With only 3 measurements in each group you are very unlikely to find any meaningful results without prior knowledge outside of the data set that you can use.
One option is the 2 sample t test, but with only 3 observations per group the accuracy of the t test will be very dependent on the assumption of normality. If you are certain that the processes that produce your data are normal (or very very near normal) then you can use the t test. But this knowledge must be based on scientific knowledge and possibly other data, 6 data points will not give you enough information to guide you in this assumption.
The other main suggestion is usually the Wilcoxon-Mann-Whitney test or other permutation tests. With only 3 observations in each of 2 groups, the smallest possible p-value that you can see from a 1 tailed test is 0.05 (equal to the traditional cut-off) and 0.10 for a 2 tailed test. This means that your only chance of significance is if you a priory believe that one treatment will have higher strength and that all 3 measurements from that treatment are larger than all 3 of the measurements from the other treatment (and you use the traditional cut-off).
You would probably do better to use a larger sample size (do a power analysis ahead of time to determine a good sample size) and/or use prior information with a Bayesian model.