Industrial AI Group
The University of Auckland
About Us
The Industrial AI Group @UoA is dedicated to developing translatable intelligent system technologies to transform the workplace into a more efficient, enjoyable, and sustainable environment. We focus on cognitive sensing, reasoning, control, and human interaction technologies, with applications in manufacturing processes, assembly, human-machine collaboration, and quality assurance.
Our work has been sponsored by prestigious funding bodies (e.g., National Science Challenge and Callaghan Innovation) and leading high-tech companies (e.g., Spark, Nokia, Oasis Engineering, and Fletcher Steel), with a total portfolio of >NZD $2.5 million. Several pieces of work have been adopted in the commercial world.
News
Sponsors
Meet the Team
Principal Investigator
Yuqian Lu
Principal Investigator / Senior Lecturer
Graduate Students
Regina Lee
PhD Student
Akhilnandh Ramesh
PhD Student
Dazzle Johnson
PhD Student
Li Xu
PhD Student
Travis Augenstein
PhD Student
Cooper Li
PhD Student
Rui Zhou
Masters Student
Changjin He
Masters Student
Zeqiang Zhu
PhD Student
Xing(Sean) Wang
Master Student
Rain Mu
Master student
Zhuoyuan Yu
PhD Student
Visiting PhD Students
Shengwei Fu
Visiting PhD Student
Alumni
Luyao Xia
Visiting PhD Student
Saahil Chand
PhD Student
Zhaojun Qin
PhD Student
Benedict Liang
Master Student
Hongfei Liu
Visiting PhD Student
Jonas Wan
Master Student
Joshua Goh
Master Student
Karen Wang
Master Student
Meilin Kang
Visiting PhD Student
Meimei Su
Visiting PhD Student
Munirah Mahadi
Master Student
Qilong Wang
Visiting Scholar / Lecturer
Sidhant Raje
Master Student
Timothy Ho
Master Student
XiangFu Fu
Visiting Scholar / Associate Professor
Xurui Li
Visiting PhD Student
Yi Zhang
Visiting PhD Student
Yong Gui
Visiting PhD Student
Ziwei Su
Master Student
Equipment
AI PCs
CNC
Conveyor
HRC Station
KUKA KR10
KUKA KR16
MES
Mobile Manipulator
UR5e
Initiatives
Research Gallery
Featured Publications
Recent Publications
Quickly discover relevant content by filtering publications.
(2024).
A method for constructing a machining knowledge graph using an improved transformer.
Expert Systems with Applications.
(2024).
A multi-objective joint optimisation method for simultaneous part family formation and configuration design in delayed reconfigurable manufacturing system (D-RMS).
International Journal of Production Research.
(2024).
A systematic framework for tackling anomalous pre-welding workpiece postures with regular butt joints based on prototype features.
Journal of Manufacturing Systems.
(2024).
Dynamic decision-making for knowledge-enabled distributed resource configuration in cloud manufacturing considering stochastic order arrival.
Robotics and Computer-Integrated Manufacturing.
(2024).
Human-Robot Shared Assembly Taxonomy: A step toward seamless human-robot knowledge transfer.
Robotics and Computer-Integrated Manufacturing.
(2024).
Semantic knowledge-driven A-GASeq: A dynamic graph learning approach for assembly sequence optimization.
Computers in Industry.
(2023).
A digital thread-driven distributed collaboration mechanism between digital twin manufacturing units.
Journal of Manufacturing Systems.
(2023).
A Knowledge Graph-based knowledge representation for adaptive manufacturing control under mass personalization.
Manufacturing Letters.
(2023).
Digital twin and parameter correlation-enabled variant design of production lines.
International Journal of Computer Integrated Manufacturing.
Contact
- yuqian.lu@auckland.ac.nz
- +64 93737599 Ext.81584
- 20 Symonds Street, Auckland, 1010
- Enter Building 405 and take the stairs to Office 869 on Floor 8
- Book an appointment