模具专业英文翻译，麻烦帮我翻译一下

爱学习/

INTERPRETING THE DATA
5 }! d! T! _9 P1 D
1 g: J( F8 }( aThe beta value for your mold data will be a number between one (which is equivalent mathematically to the action of random failures throughout the life cycle, equally likely at 5 cycles as 5000 cycles) and perhaps 6 or 7 (where failures are highly limited to a specific age-range). Thus beta is the indicator for the physics or mechanics of the failure. It describes how rapidly and steeply the reliability curve looks. See Figure 6 for a relative comparison of three different beta values. For a given failure mode, the beta value will be fairly constant across your permanent molds. Of the several molds studied for this paper, both tilt-pour and stationary gravity cast types, the beta ranged between 4.3 to 5.3. This evaluation included all failure modes (but primarily erosion and thermal fatigue cracking) in a mixed fashion. No effort was made to presort the data by failure mode since historical data was involved and the actual failure mechanism was not always recorded at that time. Knowing the beta value for your life-process is very helpful in reducing the effort needed to confirm the life of other molds is the same or better. Techniques described by Wayne Nelson can dramatically affect the effort in mold validation (Nelson, 1985).

The theta value for your mold data is the chief indicator of life length for your molds. A higher theta value (often referred to as the characteristic life) means your molds will last a longer number of shots. The value of theta in the Weibull distribution occurs at the time when 63.2% of the molds have failed (or 36.8% still survive). This theta value can be observed on Figure 6 as the intersection point of the three plots. Obviously the longer you can put that point off (the higher the theta value), the better off you are. Figure 7 illustrates different theta values for the reliability plot.
; v0 ^. @* c2 e$ V6 l
2 G, H- [% R# FThe software will provide a mean value for your distribution. This can be observed pretty easily by merely looking at the pdf of your data as it plots out. The mean represents the typical number of shots to failure. When this is initially calculated, there may be great surprise among even staff who work with molds continuously. Most people estimate the average life of a mold to be a much smaller number than actual, primarily because we discount long-lived molds as flukes and short-lived molds as “just the way things are.” Because molds tend to have long lives anyway, it is difficult to gage the accuracy of our perceptions. This is one of the great advantages to actually measuring mold life and understanding the nature of its expected variability.