Yunfeng Zhang, Ph.D.
Associate Professor, Department of Mechanical Engineering,
National University of Singapore
mpezyf@nus.edu.sg
Biography:
Y F Zhang received his B.Eng. in Mechanical Engineering from Shanghai Jiao Tong University, China in 1985 and Ph.D. from the University of Bath, UK in 1991. He is currently an Associate Professor at the Department of Mechanical Engineering, National University of Singapore. His research interests include (1) operations research, in particular, computational intelligence in design and manufacturing (process planning, scheduling, and their integration, VRP, and multi‐objective optimization); (2) hybrid manufacturing (3D printing and 5-axis machining) technology for parts repair; (3) Machine/deep learning. He has authored more than 200 publications and received various international awards including the Kayamori Best Paper Award in ICRA 1999 and the IMechE Thatcher Bros Prize in 2011.
Topic title：Robot-assisted Laser Metal Deposition: The Process Planning Issues
Abstract：Laser metal deposition (LMD) is one of the key metal 3D printing technologies for surface cladding and fabrication of near-net shape parts. Robot-assisted LMD significantly improves the designing space of the to-be-built component via moving the mounted nozzle freely during the deposition process. However, process planning of the robot-assisted LMD still heavily relies on the engineers’ empirical experiences and traditional trial and error method. In this work, we proposed a comprehensive robot-assisted LMD process planning framework.
Figure 1 Robot-assisted LMD Process Planning Frame
Firstly, a LMD laser scanning path design branch is developed to design the LMD nozzle path for building a given component. Process parameters, including slicing layer thickness, one-layer scanning pattern and depositing track profile, are incorporated in the designing branch. Specifically, these parameters can be customized in separated areas in each layer, which are divided according to the component’s geometry characteristics. To address conventional part, a conventional path design module is invoked; for revolved part, a different module (offset slicing and scanning path planning) has been developed.
Secondly, a robot path simulation branch is developed to simulate and verify the robot path derived from the LMD nozzle path. Based on the designed laser scanning path, robot related parameters, including robot joint parameters, material filling angle, nozzle speed and laser status, are generated. Generated robot code is subsequently simulated to check collisions among robot, stage, nozzle and building components. Related lines in the code will be further revised back in the designing branch based on feedback information (e.g., collision).
Thirdly, a LMD deposition path thermal analysis module is proposed to evaluate the designed laser scanning pattern. A developed thermal history analysis LMD finite element (FE) model would predict the temperature field evolution of the designed LMD nozzle path. The optimal scanning pattern with minimal distortion will further be determined by a thermal field based evaluation method developed in our previous work.
The robot-assisted LMD process planning framework is semiautomatic in current version, requiring the operator’s orders and intervention during the planning process. A fully automatic version is to be developed in the future work, incorporating optimization with empirical experience and digital twin evaluation in the planning process.
