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Dehghan-Niri, Ehsan

Publications and source records attributed to Dehghan-Niri, Ehsan.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Lizard-inspired Tube Inspector (LTI) Robot

This final report summarizes the research findings of the current project. This project is a collaboration between New Mexico State University (NMSU) as a lead (Recipient) and Arizona State University (ASU) as a Co-Recipient. Tubular structures are common components of boilers and heat exchangers in power plants. Over time, these components may suffer corrosion, cracks, and stress-corrosion cracks in either the body or the welded connections. A single tube leakage can cause an outage of several weeks. Regular inspection is a key safety factor when ensuring that power plants are maintained in reliable, operational condition. This inspection, however, is challenging, time-consuming, and in many cases impossible due to accessibility issues and safety concerns. Recent developments in robotic-based inspection can offer a great solution. Hard-to-reach places can be inspected without overhauling the unit, saving time and cost. Although several robots have been designed and implemented successfully for inspecting power plant components, in particular tubular structures, their mobility and flexibility are limited. Most of these robots use wheels for mobility which reduces their maneuverability of these robots. Moreover, they usually use magnets to attach to tubes which will not work on tubes with non-ferromagnetic materials. These robots usually carry measurement tools for a particular non-destructive testing (NDT) method such as ultrasound testing (UT) that requires a couplant to perform a point-by-point (scanning) inspection of the tubular structure. In this project, we developed a versatile lizard-inspired tube inspector (LTI) robot with embedded inspection sensing components for tube inspection which removes the need for point-by-point scanning of tube surface for crack and corrosion detection. Inspired by a “lizard”, the novelty of the current project is the integration of couplant-free ultrasound sensing and transmission, advanced data-driven defect detection and imaging, and friction-based mechanical mobility components in a single robot to eliminate a need for smooth surfaces and simple geometry for mobility and scanning. The LTI robot could replace the wheel-based approach with friction-based mobility to significantly increase the flexibility and maneuverability of the robot. The LTI robot can get into a power plant unit, such as a boiler, from a small area allowing it to access a component of interest for inspection (e.g., move on OD, curved and flat surfaces, non-ferromagnetic or ferromagnetic materials, and tubes with rough surfaces and complex geometries). Additionally, an advanced data-driven method using ultrasound data was pioneered to allow the robot to detect defects in the entire area between the robot’s multi-functional mobility system (grippers). Integrating a couplant-free ultrasound sensing in the robot’s grippers as well as using advanced data-driven methods allowed the LTI to detect defects in the entire cross-section of a tube using its grippers when stationary and when mobile.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Deep Learning for In-Situ Layer Quality Monitoring during Laser-Based Directed Energy Deposition (LB-DED) Additive Manufacturing Process

Defects are a leading issue for the rejection of parts manufactured through the Directed Energy Deposition (DED) Additive Manufacturing (AM) process. In an attempt to illuminate and advance in situ quality monitoring and control of workpieces, we present an innovative data-driven method that synchronously collects sensing data and AM process parameters with a low sampling rate during the DED process. The proposed data-driven technique determines the important influences that individual printing parameters and sensing features have on prediction at the inter-layer qualification to perform feature selection. Three Machine Learning (ML) algorithms including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are used. During post-production, a threshold is applied to detect low-density occurrences such as porosity sizes and quantities from CT scans that render individual layers acceptable or unacceptable. This information is fed to the ML models for training. Training/testing are completed offline on samples deemed “high-quality” and “low-quality”, utilizing only features recorded from the build process. CNN results show that the classification of acceptable/unacceptable layers can reach between 90% accuracy while training/testing on a “high-quality” sample and dip to 65% accuracy when trained/tested on “low-quality”/“high-quality” (respectively), indicating over-fitting but showing CNN as a promising inter-layer classifier.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of nonlinear ultrasonic waves behavior while interacting with poor interlayer bonds in large-scale additive manufactured materials

Over the past decades, researchers have developed several nonlinear ultrasonic techniques for quality control of materials commonly used in different applications. Owing to the superior sensitivity of nonlinear ultrasound waves to small defects such as micro-cracks, their applicability in different nondestructive testing (NDT) problems has been investigated in numerous studies. These studies utilize frequency domain analysis to detect the generation of higher harmonics because of the formation of defects in the inspected medium. Frequency domain analysis based on the Fourier transform is a significant approach used in linear systems; however, it may not perform adequately on nonlinear systems. Hence, studies on nonlinear dynamics and physics consider analyzing systems' behavior in the phase-space domain. In contrast to the frequency domain analysis, which can result in information loss, analysis in the phase-space domain retains all the information regarding a system's states. Here, we investigate the nonlinearities induced by poor interlayer bonding in polymer-based additive manufactured parts in the phase-space domain. It is convenient to characterize the nonlinearity in the phase-space domain because it provides a geometrical representation of a system's states. Two types of low quality interlayer bond are considered. The first type is simulated artificially while the second type is manufactured by reducing the bond quality during the printing process. The analysis verified that the received ultrasonic signals exhibit classical nonlinear behavior in the phase-space domain while interacting with simulated poor interlayer bonds. In addition, the results showed that the behavior of ultrasonic waves is amplitude-dependent and evolves into models that have not been previously reported. Furthermore, Largest Lyapunov Exponent (LLE) is used to quantify the behavior of nonlinear ultrasonic waves while interacting with poor interlayer bonds. Using LLE, it was observed that the divergence rate of the phase-space trajectories depends on the amplitude of the excitation. This observation quantitatively proves that nonlinear behavior of ultrasound while interacting with poor interlayer bonds can be amplitude-dependent. The results of both simulated and inherent poor interlayer bond cases showed that LLE can be used as a reliable quantitative damage-sensitive feature to detect and potentially characterize weak bonds, which are difficult to detect using conventional approaches. Additionally, the reported results in the phase-space domain provide a basis for proposing a new mathematical model for ultrasonic waves interacting with poor interlayer bonds.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Recurrence analysis of friction based dry-couplant ultrasonic Lamb waves in plate-like structures

Here, in this study, the effect of friction on the generation of dry-coupled Lamb waves is experimentally investigated. Recurrence analysis is performed to analyze the complex behavior of friction based dry-coupled Lamb waves. In particular, the effect of the normal force, which is necessary for a stronger dry-coupled Lamb wave generation and the friction, on the transmission of mechanical energy and determinism characteristics of Lamb waves are investigated. The results verify that larger friction coefficient and friction force are crucial for generation and propagation of strong Lamb waves supporting the fact that the main mechanism to transfer mechanical energy using dry-couplant is friction. The sensitivity of Lamb waves to the friction coefficient, highlights the importance of designing specific pads with respect to condition of the surface. Besides, the results show that the normal force and friction coefficient can change the determinism characteristics behavior of multimode Lamb waves. Furthermore, it is shown that the determinism value is sensitive to the friction coefficient and normal force. A similar trend is observed in the determinism values and friction coefficient. In general, a smaller friction coefficient indicates smaller determinism value. Additionally, it is shown that a normal load can change the behavior of a system, as observed from recurrence plots, owing to changes in the Lamb waves trajectories in the phase-space domain. In addition, it is shown that recurrence plots enable the detection of mode transitions in multimode Lamb waves. Recurrence analysis is a complementary tool to frequency domain methods for accurate analysis of multimode Lamb waves behavior.

42 ENGINEERING↗
DOE OSTI2021

A simplified framework for prediction of sensor network coverage in real-time structural health monitoring of plate-like structures

In this article, the coverage area prediction of piezoelectric sensor network for detecting a specific type of under-surface crack in plate-like structures is addressed. In particular, this article proposes a simplified framework to estimate the coverage of any given sensor network arrangement when a critical defect is known. Based on numerical results from finite element methods (FEM), a simplified framework to estimate coverage area of any given network arrangement is developed. Using such a simplified framework, one can avoid time-consuming procedure of evaluating numerous FEM models in estimating sensor network coverage. Back-scatter fields of partial cracks are estimated using a proposed function, whose parameters are estimated from the results of a limited number of FEM simulations. The proposed function efficiently predicts the back-scattered field of any combination of transmitters and receivers for a given crack geometry. Superposition is used to estimate the coverage area of an arbitrary piezoelectric (e.g., PZT) sensor network. It is shown that the coverage area of a sensor network depends on both sensor network geometry and defect properties (e.g., crack inclination) and it is not necessarily a linear function of the number of sensors. Furthermore, it is shown that the network arrangement has an important effect on the geometry of the coverage area. Experimental results of a network of 14 PZTs in two clusters confirm the accuracy of the method.

Engineering↗
DOE OSTI2021

Integrating Electromagnetic Acoustic Transducers in a Modular Robotic Gripper for Inspecting Tubular Components

Tubular structures are critical components in infrastructure such as power plants. Throughout their life, they are subjected to extreme conditions or suffer from defects such as corrosion and cracks. Although regular inspection of these components is necessary, such inspection is limited by safety-related risks and limited access for human inspection. Robots can provide a solution for automatic inspection. The main challenge, however, lies in integrating sensors for nondestructive evaluation with robotic platforms. As part of developing a versatile lizard-inspired tube inspector robot, in this study the authors propose to integrate electromagnetic acoustic transducers into a modular robotic gripper for use in automated ultrasonic inspection. In particular, spiral coils with cylindrical magnets are integrated into a novel friction-based gripper to excite Lamb waves in thin cylindrical structures. To evaluate the performance of the integrated sensors, the gripper was attached to a robotic arm manipulator and tested on pipes of different outer diameters. Two sets of tests were carried out on both defect-free pipes and pipes with simulated defects, including surface partial cracking and corrosion. The inspection results indicated that transmitted and received signals could be acquired with an acceptable signal-to-noise ratio in the time domain. Moreover, the simulated defects could be successfully detected using the integrated robotic sensing system.

Materials Science↗