Manufacturing Technologies

Steven Y. Liang is Regents’ Professor and Morris M. Bryan Jr. Professor in Mechanical Engineering for Advanced Manufacturing Systems at the Georgia Institute of Technology, USA. He earned his PhD in Mechanical Engineering from the University of California at Berkeley. His research interests include modeling, monitoring, and control of precision machining and additive manufacturing processes and systems. He has taken leadership roles in professional organizations including serving as President of NAMRI/SME, Chair of MED/ASME, and as a corporate president in a global publicly traded manufacturing enterprise. He currently serves as Technical Editor of the International Journal of Precision Engineering and Manufacturing and Editor-in-Chief of the Journal of Manufacturing and Materials Processing. His recognitions include awards from ASME, SME, and SAE, such as the Milton C. Shaw Manufacturing Research Medal, Blackall Machine Tool and Gage Award, and multiple distinguished research and lifelong service honors. He is a Fellow of ASME, SME, and AET.

• Part I - Engineering Material Structure Design and Performance Experiment.- Design and Testing of a Wireless Rotating Force-Measuring Tool Holder Based on a Tri-Point Piezoelectric Sensor.- Compression Strength of Golden Ratio Graded Ti-6Al-4V Auxetic Lattices Fabricated by Additive Manufacturing.- A Screening Method for Failure Mode Identification Considering the Deformation Characteristics of Composite Tubes.- A Variable Stiffness Unit Based on Electrostatic Adsorption Film.- Part II - Digital Additive Manufacturing and Material Processing Technology.- Acoustic-Based Tool Classification and Cutting Parameter Prediction in GFRP–CFRP Hybrid Composite Drilling Using XGBoost.- Simulation Analysis on Bending Performance of Foldable OLED Module.- Development of a Digital Twin Framework for Machining Process Anomalies Detection.- Optimization of EDM Parameters for Ti-6Al-4V in Deionized Water Using Taguchi Method.- Machine-Learning Approaches for Process and Tool Wear Detection Using Acoustic Signals in Ball-End Milling.- Statistical Process Monitoring Based on Automatic Extraction of ROIs for Fused Deposition Modeling.- etc.