Article Wave Propagation and Structural Health Monitoring Application on Parts Fabricated by Additive Manufacturing Alireza Modir * and Ibrahim Tansel   Citation: Modir, A.; Tansel, I. Wave Propagation and Structural Health Monitoring Application on Parts Fabricated by Additive Manufacturing. Automation 2021, 2, 173–186. https://doi.org/10.3390/ automation2030011 Academic Editors: Hamid Reza Karimi and Ahmed Abu-Siada Received: 13 July 2021 Accepted: 17 August 2021 Published: 18 August 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Mechanical and Material Engineering, Florida International University, Miami, FL 33174, USA; tanseli@fiu.edu * Correspondence: amodi004@fiu.edu Abstract: Additive manufacturing (AM) applications have been steadily increasing in many industry sectors. AM allows creating complex geometries inside of a part to leave some space empty, called infills. Lighter parts are manufactured in a shorter time with less warpage if the strength of the part meets the design requirements. While the benefits of structural health monitoring (SHM) have been proven in different structures, few studies have investigated SHM methods on AM parts. In this study, the relationship between wave propagation and infill density has been studied for the additively manufactured polymer parts. The propagation of surface waves is monitored by using piezoelectric elements. Four rectangular parts are manufactured by using the material extrusion method with 20%, 40%, 60%, and 100% rectilinear infill densities. Four piezoelectric elements were attached on the surface of each beam, one for excitation and three for monitoring the response of the part at equal distances on each part. The results demonstrated that the surface waves diminish faster at parts with lower densities. The received signal in the part with totally solid infills showed about 10 times higher amplitudes compare with the part with 20% infill. The surface response to excitation (SuRE) method was used for sensing the loading on the part. Also, the wave propagation speed was calculated with exciting parts with a pulse signal with a 10-microsecond duration. The wave propagation speed was almost the same for all infill densities. Keywords: structural health monitoring; SuRE method; additive manufacturing; infill ratio; wave propagation 1. Introduction Structural health monitoring (SHM) methods have been used for the estimation of the external force and detection of defects such as crack initiation, delamination, and loose bolts. In recent years, SHM has gained a lot of attention from researchers in different fields including the aerospace, civil, marine, and military fields [1]. Employing SHM has various beneficial aspects including assuring the safe operation of the system, enhancing the life span of the structures, and reducing the maintenance cost [2]. SHM approaches can be divided into two main groups, active and passive SHM. Strain, acoustic emission, temperature, and other similar signals are monitored by the passive SHM and the condition of the structure can be estimated based on the characteristics of the collected data. Although passive SHM is helpful, it does not provide information as effectively as active SHM. On the other hand, active SHM systems generally acquire more reliable information about the structure, independent of the operation of the system, by providing a carefully selected excitation signal in a very consistent manner [2]. The biological nervous system was an inspiration for researchers at the development of the SHM systems [3,4]. However, extensive additional research is needed to improve the capabilities and reduce the cost of the SHM systems to make them usable in consumer products. Each SHM system usually contains three main sections: sensors, data acquisition systems, and a health evaluation unit [5]. Most of the SHM studies have been focused on aerospace and civil engineering Automation 2021, 2, 173–186. https://doi.org/10.3390/automation2030011 https://www.mdpi.com/journal/automation