The Singapore Centre for 3D Printing (SC3DP) offers a full range of additive manufacturing services, from design and infrastructure to production and validation. With advanced technologies such as topology optimization, generative design, and bioinspired design, SC3DP creates optimized products using a variety of novel materials and printing processes, including hybrid printing, post-processing, and emerging technologies. Sustainability is a top priority for SC3DP, which offers material development and control services that combine artificial intelligence, big data, and other digital tools for process optimization, as well as non-destructive testing for AM parts. Backed by a team of experts and a state-of-the-art facility equipped with the latest equipment and technologies, SC3DP is committed to leading the way in sustainable additive manufacturing research and development.
The project is motivated by the goal of advancing automation in construction through the development and integration of 3D concrete printing and robotic systems for the design, monitoring, and reinforcement of volumetric construction.
Responsibilities:
Structural Printing:
Integrating and synchronizing multiple mobile robotic systems together with 3D dual-bridge gantry system into one integrated, coordinated, and automated system for the advanced construction process.
To design and implement real-time monitoring and adaptive control systems with sensors and suitable control/machine-learning algorithms to improve print quality and reduce material waste.
Large-scale structural modules printing with mobile robotic arm and gantry system.
Programing code for mobile robotic arm and gantry system
Operating the printing system including mixer, pump, mobile robotic arm, and gantry system
Printing Path Optimisation:
Computational design of 3D concrete printing structure using machine-learning-based/generative/optimisation design tools to balance performance and printability.
Design the real-time monitoring and adaptive control systems to optimise the printing path to ensure the smooth structure surface finish and excellent mechanical properties.
Data acquisition and Image processing with MATLAB and Python
Design data-driven models using sensor data, camera feedback, and process parameters for print and tool path planning and process optimization.
Experimental investigation:
Design and fabrication of experimental facilities, including nozzles and an inline control system based on Machine learning and simulation models.
Conduct experimental studies to validate robotic coordination algorithms, optimized processes, and printing performance.
Collaborate with process, material, and structural experts for cross-disciplinary investigation in Digital Construction.
Requirements:
Master degree in 3D additive manufacturing
2 Years of research experience in Machine learning, 3D Printing, robotics, with strong knowledge of 3D additive manufacturing process and path planning, design of tool path and machine code generation
Computational Design and 3D concrete printing
Strong knowledge and experience in large scale 3D cementitious material printing
Excellent in programing code for robotic arm and gantry system operation.
Strong experience in Deep Learning, Neural Network, Bayesian’s regression process
Strong experience in parametric modelling software like Rhinoceros 3D, Grasshopper, CAD/CAE tools like MasterCAM, Robotmaster.
Excellent interpersonal, problem solving and analytic skills.
Able to work in a multi-disciplinary team
We regret to inform that only shortlisted candidates will be notified.
Hiring Institution: NTU