It certainly makes sense to check the assumptions of the type 1 analysis. Namely, that the random error follows a normal distribution. This is certainly not the case, if we obtain either zero or one as error -- the residual is discrete and not continuous. Also note that the standard check to test normality -- the Anderson Darling hypothesis test -- is sensitive to discreteness. Thus, this tests is too conservative and you should probably use something like the Jarque-Bera test, but increase the critical p-value to 10% - 20%. For your dataset the p-value is 4%.
What can you do to ensure a successful qualification?
I like to get a feeling for the numbers, which I try to achieve. To do so I perform simple simulations. Using your results, i.e. SD=0.43 and BIAS=0.7 I run a simulation by drawing random numbers of a normal distribution. This result clearly shows that in order to achieve a successful qualification result (with a success probability >= 80%), we need too many measurements (approx. N>4000). Usually, I would assume a contaminated normal distribution, however, this further increases the number of runs. Also, I recommend to perform a sensitivity analysis -- i.e. how sensitive is the sample size to the input parameters (SD and BIAS). In your case it is very sensitive. However, if I add your final resolution of 1um, the needed sample size exceeds 5000 -- note that I assume a success probability of 80%.
Are there other concepts?
The concept, which you currently use for validating a gauge, is common in many industries. There exists other concepts: E.g. Donald Wheeler proposed (at least) two concepts: The first one focusses on the probability to detect trends using SPC charts. A second one assumes that the manufacturing process is well-centered within the specification limits and then estimates the probability that a part, which is measured to be "out of spec", is actually within specification. I like this idea. Unfortunately, I am unable to use it, because our specification limits are always extremely tide and our manufacturing processes are not stable -- although many people tried to stabilise the manufacturing processes. Thus, we need the measurement result to adjust the manufacturing process.