Digital Additive Manufacturing Research Articles

3D Printing of Customizable Transient Bioelectronics and Sensors

AbstractTransient electronics have emerged as a new category of devices that can degrade after their functional lifetime, offering tremendous potential as disposable sensors, actuators, wearables, and implants. Additive manufacturing methods represent a promising approach for patterning transient materials, yet examples of fully printed bioelectronic devices are scarce. This study introduces a fully digital 3D printing approach enabling the prototyping and customization of soft bioelectronics made of transient materials. The direct ink writing of poly(octamethylene maleate (anhydride) citrate) (POMaC) as an elastomeric matrix and of a shellac‐carbon ink as a conductor is investigated. Precise and repeatable deposition of both structural and conductive features is achieved by optimizing printing parameters, i.e., the dispense gap, printing speed, and inlet pressure. Multi‐material 3D printing enables the fabrication of functional transient devices. Notably, pressure and strain sensors are shown to operate in ranges relevant to implanted biomechanical monitoring. 3D‐printed transient electrodes are demonstrated to be comparable to state‐of‐the‐art devices in terms of impedance behavior. Finally, physical degradation of the materials is confirmed at physiological conditions. These fully digital additive manufacturing processes enable the monolithic fabrication of customizable transient bioelectronics with adaptable functions and geometries.

Big data, machine learning, and digital twin assisted additive manufacturing: A review

Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM data and utilize it for optimizing various aspects such as the manufacturing process, supply chain, and real-time monitoring. Data integration into proposed digital twin frameworks and the application of machine learning techniques is expected to play pivotal roles in advancing AM in the future. In this paper, we provide an overview of machine learning and digital twin-assisted AM. On one hand, we discuss the research domain and highlight the machine-learning methods utilized in this field, including material analysis, design optimization, process parameter optimization, defect detection and monitoring, and sustainability. On the other hand, we examine the status of digital twin-assisted AM from the current research status to the technical approach and offer insights into future developments and perspectives in this area. This review paper aims to examine present research and development in the convergence of big data, machine learning, and digital twin-assisted AM. Although there are numerous review papers on machine learning for additive manufacturing and others on digital twins for AM, no existing paper has considered how these concepts are intrinsically connected and interrelated. Our paper is the first to integrate the three concepts big data, machine learning, and digital twins and propose a cohesive framework for how they can work together to improve the efficiency, accuracy, and sustainability of AM processes. By exploring latest advancements and applications within these domains, our objective is to emphasize the potential advantages and future possibilities associated with integration of these technologies in AM.

A Novel Approach to the Production of Printed Patch Antennas

This paper presents the manufacturing of a patch antenna using an advanced 3D printing technology called lights-out digital additive manufacturing (LDM). This 3D LDM printing technology is mainly used for printing circuit boards (PCBs); however, it has also been used to print a patch antenna from conductive (CI) and dielectric ink (DI). This 3D LDM-printed antenna was compared with antennas on different dielectric substrates (Arlon 25N and FR4). The obtained results are compared and analyzed in this paper.

Design and Fabrication of Microarchitected Thermoelectric Generators: Prospects and Challenges

Solid‐state devices called thermoelectric generators (TEGs) can convert waste heat from production into electrical power. For emerging devices like thermally integrated energy‐autonomous devices, medicinal equipment, and Internet of Things, μ‐TEGs are essential because they can produce power even from minor temperature gradients. A review of the history and state‐of‐the‐art of μ‐TEGs is provided in this article. In addition, it concentrates on highly effective approaches for achieving high‐performance miniature vertical thermoelectric (TE) devices. Design and optimizing methodologies for vertical μ‐TEGs are also investigated since their output power, efficiency, and integrity are critical to the architecture. Improving electrical and mechanical performance may be achieved via optimization methods, including multiobjective and finite‐dimensional optimization in three dimensions (3D). Additionally, the idea and latest advancements of employing the 3D microadditive manufacturing (micro‐AM) technique for producing μ‐TEGs are discussed, along with their advantages and disadvantages. The concept of “microarchitected TEGs” as opposed to “μ‐TEGs” is provided by the new paradigm of digital AM and architected material concept, which also broadens the scope of material adaptability and innovative structural design. The difficulties experienced are summed up when increasing the output power of μ‐TEG and the prediction of future trends is the final step.

Recognition of Additive Manufacturing Parts Based on Neural Networks and Synthetic Training Data: A Generalized End-to-End Workflow

Additive manufacturing (AM) is becoming increasingly relevant among established manufacturing processes. AM parts must often be recognized to sort them for part- or order-specific post-processing. Typically, the part recognition is performed manually, which represents a bottleneck in the AM process chain. To address this challenge, a generalized end-to-end workflow for automated visual real-time recognition of AM parts is presented, optimized, and evaluated. In the workflow, synthetic training images are generated from digital AM part models via rendering. These images are used to train a neural network for image classification, which can recognize the printed AM parts without design adaptations. As each production batch can consist of new parts, the workflow is generalized to be applicable to individual batches without adaptation. Data generation, network training and image classification are optimized in terms of the hardware requirements and computational resources for industrial applicability at low cost. For this, the influences of the neural network structure, the integration of a physics simulation in the rendering process and the total number of training images per AM part are analyzed. The proposed workflow is evaluated in an industrial case study involving 215 distinct AM part geometries. Part classification accuracies of 99.04% (top three) and 90.37% (top one) are achieved.

Blockchain-based quality management for a digital additive manufacturing part record

Quality management (QM) of additive manufacturing (AM) processes is currently immature in terms of transparency, traceability and security. In particular, quality-relevant documents are not documented and communicated in a traceable and transparent manner, which often leads to quality deficiencies. However, combining AM with blockchain technology can enable a solution that maps the AM value chain digitally, transparently, traceably and securely in a part record. In this work, a quality assurance (QA) concept for the metal-based material extrusion (MEX) process is developed that enables a digital representation of the value chain in the form of an AM part record. The decentralized solution presented in this work uses an architecture consisting of a web application for data acquisition, a decentralized storage solution for storing larger amounts of data, a smart contract for capturing manufacturing events and the Ethereum blockchain for transparent, secure and traceable storage of blockchain data. The AM part record enables traceable and constantly available digital documentation of quality information. The cost-effectiveness of the solution is also shown in a demonstration study. The research results highlight the benefits of a blockchain-based AM part record for digital manufacturing documentation and represent an efficient alternative or extension to existing QM and QA solutions in AM.

Management of digital additive manufacturing: Features and perspectives

Aim. To find and argue points of growth of additive manufacturing, as well as to justify the rel­evance and significance of new business models using digital platforms.Tasks. To consider the features of management of digital additive manufacturing; to analyze the reasons for the low rate of additive technology development; to show the prospects of dig­ital development of additive manufacturing; to justify the importance of digital platforms for additive services as a new business model and prove that digital additive manufacturing can be characterized as a system innovation.Methods. General scientific methods (analysis, synthesis, monographic, grouping) were used in the research. The best Russian and world practices, fundamental and applied works of na­tional and foreign researchers, scientists, practitioners in the field of management, digital transformation were studied using the search of relevant research in open sources, both in ge­neral purpose search engines, and in specialized search engines and databases.Results. It is proved that digital additive manufacturing should be regarded as a system in­novation, and its large-scale benefits can be realized if the technology is complemented by va­rious innovations in products, processes and services. From the author's point of view, the po­tential and prospects of a new business model in the online market of additive design and manu­facturing are obvious: a digital platform of additive services for manufacturing parts from various materials.Conclusions. Additive manufacturing plays a vital role in Industry 4.0 because of the shortened cycle time of the lean process and the high degree of decentralization of production processes. Therefore, a systematic approach using digital platforms is required to unlock their capabilities. Customers who require parts produced using this technology can place their designs on a plat­form and find a manufacturing partner based on available technical resources, geographic loca­tion, and the amount of anticipated cost. Implementing a digital platform design strategy that integrates all stages of the design and production process for different products, facilitates data traceability and product reproducibility on different additive manufacturing machines, greatly increases customer value.

Monolithically Integrated Coaxial Resonator-Based Filtennas Using SLA Three-Dimensional Printing

This letter reports on a new class of highly-miniaturized coaxial-resonator-based filtennas alongside a monolithic integration concept using digital additive manufacturing. The filtenna approach is based on the co-design of a coaxial cavity resonator and an annular slot antenna that collectively perform as a radiating resonating node within a coupled-resonator-based bandpass filter. Beam direction can be controlled by altering the position of the radiating slot. A stereolithography apparatus (SLA) monolithic integration concept is proposed for size compactness and reduced weight. For proof-of-concept validation purposes, first-, second-, and third-order filtennas were analyzed through electromagnetic simulations. Experimental validation was performed through the manufacturing and testing of a second-order top-slot filtenna, a second-order and a third-order side-slot filtenna with the following characteristics: center frequency: 4.75 GHz, 4.8 GHz and 4.6 GHz, fractional bandwidth: 7.4%, 8.3%, and 7.6% and realized gain: 5.5 dBi, 2.6 dBi, and 2.6 dBi.

A Synergic Approach of Deep Learning towards Digital Additive Manufacturing: A Review

Deep learning and additive manufacturing have progressed together in the previous couple of decades. Despite being one of the most promising technologies, they have several flaws that a collaborative effort may address. However, digital manufacturing has established itself in the current industrial revolution and it has slowed down quality control and inspection due to the different defects linked with it. Industry 4.0, the most recent industrial revolution, emphasizes the integration of intelligent production systems and current information technologies. As a result, deep learning has received a lot of attention and has been shown to be quite effective at understanding image data. This review aims to provide a cutting-edge deep learning application of the AM approach and application. This article also addresses the current issues of data privacy and security and potential solutions to provide a more significant dimension to future studies.

Mid-Infrared Photoconductive Detectors Fabricated from Solution-Processed PbSe Nanocrystals

Lead selenide (PbSe) was the first semiconductor used for photosensors operating in the mid-wavelength infrared spectral region (3-5 um) and is still the primary choice for low-cost, uncooled photoconductive detector applications. Even through PbSe has been studied for over 70 years, more understandings on its physical mechanism for high photoresponsivity is still emerging. Common methods of fabricating PbSe detectors involve physical/chemical vapor deposition or chemical bath deposition (CBD) techniques, followed by post-deposition sensitization processes including oxygen annealing and iodization. Here, we present a new approach to fabricating photoconductive devices based on PbSe colloidal nanocrystals. This infrared colloidal ink can serve as a critical component to enabling digital additive manufacturing of mid-infrared sensor arrays and simultaneously lower the fabrication cost by removing multiple masking steps required in conventional approaches. We report on the synthesis and fabrication of PbSe nanostructured film and the effects of thermal annealing as well as oxygen and iodide incorporation on the photoconductive properties.

Error propagation from intraoral scanning to additive manufacturing of complete-arch dentate models: An in vitro study

ObjectivesTo evaluate deviation propagation from data acquisition with an intraoral scanner to additive manufacturing of complete-arch dentate models. MethodsA reference (Ref) mandibular dentate model having 5 precision spheres was scanned with a coordinate measurement machine equipped with a laser scanning head (ALTERA; Nikon) producing a Ni reference data set (n = 1). Digital impressions were taken of the Ref model with intraoral scanner (IOS) (Trios4; 3Shape) with Insane (T4_Imo) and Classic (T4_Cmo) scanning modes (each n = 10). T4_Imo scans were used as a second reference data set and to produce test models with two additive manufacturing (AM) devices (each n = 10): MAX UV385 (Asiga) and NextDent 5100 (3DSystems). As for the control group, dual viscosity vinyl polysiloxane impressions were taken of the Ref model and poured with Type IV dental stone (n = 10). All AM and stone models were scanned with a laboratory scanner (E4; 3Shape). Trueness and precision of linear (intermolar and intercanine width, arch length) and surface deviations were measured between reference (Ni, T4_Imo), test (T4_Cmo, AM), and control (stone) groups using best-fit alignments (Geomagic Control X; 3D Systems). The normality of data and differences between the groups were analyzed using Shapiro-Wilk, Levene's, Mann-Whitney U, Welch's t-test statistical analysis (p<0.05). ResultsThe accuracy of the IOS impression was not significantly affected by the scanning mode (p>0.05). Stone models showed significantly better trueness than IOS impressions (p<0.05). AM models had higher trueness than IOS Imo digital impressions (p<0.05). The precision of AM models was comparable (linear, p>0.05) or lower (surface, p<0.05) than of IOS Imo digital impressions. Trueness was insignificantly different among the stone and AM models (p>0.05). Higher trueness was achieved by Max UV385 than with Nextdent 5100 (p<0.05). The majority of linear and all surface deviations of IOS impressions and AM models were below 200 μm. ConclusionsWithin the limitations of this in vitro study, digital IOS impressions and AM models using the aforementioned equipment have acceptable accuracy for orthodontic and prosthodontic applications when complete-arch dentate records are used. Clinical SignificanceIOS and AM devices can have a significant influence on error propagation when applying digital workflow with complete-arch dentate models

Potentials of 3D extrusion‐based printing in resolving food processing challenges: A perspective review

AbstractThree‐dimensional (3D) printing has promising application potentials in improving food product manufacturing, increasingly helping in simplifying the supply chain, as well as expanding the utilization of food materials. To further understand the current situation of 3D food printing in providing food engineering solutions with customized design, the authors checked recently conducted reviews and considered the extrusion‐based type to deserve additional literature synthesis. In this perspective review, therefore, we scoped the potentials of 3D extrusion‐based printing in resolving food processing challenges. The evolving trends of 3D food printing technologies, fundamentals of extrusion processes, food printer, and printing enhancement, (extrusion) food systems, algorithm development, and associated food rheological properties were discussed. The (extrusion) mechanism in 3D food printing involving some essentials for material flow and configuration, its uniqueness, suitability, and printability to food materials, (food material) types in the extrusion‐based (3D food printing), together with essential food properties and their dynamics were also discussed. Additionally, some bottlenecks/concerns still applicable to extrusion‐based 3D food printing were brainstormed. Developing enhanced calibrating techniques for 3D printing materials, and designing better methods of integrating data will help improve the algorithmic representations of printed foods. Rheological complexities associated with the extrusion‐based 3D food printing require both industry and researchers to work together so as to tackle the (rheological) shifts that make (food) materials unsuitable.Practical ApplicationsAs a processing technology with digital additive manufacturing methodology, 3D food printing over the decades has evolved greatly with the extrusion‐based type increasingly studied. This perspective review scoped the potentials of 3D extrusion‐based printing in resolving food processing challenges. In this work, we demonstrated how this extrusion‐based technique increasingly contributes to situate the 3D food printing as among innovative technologies with an upscale dimension. To fully embrace the extrusion‐based 3D printing, the food industry needs to primarily understand the potentials this technology would provide in enhancing food material properties/types.

3D Printing in Digital Prosthetic Dentistry: An Overview of Recent Developments in Additive Manufacturing.

Popular media now often present 3D printing as a widely employed technology for the production of dental prostheses. This article aims to show, based on factual information, to what extent 3D printing can be used in dental laboratories and dental practices at present. It attempts to present a rational evaluation of todays´ applications of 3D printing technology in the context of dental restorations. In addition, the article discusses future perspectives and examines the ongoing viability of traditional dental laboratory services and manufacturing processes. It also shows which expertise is needed for the digital additive manufacturing of dental restorations.

Structural Optimization through Biomimetic-Inspired Material-Specific Application of Plant-Based Natural Fiber-Reinforced Polymer Composites (NFRP) for Future Sustainable Lightweight Architecture.

Under normal conditions, the cross-sections of reinforced concrete in classic skeleton construction systems are often only partially loaded. This contributes to non-sustainable construction solutions due to an excess of material use. Novel cross-disciplinary workflows linking architects, engineers, material scientists and manufacturers could offer alternative means for more sustainable architectural applications with extra lightweight solutions. Through material-specific use of plant-based Natural Fiber-Reinforced Polymer Composites (NFRP), also named Biocomposites, a high-performance lightweight structure with topology optimized cross-sections has been here developed. The closed life cycle of NFRPs promotes sustainability in construction through energy recovery of the quickly generative biomass-based materials. The cooperative design resulted in a development that were verified through a 1:10 demonstrator, whose fibrous morphology was defined by biomimetically-inspired orthotropic tectonics, generated with by the fiber path optimization software tools, namely EdoStructure and EdoPath in combination with the appliance of the digital additive manufacturing technique: Tailored Fiber Placement (TFP).

Real‐Time Optical Process Monitoring for Structure and Property Control of Aerosol Jet Printed Functional Materials

AbstractAerosol jet printing is a popular digital additive manufacturing method for flexible and hybrid electronics, but it lacks sophisticated real‐time process control schemes that would enable more widespread adoption in manufacturing environments. Here, an optical measurement system is introduced to track the aerosol density upstream of the printhead. The measured optical extinction, combined with the aerosol flow rate, is directly related to deposition rate and accurately predicts functional materials properties such as the electrical resistance of printed graphene films. This real‐time system offers a compelling solution for process drift and batch‐to‐batch variability, rendering it a valuable tool for both real‐time control of aerosol jet printing and fundamental studies of the underlying process science.

Additive Manufacturing of Functional Microarchitected Reactors for Energy, Environmental, and Biological Applications

The use of microreactors in the continuous fluidic system has been rapidly expanded over the past three decades. Developments in materials science and engineering have accelerated the advancement of the microreactor technology, enabling it to play a critical role in chemical, biological, and energy applications. The emerging paradigm of digital additive manufacturing broadens the range of the material flexibility, innovative structural design, and new functionality of the conventional microreactor system. The control of spatial arrangements with functional printable materials determines the mass transport and energy transfer within architected microreactors, which are significant for many emerging applications, including use in catalytic, biological, battery, or photochemical reactors. However, challenges such as lack of design based on multiphysics modeling and material validation are currently preventing the broader applications and impacts of functional microreactors conjugated with digital manufacturing beyond the laboratory scale. This review covers a state-of-the-art of research in the development of some of the most advanced digital manufactured functional microreactors. We then the outline major challenges in the field and provide our perspectives on future research and development directions.

Aerosol jet printing on paper substrate with conductive silver nano material

Abstract Aerosol jet printing (AJP) is a promising method for micro-scaled digital additive manufacturing for printed electronics. It usually requires a high sintering temperature (280 °C) and a long sintering time (e.g., 12 h on a glass substrate) to guarantee a high conductivity of the printed structures, which has limited AJP to only a few types of substrates. Moreover, the printed metallic structure peels off from the substrate easily. In this paper, a procedure of AJP printing on cellulose fiber paper is proposed, which includes the use of cellulose fiber paper as the substrate, and a new sintering method, i.e., hot-air sintering with an optimized sequence. With the proposed approach, the sintering temperature is significantly lowered (80 °C), and the sintering time is considerably shortened (40 min). The printed structure has a measured sheet resistance of 1.13 × 10−2 Ω/m2 which is equivalent to a conductivity of greater than 106 S/m and close to that of the bulk silver (6.30 × 107 S/m), and it has good adhesion to the substrate. The determinant factors underlying the diffusion and curing processes of AJP, i.e., the properties of the substrate (cellulose fiber paper), the printing parameters, the sintering parameters, and the sequence were systematically investigated. The investigation is carried out through evaluation of the morphologies of the printed structures based on scanning electron microscope (SEM) images and through the study of the correlation between the morphology and the conductivity of the structures. Moreover, the proposed paper-based AJP electronics offer tremendous flexibility. They can be folded, bent, and pasted to any surface. This proposed cellulose-fiber-paper-based AJP opens a window for low-cost, eco-friendly, flexible, and high-resolution printed electronics. It will be an essential alternative fabrication approach for flexible electronic circuits, antennas, and electromagnetic-functioning surfaces, such as reconfigurable meta-surfaces.

4D printed multi-stable metamaterials with mechanically tunable performance

The function of the metamaterials is determined by the configuration and spatial arrangement of the lattice microstructure. Once manufactured, the geometry and function of the metamaterials are irreversible and cannot be adapted to the environment to be variable and adjustable. This paper studies a shape-reconfigurable, functionally deployable, mechanically adjustable and reusable intelligent multi-stable metamaterial. Based on a 4D printing method that combines digital additive manufacturing technique and thermally induced shape memory polymer exhibiting significant change in modulus of elasticity, this metamaterial is created with reconfigurable, self-expandable and mechanical properties adjustable features. The macroscopic deformation and the morphology change of the lattice microstructure on the metamaterial during the compression test are analyzed using experiment and finite element method. The adjustable, selectable, and controllable for micro-lattice in the metamaterial during deformation and recovery can be achieved by microstructure gradients and composite design methods. The new micro-lattice programmable mechanical metamaterial has excellent versatility and the ability to adapt to environmental changes. This 4D printed multi-stable metamaterial has broad application prospects, such as in soft robots, smart damping interfaces, aerospace adjustable and expandable structures, and tunable function devices.

Additive manufacturing effect on the mechanical behaviour of architectural stoneware bricks

Recent experiments showed the potential of integrating digital additive manufacturing techniques in the architectural design and production processes of free-form stoneware bricks for building envelopes, enabling a broad geometrical freedom with specific design requirements and challenging functions.The ceramic paste properties is known to be a key factor in the process of extrusion. The extrudability can be influenced by several parameters, such as the mineralogical clay composition and its moisture. In this study the extrusion parameters changes and its influence in the mechanical properties of stoneware ceramic bodies are investigated.This paper also explores the effect of additive manufacturing on the mechanical behaviour of architectural stoneware bricks and presents the main challenges and outcomes achieved during this extensive experimental research which adds significant knowledge to this emergent and promising field.

Digital manufacturing of advanced materials: challenges and perspective.

The rapid development in materials science and engineering requests the manufacturing of materials in a more rational and designable manner. Beyond traditional manufacturing techniques, such as casting and coating, digital control of material morphology, composition, and structure represents a highly integrated and versatile approach. Digital manufacturing systems enable users to fabricate freeform materials, which lead to new functionalities and applications. Digital additive manufacturing (AM), which is a layer-by-layer fabrication approach to create three-dimensional (3D) products with complex geometries, is changing the way materials manufacturing is approached in traditional industry. More recently, digital printing of chemically synthesized colloidal nanoparticles has paved the way towards manufacturing a class of designer nanomaterials with properties precisely tailored by the nanoparticles and their interactions down to atomic scales. Despite the tremendous progress being made so far, multiple challenges have prevented the broader applications and impacts of the digital manufacturing technologies. This review features cutting-edge research in the development of some of the most advanced digital manufacturing methods. We focus on outlining major challenges in the field and providing our perspectives on the future research and development directions.