You can add a column for the mean diameter and standard deviation for the measurement as well. I assume you have a diameter target.
If you have 12 runs, you should have 12 rows and the 5 measurements should be added next to the rows. If you add them as rows you will be treating them as if they were independent experiments whereas from your question it appears that you have only 12 experiments.
You can add a column for the mean diameter and standard deviation for the measurement as well. I assume you have a diameter target.
My thoughts:
What you do depends on how the actual data was gathered. It is possible you have multiple components confounded in the measurements:
1. Part-to-part,
2. Within part, and
3. Measurement
I would first look graphically at the within treatment and possibly assess the consistency within treatment. Then, as suggested calculate appropriate enumerative statistics to summarize the within treatment data (mean and variance) and then model both response variables.
Are the 5 measurements the same response variable? Assuming you are measuring the the same Y's, you have 1 experimental unit for each treatment and it consists of 5 repeated measures. Follow repeated measures analysis.