1. Why other kinds of drifting charts, like trend charts, shows measurement errors instead of process characteristic?

The key is what the process behavior

*should* be. If it

*makes sense* that the process behavior should be random, but suddenly you see a trend, then you have evidence of a special cause. All Shewhart charts are designed to do is sense signals that the process that is expected to have a random and independent output has a special cause that is causing it to be non-random. However, if your output is a function, such as tool wear, plating bath concentration (from drag-in and drag-out), etc. Then you are looking for a special cause that would cause the output to long longer exhibit the function output - such as become random or have an incorrect function.

Measurement error tends to be random, independent and (for bilateral characteristics) normal. When it is large enough it will mask the underlying function, if it exists. That is where you run into problems.

2. Why is necessary in Xhi-lo-R chart of two bands to verify the mean? Or better, it's about the diameter measurement method, another issue, and then, the better method is to use a GD&T machine, because it's not the max. and min. diameter the issue, it's the larger and smaller points in the surface found, a different thing, isn't it?

As shown in the videos, ignoring the within-part variation when trying to determine the process variation, you will confuse measurement error with process variation. The Hi/Lo values eliminates the error of within-part variation. The definition of a round feature is NOT its average size (in fact physically it is meaningless.) A diameter is defined by the zone of values exhibited about a central point - described as either the high and low or the average AND range (never average alone.)

3. Why 75% of USL and LSL value to get the process and then chart limits? If the process is not ruled with a normal distribution, but with a uniform distribution, so all values with the same frequency, there're no tails that justify an inner compression of process limits then specification limits, do I make wrong statistical thinking?

The 75% goes back to using 1.33 as a comfortable capability - for customers and producers - that both accommodates the various errors that exist in the process in addition to the process variation found in the Total Variance Equation,

*especially *sampling error (the process variation between samples), but also including gage error, etc. If it wasn't for the other errors in the Total Variance Equation, you could run to the specification. But, they are there, so you can

*never* run to the spec on the shop floor without risking making bad parts - even in control.

Hopefully,that will help.