Additive manufacturing

As a modern manufacturing technology, additive manufacturing has been developed rapidly in recent decades. Compared with traditional subtractive manufacturing techniques such as drilling, milling, cutting, etc., additive manufacturing uses a “bottom-up” processing method to manufacture physical parts. Additive manufacturing has benefits such as higher design freedom, reduced manufacturing time and assembly process, reduced material consumption and others. The materials that can be used in additive manufacturing include metals, plastics, ceramics, concrete, biomaterials, composite materials, etc. Additive manufacturing has broad applications in aerospace, electronic devices, construction, new materials, new energy, medical and other fields. Different additive manufacturing processes such as stereolithography, material jetting, binder jetting, powder bed fusion, material extrusion, directed energy deposition and thin material lamination are available for different materials and applications.

Laser manufacturing

 Novel laser manufacturing technologies can provide new options for advanced materials production. With the laser-induced forward transfer and laser-plasma printing process, advanced materials with novel properties can provide novel solutions against grand global challenges, such as aging societies, water scarcity, energy shortage, and pandemics. Building on our secured collaborative funds for laser manufacturing of energy storage and antiviral materials, we will further collaborate with the team members of the proposed center for smart manufacturing and push the research output to higher levels with a broader impact.

Bio/Soft/flexible material manufacturing

This research area focuses on the manufacturing of materials, products, and robots that directly interact with biological systems. Such applications typically require unique material properties such as softness/flexibility, biocompatibility, and biodegradability. Topics of interest include the manufacturing of soft material robots (soft robotics) and the use of natural and/or living materials. Inspired by nature, these materials are often arranged in intricate geometries and patterns to obtain desired properties. Additive manufacturing plays an important role in realizing such geometries while simultaneously allowing for a high degree of customization to the biological system. Research in this area will address material, manufacturing process, and design aspects. This research has applications in elderly care, orthotics and prosthetics, tissue engineering, drug delivery, and environmental monitoring.

Advanced robotics manufacturing and manufacturing automation

Future manufacturing is characterized by high customization, i.e., low-volume production with large varieties and low cost will be highly competitive. In this global competition, manufacturing solutions with high flexibility, adaptivity, reconfigurability, and cost efficiency are desired. Robotic manufacturing is an ideal solution for these purposes. Compared to conventional manufacturing methods, robotic manufacturing has the following advantages: (i) Robotic manufacturing can produce parts with intricate details and complex shapes, which can be challenging to access with conventional machine tools. (ii) Robots can also be quickly and flexibly deployed to the site of manufacturing, enhancing the level of production flexibility and reconfigurability, which is highly demanded in automotive assembly lines, construction sites, etc. (iii) Robots are particularly useful in manufacturing large-scale structures, which is prevalent in civil and aerospace manufacturing applications. (iv) Robots further enable the collaboration between the workforce via human-robot collaboration in manufacturing.

Digital twin of manufacturing systems

Digital twins as the replicas of physical systems in virtual environments enable us to better monitor, control, and optimize the manufacturing processes by seamlessly integrating digital models and physical systems with information exchange. Sensor data collected in physical experiments are incorporated into digital models. The digital models are then updated and used to perform the feedback control. The ultimate goal of digital twins is to reduce the overall lifecycle cost of physical systems. The enabling technologies of manufacturing digital twins include sensing, control, modelling and simulation, predictive maintenance, data analytics, lifecycle information management, and others.

Big data and AI-driven manufacturing

The manufacturing sensor data and monitoring signals/videos are increasingly crucial in smart manufacturing processes. Using data and AI enhances the manufacturing performances of conventional manufacturing processes and enables novel manufacturing processes that traditional techniques cannot achieve. One representative example is using thermal images and other sensor data to enhance the additive manufacturing processes. In addition, the data and the AI provide computationally efficient modeling and optimization tools, which pose significant roles throughout the design to manufacturing stages of production activities.