Additive manufacturing (AM) is a digital manufacturing process that can directly convert a computer-aided design model into a physical object in a layer-by-layer manner. Due to the additive and discrete nature of the digital manufacturing process, AM needs to find a trade-off between process resolution and production efficiency. Traditional AM processes balance the resolution and efficiency by tuning the processes either in the

Bringing sensing and augmentation functions to the intimate surfaces of biological systems underpins technological advances in precision health, biology-machine interactions, and basic scientific discoveries. Contemporary wearable and on-skin sensing devices, produced by centralized mass manufacturing, are usually standardized, rigid, and invariable; they are intrinsically incompatible with the diversity, delicacy, and dynamism of living systems.

The development of efficient and scalable software systems is crucial for the growth of Industry 4.0 and its related applications, such as smart manufacturing and design technologies. In this talk, I will share our projects on developing such systems for automated robot operation, manipulation, and surveillance, with applications in diverse components of Industry 4.0. First, I will introduce our research framework, which focuses on the localization and perception of robotic systems.

Additive Manufacturing (AM) has been widely recognized as a disruptive manufacturing technology for various applications thanks to its capability of fabricating 3D objects with unprecedented geometric complexity and multiple functionalities. Also, AM enables revolutionary designs by using complex 3D shapes and heterogeneous materials. The structures with various material compositions and geometric feature sizes spanning from 10 μm up to 200 mm have a wide variety of interesting properties. However, the fabrication of such multi-scale and multi-material structures requires the integration of micro-, meso- and macro-scale manufacturing processes. How to develop AM processes to effectively and efficiently fabricate such structures with various engineering materials is still an open question.

Metal additive manufacturing (AM) is a family of rising technologies that are well-known for their flexibility in fabricating geometrically and/or material-wisely complex metal parts that are difficult for traditional manufacturing processes. However, metal AM processes are often plagued with a lack of reproducibility and reliability. Computational modeling has become an appealing approach in understanding the Process-Structure-Property (PSP) relationship in AM processes, critical for process control and optimization.

Uncertainty widely exists in many engineering problems, such as measurement uncertainty, model uncertainty, parameter uncertainty, and manufacturing uncertainty. These uncertainties will affect the design and manufacture of engineering products. An important issue is how to properly deal with these uncertainties and get more reliable and robust engineering products. In this talk, I will discuss using Bayesian statistics to deal with uncertainty and help provide a better model prediction, especially when the physical model is not accurate (a low-fidelity model), combing experimental measurement data. In addition, I will also discuss using Bayesian statistics for reliability sensitivity analysis, i.e., measuring the effects of random parameters on the failure of structures, which will be helpful for failure control and reliability-based design. Some results will be shown with composite material coupon structures.

Carbon fibre reinforced polymer composites have seen successful applications in aerospace, automotive and renewable energy industries over the past six decades. A number of processing and manufacturing technologies have been developed to make the composites but essentially they are all an additive process to some extent. From initial hand layup of carbon fibre prepregs (Composites 1.0) to later automated fibre/tape placement (Composites 1.5), there are increasingly released freedoms to manufacturing more complex, higher performance composites. The conventional manufacturing processes aim at scaling up the meso-scale material (such as lamina and tape) to the macro-scale composite structure, and as such the design and analysis theories for the manufactured composites are still within framework of composites laminated plate theory (CLPT), i.e., Design-for-Manufacturing. Although numerous design tools and failure criteria have been developed, there is still lack of confidence in adopting them in real-world composites design – consequently extensive and expensive experimental tests are still required for the certification of the manufactured composite structures. In this talk, I will introduce our recent advancement in real additive manufacturing of composites with be-spoke material system at multiple length scales to produce highly customised composites. Supported by the high-fidelity micromechanics based data-driven prediction and design, I will share my vision of Manufacturing-by-Design for next-generation digital, multifunctional lightweight composites in the context of Composites 2.0.

Manufacturing speed is very important in commercial applications of additive manufacturing. On the other hand, current additive manufacturing techniques are not readily applicable to on-orbit manufacturing (e.g., in space stations, due to microgravity) and unstable working environments (e.g., on vehicles in maneuvering, due to random vibration). Volumetric additive manufacturing is a newly developed high-speed approach. However, same as all other rapid approaches in current additive manufacturing, the materials to be cross-linked are liquid resins.

We have proposed a new way for volumetric additive manufacturing using solids instead of liquids in UV cross-linking. It appears to be similar to conventional laser engraving inside glass, but the un-cross-linked parts can be separated from the cross-linked parts to create the desired shapes. Since cross-linked polymers tend to have excellent shape memory effect, high dimensional accuracy is ensured after separation.