Digital Commons Network^™

Experimental, Computational, And Machine Learning Methods For Prediction Of Residual Stresses In Laser Additive Manufacturing: A Critical Review, Sung Heng Wu, Usman Tariq, Ranjit Joy, Todd Sparks, Aaron Flood, Frank W. Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions …

On The Use Of Machine Learning And Data-Transformation Methods To Predict Hydration Kinetics And Strength Of Alkali-Activated Mine Tailings-Based Binders, Sahil Surehali, Taihao Han, Jie Huang, Aditya Kumar, Narayanan Neithalath

Electrical and Computer Engineering Faculty Research & Creative Works

The escalating production of mine tailings (MT), a byproduct of the mining industry, constitutes significant environmental and health hazards, thereby requiring a cost-effective and sustainable solution for its disposal or reuse. This study proposes the use of MT as the primary ingredient (≥70%mass) in binders for construction applications, thereby ensuring their efficient upcycling as well as drastic reduction of environmental impacts associated with the use of ordinary Portland cement (OPC). The early-age hydration kinetics and compressive strength of MT-based binders are evaluated with an emphasis on elucidating the influence of alkali activation parameters and the amount of slag or cement …

Optical Fiber Sensors Based On Advanced Vernier Effect - A Review, Wassana Naku, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

The Optical Vernier Effect Has Emerged as a Powerful Tool for Enhancing the Sensitivity of Optical Fiber Interferometer-Based Sensors, Ushering in a New Era of Highly Sensitive Fiber Sensing Systems. While Previous Research Has Primarily Focused on the Physical Implementation of Vernier Effect-Based Sensors using Different Combinations of Interferometers, Conventional Vernier Sensors Face Several Challenges. These Include the Stringent Requirements on the Sensor Fabrication Accuracy to Achieve a Large Amplification Factor, the Necessity of using a Source with a Very Large Bandwidth and a Bulky Optical Spectrum Analyzer, and the Associated Complex Signal Demodulation Processes. This Article Delves into Recent …

Assessing The Potential Of Uav-Based Multispectral And Thermal Data To Estimate Soil Water Content Using Geophysical Methods, Yunyi Guan, Katherine R. Grote

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a …

Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Smart water flooding is a promising eco-friendly method for enhancing oil recovery in carbonate reservoirs. The optimal salinity and ionic composition of the injected water play a critical role in the success of this method. This study advances the field by employing machine learning and data analytics to streamline the determination of these critical parameters, which are traditionally reliant on time-intensive laboratory work. The primary objectives are to utilize data analytics to examine how smart water flooding influences wettability modification, identify key parameter ranges that notably alter the contact angle, and formulate guidelines and screening criteria for successful lab design. …

Optimal Tilt-Wing Evtol Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks, Shuan Tai Yeh, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to address large-scale problems. Therefore, we showcase a novel regression generative adversarial network (regGAN) surrogate for fast interactive optimal takeoff trajectory predictions of …

Prediction Of Self-Consolidating Concrete Properties Using Xgboost Machine Learning Algorithm: Part 1–Workability, Amine El Mahdi Safhi, Hamed Dabiri, Ahmed Soliman, Kamal Khayat

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The Interest in Implementing Self-Consolidating Concrete (SCC) in Major Construction Projects Has Increased Significantly in Recent Years. This Paper Reports the Results of an Extensive Survey of Experimental Data of More Than 1700 SCC Mixtures from over 100 Studies Published in the Last Decade. the Survey Included the SCC Mixture Proportioning, Key Fresh Properties Including Flowability, Passing Ability, and Segregation Resistance, as Well as Some of the Derived Properties (E.g., Paste Volume). the Statistical Analysis of the Reported Parameters Showed Wide Variations in Values. the Outcome of the Survey Indicates that SCC Mixture Design and Workability Properties Do Not Systematically …

Fabrication Process Independent And Robust Aggregation Of Detonation Nanodiamonds In Aqueous Media, Inga C. Kuschnerus, Haotian Wen, Xinrui Zeng, Yee Yee Khine, Juanfang Ruan, Chun Jen Su, U. Ser Jeng, Hugues A. Girard, Jean Charles Arnault, Eiji Ōsawa, Olga Shenderova, Vadym Mochalin, Ming Liu, Masahiro Nishikawa

Chemistry Faculty Research & Creative Works

In the past detonation nanodiamonds (DNDs), sized 3–5 nm, have been praised for their colloidal stability in aqueous media, thereby attracting vast interest in a wide range of applications including nanomedicine. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication process, with their aggregate formation dynamics poorly understood. Here we reveal that DNDs in aqueous solution, regardless of their post-synthesis de-agglomeration and purification methods, exhibit hierarchical aggregation structures consisting of chain-like and cluster aggregate morphologies. With a novel characterization approach combining machine learning with direct cryo-transmission electron microscopy and with X-ray scattering and vibrational spectroscopy, …

On The Prediction Of The Mechanical Properties Of Limestone Calcined Clay Cement: A Random Forest Approach Tailored To Cement Chemistry, Taihao Han, Bryan K. Aylas-Paredes, Jie Huang, Ashutosh Goel, Narayanan Neithalath, Aditya Kumar

Materials Science and Engineering Faculty Research & Creative Works

Limestone calcined clay cement (LC3) is a sustainable alternative to ordinary Portland cement, capable of reducing the binder's carbon footprint by 40% while satisfying all key performance metrics. The inherent compositional heterogeneity in select components of LC3, combined with their convoluted chemical interactions, poses challenges to conventional analytical models when predicting mechanical properties. Although some studies have employed machine learning (ML) to predict the mechanical properties of LC3, many have overlooked the pivotal role of feature selection. Proper feature selection not only refines and simplifies the structure of ML models but also enhances these models' prediction performance and interpretability. This …

Experimental And Machine Learning Studies On Chitosan-Polyacrylamide Copolymers For Selective Separation Of Metal Sulfides In The Froth Flotation Process, Keitumetse Monyake, Taihao Han, Danish Ali, Lana Z. Alagha, Aditya Kumar

Mining Engineering Faculty Research & Creative Works

The froth flotation process is extensively used for the selective separation of valuable base metal sulfides from uneconomic associated minerals. However, in this complex multiphase process, various parameters need to be optimized to ensure separation selectivity and peak performance. In this study, two machine learning (ML) models, artificial neural network (ANN) and random forests (RF), were used to predict the efficiency of in-house synthesized chitosan-polyacrylamide copolymers (C-PAMs) in the depression of iron sulfide minerals (i.e., pyrite) while valuable base metal sulfides (i.e., galena and chalcopyrite) were floated using nine flotation variables as inputs to the models. The prediction performance of …

A Machine Learning Specklegram Wavemeter (Maswave) Based On A Short Section Of Multimode Fiber As The Dispersive Element, Ogbole C. Inalegwu, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Wavemeters are very important for precise and accurate measurements of both pulses and continuous-wave optical sources. Conventional wavemeters employ gratings, prisms, and other wavelength-sensitive devices in their design. Here, we report a simple and low-cost wavemeter based on a section of multimode fiber (MMF). The concept is to correlate the multimodal interference pattern (i.e., speckle patterns or specklegrams) at the end face of an MMF with the wavelength of the input light source. Through a series of experiments, specklegrams from the end face of an MMF as captured by a CCD camera (acting as a low-cost interrogation unit) were analyzed …

Advanced Ensemble Modeling Method For Space Object State Prediction Accounting For Uncertainty In Atmospheric Density, Smriti Nandan Paul, Richard J. Licata, Piyush M. Mehta

Mechanical and Aerospace Engineering Faculty Research & Creative Works

For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space traffic management activities such as the satellite conjunction analysis. This paper investigates the evolution of orbit error distribution in the presence of atmospheric density uncertainties, which are modeled using probabilistic machine learning techniques. The recently proposed "HASDM-ML," "CHAMP-ML," and "MSIS-UQ" machine learning models for density estimation (Licata and Mehta, 2022b; Licata et al., 2022b) are used in this work. The investigation is convoluted because of the spatial and temporal correlation of the atmospheric density …

A Genome-Wide Association Study Coupled With Machine Learning Approaches To Identify Influential Demographic And Genomic Factors Underlying Parkinson’S Disease, Md Asad Rahman, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson's disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson's disease. Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. …

A Fiber-Optic Sensor-Embedded And Machine Learning Assisted Smart Helmet For Multi-Variable Blunt Force Impact Sensing In Real Time, Yiyang Zhuang, Taihao Han, Qingbo Yang, Ryan O'Malley, Aditya Kumar, Rex E. Gerald, Jie Huang

Materials Science and Engineering Faculty Research & Creative Works

Early on-site diagnosis of mild traumatic brain injury (mTBI) will provide the best guidance for clinical practice. However, existing methods and sensors cannot provide sufficiently detailed physical information related to the blunt force impact. In the present work, a smart helmet with a single embedded fiber Bragg grating (FBG) sensor is developed, which can monitor complex blunt force impact events in real time under both wired and wireless modes. The transient oscillatory signal "fingerprint" can specifically reflect the impact-caused physical deformation of the local helmet structure. By combination with machine learning algorithms, the unknown transient impact can be recognized quickly …

Additive Manufacturing Of Complexly Shaped Sic With High Density Via Extrusion-Based Technique – Effects Of Slurry Thixotropic Behavior And 3d Printing Parameters, Ruoyu Chen, Adam Bratten, Joshua Rittenhouse, Tian Huang, Wenbao Jia, Ming-Chuan Leu, Haiming Wen

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Additive manufacturing of dense SiC parts was achieved via an extrusion-based process followed by electrical-field assisted pressure-less sintering. The aim of this research was to study the effect of the rheological behavior of SiC slurry on the printing process and quality, as well as the influence of 3D printing parameters on the dimensions of the extruded filament, which are directly related to the printing precision and quality. Different solid contents and dispersant- Darvan 821A concentrations were studied to optimize the viscosity, thixotropy and sedimentation rate of the slurry. The optimal slurry was composed of 77.5 wt% SiC, Y2O3 and Al2O3 …

Predicting Defects In Laser Powder Bed Fusion Using In-Situ Thermal Imaging Data And Machine Learning, Sina Malakpour Estalaki, Cody S. Lough, Robert G. Landers, Edward C. Kinzel, Tengfei Luo

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. …

Artificial Intelligence In Civil Infrastructure Health Monitoring—Historical Perspectives, Current Trends, And Future Visions, Tarutal Ghosh Mondal, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the …

Machine Learning Assisted High-Sensitivity And Large-Dynamic-Range Curvature Sensor Based On No-Core Fiber And Hollow-Core Fiber, Chen Zhu, Yiyang Zhuang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Simultaneously Increasing the Sensitivity and Dynamic Range of an Optical Fiber Sensor is Desired and Yet Challenging. in This Article, We Demonstrate an Optical Fiber Curvature Sensor based on a No-Core Fiber (NCF) Cascaded with a Hollow-Core Fiber (HCF), Realizing Simultaneously High Sensitivity and a Large Dynamic Range with the Assistance of Machine Learning Analysis. the Sensor is Fabricated by Simply Fusion Splicing a Section of NCF and HCF to Two Single-Mode Fibers (SMFs), Forming the SMF-NCF-HCF-SMF Hybrid Structure. It is Shown that the Multimode Interference in the NCF Can Increase the Sensitivity of the Device for Curvature Measurements, Compared …

Snow Parameters Inversion From Passive Microwave Remote Sensing Measurements By Deep Convolutional Neural Networks, Heming Yao, Yanming Zhang, Lijun Jiang, Hong Tat Ewe, Michael Ng

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel inverse method based on the deep convolutional neural network (ConvNet) to extract snow's layer thickness and temperature via passive microwave remote sensing (PMRS). The proposed ConvNet is trained using simulated data obtained through conventional computational electromagnetic methods. Compared with the traditional inverse method, the trained ConvNet can predict the result with higher accuracy. Besides, the proposed method has a strong tolerance for noise. The proposed ConvNet composes three pairs of convolutional and activation layers with one additional fully connected layer to realize regression, i.e., the inversion of snow parameters. The feasibility of the proposed method …

Classification Of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach, Yanwei Zhang, Stephen S. Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Shear wave splitting (SWS) analysis is widely used to provide critical constraints on crustal and mantle structure and dynamic models. In order to obtain reliable splitting measurements, an essential step is to visually verify all the measurements to reject problematic measurements, a task that is increasingly time consuming due to the exponential increase in the amount of data. In this study, we utilized a convolutional neural network (CNN) based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data …

Predicting Compressive Strength Of Alkali-Activated Systems Based On The Network Topology And Phase Assemblages Using Tree-Structure Computing Algorithms, Rohan Bhat, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

Alkali-activated system is an environment-friendly, sustainable construction material utilized to replace ordinary Portland cement (OPC) that contributes to 9% of the global carbon footprint. Moreover, the alkali-activated system has exhibited superior strength at early ages and better corrosion resistance compared to OPC. The current state of analytical and machine learning models cannot produce highly reliable predictions of the compressive strength of alkali-activated systems made from different types of aluminosilicate-rich precursors owing to substantive variation in the chemical compositions and reactivity of these precursors. In this study, a random forest model with two constraints (i.e., topological network and thermodynamic constraints) is …

Quantitative Evaluation Of Steel Corrosion Induced Deterioration In Rubber Concrete By Integrating Ultrasonic Testing, Machine Learning And Mesoscale Simulation, Jinrui Zhang, Mengxi Zhang, Biqin Dong, Hongyan Ma

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Chloride-induced steel corrosion seriously affects the durability of reinforced concrete structures. Rubber concrete, an environmentally friendly construction material in which waste rubber is recycled as a concrete component, has demonstrated superior resistance to chloride-induced steel corrosion and the subsequent concrete deterioration. However, quantitative evaluation of the degree of deterioration in rubber concrete based on nondestructive detection is challenging due to the complexity of the material. In this paper, reinforced concrete specimens with rubber contents of 0, 10% and 20% are subjected to the electrochemically accelerated corrosion experiments and monitored by ultrasonic testing. Six machine learning models are trained by the …

Prediction Of Soil Water Content And Electrical Conductivity Using Random Forest Methods With Uav Multispectral And Ground-Coupled Geophysical Data, Yunyi Guan, Katherine R. Grote, Joel Schott, Kelsi Leverett

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling …

An Intelligent Distributed Ledger Construction Algorithm For Iot, Charles Rawlins, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed …

A Dnn-Ensemble Method For Error Reduction And Training Data Selection In Dnn Based Modeling, Ling Zhang, Da Li, Jiayi He, Bhyrav Mutnury, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Jun Fan, James L. Drewniak, Er Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

Deep neural networks (DNNs) have been widely adopted in modeling electromagnetic compatibility (EMC) problems, but the training data acquisition is usually time-consuming through various simulators. This paper presents a powerful approach using an ensemble of DNN s to effectively reduce the training data size in DNN-based modeling problems. A batch of training data with the largest uncertainties is selected using active learning through the variance among the ensemble of DNNs. Subsequently, a greedy sampling algorithm is applied to select a data subset using diversity. Thus, the proposed method can achieve both uncertainty and diversity in data selection. By averaging the …

Self-Vernier Effect-Assisted Optical Fiber Sensor Based On Microwave Photonics And Its Machine Learning Analysis, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Optical Vernier Effect Has Been Recently Demonstrated as a Tool to Enhance the Sensitivity of Optical Fiber Interferometric Sensors and Has Become a Hot Topic in the Last Few Years. the Generation of the Vernier Effect Relies on the Superposition of Interferograms of Two Interferometers (A Sensing One and a Reference One) with Marginally Different Optical Path Differences (OPDs), Where an Amplitude Modulation-Like Signal is Sustained in the Output Spectrum. the Vernier Modulation Envelope Exhibits Significantly Magnified Sensitivity in Response to External Perturbations, compared to the Individual Sensing Interferometer, Providing a New Route to New Generations of Ultra-Sensitive Optical Fiber …

Depression Of Pyrite In Polymetallic Sulfide Flotation Using Chitosan-Grafted-Polyacrylamide Polymers, Keitumetse Cathrine Monyake

Doctoral Dissertations

“In this work, Chitosan-grafted-Polyacrylamides (Chi-g-PAMs) were studied, for the first time, as selective depressants of pyrite in the flotation of base metal sulfides. Fundamental studies of the adsorption behavior of Chi-g-PAM on model sulfide minerals indicated that Chi-g-PAM was more selective to pyrite’s surfaces as compared to base metal sulfides. Results suggested that the adsorption of Chi-g-PAM at pyrite-water interface was a chemisorption in nature which involved the amine, amide, and hydroxyl groups of Chi-g-PAM. Batch flotation studies of complex sulfide ore of Mississippi Valley Type (MVT) showed that Chi-g-PAM outperformed other pyrite’s depressants at producing less pyrite-diluted concentrates. Statistical …

Groundwater Withdrawal Estimation Using Integrated Remote Sensing Products And Machine Learning, Sayantan Majumdar

Doctoral Dissertations

"The rising demands for water, food, and energy primarily driven by the increasing global population constitute a pressing issue worldwide. Therefore, the water-food-energy nexus plays a substantial role in developing globally applicable sustainable solutions. Recent technological advancements, including the earth observation programs using spaceborne remote sensing platforms, have enabled us to monitor various critical components affecting the globe. Groundwater, which comprises the world's 30% freshwater, is one such key component of the global water resources and supplies nearly half of the global drinking water.

Despite groundwater overdraft in many parts of the world, including the United States (US), there are …

Integrating Remote Sensing And Model-Based Datasets In A Machine Learning Model To Map Global Subsidence Associated With Groundwater Withdrawal, Md Fahim Hasan

Masters Theses

"Quantifying groundwater storage loss is becoming increasingly essential globally due limited availability of this major hydrologic component and its long recharge time. Groundwater overdraft gives rises to multiple adverse impacts including land subsidence and permanent groundwater storage loss. In absence of spatially dense monitoring network, publicly available in-situ data, and uniform monitoring strategies, it is challenging to assess the sustained losses from overexploitation of this resource. Remote sensing based techniques have the capacity to fill this gap to increase our groundwater monitoring capacities. Exploring the interrelation between groundwater pumping and land subsidence using remote sensing datasets can be a very …

A Convolutional Neural Network (Cnn) For Defect Detection Of Additively Manufactured Parts, Musarrat Farzana Rahman

Masters Theses

“Additive manufacturing (AM) is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. The convolutional neural network (CNN) has been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes. A network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the …