Materials Engineering


Predicting material composition via Non Destructive Testing and Neural Networks

Material selection can at times be very tricky, sometimes you want certain aspects of a material such as high strength but you don’t want the low ductility that generally trends along with it.
(for information on the techniques discussed in this article please look at the wiki links below)

To solve this problem alloys and composite materials are developed by materials scientists and engineers that have particular characteristics that are well suited for a particular application; however this process is very time consuming an could take months if not years to completely map the strengths and weaknesses of a new alloy or composite design.

There are ways around this time sink, and one of them is instead of empirically measuring the resultant properties of a material for each change of a materials chemistry or micro-structure, it may be possible to predict the resultant structure based on non-destructive analysis methods and neural networks.

The first step with any neural network is to prepare the training data for the system to adapt and successfully understand the underlying natural laws the govern the kinetics. Input data would be generated from the resultant Electrochemical Impedance Spectroscopy (EIS) and/or Ultrasonic Testing (UT) of a sample training material, and the output data would come from slicing the specimen into very thin (~10 micron) plates which would then be analysed with EDS to determine the real material parameters.

The data would then be fed into a neural network designed to handle 3D input parameters (IE: It would have to have at least 2 hidden neuron layers), and a trained neural network will be generated. This network will now be able to take NDT data from a new material and generate a 3-D Finite Element Model of what it predicts to be the composition and micro-structure.

This process can be used in place with existing NDT QA techniques, but can also be used as an initial step with my previous article (see  to create a material optimisation program. – EIS – UT – NDT – EDS
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How machine learning unlocks the secrets of materials science

Imagine for a second, that you had the power to simulate atoms and molecules and how they interact with each other to create a material, sounds like it would make material selection pretty easy right?

Unfortunately the technology doesn’t yet exist to model so many individual elements and FEMAP (Finite Element Modelling and Processing) can only do so much; it still requires something to interpret the resultant data.

Now imagine if you were able to teach a computer to look for the “natural laws” that govern the mechanical/chemical/electronic properties of a material based on its alloy (or composite)  content.

This idea brings us to machine learning and neural nets which are capable of discovering patterns within current materials, and use those results as “training data” to try and predict the material/chemical/and electronic properties of a yet-to-be tested material.

The length of time it took us to completely comprehend steel-making took us almost a millennia, imagine if we were able to cut that time down to a few days via material simulations and predictive modelling!

It won’t take long before we’re able to build space elevators and room temperature super-conductors when this technology comes of age.