Scientists at Nanyang Technological University, Singapore (NTU Singapore), Massachusetts Institute of Technology (MIT), and Brown University have developed new approaches that significantly improve the accuracy of an important material-testing technique by harnessing the power of machine learning
Nano-indentation, the process of poking a sample of a material with a sharp needle-like tip to see how the material responds by deforming, is important in many manufacturing applications, but its poor accuracy in obtaining certain key mechanical properties of a material has prevented it from being used widely in industry.
Using the standard nano-indentation process and feeding its experimentally-measured data to a neural-network machine-learning system, the scientists developed and trained the system to predict samples’ yield strength 20 times more accurately than existing methods.
The new analytical technique could reduce the need for time-consuming and costly computer simulations, to ensure that manufactured parts used in structural applications, such as airplanes and automobiles, and those made from digital-manufacturing techniques, such as 3D printing, are safe to use in real-life conditions.
The senior corresponding author of this paper,NTU Distinguished University Professor Subra Suresh, who is also the university president, said “By incorporating the latest advances in machine learning with nano-indentation, we have shown that it is possible to improve the precision of the estimates of material properties by as much as 20 times. We have also validated this system’s predictive capability and accuracy enhancement on conventionally manufactured aluminum alloys and 3D-printed titanium alloys. This points to our method’s potential for digital manufacturing applications in Industry 4.0, especially in areas such as 3D-printing.”
The findings will be published in the Proceedings of the National Academy of Sciences of the United States of America this week.
Material benefits from a hybrid approach
The method is a hybrid approach that combines machine learning with state-of-the-art nano-indentation techniques [see illustration].
The process starts with pressing a hard tip (typically made of a material like diamond) into the sample material at a controlled rate with precisely calibrated force, while constantly measuring the penetration depth of the tip into the material being deformed.
The challenge arises because the process of decoding the resulting experimentally-measured data is extremely complex and is currently preventing the widespread use of the nano-indentation testing technique.
MIT principal research scientist and NTU Visiting Professor Ming Dao said that previous attempts at using machine learning to analyze material properties mostly involved the use of “synthetic” data generated by the computer under unrealistically perfect conditions—for instance where the shape of the indenter tip is perfectly sharp, and the motion of the indenter is perfectly smooth. The measurements predicted by machine learning were inaccurate as a result.
Training the neural network initially with synthetic data, then incorporating a relatively small number of real experimental data points, however, can substantially improve the accuracy of the results, the team found.
They also report that the training with synthetic data can be done ahead of time, with a small number of real experimental results to be added for calibration when it comes to evaluating the properties of actual materials.
Suresh said: “The use of real experimental data points helps to compensate for the ideal world that is assumed in the synthetic data. By using a good mix of data points from the idealized and real-world, the end result is drastically reduced error.”
Illustration of the novel technique developed by NTU Singapore, MIT, and Brown University. Credit: MIT
