Browsing by Author "Rao, Prahalad"

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    Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis
    Bevans, Benjamin; Ramalho, Andre; Smoqi, Ziyad; Gaikwad, Aniruddha; Santos, Telmo G.; Rao, Prahalad; Oliveira, J. P. (Elsevier, 2023-01)
    The goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw for-mation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray com-puted tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objec-tive, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
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    Sensor-based Online Process Monitoring in Advanced Manufacturing
    Roberson, David Mathew III (Virginia Tech, 2016-09-09)
    Effective quality improvement in the manufacturing industry is continually pursued. There is an increasing demand for real-time fault detection, and avoidance of destructive post-process testing. Therefore, it is desirable to employ sensors for in-process monitoring, allowing for real-time quality assurance. Chapter 3 describes the application of sensor based monitoring to additive manufacturing, in which sensors are attached to a desktop model fused deposition modeling machine, to collect data during the manufacturing process. A design of experiments plan is conducted to provide insight into the process, particularly the occurrence of process failure. Subsequently, machine learning classification techniques are applied to detect such failure, and successfully demonstrate the future potential of this platform and methodology. Chapter 4 relates the application of online, image-based quantification of the surface quality of workpieces produced by cylindrical turning. Representative samples of cylindrical shafts, machined by turning under various conditions, are utilized, and an apparatus is constructed for acquiring images while the part remains mounted on a lathe. The surface quality of these specimens is analyzed, employing an algebraic graph theoretic approach, and preliminary regression modeling displays an average surface roughness (Ra) prediction error of less than 8%. Prediction occurs in less than 2 seconds, showing the capability for future application in a real-time, quality control setting. Both of these cases, in additive manufacturing and in turning, are validated using real experimental data and analysis, showing application of sensor-based online process monitoring in multiple manufacturing areas.