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Abstract In recent years, inkjet 3D printing has rapidly gained prominence as a disruptive fabrication technique that has witnessed ever-increasing demand in the fields of biomedicine, metal manufacturing, electronics, and functional material production. This innovative approach involves precise deposition of controlled amounts of material onto a moving substrate through a nozzle, achieving impressive sub-millimeter scale resolution by leveraging the concepts of micro-droplet deposition. However, the dynamic nature of the process introduces significant challenges related to consistency and quality control, especially in terms of reproducibility and repeatability. The key input parameters governing this process, such as pressure, voltage, jetting frequency, and duty cycle, are interrelated, entailing the identification of optimal settings in order to realize high-quality jetting. At present, the data collection heavily relies on image-based methods which are inherently slow and often fail to encompass the entirety of the data, making it difficult to determine the relation between the input parameters and jet characteristics. To address this multidimensional difficulty, we developed a unique approach based on light-beam field interruption to collect critical jet data at high speeds. This novel approach collects both temporal and spatial information on droplet evolution, making it a vital tool for enhancing our ability to attain high accuracy and control in inkjet 3D printing. To illustrate the efficacy of our approach, we model the extracted features derived from the process parameters and the extracted data to predict the droplet jetting behavior and droplet size. Specifically, a decision tree classifier is used to predict the jetting behavior and discern between “ideal” and “non-ideal” jetting behaviors. Simultaneously, a linear regression model was employed to predict the droplet size within the “ideal jetting” class based on the interplay of process parameters and the extracted features. The results emphasize the system’s accuracy in capturing the droplet behavior and size using our light-beam field interference sensing module. Furthermore, these findings establish a crucial foundation for the implementation of real-time feedback control loop in the inkjet printing process, promising advancements in adaptability and precision. 

Inkjet three-dimensional (3D) printing has emerged as a transformative manufacturing technique, finding applications in diverse fields such as biomedical, metal fabrication, and functional materials production. It involves precise deposition of materials onto a moving substrate through a nozzle, achieving submillimeter scale resolution. However, the dynamic nature of droplet deposition introduces uncertainties, challenging consistent quality control. Current process monitoring, relying on image-based techniques, is slow and limited, hindering real-time feedback in erratic droplet ejection. In response to these challenges, we present the zero-dimensional ultrafast sensing (0-DUS) system, a novel, cost-effective, in situ monitoring tool designed to assess the quality of drop-on-demand inkjet printing. The 0-DUS system leverages the sensitivity of the light-beam field interference effect to rapidly and precisely detect and analyze localized droplets. Two core technical advancements drive this innovation: first, the exploration of integral sensing of the computational light-beam field, which allows for efficient extraction of temporal and spatial information about droplet evolution, introducing a novel in situ sensing modality; second, the establishment of a robust mapping mechanism that aligns sensor data with image-based data, facilitating accurate estimation of droplet characteristics. We successfully implemented the 0-DUS system within a commercial inkjet printer and conducted a comparative analysis with ground truth data. Our experimental results demonstrate a detection accuracy exceeding 95%, even at elevated speeds, allowing for an impressive in situ certification throughput of up to 500 Hz. Consequently, our proposed 0-DUS system meets the stringent quality assurance requirements, thereby expanding the potential applications of inkjet printing across a wide spectrum of industrial sectors. 

Thermal insulation materials reduce heat transfer and are typically made from materials like fiberglass, foam, or mineral wool, which are engineered to trap air and hinder heat conduction and convection. The traditional manufacturing processes of thermal insulation materials are often energy-intensive and result in significant greenhouse gas emissions. In the current global drive for sustainability, these energy-intensive manufacturing processes raise environmental concerns and need to be addressed. In this work, with the objective of addressing both material sustainability and manufacturing sustainability, we present an additive manufacturing strategy to fabricate biomass materials for thermal insulation applications. Firstly, we propose to use biomass materials, such as wheat straw, as the primary feedstock materials for manufacturing. Such biomass materials offer the unique capacity to sequester carbon dioxide during their growth, and when incorporated into thermal insulation structures, they effectively capture and store carbon inside the structure. Concurrently, our pursuit of manufacturing process sustainability is driven by using a cost-effective additive manufacturing technology to fabricate durable thermal insulation structures. In the presented work, we first demonstrate the formulation of a 3D-printable ink using chopped straw fibers. We conduct comprehensive rheological characterizations to reveal the shear-thinning properties and the printability of the straw fiber ink. Utilizing the direct ink writing (DIW) process, the straw fiber material is deposited into 3D structures. Following bulk material characterization tests, including microstructure, mechanical, and thermal tests. We unveil the low thermal conductivity and robust mechanical properties. This paper marks the first work of 3D printing of wheat straw fibers for thermal insulation structures. The discoveries in this pilot work demonstrate the potential to leverage additive manufacturing technologies and sustainable biomass materials to create both functional and value-added wheat straw parts tailored for thermal insulation applications.