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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. 

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. 

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. 

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. 

Micro Projection-based Stereolithography (µPSL), also known as micro vat photopolymerization, is a promising technology that could revolutionize microfabrication by providing benefits similar to traditional lithography while reducing production time and cost. However, it faces a significant challenge in the form of the "proximity effect." This effect occurs when adjacent features are too close together, causing undesirable artifacts and limiting the achievable fabrication resolution. The proximity effect is caused by interactions between adjacent pixels of light and affects both the spatial and temporal domains of the fabrication process. Although researchers have been aware of this issue for some time, there has been little progress in understanding and addressing the proximity effect in micro vat photopolymerization. Existing models developed for laser-based systems can explain the effect to some extent, but they do not fully account for the impact of large area projection or explain how local threshold changes affect part size. This research aims to fill this knowledge gap by using in-situ observation systems to experimentally study the spatial and temporal proximity effects in single-shot vat photopolymerization microfabrication. We also investigate the role of oxygen in the proximity effect and lay the groundwork for better understanding how the effect impacts periodic structures with micronic inter-feature distances. In conclusion, while micro vat photopolymerization offers significant advantages over traditional lithography, the proximity effect remains a significant obstacle. This research represents an important step forward in addressing this challenge and improving the accuracy and resolution of vat photopolymerization in microfabrication. 

Abstract Digital maskless lithography is gaining popularity due to its unique ability to quickly fabricate high-resolution parts without the use of physical masks. By implementing controlled grayscaling and exposure control, it has the potential to replace conventional lithography altogether. However, despite the existence of a theoretical foundation for photopolymerization, observing the voxel growth process in situ is a significant challenge. This difficulty can be attributed to several factors, including the microscopic size of the parts, the low refractive index difference between cured and uncured resin, and the rapid rate of photopolymerization once it crosses a certain threshold. As such, there is a pressing need for a system that can address these issues. To tackle these challenges, the paper proposes a modified Schlieren-based observation system that utilizes confocal magnifying optics to create a virtual screen at the camera's focal plane. This system allows for the visualization of the minute changes in refractive indices made visible by the use of Schlieren optics, specifically the deflection of light by the changing density-induced refractive index gradient. The use of focusing optics provides the system with the flexibility needed to position the virtual screen and implement optical magnification. The researchers employed single-shot binary images with different pixel numbers to fabricate voxels and examine the various factors affecting voxel shape, including chemical composition and energy input. The observed results were then compared against simulations based on Beer–Lambert's law, photopolymerization curve, and Gaussian beam propagation theory. The physical experimental results validated the effectiveness of the proposed observation system. The paper also briefly discusses the application of this system in fabricating microlenses and its advantages over theoretical model-based profile predictions. 

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 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 Inkjet printing (IJP) is an additive manufacturing process capable to produce intricate functional structures. The IJP process performance and the quality of the printed parts are considerably affected by the deposited droplets’ volume. Obtaining consistent droplets volume during the process is difficult to achieve because the droplets are prone to variations due to various material properties, process parameters, and environmental conditions. Experimental (i.e., IJP setup observations) and computational (i.e., computational fluid dynamics (CFD)) analysis are used to study the droplets variability; however, they are expensive and computationally inefficient, respectively. The objective of this paper is to propose a framework that can perform fast and accurate droplet volume predictions for unseen IJP driving voltage regimes. A two-step approach is adopted: (1) an emulator is constructed from the physics-based droplet volume simulations to overcome the computational complexity and (2) the emulator is calibrated by incorporating the experimental IJP observations. In particular, a scaled Gaussian stochastic process (s-GaSP) is deployed for the emulation and calibration. The resulting surrogate model is able to rapidly and accurately predict the IJP droplets volume. The proposed methodology is demonstrated by calibrating the simulated data (i.e., CFD droplet simulations) emulator with experimental data from two distinct materials, namely glycerol and isopropyl alcohol.