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Abstract 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. We propose utilizing wheat straw as a biomass feedstock for manufacturing sustainable thermal insulation. This approach captures carbon during growth and stores it within the insulation structure. 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. Through material characterization tests, which include microstructure, mechanical, and thermal analyses, we demonstrate 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. 

Abstract Over the course of millions of years, nature has evolved to ensure survival and presents us with a myriad of functional surfaces and structures that can boast high efficiency, multifunctionality, and sustainability. What makes these surfaces particularly practical and effective is the intricate micropatterning that enables selective interactions with microstructures. Most of these structures have been realized in the laboratory environment using numerous fabrication techniques by tailoring specific surface properties. Of the available manufacturing methods, additive manufacturing (AM) has created opportunities for fabricating these structures as the complex architectures of the naturally occurring microstructures far exceed the traditional ways. This paper presents a concise overview of the fundamentals of such patterned microstructured surfaces, their fabrication techniques, and diverse applications. A comprehensive evaluation of micro fabrication methods is conducted, delving into their respective strengths and limitations. Greater emphasis is placed on AM processes like inkjet printing and micro digital light projection printing due to the intrinsic advantages of these processes to additively fabricate high resolution structures with high fidelity and precision. The paper explores the various advancements in these processes in relation to their use in microfabrication and also presents the recent trends in applications like the fabrication of microlens arrays, microneedles, and tissue scaffolds. 

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. 

Abstract Inkjet printing (IJP) is one of the promising additive manufacturing techniques that yield many innovations in electronic and biomedical products. In IJP, the products are fabricated by depositing droplets on substrates, and the quality of the products is highly affected by the droplet pinch-off behaviors. Therefore, identifying pinch-off behaviors of droplets is critical. However, annotating the pinch-off behaviors is burdensome since a large amount of images of pinch-off behaviors can be collected. Active learning (AL) is a machine learning technique which extracts human knowledge by iteratively acquiring human annotation and updating the classification model for the pinch-off behaviors identification. Consequently, a good classification performance can be achieved with limited labels. However, during the query process, the most informative instances (i.e., images) are varying and most query strategies in AL cannot handle these dynamics since they are handcrafted. Thus, this paper proposes a multiclass reinforced active learning (MCRAL) framework in which a query strategy is trained by reinforcement learning (RL). We designed a unique intrinsic reward signal to improve the classification model performance. Moreover, how to extract the features from images for pinch-off behavior identification is not trivial. Thus, we used a graph convolutional network for droplet image feature extraction. The results show that MCRAL excels AL and can reduce human efforts in pinch-off behavior identification. We further demonstrated that, by linking the process parameters to the predicted droplet pinch-off behaviors, the droplet pinch-off behavior can be adjusted based on MCRAL. 

Abstract Direct ink writing (DIW) process is a facile additive manufacturing technology to fabricate three-dimensional (3D) objects with various materials. Its versatility has attracted considerable interest in academia and industry in recent years. As such, upsurging endeavors are invested in advancing the ink flow behaviors in order to optimize the process resolution and the printing quality. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and its underlying theories. Here, we present a comprehensive analytical study of non-Newtonian ink flow behavior during the DIW process. Different syringe-nozzle geometries are modeled for the comparative case studies. By using the computational fluid dynamics (CFD) simulation method, we reveal the shear-thinning property during the ink extrusion process. Besides, we study the viscosity, shear stress, and velocity fields, and analyze the advantages and drawbacks of each syringe-nozzle model. On the basis of these investigations and analyses, we propose an improved syringe-nozzle geometry for stable extrusion and high printing quality. A set of DIW printing experiments and rheological characterizations are carried out to verify the simulation studies. The results developed in this work offer an in-depth understanding of the ink flow behavior in the DIW process, providing valuable guidelines for optimizing the physical DIW configuration toward high-resolution printing and, consequently, improving the performance of DIW-printed objects. 

Abstract As a facile and versatile additive manufacturing technology, direct ink writing (DIW) has attracted considerable interest in academia and industry to fabricate three-dimensional structures with unique properties and functionalities. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and the underlying theories. Here, we presented a comprehensive simulation study of non-Newtonian ink flow during the DIW process. We used the computational fluid dynamics (CFD) method and revealed the shear-thinning behavior during the extrusion process. Different nozzle geometry models were adopted in the simulation. The advantages and drawbacks of each syringe-nozzle geometry were analyzed. In addition, the ink shear stress and velocity fields were investigated and compared in the case studies. Based on these investigations and analysis, we proposed an improved syringe-nozzle geometry towards high-resolution DIW. Consequently, the high-resolution and high shape fidelity DIW could enhance the DIW product performance. The results developed in this work offer valuable guidelines and could accelerate further advancement of DIW. 

Abstract The macro-porous ceramics has promising durability and thermal insulation performances. A cost-effective and scalable additive manufacturing technique for the fabrication of macro-porous ceramics, with a facile approach to control the printed porosity is reported in the paper. Several ceramic inks were prepared, the foaming agent was used to generate gaseous bubbles in the ink, followed by the direct ink writing and the ambient-pressure and room-temperature drying to create the three-dimensional geometries. The experimental studies were performed to optimize the printing quality. A set of studies revealed the optimal printing process parameters for printing the foamed ceramic ink with a high spatial resolution and fine surface quality. Varying the concentration of the foaming agent enabled the controllability of the structural porosity. The maximum porosity can reach 85%, with a crack-free internal porous structure. The tensile tests showed that the printed macro-porous ceramics have enhanced durability with the addition of fiber. With a high-fidelity 3D printing process and precise control of the porosity, the printed samples exhibited a low thermal conductivity and high mechanical strength. 

Abstract The conventional manufacturing process of aerogel insulation material relies largely on the supercritical drying, which suffers from issues of massive energy consumption, high-cost equipment and prolonged processing time. With the consideration of large market demand of the aerogel insulation material in the next decade, a low-cost and scalable fabrication technique is highly desired. In this paper, a direct ink writing (DIW) method is used to three-dimensionally fabricate the silica aerogel insulation material, followed by room-temperature and ambient pressure drying. Compared to the supercritical drying and freeze-drying, the reported method significantly reduces the fabrication time and costs. The cost-effective DIW technique offers the capability to print complex hollow internal structures, coupled with the porous structure, is found to be beneficial to the thermal insulation property. The addition of fiber to the ink assures the durability of the fabricated product. The foam ink preparation methods and the printability are demonstrated in this paper, along with the printed samples for characterizing thermal insulation performance and mechanical properties. 

Abstract Electrospinning is a promising process to fabricate functional parts from macrofibers and nanofibers of bio-compatible materials including collagen, polylactide (PLA), and polyacrylonitrile (PAN). However, the functionality of the produced parts highly rely on quality, repeatability, and uniformity of the electrospun fibers. Due to the variations in material composition, process settings, and ambient conditions, the process suffers from large variations. In particular, the fiber formation in the stable regime (i.e., Taylor cone and jet) and its propagation to the substrate plays the most significant role in the process stability. This work aims to designing a fast process monitoring tool from scratch for monitoring the dynamic electrospinning process based on the Taylor cone and jet videos. Nevertheless, this is challenging since the videos are of high frequency and high dimension, and the monitoring statistics may not have a parametric distribution. To achieve this goal, a framework integrating image analysis, sketch-based tensor decomposition, and non-parametric monitoring, is proposed. In particular, we use Tucker tensor-sketch (Tucker-TS) based tensor decomposition to extract the sparse structure representations of the videos. Additionally, the extracted monitoring variables are non-normally distributed, hence non-parametric bootstrap Hotelling T2 control chart is deployed to handle this issue during the monitoring. The framework is demonstrated by electrospinning a PAN-based polymeric solution. Finally, it is demonstrated that the proposed framework, which uses Tucker-TS, largely outperformed the computational speed of the alternating least squares (ALS) approach for the Tucker tensor decomposition, i.e., Tucker-ALS, in various anomaly detection tasks while keeping the comparable anomaly detection accuracy. 

Abstract Inkjet 3D printing has broad applications in areas such as health and energy due to its capability to precisely deposit micro-droplets of multi-functional materials. However, the droplet of the inkjet printing has different jetting behaviors including drop initiation, thinning, necking, pinching and flying, and they are vulnerable to disturbance from vibration, material inhomogeneity, etc. Such issues make it challenging to yield a consistent printing process and a defect-free final product with desired properties. Therefore, timely recognition of the droplet behavior is critical for inkjet printing quality assessment. In-situ video monitoring of the printing process paves a way for such recognition. In this paper, a novel feature identification framework is presented to recognize the spatiotemporal feature of in-situ monitoring videos for inkjet printing. Specifically, a spatiotemporal fusion network is used for droplet printing behavior classification. The categories are based on inkjet printability, which is related to both the static features (ligament, satellite, and meniscus) and dynamic features (ligament thinning, droplet pinch off, meniscus oscillation). For the recorded droplet jetting video data, two streams of networks, the frames sampled from video in spatial domain (associated with static features) and the optical flow in temporal domain (associated with dynamic features), are fused in different ways to recognize the droplet evolving behavior. Experiments results show that the proposed fusion network can recognize the droplet jetting behavior in the complex printing process and identify its printability with learned knowledge, which can ultimately enable the real-time inkjet printing quality control and further provide guidance to design optimal parameter settings for the inkjet printing process.