Quality Assurance


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 https://learningann.wordpress.com/2014/03/10/how-machine-learning-unlocks-the-secrets-of-materials-science/)  to create a material optimisation program.

http://en.wikipedia.org/wiki/Dielectric_spectroscopy – EIS

http://en.wikipedia.org/wiki/Ultrasonic_testing – UT

http://en.wikipedia.org/wiki/Non_Destructive_Testing – NDT

http://en.wikipedia.org/wiki/Energy-dispersive_X-ray_spectroscopy – EDS
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