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.

The integration of 3D printed sensors into hosting structures has become a growing area of research due to simplified assembly procedures, reduced system complexity, and lower fabrication cost. Embedding 3D printed sensors into structures or bonding the sensors on surfaces are the two techniques for the integration of sensors. This review extensively discusses the fabrication of sensors through different additive manufacturing techniques. Various additive manufacturing techniques dedicated to manufacture sensors as well as their integration techniques during the manufacturing process will be discussed. This review will also discuss the basic sensing mechanisms of integrated sensors and their applications. It has been proven that integrating 3D printed sensors into infrastructures can open new possibilities for research and development in additive manufacturing and sensor materials for smart goods and the Internet of Things.

Artificial intelligence (AI) and additive manufacturing (AM) have propelled the next wave of technological innovation by integrating data-driven intelligence with design freedom, thereby enabling adaptive, efficient, and multifunctional systems. This review highlights the transformative role of AI and machine learning (ML) in addressing the key challenges associated with process complexity, parameter tuning, and multifunctional design in AM. Starting with the historical evolution of AI-AM integration, the progression from rule-based modeling to contemporary deep learning, reinforcement learning, and physics-informed frameworks that enable autonomous and self-optimizing manufacturing systems was summarized. Attention is directed toward ML-driven topology and lattice optimization, data-driven methods for predicting process and structural properties, and Multiphysics optimization, demonstrating how AI replaces labor-intensive experimentation with predictive and adaptive models. In parallel, the integration of AI into smart materials (SMs) and 4D printing has been explored, with emphasis on property tuning for piezoelectric, shape-memory, and self-healing systems. The review concludes by highlighting key challenges, including data scarcity, limited interpretability, and the lack of standardized datasets, while pointing toward hybrid physics-informed ML and digital twin approaches for closed-loop and intelligent manufacturing. Collectively, this study provides a comprehensive roadmap illustrating how AI enables AM to evolve from empirical fabrication to autonomous, multifunctional manufacturing paradigms.

The ability to detect impact waves and their propagation across materials is the key to structural health monitoring and defect detection of materials. To detect impact waves from a certain type of structures, it is important for a sensor to be highly flexible and complex in shape. Direct ink write (DIW) allows for the manufacturing of complex sensors. This article presents the fabrication of a flexible impact wave propagation sensor (IWPS) through the DIW technique. The dispersion of a ferroelectric ceramic material barium titanate (BaTiO _3, or BTO) in polydimethylsiloxane (PDMS), not only enhances the flexibility of the 3D (three-dimensional) printed sensor but also ensures the uniform piezoelectric response throughout the whole sensor. This research explored the impact load generated impact wave in the flexible sensor and sensing response. The capability of DIW for multi-material printing was utilized to print multi-walled carbon nanotube based electrodes on BTO/PDMS stretchable composites. A total of 50 wt% of BTO in the PDMS matrix resulted in a piezoelectric coefficient of 20 pC N ⁻¹ after contact poling of IWPS. Upon applying impact loading at the center of the sensor, an impact wave was generated which gradually diminished with the distance from the origin of the applied impact load. The impact wave propagation was quantitatively characterized by measuring output voltage from different nodes of IWPS. Additionally, from the voltage response time difference at different locations of the sensor, the particle-wave velocity of a certain material attached to IWPS was determined in this research. Using the custom-designed IWPS, it was found that the particle-wave velocity of stainless steel and low-density polyethylene were 5625 m s ⁻¹ and 2000 m s ⁻¹ respectively, which are consistent with their theoretical values.