The ability to 3D print metal parts presents exciting opportunities to simplify the designs of many advanced technologies, and improve their performance. However, on microscopic scales, printed metals can have defects that cause their mechanical properties to vary unpredictably, lowering the quality of final products. To assess these variations, researchers use a technique named profilometry-based indentation plastometry, or PIP. This technique involves pressing a hard tip into a material on a flat surface, and then scanning a probe across the crater to measure the shape left behind. Read More
By measuring how far the tip penetrates and the crater shape, manufacturers can determine important mechanical characteristics of a part, such as strength. By repeating the test at different places on a part, they can assess how these properties vary.
With this method, manufacturers can determine which 3D printing techniques will produce the most reliable metal parts, helping them to improve their designs. However, the measurement uncertainty associated with PIP tests hasn’t been fully clarified, preventing the PIP method from reaching its potential.
Recently, Dr Aaron Tallman and colleagues at Florida International University developed a new technique for quantifying these uncertainties. Their approach could provide vital new insights for manufacturers on how they can optimize their 3D printing techniques.
In their experiment, Dr Tallman’s team first carried out 99 PIP tests on different sections of an aluminum alloy plate (not a 3D printed sample). The aluminum plate did not have the defects found in 3D printed samples. Despite this, each test gave a slightly different strength.
To predict how much these differences were due to the material and how much they were due to the test’s limitations, they used a mathematical model to analyze the 99 results. The idea was to “measure the measurement”, to guide how the tests might be used in demanding situations. This work would answer questions such as: When one test is not accurate enough, can two tests be combined to get a better answer?
Using their experimental data, Dr Tallman’s team developed an uncertainty model using “principal component analysis”: a technique that involves simplifying the mathematical descriptions of materials, while preserving the most important information about them.
For the first time, this allowed them to precisely calculate the inherent uncertainties associated with the PIP method. By modelling uncertainties in this way, the researchers are confident that PIP tests can become far more reliable, allowing their widespread use in assessing the performance of 3D printed metals.
As the field of metal-based manufacturing shifts towards 3D printing, the team’s work could inform future manufacturing processes: helping them to become more time- and cost-efficient, while improving the quality of final products.
