Optimization model and solution model Solving the robust design of the solution process is generally divided into three phases: 1 system design (also known as the first design), propose the initial design; 2 parameter design (also known as the second design), seeking parameters The best match, to improve the stability of product performance, it is the core of three designs; 3 tolerance design (also known as the third design), using the loss function to give the key parts the appropriate tolerance (tolerance) range. The three-time design uses orthogonal tables as the basic tool, simulates various interferences with error factors, and uses signal-to-noise ratio as an indicator to measure product quality stability. It uses inexpensive components to assemble high-quality, low-cost, stable and reliable products. The mathematical model of multi-objective robust optimization design is as follows: X=(x1,x2,...,xn)TminF(X)= When the parameter design and fuzzy comprehensive evaluation are optimized by orthogonal table, the optimized design variables are taken as the factors in the orthogonal table, and the objective function is used as the test index. Generally, two orthogonal tables are used for design, so-called inner table and outer table. The inner table is used for design calculation of controllable factors (design variables), and the outer table is used for error calculation of each test plan in the inner table. Determine the level of each variable before designing the inner table. The feasible domain of robust optimization design is generally located in the feasible domain of fuzzy optimization design. The fuzzy optimization value obtained by fuzzy optimization in model solving can be used as the initial value, and its horizontal value is reasonably determined for robust optimization design. After calculating the signal-to-noise ratio of each objective function under different experimental schemes from the inner and outer tables, the data can be analyzed according to the orthogonal analysis method to determine the optimization scheme. For the signal-to-noise ratio of each objective function obtained by multi-objective robust optimization, the analysis method of fuzzy mathematical theory can be used for comprehensive analysis and evaluation. The water average value of the signal-to-noise ratio of each level of each factor in the internal table is as follows: RSNP=∑l(RSNl)PK. (4) where: P represents the horizontal number of the factor; l ranges from the test number corresponding to the P level; PK is the number of trials of the level. The signal-to-noise ratio is superior, but because the signal-to-noise ratio has a negative value, the signal-to-noise ratio is incrementally transformed by equation (5): RSNP'=RSNPRSNP≥01RSNPRSNP<0. (5) A fuzzy comprehensive evaluation subset is formed by the mean value of the signal-to-noise ratio of each objective function for each level of each factor. The full RSNP of each factor can constitute the fuzzy evaluation matrix R~ of the factor. Using equations (4) and (5), the calculation formula of the signal-to-noise ratio is selected according to the type of signal-to-noise ratio in the optimization process. Application example The main parameters of an internal combustion engine valve spring are: installation load F1=275N, spring is 10mm from the installation height when the valve is open maximum, compression load F2=680N, maximum speed 1400rmin, material is 50CrVA. The required fatigue strength reliability is 0199 ( The corresponding reliability factor is 21,326,367, which is 2133 when designed. The purpose of optimization is to improve the performance of the spring. Under the premise of ensuring the size and performance, the quality should be as small as possible. Therefore, the objective function is established: f(x)=Π2d(n1+n2)ΘD4. Where: d, D respectively The diameter of the spring wire and the diameter of the spring; n1 is the number of spring working rings; n2 is the number of spring support turns, the value is 2; Θ is the material density, Θ = 718 × 103 kgm3. Conclusion The high inclusions in the strip and the high roughness of the specimen are a factor that leads to unqualified performance. The unqualified structure is a Wei's body structure or a granular bainite structure. It is a typical fast-cooling structure. It is good for eliminating the horizontal fold, but reduces the plasticity and improves the strength, thus causing the performance to fail. . Pot Bearings,Bridge Pot Bearings,Pot Bearings for Bridges Rubber Seal Strip Co., Ltd. , http://www.nsrubberseal.com