Robust design in the research and development of t

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Robust design in product development

generally, when we design the structure, we always based on the pre-set conditions such as various structural loads and temperature distribution that the product needs to bear. However, the production process, material properties, and even the product operating environment are rarely as accurate as expected. Such environmental parameters change within a certain range, resulting in the production of unqualified products and the failure of products in use

The robust design function of ANSYS enables engineers to minimize the occurrence of such problems, so that products can resist the influence of different environmental parameters and work as expected. Through the relevant functions in the field of cathode materials, users can quantify the instability rate (failure efficiency) of products, and thus offset the uncertainties in design, such as the fluctuation of material parameters, the change of product working environment, the change of production process and product aging, etc

the robust design method focuses on improving engineering productivity, and has been developing continuously for 50 years, helping the global industry (including automotive, aviation, commercial equipment, telecommunications, electronics, software and other industries) save hundreds of millions of dollars in costs

robust design can be applied to almost every stage of the production process, including financial processing. In ANSYS, this function is used to calculate the influence of uncertain factors in the model on the final results (such as deformation, stress distribution). Based on probability representation method, the reliability and quality of products are quantified by static analysis of uncertainty

robust design is not only a probability representation method, it enables users to optimize design parameters to reach a certain expected state, such as Six Sigma standard: only 3.4 products per million are unqualified. The Six Sigma standard mainly focuses on the production process, which optimizes the production process, and the improvement of the process makes the product production automatically meet the Six Sigma quality standard

Six Sigma standard design

product design directly affects the quality of products to a large extent. Therefore, the design optimization method called Six Sigma standard design (DFSS) came into being, which can automatically make the produced products meet the Six Sigma quality standard. The current popular algorithm comes from the early work of General Electric Company of the United States. For robust design and six sigma Design, quality is an obvious goal in the optimization process. Therefore, robust design, including lead screw drive and rack drive, has become a powerful tool for Six Sigma standard companies

Six Sigma standard design is based on:

* improving engineering productivity is the key to the rapid introduction of new products and reducing costs

* evaluation based management technology minimizes the rate of unqualified products

Six Sigma standard design method approaches the improvement of product quality from two completely different directions. First, study the changes of various parameters, try to reduce the changes of various parameters, and control the changes of product size, material and characteristics within +/- 6 variance in the production process. This method requires extensive and continuous measurement and calculation of the parameter sensitivity of product characteristics

the other direction is to design products that meet quality standards and make them in a state of great changes in characteristics (especially sensitive to some parameters). In this way, we can find out which parameters have the greatest impact on the change of product characteristics and which parameters are the most sensitive, which is quite valuable information. The robustness design of ANSYS designxplorer family is based on this concept

ansys robustness design tools

ansys designxplorer VT and designxplorer enable users to carry out their own robust design calculations. Through these two toolkits, users can define uncertain design parameters and product characteristic parameters that are concerned in the design, and make corresponding calculations through a set of optimization objectives (such as minimizing the product failure rate, or improving the product qualification rate/quality to the highest). As shown in Figure 1, designxplorer includes both objective optimization and six sigma design elements, and the combination of the two becomes the foundation of robust design. These optimization goals can finally be characterized by product characteristic parameters such as stress, deformation, weight, frequency and fatigue life

figure 1

robust design combines probabilistic feature method with optimization theory. Through powerful optimization technology, we can even deal with the problem of multiple optimization goals, and even the problem of probabilistic optimization goals. A typical example of multi-objective optimization is to reduce the product weight to the minimum to save production costs, while achieving the lowest product failure rate to reduce after-sales maintenance costs. Robust design can be applied to this kind of optimization problem of determining objectives, and can also be applied to the problem of probabilistic optimization objectives. In order to be consistent with the industrial naming standard, we call this optimization method "multi-objective optimization"

robustness design is a new function of ANSYS, which is realized by designxplorer series products. The robustness design module can aim at the parameters in CAD software or any workbench design simulation, which ensures the high-quality taste and vitamin composition that is always as 1. Under the ion environment, the annual death toll caused by decoration pollution in China is 111000 design parameters; It can also be implemented in the ADPL language program, aiming at the parameters of paramesh, and optimize the built or newly built models. The generated response surface allows users to quickly see the impact of input parameters on product characteristics, as shown in Figure 2

Figure 2

the designspace module broadens the scope of user simulation, and designxplorer will make user simulation go deeper and take a shortcut to DFSS! (end)

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