The correlation of 0.333 means that the estimated effect of Temperature is increased by the effect of the gind*time-interaction multiplied by 1/3. So basically you are estimating in the analysis
[Temperature] = Temperature + 1/3*Grind*Time
Thus if there is a strong interaction effect it will inflate your main effect estimator.
You can see that by simulating some simple data. For demonstration purposes I will use a Plackett-Burrman screening design with the following correlation structure:
![PB Design.png PB Design.png](https://community.jmp.com/t5/image/serverpage/image-id/10547iB3016BA848FCD736/image-size/large?v=v2&px=999)
As you can see the main effect X1 has a correlation of 0.333 with the X2*X3-interaction. I generated the design and added some simulated data:
![simulated data.PNG simulated data.PNG](https://community.jmp.com/t5/image/serverpage/image-id/10548i0B970277D923C6C3/image-size/large?v=v2&px=999)
As you can see, the true effect of X1 = 10, the true effect of the interaction X2*X3 = 18. If we now fit a main effects model you get the following results:
![Model Parameters.PNG Model Parameters.PNG](https://community.jmp.com/t5/image/serverpage/image-id/10549iB9EDC673925FD4BA/image-size/large?v=v2&px=999)
The estimated effect of X1 = 16, which is the true effect of X1 - which is 10 - and it is biased by 1/3*18 (the effect of X2*X3).
Hope this helps.
Sebastian