Accelerated discovery of 3D printing materials using data-driven multiobjective optimization

Machine-Learning-Enhanced Trial-and-Error for Efficient Optimization of Rubber Composites

The traditional trial-and-error approach, although effective, is inefficient for optimizing rubber composites. The latest developments in machine learning (ML)-assisted methodologies are also not suitable for predicting and optimizing rubber composite properties. This is due to the dependency of the properties on processing conditions, which prevents the alignment of data collected from different sources. In this work, a novel workflow called the ML-enhanced trial-and-error approach is proposed. This approach integrates orthogonal experimental design with symbolic regression (SR) to effectively extract empirical principles. This combination enables the optimization process to retain the characteristics of the traditional trial-and-error approach while significantly improving efficiency and capability. Using rubber composites as the model system, the ML-enhanced trial-and-error approach effectively extracts empirical principles encapsulated by high-frequency terms in the SR-derived mathematical formulas, offering clear guidance for material property optimization. An online platform has been developed that allows for no-code usage of the proposed methodology, designed to seamlessly integrate into the existing experimental optimization process.

NeuroDISK: An AI Approach to Automate Continuous Inquiry-Driven Discoveries in Neuroimaging Genetics

Collaborative and multi-site neuroimaging studies have greatly accelerated the rate at which new and existing data can be aggregated to answer a neuroscientific question. New research initiatives are continuously collecting more data, allowing opportunities to refine previous published findings through continuous and dynamic updates. Yet, we lack a practical framework for researchers to systematically, automatically, and continuously update published findings. We developed NeuroDISK, an automated artificial intelligence based framework that: 1) performs automated and inquiry-driven analyses, and 2) continuously updates these analyses as new data becomes available. NeuroDISK was evaluated using published results from the ENIGMA consortium's work on the genetic architecture of the cerebral cortex. We incorporate both meta-analysis and meta-regression options to showcase our framework on the effect of specific genotypes and moderators on select brain regions. Initial NeuroDISK meta-analysis results replicate the original publication, and we show result updates after adding new data. The NeuroDISK framework can be generalized for users to define question(s), run corresponding workflow(s) and access results interactively and continuously.

Real-time autonomous control of a continuous macroscopic process as demonstrated by plastic forming

To meet the need for more adaptable and expedient approaches in research and manufacturing, we present a continuous autonomous system that leverages real-time, in situ characterization and an active-learning-based decision-making processor. This system was applied to a plastic film forming process to demonstrate its capability in autonomously determining process conditions for specified film dimensions without human intervention. Application of the system to nine film dimensions (width and thickness) highlighted its ability to explore the search space and identify appropriate and stable process conditions, with an average of 11 characterization-adjustment iterations and a processing time of 19 minutes per width, thickness combination. The system successfully avoided common pitfalls, such as repetitive over-correction, and demonstrated high accuracy, with R2 values of 0.87 and 0.90 for film width and thickness, respectively. Moreover, the active learning algorithm enabled the system to begin exploration with zero training data, effectively addressing the complex and interdependent relationships between control factors (material supply rate, applied force, material viscosity) in the continuous plastic forming process. Given that the core concept of this autonomous process can, in principle, be transferred to other continuous material processing systems, these results have implications for accelerating progress in both research and industry.

Characterization and Machine Learning-Driven Property Prediction of a Novel Hybrid Hydrogel Bioink Considering Extrusion-Based 3D Bioprinting

The field of tissue engineering has made significant advancements with extrusion-based bioprinting, which uses shear forces to create intricate tissue structures. However, the success of this method heavily relies on the rheological properties of bioinks. Most bioinks use shear-thinning. While a few component-based efforts have been reported to predict the viscosity of bioinks, the impact of shear rate has been vastly ignored. To address this gap, our research presents predictive models using machine learning (ML) algorithms, including polynomial fit (PF), decision tree (DT), and random forest (RF), to estimate bioink viscosity based on component weights and shear rate. We utilized novel bioinks composed of varying percentages of alginate (2-5.25%), gelatin (2-5.25%), and TEMPO-Nano fibrillated cellulose (0.5-1%) at shear rates from 0.1 to 100 s-1. Our study analyzed 169 rheological measurements using 80% training and 20% validation data. The results, based on the coefficient of determination (R2) and mean absolute error (MAE), showed that the RF algorithm-based model performed best: [(R2, MAE) RF = (0.99, 0.09), (R2, MAE) PF = (0.95, 0.28), (R2, MAE) DT = (0.98, 0.13)]. These predictive models serve as valuable tools for bioink formulation optimization, allowing researchers to determine effective viscosities without extensive experimental trials to accelerate tissue engineering.

3D printed dosage forms, where are we headed?

INTRODUCTION

3D Printing (3DP) is an innovative fabrication technology that has gained enormous popularity through its paradigm shifts in manufacturing in several disciplines, including healthcare. In this past decade, we have witnessed the impact of 3DP in drug product development. Almost 8 years after the first USFDA approval of the 3D printed tablet Levetiracetam (Spritam), the interest in 3DP for drug products is high. However, regulatory agencies have often questioned its large-scale industrial practicability, and 3DP drug approval/guidelines are yet to be streamlined.

AREAS COVERED

In this review, major technologies involved with the fabrication of drug products are introduced along with the prospects of upcoming technologies, including AI (Artificial Intelligence). We have touched upon regulatory updates and discussed the burning limitations, which require immediate focus, illuminating status, and future perspectives on the near future of 3DP in the pharmaceutical field.

EXPERT OPINION

3DP offers significant advantages in rapid prototyping for drug products, which could be beneficial for personalizing patient-based pharmaceutical dispensing. It seems inevitable that the coming decades will be marked by exponential growth in personalization, and 3DP could be a paradigm-shifting asset for pharmaceutical professionals.

Recent advances and applications of artificial intelligence in 3D bioprinting

3D bioprinting techniques enable the precise deposition of living cells, biomaterials, and biomolecules, emerging as a promising approach for engineering functional tissues and organs. Meanwhile, recent advances in 3D bioprinting enable researchers to build in vitro models with finely controlled and complex micro-architecture for drug screening and disease modeling. Recently, artificial intelligence (AI) has been applied to different stages of 3D bioprinting, including medical image reconstruction, bioink selection, and printing process, with both classical AI and machine learning approaches. The ability of AI to handle complex datasets, make complex computations, learn from past experiences, and optimize processes dynamically makes it an invaluable tool in advancing 3D bioprinting. The review highlights the current integration of AI in 3D bioprinting and discusses future approaches to harness the synergistic capabilities of 3D bioprinting and AI for developing personalized tissues and organs.

Constraint based Bayesian optimization of bioink precursor: a machine learning framework

Current research practice for optimizing bioink involves exhaustive experimentation with multi-material composition for determining the printability, shape fidelity and biocompatibility. Predicting bioink properties can be beneficial to the research community but is a challenging task due to the non-Newtonian behavior in complex composition. Existing models such as Cross model become inadequate for predicting the viscosity for heterogeneous composition of bioinks. In this paper, we utilize a machine learning framework to accurately predict the viscosity of heterogeneous bioink compositions, aiming to enhance extrusion-based bioprinting techniques. Utilizing Bayesian optimization (BO), our strategy leverages a limited dataset to inform our model. This is a technique especially useful of the typically sparse data in this domain. Moreover, we have also developed a mask technique that can handle complex constraints, informed by domain expertise, to define the feasible parameter space for the components of the bioink and their interactions. Our proposed method is focused on predicting the intrinsic factor (e.g. viscosity) of the bioink precursor which is tied to the extrinsic property (e.g. cell viability) through the mask function. Through the optimization of the hyperparameter, we strike a balance between exploration of new possibilities and exploitation of known data, a balance crucial for refining our acquisition function. This function then guides the selection of subsequent sampling points within the defined viable space and the process continues until convergence is achieved, indicating that the model has sufficiently explored the parameter space and identified the optimal or near-optimal solutions. Employing this AI-guided BO framework, we have developed, tested, and validated a surrogate model for determining the viscosity of heterogeneous bioink compositions. This data-driven approach significantly reduces the experimental workload required to identify bioink compositions conducive to functional tissue growth. It not only streamlines the process of finding the optimal bioink compositions from a vast array of heterogeneous options but also offers a promising avenue for accelerating advancements in tissue engineering by minimizing the need for extensive experimental trials.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research

Microreactor technologies have emerged as versatile platforms with the potential to revolutionize chemistry and materials research, offering sustainable solutions to global challenges in environmental and health domains. This survey paper provides an in-depth review of recent advancements in microreactor technologies, focusing on their role in facilitating accelerated discoveries in chemistry and materials. Specifically, we examine the convergence of microfluidics with machine intelligence and automation, enabling the exploitation of the cyber-physical environment as a highly integrated experimentation platform for rapid scientific discovery and process development. We investigate the applicability and limitations of microreactor-enabled discovery accelerators in various chemistry and materials contexts. Despite their tremendous potential, the integration of machine intelligence and automation into microreactor-based experiments presents challenges in establishing fully integrated, automated, and intelligent systems. These challenges can hinder the broader adoption of microreactor technologies within the research community. To address this, we review emerging technologies that can help lower barriers and facilitate the implementation of microreactor-enabled discovery accelerators. Lastly, we provide our perspective on future research directions for democratizing microreactor technologies, with the aim of accelerating scientific discoveries and promoting widespread adoption of these transformative platforms.

Intelligent Optimization Method of Piezoelectric Ejection System Design Based on Finite Element Simulation and Neural Network

This study describes an intelligent method for modeling and optimization of piezoelectric ejection system design for additive manufacturing. It is a combination of neural network (NN) techniques and finite element simulation (FES) that allows designing each parameter of a piezoelectric ejection system faster and more reliably than conventional methods. Using experimental and literature data, a FE model of the droplet ejection process was developed and validated to predict two indexes of droplet ejection behavior (DEB): jetting velocity and droplet diameter. Two artificial neural network (ANN) models based on feed-forward back propagation were developed and optimized by genetic algorithm (GA). A database was established by FE calculations, and the models were trained to establish the relationship between the piezoelectric ejection system design input parameters and each DEB indicator. The results show that both NN models can independently predict the droplet jetting velocity and droplet diameter values from the training and testing data with high accuracy to determine the optimal piezoelectric ejection system design. Finally, the accuracy of the prediction results of the FES and ANN-GA models was verified experimentally. It was found that the errors between the predicted and experimental results were 4.48% and 3.18% for the jetting velocity and droplet diameter, respectively, verifying that the optimization method is reliable and robust for piezoelectric ejection system design optimization.

Machine-Guided Discovery of Acrylate Photopolymer Compositions

Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.

Bayesian Optimization-guided Discovery of High-performance Methane Combustion Catalysts based on Multi-component PtPd@CeZrOx Core-Shell Nanospheres

Formula regulation of multi-component catalysts by manual search is undoubtedly a time-consuming task, which has severely impeded the development efficiency of high-performance catalysts. In this work, PtPd@CeZrOx core-shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33-1/9.09, and Ce/Zr from 1/0.22-1/0.35), which directly results in a lower conversion temperature (T50 approaching to 330 °C) than ones reported hitherto. Consequently, the best sample obtained could be efficiently developed after two rounds of iterations, containing only 18 experiments in all that is far less than the common human workload via the traditional trial-and-error search for optimal compositions. Further, this BO-based machine learning strategy can be straightforward extended to serve the autonomous discovery in multi-component material systems, for other desired properties, showing promising opportunities to practical applications in future.

A Mucin-Based Bio-Ink for 3D Printing of Objects with Anti-Biofouling Properties

With its potential to revolutionize the field of personalized medicine by producing customized medical devices and constructs for tissue engineering at low costs, 3D printing has emerged as a highly promising technology. Recent advancements have sparked increasing interest in the printing of biopolymeric hydrogels. However, owing to the limited printability of those soft materials, the lack of variability in available bio-inks remains a major challenge. In this study, a novel bio-ink is developed based on functionalized mucin-a glycoprotein that exhibits a multitude of biomedically interesting properties such as immunomodulating activity and strong anti-biofouling behavior. To achieve sufficient printability of the mucin-based ink, its rheological properties are tuned by incorporating Laponite XLG as a stabilizing agent. It is shown that cured objects generated from this novel bio-ink exhibit mechanical properties partially similar to that of soft tissue, show strong anti-biofouling properties, good biocompatibility, tunable cell adhesion, and immunomodulating behavior. The presented findings suggest that this 3D printable bio-ink has a great potential for a wide range of biomedical applications, including tissue engineering, wound healing, and soft robotics.

Development, Physiochemical characterization, Mechanical and Finite element analysis of 3D printed Polylactide-β-TCP/α-Al2O3 composite

Herein, material extrusion (MEX) technique is utilized to develop 3D printed models based on reinforcing β-Ca3(PO4)2/α-Al2O3 composite in polylactide (PLA) matrix. β-Ca3(PO4)2/α-Al2O3 composite has been synthesized through co-precipitation method and the phase content of β-Ca3(PO4)2 and α-Al2O3 components are respectively determined as 64 and 36 wt%. The resultant β-Ca3(PO4)2/α-Al2O3 composite mixed with PLA at various weight ratios were extruded as filaments and subsequently 3D printed into definite shapes for the physiochemical, morphological and mechanical evaluation. 3D printed bodies that comprise 5 wt % β-Ca3(PO4)2/α-Al2O3 composite yielded an increasing tensile, compressive and flexural strength in the corresponding order of ∼15, ∼15 and 22% than 3D printed pure PLA. Further, the Representative volume element (RVE) unit cells developed based on the various investigated compositions of PLA-β-Ca3(PO4)2/α-Al2O3 were subjected to mechanical evaluation through Finite element analysis (FEA) under both static and dynamic loading conditions on ASTM standard specimens. The results from experimental and FEA analysis demonstrated good uniformity that confirmed the reinforcement of 5 wt % β-Ca3(PO4)2/α-Al2O3 in PLA matrix as an optimum combination to yield better mechanical strength.

Machine learning-enabled constrained multi-objective design of architected materials

Architected materials that consist of multiple subelements arranged in particular orders can demonstrate a much broader range of properties than their constituent materials. However, the rational design of these materials generally relies on experts' prior knowledge and requires painstaking effort. Here, we present a data-efficient method for the high-dimensional multi-property optimization of 3D-printed architected materials utilizing a machine learning (ML) cycle consisting of the finite element method (FEM) and 3D neural networks. Specifically, we apply our method to orthopedic implant design. Compared to uniform designs, our experience-free method designs microscale heterogeneous architectures with a biocompatible elastic modulus and higher strength. Furthermore, inspired by the knowledge learned from the neural networks, we develop machine-human synergy, adapting the ML-designed architecture to fix a macroscale, irregularly shaped animal bone defect. Such adaptation exhibits 20% higher experimental load-bearing capacity than the uniform design. Thus, our method provides a data-efficient paradigm for the fast and intelligent design of architected materials with tailored mechanical, physical, and chemical properties.

"Freedom of design" in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties

The rational design of molecules with targeted quantum-mechanical (QM) properties requires an advanced understanding of the structure-property/property-property relationships (SPR/PPR) that exist across chemical compound space (CCS). In this work, we analyze these fundamental relationships in the sector of CCS spanned by small (primarily organic) molecules using the recently developed QM7-X dataset, a systematic, extensive, and tightly converged collection of 42 QM properties corresponding to ≈4.2M equilibrium and non-equilibrium molecular structures containing up to seven heavy/non-hydrogen atoms (including C, N, O, S, and Cl). By characterizing and enumerating progressively more complex manifolds of molecular property space-the corresponding high-dimensional space defined by the properties of each molecule in this sector of CCS-our analysis reveals that one has a substantial degree of flexibility or "freedom of design" when searching for a single molecule with a desired pair of properties or a set of distinct molecules sharing an array of properties. To explore how this intrinsic flexibility manifests in the molecular design process, we used multi-objective optimization to search for molecules with simultaneously large polarizabilities and HOMO-LUMO gaps; analysis of the resulting Pareto fronts identified non-trivial paths through CCS consisting of sequential structural and/or compositional changes that yield molecules with optimal combinations of these properties.

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.

Kirigami-Origami-Inspired Lead-Free Piezoelectric Ceramics

Kirigami- and oirigami-inspired techniques have emerged as effective strategies for material structure design; however, the use of these techniques is usually limited to soft and deformable materials. Piezoelectric ceramics, which are typical functional ceramics, are widely used in electronic and energy devices; however, the processing options for piezoelectric ceramics are limited by their brittleness and feedstock viscosity. Here, a design strategy is proposed for the preparation of lead-free piezoelectric ceramics inspired by kirigami/origami. This strategy involves direct writing printing and control over the external gravity during the calcination process for the preparation of curved and porous piezoelectric ceramics with specific shapes. The sintered BaTiO3 ceramics with curved geometries produced using this strategy exhibit a high piezoelectric constant (d33 = 275 pC N-1 ), which is 45% higher than that of conventionally sintered sheet ceramics. The curved structure of the ceramics is well-suited for use in the human body and it was determined that these curved ceramics can detect pulse signals. This strategy can be applied in the large-scale and low-cost production of other piezoelectric ceramics with various curved shapes and provides a new approach for the preparation of complex-shaped ceramics.

Sustainable Design of Structural and Functional Polymers for a Circular Economy

To achieve a sustainable circular economy, polymer production must start transitioning to recycled and biobased feedstock and accomplish CO₂ emission neutrality. This is not only true for structural polymers, such as in packaging or engineering applications, but also for functional polymers in liquid formulations, such as adhesives, lubricants, thickeners or dispersants. At their end of life, polymers must be either collected and recycled via a technical pathway, or be biodegradable if they are not collectable. Advances in polymer chemistry and applications, aided by computational material science, open the way to addressing these issues comprehensively by designing for recyclability and biodegradability. This Review explores how scientific progress, together with emerging regulatory frameworks, societal expectations and economic boundary conditions, paint pathways for the transformation towards a circular economy of polymers.

Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems

A new technological passage has emerged in the pharmaceutical field, concerning the management, application, and transfer of knowledge from humans to machines, as well as the implementation of advanced manufacturing and product optimisation processes. Machine Learning (ML) methods have been introduced to Additive Manufacturing (AM) and Microfluidics (MFs) to predict and generate learning patterns for precise fabrication of tailor-made pharmaceutical treatments. Moreover, regarding the diversity and complexity of personalised medicine, ML has been part of quality by design strategy, targeting towards the development of safe and effective drug delivery systems. The utilisation of different and novel ML techniques along with Internet of Things sensors in AM and MFs, have shown promising aspects regarding the development of well-defined automated procedures towards the production of sustainable and quality-based therapeutic systems. Thus, the effective data utilisation, prospects on a flexible and broader production of "on demand" treatments. In this study, a thorough overview has been achieved, concerning scientific achievements of the past decade, which aims to trigger the research interest on incorporating different types of ML in AM and MFs, as essential techniques for the enhancement of quality standards of customised medicinal applications, as well as the reduction of variability potency, throughout a pharmaceutical process.

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.

3D printed microfluidics: advances in strategies, integration, and applications

The ability to construct multiplexed micro-systems for fluid regulation could substantially impact multiple fields, including chemistry, biology, biomedicine, tissue engineering, and soft robotics, among others. 3D printing is gaining traction as a compelling approach to fabricating microfluidic devices by providing unique capabilities, such as 1) rapid design iteration and prototyping, 2) the potential for automated manufacturing and alignment, 3) the incorporation of numerous classes of materials within a single platform, and 4) the integration of 3D microstructures with prefabricated devices, sensing arrays, and nonplanar substrates. However, to widely deploy 3D printed microfluidics at research and commercial scales, critical issues related to printing factors, device integration strategies, and incorporation of multiple functionalities require further development and optimization. In this review, we summarize important figures of merit of 3D printed microfluidics and inspect recent progress in the field, including ink properties, structural resolutions, and hierarchical levels of integration with functional platforms. Particularly, we highlight advances in microfluidic devices printed with thermosetting elastomers, printing methodologies with enhanced degrees of automation and resolution, and the direct printing of microfluidics on various 3D surfaces. The substantial progress in the performance and multifunctionality of 3D printed microfluidics suggests a rapidly approaching era in which these versatile devices could be untethered from microfabrication facilities and created on demand by users in arbitrary settings with minimal prior training.

Advances in Digital Light Processing of Hydrogels

Hydrogels, three-dimensional (3D) networks of hydrophilic polymers formed in water, are a significant type of soft matter used in fundamental and applied sciences. Hydrogels are of particular interest for biomedical applications, owing to their soft elasticity and good biocompatibility. However, the high water content and soft nature of hydrogels often make it difficult to process them into desirable solid forms. The development of 3D printing (3DP) technologies has provided opportunities for the manufacturing of hydrogels, by adopting a freeform fabrication method. Owing to its high printing speed and resolution, vat photopolymerization 3DP has recently attracted considerable interest for hydrogel fabrication, with digital light processing (DLP) becoming a widespread representative technique. Whilst acknowledging that other types of vat photopolymerization 3DP have also been applied for this purpose, we here only focus on DLP and its derivatives. In this review, we first comprehensively outline the most recent advances in both materials and fabrication, including the adaptation of novel hydrogel systems and advances in processing (e.g., volumetric printing and multimaterial integration). Secondly, we summarize the applications of hydrogel DLP, including regenerative medicine, functional microdevices, and soft robotics. To the best of our knowledge, this is the first time that either of these specific review focuses has been adopted in the literature. More importantly, we discuss the major challenges associated with hydrogel DLP and provide our perspectives on future trends. To summarize, this review aims to aid and inspire other researchers investigatng DLP, photocurable hydrogels, and the research fields related to them.

Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing

3D printing of assistive devices requires optimization of material selection, raw materials formulas, and complex printing processes that have to balance a high number of variable but highly correlated variables. The performance of patient-specific 3D printed solutions is still limited by both the increasing number of available materials with different properties (including multi-material printing) and the large number of process features that need to be optimized. The main purpose of this study is to compare the optimization of 3D printing properties toward the maximum tensile force of an exoskeleton sample based on two different approaches: traditional artificial neural networks (ANNs) and a deep learning (DL) approach based on convolutional neural networks (CNNs). Compared with the results from the traditional ANN approach, optimization based on DL decreased the speed of the calculations by up to 1.5 times with the same print quality, improved the quality, decreased the MSE, and a set of printing parameters not previously determined by trial and error was also identified. The above-mentioned results show that DL is an effective tool with significant potential for wide application in the planning and optimization of material properties in the 3D printing process. Further research is needed to apply low-cost but more computationally efficient solutions to multi-tasking and multi-material additive manufacturing.