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Three-dimensional (3D) printed preoperative planning models serve a critical role in the success of many medical procedures. However, many of these models do not portray the patient's complete anatomy due to their monolithic and static nature. The use of dynamic 3D-printed models can better equip physicians by providing a more anatomically accurate model due to its movement capabilities and the ability to remove and replace printed anatomies based on planning stages. A dynamic 3D-printed preoperative planning model has the capability to move in similar ways to the anatomy that is being represented by the model, or reveal additional issues that may arise during the use of a movement mechanism. The 3D-printed models are constructed in a similar manner to their static counterparts; however, in the digital post-processing phase, additional care is needed to ensure the dynamic functionality of the model. Here, we discuss the process of creating a dynamic 3D-printed model and its benefits and uses in modern medicine.

The advent of additive manufacturing has opened opportunities to rapidly prototype devices and products ranging from automotive and aerospace applications to micro/nanoscale metastructures, as examples). Three-dimensional (3D) printing has become relevant for electromagnetic structures, integrated optics and photonics systems, however, the optical properties of commercially available 3D printed polymers at telecommunication wavelengths (wavelength 1550nm) is not always available. Provided the importance of 3D printing technologies, in this work, we evaluate both theoretically and experimentally the complex refractive index of four polymers including some recycled versions (namely Butenediol Vinyl Alcohol (BVOH), Polylactic Acid (PLA), Recycled Polyethylene Terephthalate (rPET), and recycled Polylactic Acid (rPLA)) as potential candidates for photonics applications. The 3D printed samples have thicknesses from ~100 to 400 nm (~64wavelengths to ~258wavelengths, respectively). The experimental reflectance and transmittance spectra are extracted and used to retrieve the complex refractive index of each printed material demonstrating extinction coefficients in the order of 10^-4 at wave

Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to large feature sizes, printed classifiers are limited by high power, area, and energy overheads, which restricts the realization of battery-powered systems. In this work, we design sequential printed bespoke Support Vector Machine (SVM) circuits that adhere to the power constraints of existing printed batteries while minimizing energy consumption, thereby boosting battery life. Our results show 6.5x energy savings while maintaining higher accuracy compared to the state of the art.

Printed electronics offer ultra-low manufacturing costs and the potential for on-demand fabrication of flexible hardware. However, significant intrinsic constraints stemming from their large feature sizes and low integration density pose design challenges that hinder their practicality. In this work, we conduct a holistic exploration of printed neural network accelerators, starting from the analog-to-digital interface - a major area and power sink for sensor processing applications - and extending to networks of ternary neurons and their implementation. We propose bespoke ternary neural networks using approximate popcount and popcount-compare units, developed through a multi-phase evolutionary optimization approach and interfaced with sensors via customizable analog-to-binary converters. Our evaluation results show that the presented designs outperform the state of the art, achieving at least 6x improvement in area and 19x in power. To our knowledge, they represent the first open-source digital printed neural network classifiers capable of operating with existing printed energy harvesters.

Printed and flexible electronics (PFE) have emerged as the ubiquitous solution for application domains at the extreme edge, where the demands for low manufacturing and operational cost cannot be met by silicon-based computing. Built on mechanically flexible substrates, printed and flexible devices offer unparalleled advantages in terms of form factor, bio-compatibility and sustainability, making them ideal for emerging and uncharted applications, such as wearable healthcare products or fast-moving consumer goods. Their desirable attributes stem from specialized fabrication technologies, e.g., Pragmatic's FlexIC, where advancements like ultra-thin substrates and specialized printing methods expand their hardware efficiency, and enable penetration to previously unexplored application domains. In recent years, significant focus has been on machine learning (ML) circuits for resource-constrained on-sensor and near-sensor processing, both in the digital and analog domains, as they meet the requirements of target applications by PFE. Despite their advancements, challenges like reliability, device integration and efficient memory design are still prevalent in PFE, spawning several researc

While analyzing scanned documents, handwritten text can overlap with printed text. This overlap causes difficulties during the optical character recognition (OCR) and digitization process of documents, and subsequently, hurts downstream NLP tasks. Prior research either focuses solely on the binary classification of handwritten text or performs a three-class segmentation of the document, i.e., recognition of handwritten, printed, and background pixels. This approach results in the assignment of overlapping handwritten and printed pixels to only one of the classes, and thus, they are not accounted for in the other class. Thus, in this research, we develop novel approaches to address the challenges of handwritten and printed text segmentation. Our objective is to recover text from different classes in their entirety, especially enhancing the segmentation performance on overlapping sections. To support this task, we introduce a new dataset, SignaTR6K, collected from real legal documents, as well as a new model architecture for the handwritten and printed text segmentation task. Our best configuration outperforms prior work on two different datasets by 17.9% and 7.3% on IoU scores. The

This article focuses on the influence of long-term UV radiation exposure on mechanical and structural properties of selected polymeric materials (PLA, ABS, PC, PETG, HIPS) prepared using 3D-print based Fused Filament Fabrication (FFF) method. Existing research in the field of polymers weathering has been focused more on the combined effects so far, moreover on time scales not exceeding units of months. However, it is important to separate individual effects to understand the dynamics of material changes and design strategies to improve material resistance. Our research thus focuses on UV-affected ageing of the selected polymers for time duration exceeding 10 months (7744 consecutive hours, i.e. 322 days), performed in an environmental cell with controlled humidity and temperature. Mechanical properties were evaluated by a locally sensitive nanoindentation method. Surface properties, depth property profiles, and creep were studied as well. Hardness and modulus of elasticity data were obtained for a wide range of samples. Based on our analysis of mechanical properties, the highest UV resistances are characteristic for PLA and PC. On the contrary, noticeable changes of mechanical prop

The demand of many application domains for flexibility, stretchability, and porosity cannot be typically met by the silicon VLSI technologies. Printed Electronics (PE) has been introduced as a candidate solution that can satisfy those requirements and enable the integration of smart devices on consumer goods at ultra low-cost enabling also in situ and ondemand fabrication. However, the large features sizes in PE constraint those efforts and prohibit the design of complex ML circuits due to area and power limitations. Though, classification is mainly the core task in printed applications. In this work, we examine, for the first time, the impact of neural minimization techniques, in conjunction with bespoke circuit implementations, on the area-efficiency of printed Multilayer Perceptron classifiers. Results show that for up to 5% accuracy loss up to 8x area reduction can be achieved.

In the past few years, the widespread use of 3D printing technology enables the growth of the market of 3D printed products. On Esty, a website focused on handmade items, hundreds of individual entrepreneurs are selling their 3D printed products. Inspired by the positive effects of machine-readable tags, like barcodes, on daily product marketing, we propose AnisoTag, a novel tagging method to encode data on the 2D surface of 3D printed objects based on reflection anisotropy. AnisoTag has an unobtrusive appearance and much lower extraction computational complexity, contributing to a lightweight low-cost tagging system for individual entrepreneurs. On AnisoTag, data are encoded by the proposed tool as reflective anisotropic microstructures, which would reflect distinct illumination patterns when irradiating by collimated laser. Based on it, we implement a real-time detection prototype with inexpensive hardware to determine the reflected illumination pattern and decode data according to their mapping. We evaluate AnisoTag with various 3D printer brands, filaments, and printing parameters, demonstrating its superior usability, accessibility, and reliability for practical usage.

Extrusion-based 3D printing has become one of the most common additive manufacturing methods and is widely used in engineering. This contribution presents the results of flexural creep experiments on 3D printed PLA specimens, focusing on changes in creep behavior due to physical aging. It is shown experimentally that the creep curves obtained on aged specimens are shifted to each other on the logarithmic time scale in a way that the theory of physical aging can explain. The reason for the physical aging of 3D printed thermoplastics is assumed to be the special heat treatment that the polymer undergoes during extrusion. Additionally, results of a long-term flexural creep experiment are shown, demonstrating that non-negligible creep over long periods can be observed even at temperatures well below the glass transition temperature. Such creep effects should be considered for designing components made of 3D printed thermoplastics.

High-precision 3D printing technology opens to almost endless opportunities to design complex shapes present in tailored architected materials. The scope of this work is to review the latest studies regarding 3D printed lattice structures that involve the use of photopolymers fabricated by Material Jetting (MJ), with a focus on the widely used Polyjet and MultiJet techniques. The main aspects governing this printing process are introduced to determine their influence during the fabrication of 3D printed lattices. Performed experimental studies, considered assumptions, and constitutive models for the respective numerical simulations are analyzed. Furthermore, an overview of the latest extensively studied 3D printed architected lattice materials is exposed by emphasizing their achieved mechanical performances through the use of Ashby plots. Then, we highlight the advantages, limitations, and challenges of the material jetting technology to manufacture tunable architected materials for innovative devices, oriented to several engineering applications. Finally, possible approaches for future works and gaps to be covered by further research are indicated, including cost and environmental

We present a deep neural network based method for the retrieval of watermarks from images of 3D printed objects. To deal with the variability of all possible 3D printing and image acquisition settings we train the network with synthetic data. The main simulator parameters such as texture, illumination and camera position are dynamically randomized in non-realistic ways, forcing the neural network to learn the intrinsic features of the 3D printed watermarks. At the end of the pipeline, the watermark, in the form of a two-dimensional bit array, is retrieved through a series of simple image processing and statistical operations applied on the confidence map generated by the neural network. The results demonstrate that the inclusion of synthetic DR data in the training set increases the generalization power of the network, which performs better on images from previously unseen 3D printed objects. We conclude that in our application domain of information retrieval from 3D printed objects, where access to the exact CAD files of the printed objects can be assumed, one can use inexpensive synthetic data to enhance neural network training, reducing the need for the labour intensive process

Although Printed Electronics (PE) cannot compete with silicon-based systems in conventional evaluation metrics, e.g., integration density, area and performance, PE offers attractive properties such as on-demand ultra-low-cost fabrication, flexibility and non-toxicity. As a result, it targets application domains that are untouchable by lithography-based silicon electronics and thus have not yet seen much proliferation of computing. However, despite the attractive characteristics of PE, the large feature sizes in PE prohibit the realization of complex printed circuits, such as Machine Learning (ML) classifiers. In this work, we exploit the hardware-friendly nature of Decision Trees for machine learning classification and leverage the hardware-efficiency of the approximate design in order to generate approximate ML classifiers that are suitable for tiny, ultra-resource constrained, and battery-powered printed applications.

The recent development of Liquid Phase Exfoliation (LPE) of 2D materials has enabled the formulation of inks with rheological properties adapted to numerous liquid deposition methods. This has allowed the fabrication of various types of printed devices with unique features stemming from the nano-structure of the printed 2D layers. In this short communication, we demonstrate that the preferred crystalline orientation of printed MoS2 flakes depends drastically upon the printing method employed to deposit the layers. Using angle resolved X-Ray Diffraction (XRD) to measure Pole Figure and subsequently calculate Orientation Distribution Functions (ODF), we show that the spin-coating method yields the best basal fiber texture, most likely because of the shear force at work on the flakes during the deposition process. This interim report thereby paves the way to further investigations and fine control of the preferred crystalline orientation of printed 2D flakes for the development of dedicated devices.

Assessing the quality of 3D printed models before they are printed remains a challeng- ing problem, particularly when considering point cloud-based models. This paper introduces an approach to quality assessment, which uses techniques from the field of Topological Data Analy- sis (TDA) to compute a topological abstraction of the eventual printed model. Two main tools of TDA, Mapper and persistent homology, are used to analyze both the printed space and empty space created by the model. This abstraction enables investigating certain qualities of the model, with respect to print quality, and identifies potential anomalies that may appear in the final product.

We introduce OmniPrint, a synthetic data generator of isolated printed characters, geared toward machine learning research. It draws inspiration from famous datasets such as MNIST, SVHN and Omniglot, but offers the capability of generating a wide variety of printed characters from various languages, fonts and styles, with customized distortions. We include 935 fonts from 27 scripts and many types of distortions. As a proof of concept, we show various use cases, including an example of meta-learning dataset designed for the upcoming MetaDL NeurIPS 2021 competition. OmniPrint is available at https://github.com/SunHaozhe/OmniPrint.