Four-dimensional (4D) printing is an advanced manufacturing technology that has rapidly emerged as a transformative tool with the capacity to reshape various research domains and industries. Distinguished by its integration of time as a dimension, 4D printing allows objects to dynamically respond to external stimuli, setting it apart from conventional 3D printing. This roadmap has been devised, by contributions of 44 active researchers in this field from 32 affiliations world-wide, to navigate the swiftly evolving landscape of 4D printing, consolidating recent advancements and making them accessible to experts across diverse fields, ranging from biomedicine to aerospace, textiles to electronics. The roadmap’s goal is to empower both experts and enthusiasts, facilitating the exploitation of 4D printing’s transformative potential to create intelligent, adaptive objects that are not only feasible but readily attainable. By addressing current and future challenges and proposing advancements in science and technology, it sets the stage for revolutionary progress in numerous industries, positioning 4D printing as a transformative tool for the future.

3D-printing (3DP) technology has been developing rapidly. However, limited studies on the contribution of 3DP technology, especially multimaterial 3DP technology, to droplet-microfluidics have been reported. In this paper, multimaterial 3D-printed devices for the pneumatic control of emulsion generation have been reported. A 3D coaxial flexible channel with other rigid structures has been designed and printed monolithically. Numerical and experimental studies have demonstrated that this flexible channel can be excited by the air pressure and then deform in a controllable way, which can provide the active control of droplet generation. Furthermore, a novel modular microfluidic device for double emulsion generation has been designed and fabricated, which consists of three modules: function module, T-junction module, and co-flow module. The function module can be replaced by (1) Single-inlet module, (2) Pneumatic Control Unit (PCU) module and (3) Dual-inlet module. Different modules can be easily assembled for different double emulsion production. By using the PCU module, double emulsions with different number of inner droplets have been successfully produced without complicated operation of flow rates of different phases. By using single and dual inlet module, various double emulsions with different number of encapsulated droplets or encapsulated droplets with different compositions have been successfully produced, respectively.

Droplet microfluidics has become the most promising subcategory of microfluidics since it contributes numerous applications to diverse fields. However, fabrication of microfluidic devices for droplet formation, manipulation and applications is usually complicated and expensive. Three-dimensional printing (3DP) provides an exciting alternative to conventional techniques by simplifying the process and reducing the cost of fabrication. Complex and novel structures can be achieved via 3DP in a simple and rapid manner, enabling droplet microfluidics accessible to more extensive users. In this article, we review and discuss current development, opportunities and challenges of applications of 3DP to droplet microfluidics.

The development of integrated microfluidic systems/platforms concerns many fields. Current remarkable integrated systems based on stacking multi-layer polydimethylsiloxane (PDMS) require complicated fabrication and operation and still remain challenging. We propose a novel micro-millifluidic processing system (MPS) comprising three core modules: a motherboard, a control panel and microfluidic chips. Fluids are handled in sub-millichannels in a motherboard and functional operations occur in microchannels in microfluidic chips. A motherboard with versatile functional units for fluid handling was monolithically fabricated via multimaterial 3D printing, which avoids multi-layer structures, and the major disadvantage of current 3D printing, i.e. low resolution, has been overcome by integrating novel microfluidic chips based on our developed maskless lithography platform. Both numerical and experimental studies were conducted to validate our system. Potential applications such as droplet generation and distribution, microfluidic mixing and simple bacterial resistance tests have been demonstrated via our MPS.

Digital twin (DT) models are computational models that can effectively represent different assets and processes in the manufacturing environment. Moreover, the DT models can support intelligent automation by integrating with the digital foundation and the data analytics provided by the cyber-physical system (CPS) in an industrial environment. To properly model a physical process, a DT model should be updated online to closely and timely model the underlying process and reduce modeling uncertainty in the CPS. However, most DT models are created offline and implemented online, which cannot be easily updated by using online data from heterogeneous product designs or manufacturing processes. This limitation arises from existing online learning methods, which are typically designed for identical structures, while real manufacturing CPS involves personalized designs and diverse processes. More importantly, there are limited samples for the same product design or manufacturing process due to manufacturing personalization, which slows down the online updating of DT models. In this article, the authors investigated online DT model updating based on data collected from different product designs and/or processes. The authors proposed a mutual active learning framework to identify informative samples from different designs or processes for online DT model updating. Specifically, by properly balancing the gradient-based features of the DT models and the similarity among these heterogeneous designs or processes, the proposed method can effectively query the most informative samples among heterogeneous processes to update the corresponding DT model in a timely manner. The advantages of the proposed method are illustrated by an engineering-driven statistical DT model for an additive manufacturing process (i.e., fused deposition modeling).