Rapid Material Characterization using Smart Skin with functional Data Analysis

Abstract

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.