This thesis explores the limitations of conventional mechanical testing methods and assesses two miniaturised specimen testing techniques: the small ring test (SRT) and the small punch test (SPT). Given the nascency of the SRT, especially as a tensile test, this thesis rigorously assesses the SRT for 48 pin displacement rates on Stainless Steel (Grade 316L), employing machine learning and inter-test comparison to evaluate rate dependency. The gathered data facilitates new conversion relationships for translating SRT data to equivalent stress-strain data. Material properties are obtained through inverse finite element analysis and optimisation routines, with test standardisation recommendations also proposed. Next, Digital image correlation (DIC) is applied to various SRT ring specimen locations, suggesting the design of an extensometer for the SRT. A 30° claw-like extensometer, gripping the ring at 30° points on either side of the horizontal axis, is found to be optimal. This thesis also studies the concurrent use of SRT and SPT, where SPT specimens are machined from the SRT’s blank space. Consequently, two discs are extracted from each SRT ring’s blank space and their results are compared with those from discs extracted normally. This combined testing method is applied to SS-316L, Nimonic-75 creep tests, and a plate with weld deposits. The SRT reveals encouraging results, showing a good match between the rings extracted normally and those with discs taken from their blank space. Despite rig compliance issues inhibiting proper result conversion for the SPT, the approach exhibits potential, as the results for discs from the ring’s blank space align well with normally extracted discs. The combination shows encouraging results, suggesting future research on different materials. It offers substantial material savings by theoretically reducing the volumetric material required by 97.89% compared to conventional uniaxial testing.