Hybrid Machine Learning Method to Determine the Optimal Operating Process Window in Aerosol Jet 3D Printing

Attentive ink MLP droplet detection algorithm based on the satellite droplets threshold domain

Satellite droplet are trailing droplets caused by improper voltage waveform control in the nozzles, which directly affect the quality of inkjet printing. Traditional empirical tuning methods struggle to effectively control and predict satellite droplet. In response, this paper establishes a simulation model of a ring-shaped piezoelectric ceramic device. establishes a simulation model of a ring-shaped piezoelectric ceramic nozzle through numerical analysis, collecting droplet behavior parameters for 13,650 different waveforms. It was found that the relative differential of the negative pressure peak at the nozzle is the primary cause of satellite droplet formation. Furthermore, the critical values of various behavior parameters leading to satellite droplet formation were investigated, resulting in the construction of a "Satellite droplet formation". resulting in the construction of a "Satellite droplet threshold domain." Based on this domain, an attention-based MLP algorithm, the Attentive Ink MLP Based on this domain, an attention-based MLP algorithm, the Attentive Ink MLP, was proposed to automatically predict whether a waveform will generate satellite droplet. algorithm reduces prediction time from 40 min to 5 min, with a validation accuracy of 0.988. The numerical analysis and algorithm were further validated through experiments with a droplet. The numerical analysis and algorithm were further validated through experiments with a droplet observation system.

3D Nanofabrication via Directed Material Assembly: Mechanism, Method, and Future

Fabrication of complex three-dimensional (3D) structures at nanoscale is the core of nanotechnology, as it enables the creation of various micro-/nano-devices such as micro-robots, metamaterials, sensors, photonic devices, etc. Among most 3D nanofabrication strategies, the guided material assembly, an efficient bottom-up approach capable of directly constructing designed structures from precise integration of material building blocks, possesses compelling advantages in diverse material compatibility, sufficient driving forces, facile processing steps, and nanoscale resolution. In this review, we focus on assembly-based fabrication methods capable of creating complex 3D nanostructures (instead of periodic or 2.5D-only structures). Recent advances are classified based on the different assembly mechanisms, i.e., assembly driven by chemical reactions, physical interactions, and the synergy of multiple microscopic interactions. The design principles of representative fabrication strategies with an emphasis on their respective advantages, e.g., structural design flexibility, material compatibility, resolution, or applications are analyzed. In the summary and outlook, existing challenges, as well as possible paths to solutions for future development are reviewed. We believe that with recent advances in assembly-based nanofabrication strategies, 3D nanofabrication has achieved tremendous progress in resolution, material generality, and manufacturing cost, for it to make a greater impact in the near future.

Advances in materials and technologies for digital light processing 3D printing

Digital light processing (DLP) is a projection-based vat photopolymerization 3D printing technique that attracts increasing attention due to its high resolution and accuracy. The projection-based layer-by-layer deposition in DLP uses precise light control to cure photopolymer resin quickly, providing a smooth surface finish due to the uniform layer curing process. Additionally, the extensive material selection in DLP 3D printing, notably including existing photopolymerizable materials, presents a significant advantage compared with other 3D printing techniques with limited material choices. Studies in DLP can be categorized into two main domains: material-level and system-level innovation. Regarding material-level innovations, the development of photocurable resins with tailored rheological, photocuring, mechanical, and functional properties is crucial for expanding the application prospects of DLP technology. In this review, we comprehensively review the state-of-the-art advancements in DLP 3D printing, focusing on material innovations centered on functional materials, particularly various smart materials for 4D printing, in addition to piezoelectric ceramics and their composites with their applications in DLP. Additionally, we discuss the development of recyclable DLP resins to promote sustainable manufacturing practices. The state-of-the-art system-level innovations are also delineated, including recent progress in multi-materials DLP, grayscale DLP, AI-assisted DLP, and other related developments. We also highlight the current challenges and propose potential directions for future development. Exciting areas such as the creation of photocurable materials with stimuli-responsive functionality, ceramic DLP, recyclable DLP, and AI-enhanced DLP are still in their nascent stages. By exploring concepts like AI-assisted DLP recycling technology, the integration of these aspects can unlock significant opportunities for applications driven by DLP technology. Through this review, we aim to stimulate further interest and encourage active collaborations in advancing DLP resin materials and systems, fostering innovations in this dynamic field.

Efficient Creation of Jettability Diagrams Using Active Machine Learning

The ability to jet a wide variety of materials consistently from print heads remains a key technical challenge for inkjet-based additive manufacturing processes. Drop watching is the most direct method for testing new inks and print head designs but such experiments are also resource consuming. In this work, a data-efficient machine learning technique called active learning is used to construct detailed jettability diagrams that identify complex regions corresponding to "no jetting," "jetting," and "desired jetting," rather than only individually sampled points. Crucially, our active learning method has resolved challenges with model selection that previously limited the accuracy of active learning in practical settings with very small experimental budgets. In addition, the key "desired jetting" zone may be quite small which is a challenge for initializing active learning. We leverage the physical intuition that the "desired jetting" zone tends to exist between the "jetting" and "no jetting" zone, to improve the performance of this highly imbalanced classification problem by performing two binary classifications in sequence. The first binary classification aims to map out the "jetting" zone versus the "no jetting" zone, while the second binary classification targets identifying the "desired jetting" zone with primary drops only. Our experiments use a stroboscopic drop watcher to visualize the jetting behavior of two fluids from a piezoelectric print head with different jetting waveforms. The results obtained from active learning were compared to a grid search method, which involves running more than 200 experiments for each fluid. The active learning method significantly reduces the number of experiments by 80% while achieving a test accuracy of more than 95% in the "jetting" zone prediction for the test fluids. The ability to construct these jettability diagrams will further accelerate new ink and print head developments.

Machine learning enables electrical resistivity modeling of printed lines in aerosol jet 3D printing

Among various non-contact direct ink writing techniques, aerosol jet printing (AJP) stands out due to its distinct advantages, including a more adaptable working distance (2-5 mm) and higher resolution (~ 10 μm). These characteristics make AJP a promising technology for the precise customization of intricate electrical functional devices. However, complex interactions among the machine, process, and materials result in low controllability over the electrical performance of printed lines. This significantly affects the functionality of printed components, thereby limiting the broad applications of AJP. Therefore, a systematic machine learning approach that integrates experimental design, geometrical features extraction, and non-parametric modeling is proposed to achieve printing quality optimization and electrical resistivity prediction for the printed lines in AJP. Specifically, three classical convolutional neural networks (CNNs) architectures are compared for extracting representative features of printed lines, and an optimal operating window is identified to effectively discriminate better line morphology from inferior printed line patterns within the design space. Subsequently, three representative non-parametric machine learning techniques are employed for resistivity modeling. Following that, the modeling performances of the adopted machine learning methods were systematically compared based on four conventional evaluation metrics. Together, these aspects contribute to optimizing the printed line morphology, while simultaneously identifying the optimal resistivity model for accurate predictions in AJP.

Deep Learning-Based Inkjet Droplet Detection for Jetting Characterizations and Multijet Synchronization

Inkjet printing is a powerful direct material writing process. It can be used to deposit microfluidic droplets in designated patterns at submicrometer resolution, which reduces materials usage. Nonetheless, predicting jetting characterizations is not easy because of the intrinsic complexity of the ink-nozzle-air interactions. Thus, inkjet processes are monitored by skilled engineers to ensure process reliability. This is a bottleneck in industry, resulting in high labor costs for multiple nozzles. To address this, we present a deep learning-based method for jetting characterizations. Inkjet printing is recorded by an in situ CCD camera and each droplet is detected by YOLOv5, a 1-stage detector using a convolutional neural network (CNN). The precision, recall, and mean average precision (mAP) at a 0.5 intersection over the union (IoU) threshold of the trained model were 0.86, 0.89, and 0.90, respectively. Each regression result for a detected droplet is accumulated in chronological order for each class of droplet and nozzle. The quantified information includes velocity, diameter, length, and translation, which can be used to synchronize multinozzle jetting and, eventually, the printed patterns. This demonstrates the feasibility of autonomous real-time process testing for large-scale electronics manufacturing, such as the high-resolution patterning of biosensor electrodes and QD display pixels while exploiting big data obtained from jetting characterizations.

Unraveling the influence of solvent composition on Drop-on-Demand binder jet 3D printed tablets containing calcium sulfate hemihydrate

Recently, binder jet printed modular tablets were loaded with three anti-viral drugs via Drop on Demand (DoD) technology where drug solutions prepared in ethanol showed faster release than those prepared in water. During printing, water is used as a binding agent, whereas ethanol is added to maintain the porous structure of the tablets. Thus, the hypothesis is that the porosity would be controlled by manipulating the percentage of water and ethanol. In this study, Rhodamine 6G (R6G) was selected as a model drug due to its high solubility in water and ethanol, visualization function as a fluorescent dye, and potential therapeutic effects for cancer treatment. Approximately, 10 mg/ml R6G solutions were prepared with five different water-ethanol ratios (0-100, 75-25, 50-50, 75-25, 100-0). The ink solutions were printed onto blank binder jet 3D-printed tablets containing calcium sulphate hemihydrate using DoD technology. The tablets were dried at room temperature and then characterized using SEM-EDX, fluorescent microscope, TGA, XRD, FTIR, and DSC as well as in vitro release studies to investigate the impact of water-ethanol ratio on the release profile of R6G. Results indicated that the solution with higher ethanol ratio penetrated the tablets faster than the lower ethanol ratio, while the solution prepared with pure water was first accumulated onto the tablets' surface and then absorbed by the tablets. Moreover, tablets with more water content gained more weight and thickness. The EDX analysis and fluorescent microscope showed the uniform surface distribution of the drug. The SEM images revealed the difference in the tablet surface among the five formulations. Furthermore, the TGA data presents a notable increase in water loss, with XRD analysis suggesting the formation of gypsum in tablets containing elevated water content. The release study exhibited that the fastest release was from WE0-100, whereas the release rate decreases as the content of water increases. The WE0-100 releases more than 40 % drug within the first hour which is almost twice as high of the WE100-0 formulation. This DoD technology could distribute drugs onto the tablet's surface uniformly. The calcium sulfate would transform from hemihydrate to dihydrate form in the presence of water and therefore, those tablets treated with higher water content led to slower release. In conclusion, this study underscores the substantial impact of the water-ethanol ratio on drug release from binder jet printed tablets and highlights the potential of DoD technology for uniform drug distribution and controlled release.

3D printing of biologics-what has been accomplished to date?

Three-dimensional (3D) printing is a promising approach for the stabilization and delivery of non-living biologics. This versatile tool builds complex structures and customized resolutions, and has significant potential in various industries, especially pharmaceutics and biopharmaceutics. Biologics have become increasingly prevalent in the field of medicine due to their diverse applications and benefits. Stability is the main attribute that must be achieved during the development of biologic formulations. 3D printing could help to stabilize biologics by entrapment, support binding, or crosslinking. Furthermore, gene fragments could be transited into cells during co-printing, when the pores on the membrane are enlarged. This review provides: (i) an introduction to 3D printing technologies and biologics, covering genetic elements, therapeutic proteins, antibodies, and bacteriophages; (ii) an overview of the applications of 3D printing of biologics, including regenerative medicine, gene therapy, and personalized treatments; (iii) information on how 3D printing could help to stabilize and deliver biologics; and (iv) discussion on regulations, challenges, and future directions, including microneedle vaccines, novel 3D printing technologies and artificial-intelligence-facilitated research and product development. Overall, the 3D printing of biologics holds great promise for enhancing human health by providing extended longevity and enhanced quality of life, making it an exciting area in the rapidly evolving field of biomedicine.

Optimization of process parameters in micro-scale pneumatic aerosol jet printing for high-yield precise electrodes

Aerosol jet printing (AJP) is a new non-contact direct writing technique designed to achieve precise and intricate patterns on various substrates. Specifically, the pneumatic AJP process breaks down the ink into fine particles, significantly reducing the risk of nozzle clogging and rendering it highly advantageous for industrial applications. This paper focuses on the optimization of the line electrode formation process using soluble silver clusters as the conductive ink, along with the aerosol formation procedure. The main parameters of the AJP process, namely sheath flow rate, atomizer flow rate, and dispensing speed, were identified and examined for their influence on line width and resistivity. Through this analysis, an operability window, including optimized conditions for printing high-quality lines using the AJP process, was established, along with a regression equation enabling the statistical estimation of line width. In summary, the outcomes of this investigation underscore the feasibility of an integrated printing system capable of precision control over line width, achieved through the optimization of AJP process parameters. Furthermore, it was established that pneumatic AJP offers robust process stability. The practical applicability of the proposed optimization techniques was assessed, highlighting their potential utilization in electrode formation processes within the electronic and display industry.

Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning

Scanning probe lithography (SPL) is a promising technology to fabricate high-resolution, customized and cost-effective features at the nanoscale. However, the quality of nano-fabrication, particularly the critical dimension, is significantly influenced by various SPL fabrication techniques and their corresponding process parameters. Meanwhile, the identification and measurement of nano-fabrication features are very time-consuming and subjective. To tackle these challenges, we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning (ML). Different from traditional SPL techniques that rely on manual labeling-based experimental methods, the proposed framework intelligently extracts reliable and global information for statistical analysis to fine-tune and optimize process parameters. Based on the proposed framework, we realized the processing of smaller critical dimensions through the optimization of process parameters, and performed direct-write nano-lithography on a large scale. Furthermore, data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.

High-throughput printing of combinatorial materials from aerosols

The development of new materials and their compositional and microstructural optimization are essential in regard to next-generation technologies such as clean energy and environmental sustainability. However, materials discovery and optimization have been a frustratingly slow process. The Edisonian trial-and-error process is time consuming and resource inefficient, particularly when contrasted with vast materials design spaces1. Whereas traditional combinatorial deposition methods can generate material libraries2,3, these suffer from limited material options and inability to leverage major breakthroughs in nanomaterial synthesis. Here we report a high-throughput combinatorial printing method capable of fabricating materials with compositional gradients at microscale spatial resolution. In situ mixing and printing in the aerosol phase allows instantaneous tuning of the mixing ratio of a broad range of materials on the fly, which is an important feature unobtainable in conventional multimaterials printing using feedstocks in liquid-liquid or solid-solid phases4-6. We demonstrate a variety of high-throughput printing strategies and applications in combinatorial doping, functional grading and chemical reaction, enabling materials exploration of doped chalcogenides and compositionally graded materials with gradient properties. The ability to combine the top-down design freedom of additive manufacturing with bottom-up control over local material compositions promises the development of compositionally complex materials inaccessible via conventional manufacturing approaches.

Machine Learning Enables Process Optimization of Aerosol Jet 3D Printing Based on the Droplet Morphology

Aerosol jet printing (AJP) is a promising noncontact direct ink writing technology that enables flexible and conformal electronic devices to be fabricated onto planar and nonplanar substrates with higher resolution and less waste. Despite possessing many advantages, the limited electrical performance of microelectronic devices caused by the poor printing quality is still the greatest hurdle to overcome for AJP technology. With the motivation to improve the printing quality, a novel hybrid machine learning method is proposed to analyze and optimize the AJP process based on the deposited droplet morphology in this study. The proposed method consists of classic machine learning approaches, including space-filling-based experimental design, clustering, classification, regression, and multiobjective optimization. In the proposed method, a two-dimensional (2D) design space is fully explored using a Latin hypercube sampling approach for experimental design, and a K-means clustering approach is employed to reveal the cause-effect relationship between the deposited droplet morphology and printed line characteristics. Following that, an optimal operating window with respect to the deposited droplet morphology is identified using a support vector machine to ensure the printing quality in a design space. Finally, to achieve high-controllability and sufficient-thickness droplets, Gaussian process regression is adopted to develop the process model of droplet geometrical properties, and the deposited droplet morphology is optimized under dual conflicting objectives of customizing the droplet diameter and maximizing droplet thickness. Different from previous printing quality optimization approaches, the proposed method enables a systemic investigation on the formation mechanisms of printed line characteristics, and the printing quality is fundamentally optimized based on the deposited droplet morphology. Moreover, data-driven-based characteristics can help the proposed approach serve as a guideline for printing quality optimization in other noncontact direct ink writing technologies.

A hybrid multi-objective optimization of functional ink composition for aerosol jet 3D printing via mixture design and response surface methodology

The limited electrical performance of microelectronic devices caused by low inter-particle connectivity and inferior printing quality is still the greatest hurdle to overcome for Aerosol jet printing (AJP) technology. Despite the incorporation of carbon nanotubes (CNTs) and specified solvents into functional inks can improve inter-particle connectivity and ink printability respectively, it is still challenging to consider multiple conflicting properties in mixture design simultaneously. This research proposes a novel hybrid multi-objective optimization method to determine the optimal functional ink composition to achieve low electrical resistivity and high printed line quality. In the proposed approach, silver ink, CNTs ink and ethanol are blended according to mixture design, and two response surface models (ReSMs) are developed based on the Analysis of Variance. Then a desirability function method is employed to identify a 2D optimal operating material window to balance the conflicting responses. Following that, the conflicting objectives are optimized in a more robust manner in the 3D mixture design space through the integration of a non-dominated sorting genetic algorithm III (NSGA-III) with the developed ReSMs and the corresponding statistical uncertainty. Experiments are conducted to validate the effectiveness of the proposed approach, which extends the methodology of designing materials with multi-component and multi-property in AJP technology.

Liquid Metal Patterning and Unique Properties for Next-Generation Soft Electronics

Room-temperature liquid metal (LM)-based electronics is expected to bring advancements in future soft electronics owing to its conductivity, conformability, stretchability, and biocompatibility. However, various difficulties arise when patterning LM because of its rheological features such as fluidity and surface tension. Numerous attempts are made to overcome these difficulties, resulting in various LM-patterning methods. An appropriate choice of patterning method based on comprehensive understanding is necessary to fully utilize the unique properties. Therefore, the authors aim to provide thorough knowledge about patterning methods and unique properties for LM-based future soft electronics. First, essential considerations for LM-patterning are investigated. Then, LM-patterning methods-serial-patterning, parallel-patterning, intermetallic bond-assisted patterning, and molding/microfluidic injection-are categorized and investigated. Finally, perspectives on LM-based soft electronics with unique properties are provided. They include outstanding features of LM such as conformability, biocompatibility, permeability, restorability, and recyclability. Also, they include perspectives on future LM-based soft electronics in various areas such as radio frequency electronics, soft robots, and heterogeneous catalyst. LM-based soft devices are expected to permeate the daily lives if patterning methods and the aforementioned features are analyzed and utilized.

Aerosol jet printing of piezoelectric surface acoustic wave thermometer

Surface acoustic wave (SAW) devices are a subclass of micro-electromechanical systems (MEMS) that generate an acoustic emission when electrically stimulated. These transducers also work as detectors, converting surface strain into readable electrical signals. Physical properties of the generated SAW are material dependent and influenced by external factors like temperature. By monitoring temperature-dependent scattering parameters a SAW device can function as a thermometer to elucidate substrate temperature. Traditional fabrication of SAW sensors requires labor- and cost- intensive subtractive processes that produce large volumes of hazardous waste. This study utilizes an innovative aerosol jet printer to directly write consistent, high-resolution, silver comb electrodes onto a Y-cut LiNbO₃ substrate. The printed, two-port, 20 MHz SAW sensor exhibited excellent linearity and repeatability while being verified as a thermometer from 25 to 200 ^∘C. Sensitivities of the printed SAW thermometer are - 96.9 × 1 0 - 6 ∘ C^-1 and - 92.0 × 1 0 - 6 ∘ C^-1 when operating in pulse-echo mode and pulse-receiver mode, respectively. These results highlight a repeatable path to the additive fabrication of compact high-frequency SAW thermometers.

Recent Advances in Multi-Material 3D Printing of Functional Ceramic Devices

In recent years, functional ceramic devices have become smaller, thinner, more refined, and highly integrated, which makes it difficult to realize their rapid prototyping and low-cost manufacturing using traditional processing. As an emerging technology, multi-material 3D printing offers increased complexity and greater freedom in the design of functional ceramic devices because of its unique ability to directly construct arbitrary 3D parts that incorporate multiple material constituents without an intricate process or expensive tools. Here, the latest advances in multi-material 3D printing methods are reviewed, providing a comprehensive study on 3D-printable functional ceramic materials and processes for various functional ceramic devices, including capacitors, multilayer substrates, and microstrip antennas. Furthermore, the key challenges and prospects of multi-material 3D-printed functional ceramic devices are identified, and future directions are discussed.

Accurate Estimation of Tensile Strength of 3D Printed Parts Using Machine Learning Algorithms

Manufacturing processes need optimization. Three-dimensional (3D) printing is not an exception. Consequently, 3D printing process parameters must be accurately calibrated to fabricate objects with desired properties irrespective of their field of application. One of the desired properties of a 3D printed object is its tensile strength. Without predictive models, optimizing the 3D printing process for achieving the desired tensile strength can be a tedious and expensive exercise. This study compares the effectiveness of the following five predictive models (i.e., machine learning algorithms) used to estimate the tensile strength of 3D printed objects: (1) linear regression, (2) random forest regression, (3) AdaBoost regression, (4) gradient boosting regression, and (5) XGBoost regression. First, all the machine learning models are tuned for optimal hyperparameters, which control the learning process of the algorithms. Then, the results from each machine learning model are compared using several statistical metrics such as 𝑅2, mean squared error (MSE), mean absolute error (MAE), maximum error, and median error. The XGBoost regression model is the most effective among the tested algorithms. It is observed that the five tested algorithms can be ranked as XG boost > gradient boost > AdaBoost > random forest > linear regression.

Printing Technologies as an Emerging Approach in Gas Sensors: Survey of Literature

Herein, we review printing technologies which are commonly approbated at recent time in the course of fabricating gas sensors and multisensor arrays, mainly of chemiresistive type. The most important characteristics of the receptor materials, which need to be addressed in order to achieve a high efficiency of chemisensor devices, are considered. The printing technologies are comparatively analyzed with regard to, (i) the rheological properties of the employed inks representing both reagent solutions or organometallic precursors and disperse systems, (ii) the printing speed and resolution, and (iii) the thickness of the formed coatings to highlight benefits and drawbacks of the methods. Particular attention is given to protocols suitable for manufacturing single miniature devices with unique characteristics under a large-scale production of gas sensors where the receptor materials could be rather quickly tuned to modify their geometry and morphology. We address the most convenient approaches to the rapid printing single-crystal multisensor arrays at lab-on-chip paradigm with sufficiently high resolution, employing receptor layers with various chemical composition which could replace in nearest future the single-sensor units for advancing a selectivity.

Advancing pharmacy and healthcare with virtual digital technologies

Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.

Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing

Recently, machine learning has gained considerable attention in noncontact direct ink writing because of its novel process modeling and optimization techniques. Unlike conventional fabrication approaches, noncontact direct ink writing is an emerging 3D printing technology for directly fabricating low-cost and customized device applications. Despite possessing many advantages, the achieved electrical performance of produced microelectronics is still limited by the printing quality of the noncontact ink writing process. Therefore, there has been increasing interest in the machine learning for process optimization in the noncontact direct ink writing. Compared with traditional approaches, despite machine learning-based strategies having great potential for efficient process optimization, they are still limited to optimize a specific aspect of the printing process in the noncontact direct ink writing. Therefore, a systematic process optimization approach that integrates the advantages of state-of-the-art machine learning techniques is in demand to fully optimize the overall printing quality. In this paper, we systematically discuss the printing principles, key influencing factors, and main limitations of the noncontact direct ink writing technologies based on inkjet printing (IJP) and aerosol jet printing (AJP). The requirements for process optimization of the noncontact direct ink writing are classified into four main aspects. Then, traditional methods and the state-of-the-art machine learning-based strategies adopted in IJP and AJP for process optimization are reviewed and compared with pros and cons. Finally, to further develop a systematic machine learning approach for the process optimization, we highlight the major limitations, challenges, and future directions of the current machine learning applications.

Biomaterials by design: Harnessing data for future development

Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.

Droplet-Based Techniques for Printing of Functional Inks for Flexible Physical Sensors

Printed electronics (PE) is an emerging technology that uses functional inks to print electrical components and circuits on variety of substrates. This technology has opened up new possibilities to fabricate flexible, bendable, and form-fitting devices at low-cost and fast speed. There are different printing technologies in use, among which droplet-based techniques are of great interest as they provide the possibility of printing computer-controlled design patterns with high resolution, and greater production flexibility. Nanomaterial inks form the heart of this technology, enabling different functionalities. To this end, intensive research has been carried out on formulating inks with conductive, semiconductive, magnetic, piezoresistive, and piezoelectric properties. Here, a detailed landscape view on different droplet-based printing technologies (inkjet, aerosol jet, and electrohydrodynamic jet) is provided, with comprehensive discussion on their working principals. This is followed by a detailed research overview of different functional inks (metal, carbon, polymer, and ceramic). Different sintering methods and common substrates being used in printed electronics are also discussed, followed by an in-depth review of different physical sensors fabricated by droplet-based techniques. Finally, the challenges facing the field are considered and a perspective on possible ways to overcome them is provided.

A Post-Treatment Method to Enhance the Property of Aerosol Jet Printed Electric Circuit on 3D Printed Substrate

Aerosol jet printing of electronic devices is increasingly attracting interest in recent years. However, low capability and high resistance are still limitations of the printed electronic devices. In this paper, we introduce a novel post-treatment method to achieve a high-performance electric circuit. The electric circuit was printed with aerosol jet printing method on an ULTEM substrate. The ULTEM substrate was fabricated by the Fused Deposition Modelling method. After post-treatment, the electrical resistance of the printed electric circuit was changed from 236 mΩ to 47 mΩ and the electric property was enhanced. It was found that the reduction of electric resistance was caused by surface property changes. Different surface analysis methods including scanning electron microscopy (SEM) and x-ray photoelectron spectroscopy (XPS) were used to understand the effectiveness of the proposed method. The results showed that the microsurface structure remained the same original structure before and after treatment. It was found that the surface carbon concentration was significantly increased after treatment. Detailed analysis showed that the C-C bond increased obviously after treatment. The change of electrical resistance was found to be limited to the material's surface. After polishing, the circuit resistance was changed back to its original value. As the electric circuit is the basic element of electric devices, the proposed method enables the fabrication of high performance devices such as capacitors, strain gauge, and other sensors, which has potential applications in many areas such as industrial, aerospace, and military usage.

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration

Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting.