Additive manufacturing (AM), also known as 3D printing, was introduced to design complicated structures/geometries that overcome the manufacturability limitations of traditional manufacturing processes. However, like any other manufacturing technique, AM also has its limitations, such as the need of support structures for overhangs, long build time etc. To overcome these limitations of 3D printing, 4D printing was introduced, which utilizes smart materials and processes to create shapeshifting structures with the external stimuli, such as temperature, humidity, magnetism, etc. The state-of-the-art 4D printing technology focuses on the “form” of the 4D prints through the multi-material variability. However, the quantitative morphing analysis is largely absent in the existing literature on 4D printing. In this research, the inherited material anisotropic behaviors from the AM processes are utilized to drive the morphing behaviors. In addition, the quantitative morphing analysis is performed for designing and controlling the shapeshifting. A material–process–performance 4D printing prediction framework has been developed through a novel dual-way multi-dimensional machine learning model. The morphing evaluation metrics, bending angle and curvature, are obtained and archived at 99% and 93.5% R², respectively. Based on the proposed method, the material and production time consumption can be reduced by around 65–90%, which justifies that the proposed method can re-imagine the digital–physical production cycle.

Metal additive manufacturing (MAM) offers a larger design space with greater manufacturability than traditional manufacturing. Despite continued advances, MAM processes still face huge uncertainty, resulting in variable part quality. Real-time sensing for MAM processing helps quantify uncertainty by detecting build failure and process anomalies. While the high volume of multidimensional sensor data—such as melt-pool geometries and temperature gradients—is beginning to be explored, sensor selection does not yet effectively link sensor data to part quality. To begin investigating such connections, we propose network-based models that capture in real-time (1) sensor data's association with process variables and (2) as-built part qualities’ association with related physical phenomena. These sensor models and networks lay the foundation for a comprehensive framework to monitor and manage the quality of MAM process outcomes.

Metal Additive Manufacturing (MAM) produces complex, part geometries from a variety of materials in powder and wire form. Due to complexities of MAM processes that create those geometries, especially powder bed fusion, quality assurance, and qualification remain an ongoing challenge. Quality assurance involves assessing the quality of a part’s geometry, surface finish, and mechanical properties. Currently, quality assurance is not easily achieved due to variations in the powder inputs, the MAM process itself, and environmental factors, such as temperature. Many efforts are underway to develop a new quality system that includes 1) planning, measuring, and qualifying parts and 2) enhancing quality through a processing-monitoring-qualifying framework. Creating this new system requires building the complex relationships between requirements, processes, and quality. These relationships are needed to specify, measure, analyze, and optimize variables to ensure final part quality. Thus, a processing-monitoring-quality framework could provide critical steps to identify those relationships and help meet stakeholder needs. The paper describes how to adapt the “software and systems engineering” V-model to a “metal AM quality assurance” V-model that can provide a framework for quality assurance in MAM.

Batteries are used as energy storage devices in various equipment. Today, research is focused on solid-state batteries (SSBs), replacing the liquid electrolyte with a solid separator. The solid separators provide electrolyte stability, no leakage, and provide mechanical strength to the battery. Separators are mostly manufactured by either traditional processes or 3D printing technologies. These processes involve making a slurry of plastic, active and conductive material and usually adding a plasticizer when making thin films or filaments for 3D printing. This study investigates the additive manufacturing of solid-state electrolytes (SSEs) by employing fused deposition modeling (FDM) with recyclable, bio-derived polylactic acid (PLA) filaments. Precise control of macro-porosity is achieved by systematically varying key process parameters, including raster orientation, infill percentage, and interlayer adhesion conditions, thereby enabling the formation of tunable, interconnected pore networks within the polymer matrix. Following 3D printing, these engineered porous frameworks are infiltrated with lithium hexafluorophosphate (LiPF₆), which functions as the active ionic conductor. A tailored thermal sintering protocol is then applied to promote solid-phase fusion of the embedded salt throughout the macro-porous PLA scaffold, resulting in a mechanically robust and ionically conductive composite separator. The electrochemical ionic conductivity and structural integrity of the sintered SSEs are characterized through electrochemical impedance spectroscopy (EIS) and standardized mechanical testing to assess their suitability for integration into advanced solid-state battery architectures. The solid-state separator achieved an average ionic conductivity of 2.529 × 10⁻⁵ S·cm⁻¹. The integrated FDM-sintering process enhances ion exchange at the electrode–electrolyte interface, minimizes material waste, and supports cost-efficient, fully recyclable component fabrication.

Hybrid manufacturing (HM) integrates two or more distinct manufacturing processes—most commonly additive manufacturing (AM) and subtractive machining—within a coordinated workflow (often on a single platform) to achieve capabilities that neither process provides alone: high geometric freedom and rapid material addition from AM, plus tolerance, surface finish, and datum control from machining. This review first surveys major classes of hybrid manufacturing (additive–subtractive, multi-energy, multi-material, and assistive hybrid). It then focuses on multi-axis directed energy deposition (DED) combined with in-loop subtractive machining—arguably the most mature and industrially impactful HM configuration—emphasizing process planning methods, persistent limitations, and application domains, including repair/remanufacturing and functionally graded materials (FGMs). The review highlights current CAM strategies for hybrid DED, integrated planning frameworks, and emerging trends such as digital twins and closed-loop metrology-driven replanning.