allocation.

2. The tolerance optimization allocation method based on the adaptive decision operator whale optimization algorithm according to claim 1, characterized in that: In step 1, the planetary gear error parameters include the sun gear tooth thickness reduction x1, the planetary gear tooth thickness reduction x2, the inner ring gear tooth thickness reduction x3, the sun gear eccentricity error x4, the planetary gear eccentricity error x5, the inner ring gear eccentricity error x6, the sun gear sun gear axis parallelism x7, the planetary gear axis parallelism x8, the inner ring gear axis parallelism x9, the sun gear axis position x1, the planetary gear axis parallelism x2, the inner ring gear axis parallelism x3, the sun gear axis position x4, the planetary gear axis parallelism x5, the inner ring gear axis parallelism x6, the sun gear axis position x7, the planetary gear axis parallelism x8, the inner ring gear axis parallelism x9, the sun gear axis position x1, the planetary gear axis parallelism x1, the inner ring gear axis parallelism x1, the inner ring gear axis position x1, the sun gear axis position x2, the planetary gear axis parallelism x2, the inner ring gear axis parallelism x3 10 , planetary gear shaft position x 11 , internal gear ring shaft position x 12 , radial clearance of sun gear bearing x 13 , radial clearance of planetary gear bearing x 14 , radial clearance of internal gear ring bearing x 15 .

3. The tolerance optimization allocation method based on the adaptive decision operator whale optimization algorithm according to claim 2, characterized in that: In step 1, different machining features include outer circle features, inner hole features, plane features, and positioning features.

4. The tolerance optimization allocation method based on the adaptive decision operator whale optimization algorithm according to claim 3, characterized in that: In step 1, the machining cost function of the outer circle feature of the manufacturing cost model is expressed as: The machining cost function of the inner hole feature is expressed as: The machining cost function of the plane feature is expressed as: The machining cost function of the positioning size is expressed as:

5. The tolerance optimization allocation method based on the adaptive decision operator whale optimization algorithm according to claim 4 is characterized in that: The expression of the quality loss cost model in step 2 is: in, T is the dimensional tolerance, which is determined by ym, y is the quality characteristic of the product, m is the target value, and A is the loss caused by unqualified parts.

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