In this presentation, I will focus on two crucial aspects of additive manufacturing. Firstly, I'll discuss sustainable additive manufacturing and its significance in optimizing processes and planning for additive manufacturing. This will contribute to a reduction in waste, time, and cost consumption. Secondly, I'll talk about the integration of machine learning into additive manufacturing to enhance printed parts' mechanical properties and overall quality. Additionally, machine learning can facilitate real-time monitoring leading to a reduction in material, time, and energy waste. The future perspectives of smart and sustainable additive manufacturing will also be explored. By utilizing advanced additive manufacturing techniques and state-of-the-art artificial intelligence, engineers can improve additive manufacturing processes. The ultimate goal is to make additive manufacturing smarter and reduce material, time, and energy costs.

This seminar aims to provide a comprehensive overview of the mathematical challenges in 4D printing, their implications, and the potential they hold in advancing soft robotics. The presentation delves into the underpinnings of 4D printing and the intricate mathematical quandaries it introduces, which lie at the heart of the novel functionalities this technology offers. Concurrently, the burgeoning field of soft robotics will be drawn parallelly, highlighting the transformative role 4D printing could play in this domain. 4D printing, an advanced variation of 3D printing, incorporates time as the fourth dimension. It enables the creation of materials that can change shape or properties over time in response to specific stimuli. The mathematical complexities involved in predicting and controlling these transformative behaviors will be dissected. Utilizing a range of mathematical tools such as differential geometry, topology, and non-linear dynamics, I will provide insights into the mechanisms that facilitate the seamless transition between multiple states in 4D printed materials. Simultaneously, the tremendous potential of 4D printing will be illustrated in the realm of soft robotics. As we stand on the brink of a new era where soft robots, with their inherent flexibility and adaptability, are poised to revolutionize industries from healthcare to space exploration, understanding the potential and challenges of 4D printing becomes imperative. Various applications of soft robots will be demonstrated, focusing on how the use of 4D-printed components can enhance their performance, adaptability, and autonomy.

Auxetic materials showing negative Poisson’s ratio attract great interests due to their excellent properties including extremely high indentation resistance, fracture resistance and energy absorption etc. As a type of auxetic materials, auxetic foams have attracted great attention because they are light weight and easy to manufacture. So far, conventional auxetic foam is usually prepared by volumetric compression of conventional polyurethane foam, yet achieving high energy dissipation for auxetic foam remains challenging. This talk will present a novel strategy for making ultrahigh energy-dissipation auxetic foam inspired by balloon art. As revealed by finite element analysis of balloon deformation evolution in polymer matrix, spherical balloons will turn to be reentrant auxetic shape when it is compressed uniaxially in polymer matrix with large Poisson’s ratio. By utilizing the know-how, auxetic silicone foam has been developed by using expandable microspheres as the blowing agent to form silicone foam, followed by subsequent compression and curing processes. The auxetic silicone foam shows ultrahigh energy dissipation capability of ~2000 kJ/m3, over 80 times higher than that of conventional auxetic polyurethane foams. Moreover, the developed auxetic silicone foam has low water absorption, high chemical and temperature resistance, making it can be used in harsh circumstances. Additionally, the auxetic silicone foam is found to be a thermal-responsive material, which can expand at high temperature and return to initial state when cooling down. Our work provides routes towards multifunctional and smart auxetic foams, with potential applications in protective equipment, thermal insulation, and thermal driven soft actuator.

With the rapid development of artificial intelligence (AI) technology, intelligent additive manufacturing technology has attracted widespread attention in the field of manufacturing. This report conducts research on the topic of "AI-enabled intelligent additive manufacturing technology", aiming to explore how to use AI technology to improve the efficiency, precision and sustainability of additive manufacturing technology. First of all, we pay attention to the green design of additive manufacturing technology. By proposing a multi-scale Transformer model to predict the energy consumption and emissions of manufactured products, we can consider factors of different physical scales of products in the design process and provide feasible green design solutions to reduce resources. waste and environmental load. Secondly, we focused on the defect detection of additive manufacturing technology, and developed a layered defect detection method for additive manufacturing based on deep contrastive learning technology. Provides a more reliable means of quality control. This report will introduce our research methods, experimental results and application cases in detail, and demonstrate the potential and application prospects of AI technology in intelligent additive manufacturing. The intelligent additive manufacturing technology empowered by AI will provide new ideas and solutions for promoting green sustainable development and improving the quality of the manufacturing process.

3D printing (Additive manufacturing, AM) has been established as a state-of-the-art manufacturing technology that can fabricate components by stacking materials layer by layer, achieving large design freedom. In response to requirements for lightweight and high-performance components, various metamaterials, such as shell, strut, and plate -based lattice structures, have been introduced to the AM design. As the most common form of metal AM, laser powder bed fusion (LPBF) has been increased to a level that it is possible to print metal parts with highly complex geometry and extremely fine details. This presentation aims to showcase our recent advancements in micro-LPBF, including the STL-free design & manufacturing paradigm, and geometric deviation & compensation method for thin-walled shell lattice structures. With implicit modelling, complex geometry can be efficiently modelled without any STL-related representation; with direct slicing, toolpaths can be directly generated from the implicit geometry without any intermediate steps related to STL meshes. Then, a significant reduction of both memory and time consumption can be achieved. In terms of fabrication, we propose a new dimensional deviation prediction method and compensation workflow, which provides a one-stop solution for improving geometric accuracy. The characterization results will unveil the manufacturing capability of the 3D printer in terms of its capability to print thin-walled lattices, which will serve as basic rules for lattice design when using a specific 3D printer.

Mechanical metamaterials are artificial structures whose mechanical properties are tunable by the downscale micro-architectures. Mechanical metamaterials can be made to achieve unconventional mechanical properties through appropriate design of internal architectures. Elastically isotropic metamaterials exhibit identical elastic properties along all directions and are ideal candidates for applications in case of unknown primary loading direction. Generally, open-cell metamaterials are preferred for additive manufacturing processes and engineering applications, since their open-cell property enables easier removal of residual resins or unmolten metal powders and facilitates a considerable amount of mass and heat transfer. This presentation focuses on the design, simulation, and experimental tests of elastically isotropic open-cell mechanical metamaterials with superior stiffness.

After years of dedicated research in the field of 3D nanofabrication, we now find ourselves at a critical juncture, grappling with the challenges of achieving nanoscale feature sizes, versatile material compatibility, and high-speed fabrication. As the nanotechnology community seeks to transcend these limitations, our fabrication capabilities must evolve to enable the creation of intricate, three-dimensional functional nano-devices with diverse geometries. In this talk, I will present my research aimed at addressing these challenges: a novel 3D nanofabrication method powered by temporally focused femtosecond light sheets and superabsorbent hydrogels. This approach offers remarkable resolution in the tens of nanometers, unparalleled material compatibility, and scalable throughput. Additionally, I will introduce a new type of 3D optical storage device as a practical application of this method, which exhibits revolutionary performances. As a disruptive solution, this research is expected to provide valuable insights into future developments of 3D nanofabrication, paving the way for effortless fabrication of nanophotonic, nanoelectronic, and biomedical devices.

We present the design and characterization of a maskless optical photolithography (MOL) system for high-resolution (200 nm) and high-throughput 2D and 3D nano-patterning. Specifically, a digital micromirror device (DMD) and binary holography are used to split the laser beam into 1 - 100 programmable laser foci to perform parallel processing. Accordingly, arbitrary complex surface structures can be fabricated at 500 mm2 /min via arranging the focus array in combination with a precision XY stage. Simulation and experimental results are presented to demonstrate the precision and speed of the system, which can perform fast maskless micro- and nano-patterning to directly define structures without photomasks or alternatively to fabricate photomasks.