wheel design new technology |
| 03-24 16:20:49 来源： 作者： |
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wheel design new technology Inquiry (采购产品): wheel design new technology Automobile wheel manufacturing is a very competitive industry due to the wide availability of mold-cutting and production facilities. Nevertheless, the competition niche lies in (1) Cost reduction, (2) Ability of delivery in time and (3) Reliability of products. These CAR factors can be judged and planned at the early design stage. A good design can reduce the weight of a wheel namely reducing material cost. A good design can increase the wheel strength improving reliability. More importantly, a good design can take into consideration of uncertainty factors existing in casting, testing and production. With a reliable product well designed, delivery-in-time can be fulfilled. Auto wheels carry safety liability; they have to pass three major tests including (1) radial fatigue test, (2) cornering fatigue test and (3) impact failure test. To meet the challenges of these three tests, engineers usually utilize computers and application software to design wheel shapes and perform simulations of wheel tests and possibly a simplified casting process e.g. steady-state solidification. The conventional design wisdom is recognized as the deterministic approach wherein all design parameters are set to be constants. The trial-and-error process is used to finalize the design parameters which will lead to a product most likely meeting required conditions such as maximum stresses below all test failure points. In reality, geometrical dimensions, casting temperature, pressure, cooling duration and other design related parameters are not constants but variables with probability distribution patterns or functions. Consequently, the probabilistic approach is realistically more suitable than deterministic approach in design an auto wheel with many uncertain variables. This article is intended to illustrate a probabilistic methodology to perform robust design of auto wheels. The design procedures include: (1) to gather design and production variables expressed in probability distribution functions, (2) to define a target function for meeting test requirements, such as wheel strength in terms of maximum allowable stresses, (3) to use statistic regression method to establish a relationship between the target function and the design variables, or to utilize commercially available software such as ANSYS, NASTRAN and PRO/MECH etc. to calculate the target function, and (4) to interface with UN IPASS software, a leading probabilistic software package, to carry out robust design. **** Hidden Message ***** erhrong wu ca 90275 Company: mega research inc. E-mail: erwu@excite.com **** Hidden Message ***** erwu@excite.com |
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