Advanced analytics for advanced manufacturing


The AMTex team conducts research aimed at advancing the state of the knowledge in Additive Manufacturing (AM) processes; colloquially known as 3D Printing. The multi-disciplinary research team has a unique combination of expertise in advanced analytics, uncertainty quantification, process monitoring, material science, and reliability engineering.


ICME-based Qualification and Certification


Use tools of integrated computational materials engineering (ICME) and develop AM-focused material simulation models to accelerate the qualification and certification (Q&C) of AM-fabricated metal parts. The Q&C process is broadly defined as the process of ensuring that the properties of the fabricated parts will meet design requirements


Uncertainty Quantification


Conduct systematic uncertainty quantification to identify and reduce sources of variability in metal AM. This includes:

  • Surrogate modeling, model calibration, and uncertainty propagation analysis of the ICME-based models developed by the team

  • Data-driven modeling and design of experiments of the AM process


Process Monitoring and Control


This area focuses on: (1) the integration and instrumentation of AM systems with advanced sensors to perform in-situ monitoring and, (2) develop models and methods to analyze the data streams acquired using process monitoring and conduct process control with the objective of improving the quality of fabricated parts.


Design and fabrication of customized medical implants


Leverage team expertise and research findings to design and fabricate medical implants and devices with patient- and location-specific properties to improve functionality



4D-Printed Active
Metallic Structures

Multi-stage shape recovery in a U-shaped additively manufactured NiTi build piece using Laser Powder Bed Fusion (LPBF). Two “arms” of the piece activate their shape recovery at different temperatures, creating a location-dependent active response; The location-dependent active response is created by changing the LPBF processing at different sections of the build, which results in differences in the transformation temperatures in corresponding sections.


Two different sets of LPBF processing parameters were used to process two different sections in a single Ni50.9Ti49.1 SMA U-shaped build piece. The two arms of the U-shape build piece were subsequently pre-formed at 0 °C in the martensite state. As the part is heated, the reverse transformation from martensite to austenite occurs, resulting in a two-stage shape change where different sections of the part activate the shape memory response at different temperature intervals and at different rates.


Funded projects at AMTEX


NASA Early Stage Innovations (ESI) - 2014

The Early Stage Innovation (ESI) Appendix is part of the NASA SpaceTech-REDDI Research Announcement.

Title: “Control of Variability in the Performance of Selective Laser Melting (SLM) Parts through Microstructure Control and Design”

Principal Investigators: Alaa Elwany, Ibrahim Karaman, Raymundo Arroyave

Research Fellow: Ji Ma

Airforce Office of Scientific Research (AFOSR) –Defense University Research Instrumentation Program (DOD-DURIP) - 2015

Title: “Multi-Material Bulk Deposition and Characterization System for Accelerated Materials Discovery and Design”

Principal Investigators: Ibrahim Karaman, Raymundo Arroyave, Alaa Elwany, Dimitris Lagoudas, Miladin Radovic, and Patrick Shamberger

Research Fellow: Ji Ma

Texas A&M Strategic Areas Interdisciplinary Research Seed Grant – 2016

Title: “Additive Manufacturing of Patient-specific Medical Devices: the Path from Tailored

Geometry to Tailored Material Properties”

Principal Investigators: Alaa Elwany, Ibrahim Karaman, Raymundo Arroyave, Darren Hartl

Research Fellow: Ji Ma


Relevant Resources

Here we introduce valuable resources for students, colleagues, and peers in the area of advanced manufacturing.


calibration of Multi-output computer models

This repository consists of MATLAB codes and a sample data set for the purpose of emulation (surrogate modeling) and statistical calibration of computer models using Gaussian processes. The data set used as an example come from an FEM thermal model developed for metal-based additive manufacturing. It includes the codes for loading the data, Monte Carlo simulation, and visualization of the outputs.


NANOhub is a science and engineering gateway comprising community-contributed resources and geared toward educational applications, professional networking, and interactive simulation tools for nanotechnology.



There are hundreds of industrial AM machines and materials. New products come to market weekly. Picking the best option for a manufacturing or research project is a tough call and a wrong direction can be costly. The Senvol Database™ details over 950 AM machines and more than 1,700 compatible materials. Within Granta software, you can search and compare materials based on properties, type, or compatible machines. Identify and compare machines based on supported processes, manufacturer, required part size, cost, or compatible materials (and their properties). Quickly focus on the most likely routes to achieve project goals, save time and get new ideas as you research AM options.