Rapid Material Characterization using Smart Skin with functional Data Analysis
Automotive industries in the last decade are demanding for lightweight, corrosion resistance and improved fatigue performance materials. Composites having all such properties quickly gain popularity. Due to complex fabrication methods, composites are known for various process defects. Thus, monitoring and characterizing the composite component before assembly is necessary to maintain the overall structural integrity. Further a rapid and reliable inspection is much needed addition in most automotive industries to save time.
Non-Destructive Evaluation (NDE) is popular for component testing in the automobile industry because they do not cause any permanent alteration to components. Some NDE techniques require sensors (Piezo Electric transducers, PVDF films, Optical fiber, etc.) that need to be bonded to the structure for testing. Some of these sensors are expensive and cannot be reused once detached. This work aims at the reusability of sensors. Further, the reusable sensors can be deployed in an array configuration for multi-purpose NDE. SMART Skin shall be explained as a Multiple-Transmitter-Multiple-Receiver (MTMR) Piezo-ceramic based sensor (PZT) array which is embedded to a conformable skin. The bottom layer of the skin is coated with pressure-sensitive adhesive to be attached to most curved and non-curved structural surfaces. Each PZT sensor nodes are individually controlled by a MATLAB code that actuates and receive the GW waves signals.
In this work, we have validated the ability of rapid material characterization using isotropic materials.
Using experimental data collected by the proposed methodology, we have developed an automated material classification algorithm using various machine learning algorithms. A novel application of functional data analysis (fDA) which converts discrete samples into continuous curves is used. The curves are represented as linear combinations of basis functions. The advantage of this fDA method is that we use the shape of the signal instead of extracting features from the signal. Several choices of basis coefficients such as fourier series, wavelet and B-spline are considered of which spline basis gives the best results. These basis coefficients are then feed into machine learning models like Support Vector Machine (SVM), Random Forest (RF), k nearest neighbor (knn) for classification. The efficacy of this novel method is compared with conventional feature extraction techniques such as zero-crossing coefficients, absolute maximum value, different statistical features like mean and variance, energy of the signal. Encouraging results are obtained that shows the fDA methodology is efficient over the conventional feature extraction methods as it improves the prediction performance on the classifier and result in a faster and cost-effective model by reducing the predictor dimensionality.
How to Cite
Smart skin, Non-Destructive Evaluation, material characterization, functional data analysis, Piezo Electric transducers
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.