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Advanced Additive Manufacturing Laboratory

Experimental setup for multimodal monitoring of Laser Directed Energy Deposition (LDED) processes for geometric variation detection.

The Advanced Additive Manufacturing Laboratory's research focuses on advancing smart and intelligent manufacturing technologies by enhancing the quality and efficiency of the fabrication processes through real-time monitoring, offline inspection and AI-driven techniques for quality prediction and evaluation.

While the Advanced Additive Manufacturing Laboratory's primary focus is currently centered on the Directed Energy Deposition (DED) additive manufacturing process, our research scope reaches well beyond DED.

Details of our current projects can be found in the Projects section.

Capabilities

Our group embraces a wide range of advanced manufacturing technologies as part of our ongoing exploration and innovation efforts:

Advanced Manufacturing

Smart Manufacturing: multi-modal process monitoring of manufacturing processes, such as metal additive manufacturing, polymer based additive manufacturing, integrated with AI based data processing for developing intelligent manufacturing systems.
Quality inspection in manufacturing and assembly lines using advanced sensor systems

Mechanical Testing and Material Characterization

Mechanical Property Testing: Determining fundamental material properties including tensile strength, compression, and flexural modulus.
Metallographic Sample Preparation: Preparing material samples via precision sectioning, mounting and polishing for microstructural examination.

Microscopy and Microstructural Analysis

Non-destructive, high-resolution 3D imaging of internal structures, porosity, and morphology of materials and components using Micro-CT X-ray Microscopy.
Optical Microscopy and Scanning Electron Microscopy Imaging: High-resolution imaging of polished and etched samples for microstructural analysis, phase identification, and defect detection. Example applications: measuring melt pool depth and width of printed tracks.

Structural Health Monitoring (SHM) and Non-Destructive Evaluation (NDE)

Real-time Damage Detection: Using Acoustic Emission (AE) to actively monitor and detect the initiation and growth of defects like cracks in real-time.
Defect Identification and Classification: Analyzing AE signals to classify the type and severity of damage occurring within a material or structure.
3D Scanning and Reverse Engineering: Capturing the precise geometry of physical objects to create digital models for analysis, modification, or replication.
Dimensional Analysis and Quality Control: Performing high-precision geometric measurements and automated inspections to verify part accuracy against CAD models.
Thermal Imaging and Infrared Thermography: Utilizing high-resolution infrared cameras to detect thermal anomalies, monitor heat distribution, and identify subsurface defects, delamination, or areas of stress concentration. It collects melt pool temperatures and thermal gradients in real-time during printing processes.

Souran ManoochehriPrincipal Investigator

Souran Manoochehri, Ph.D.
Professor and Chair of the Department of Mechanical Engineering

Souran Manoochehri’s research interests are in areas of design and manufacturing, including additive manufacturing, computer integrated design and manufacturing and intelligent modeling and optimization. His research on additive manufacturing and integrated design integrates mathematical modeling, machine learning methods and experimental studies, providing product and process quality assurance.

Contact information: smanooch@stevens.edu | 201-216-5562

About Souran Manoochehri

Co-Principal Investigators

Chaitanya Krishna Vallabh
Teaching Assistant Professor, Department of Mechanical Engineering

Chaitanya Vallabh’s research expertise lies in the broad area of system dynamics and vibrations with specialization in microparticle adhesion and manipulation using acoustic techniques coupled with laser interferometry. His research experience and interests also include additive manufacturing (AM), advanced, smart, and scalable manufacturing methods, elastic wave propagation, ultrasonic non-destructive evaluation, structural health monitoring (SHM), systems integration, computer vision and signal processing.

Current Research Interests: Advanced manufacturing, Additive manufacturing, Engineering Education, Machine Learning Applications in Manufacturing and Automation and Big Data Analysis

Contact information: cvallabh@stevens.edu | 201-216-5051

Chan Yu
Lecturer, Department of Mechanical Engineering

Chan Yu is extensively involved in teaching courses on manufacturing, which include design for manufacturability, design for additive manufacturing, advanced additive manufacturing and optimization principles in mechanical engineering. His research interests lie in the areas of design and manufacturing, with a focus on design optimization, Design for Manufacturing and Assembly (DFMA), and additive manufacturing. One of his recent projects focused on the development of efficient optimization techniques for defect detection in manufacturing assembly.

Contact information: cyu@stevens.edu | 201-216-5561

Student Researchers

Ke Xu

Ph.D. Candidate in Mechanical Engineering

Youmna Mahmoud

Ph.D. Candidate in Mechanical Engineering

Sudharshanan Dhabaseelan Vasugi

Ph.D. Student in Mechanical Engineering

André Colón

Ph.D. Student in Mechanical Engineering

Alumni

Javid Akhavan, Ph.D.

AI Engineer at ZS Consultants

Jiaqi Lyu, Ph.D.

Systems Software Engineer at Precision X-Ray, Inc.

Current Projects

Sensor-based Monitoring and Data-driven Quality Prediction

Sensor-based Monitoring and Data-driven Quality Prediction in Additive Manufacturing

Lead Student Researcher: Ke Xu

Multimodal Monitoring for Geometric Variation in LDED: This project, developed a novel multimodal monitoring framework that synergistically integrates contact-based acoustic emission (AE) sensing with vision-based coaxial camera monitoring. The primary goal was to enable layer-wise identification and evaluation of geometric variations in Laser Directed Energy Deposition (LDED) fabricated parts, using specimens with and without through-holes as test cases. By applying multiple machine learning algorithms including SVM, neural networks, random forest, gradient boosting, XGBoost, and logistic regression, the integrated multimodal strategy demonstrated superior classification performance of 94.4% accuracy, significantly surpassing individual sensing modalities. This research established a technical foundation for characterizing part variations and manufacturing-induced defects.

Experimental setup for multimodal monitoring of Laser Directed Energy Deposition (LDED) processes for geometric variation detection.

Optomec LDED machine chamber with an integrated AE sensor.

Predicting Mechanical Hardness in LDED using AE and Machine Learning: This project aimed to develop a non-destructive method coupled with machine learning (ML) models for predicting the mechanical hardness of samples printed by the Laser Directed Energy Deposition (LDED) process. By leveraging real-time Acoustic Emission (AE) signals captured during the printing process, the study investigated the influence of key printing parameters (overlap ratio, dwell time, and number of layers) on both AE signal characteristics and the resulting mechanical hardness. Three ML regression models—Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Gaussian Process Regression (GPR) were developed and tested to establish a predictive relationship between AE features and hardness. GBR demonstrated the highest accuracy, achieving a Mean Absolute Error (MAE) of 2.72 and a Mean Absolute Percentage Error (MAPE) of 3.43% on the testing set. This highlights the potential of integrating AE sensors with advanced machine learning algorithms for real-time, non-destructive prediction of mechanical properties in LDED additive manufacturing processes.

Poster

Melt Pool Depth Prediction in Directed Energy Deposition (DED)

Sim-to-Real Transfer Learning Approach: Real-Time Melt Pool Analysis in DED

Multi-Sensor Quality Inspection System

Equipment

Our research employs covers a host of areas, each of which is furnished with state-of-the-art equipment.

Additive and Advanced Manufacturing

Mechanical Property Testing

Metallographic Sample Preparation

Microstructure Analysis

Sensors

Publications

Peer-Reviewed Journal Publications

Conference Proceedings

Sudharshanan Dhabaseelan Vasugi, Chaitanya Krishna Prasad Vallabh, and Souran Manoochehri, "Multi-Sensor 3D Inspection System for Enhanced Manufacturing Quality," Proceedings of the ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2025), August 17–20, 2025, Anaheim, CA.
Mahmoud, Y, Lyu, J, Vallabh, CKP, & Manoochehri, S. "Melt Pool Depth Prediction in Directed Energy Deposition Single-Track Prints Using Point Cloud Analysis." Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2A: 44th Computers and Information in Engineering Conference (CIE). Washington, DC, USA. August 25–28, 2024. V02AT02A024. ASME. https://doi.org/10.1115/DETC2024-141272
Akhavan, J., Mahmoud, Y., Xu, K., Lyu, J., & Manoochehri, S. (2023, July). TDIP: Tunable Deep Image Processing, a Real Time Melt Pool Monitoring Solution. In 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) (pp. 1899-1908). IEEE.
Lyu, J, Akhavan, J, Mahmoud, Y, Xu, K, Vallabh, CKP, & Manoochehri, S. "Real-Time Monitoring and Gaussian Process-Based Estimation of the Melt Pool Profile in Direct Energy Deposition." Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering Conference. Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering. New Brunswick, New Jersey, USA. June 12–16, 2023. V001T01A024. ASME. https://doi.org/10.1115/MSEC2023-105104
Mahmoud, Y, & Manoochehri, S. "In-Situ Temperature Monitoring of ABS During Fused Filament Fabrication (FFF) Process With Varying Process Parameters." Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3A: 47th Design Automation Conference (DAC). Virtual, Online. August 17–19, 2021. V03AT03A031. ASME. https://doi.org/10.1115/DETC2021-69813
Xu, K., & Manoochehri, S. (2021). Health Monitoring Using Acoustic Emission Technique During Fused Filament Fabrication Printing Process. ASME. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, V010T10A006–V010T10A006.
Xu, K., & Manoochehri, S. (2019). Job Shop Scheduling Optimization Using Genetic Algorithm With Global Criterion Technique. ASME. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, V001T02A059–V001T02A059.

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