Ph.D. researcherXiaoyi Guan
Thermalmechanical modeling of direct powder deposition additive manufacturing process
The people
behind the work.
21 researchers in the lab today, and 45 alumni since 2014 — many now across industry and academia.
Principal investigator
01Professor and William Dawson ScholarYaoyao Fiona Zhao
Digitization changes traditional thinking and manufacturing technologies. Manufacturing in the 21st century will become more intelligent, flexible, automated, and sustainable. I seek to advance manufacturing industry with new design theories, manufacturing information technologies, and simulation and optimization infrastructures to reach smarter and more sustainable manufacturing.
Doctoral researchers
08
Ph.D. researcherMutahar Safdar
Data-driven anomaly prediction and operator guidance to support direct energy deposition process as effective repair technique
Ph.D. researcherManuel Sage
Developing an intelligent tool to suggest and assess power plant configurations
Ph.D. researcherChonghui Zhang
Investigation on applying machine learning to explore NPR (Negative Poisson’s Ratio) structure design space
Ph.D. researcherJiarui Xie
Smart Manufacturing: data analysis and predictive analysis using Machine Learning technologies in the assembly line
Ph.D. researcherBogdan Bogachov
Developing a computer vision algorithm to detect aircraft structural damage
Masters & graduate
05
M.Eng. researcherOmar Lebbar
Object detection in 2D Engineering Drawings for the Estimation of Carbon Footprint
M.Eng. researcherDaniel Amoshie
Image-based and machine learning-supported powder spreadability analysis for laser powder bed fusion (LPBF) additive manufacturing (AM) processes.
M.Eng. researcherPoobesh Kumar Subramaniam
Life Cycle and Environmental Impact Assessment of a Centralized Lubrication System
Undergraduate researchers
03
Undergraduate researcherAchraf Maine
Laser Powder Bed Fusion (LPBF) layer-wise image processing for defect prediction and improved part quality, specifically on in-situ powder-spreading data
Undergraduate researcherChris Liu
Application of machine learning and product data digitization techniques to streamline Life Cycle Assessment
Undergraduate researcherJuliette Fogarty
Shape-changing origami structures, focusing on their design, simulation, and material optimization for adaptable engineering applications.
Lab members
04
Alumni — where they are now
45 alumni
Ph.D.Pavan Tejaswi Velivela
Data Science/ML assisted Biologically Inspired Industrial Design & Engineering
Ph.D.Yuan Liu
Exchange Student at ADML; Thesis Title: Modeling and Optimization Methods for Multi-Scale Heterogeneous Lattice Structures
M.Eng.Rachel Yeung
Development of Sustainability Assessment Tool for Use within Supply Chain Management Process
M.Eng.Samuel Iyengar
Bayesian and multi-objective optimization methods for auxetic material database generation
M.Eng.Shivangi Sarabhai
Study, produce and measure cellular structures (Lattice design optimization for Lightweight Structures) related to their flow behavior.
M.Eng.Siyuan Sun
Thesis Title: Investigating Multi-label Classification for Bio-inspired Design by Using Text Mining and Natural Language Processing
M.Eng.Xin Xu
Thesis Title: Process Modeling and Sustainability Analysis of Binder-jetting Additive Manufacturing Processes
B.Eng.Josh Campbell
Developing machine learning models to forecast time-series variables for use in an energy management system
B.Eng.Lynn Cherif
Development and implementation of computer vision based deep learning models for microstructure classification in directed-energy deposition
B.Eng.Sariel Coronado
My research concerns the application of a comprehensive Information Modelling approach to LBPF build files and the creation of industry-applicable data modeling schemas for the generation and verification of build file data structures.