The world's first 3D-printed microscope costs only $60 to build

Technology Engineering How does 3D printing work? Rapid prototyping is a relatively simple process that can be scaled up or down. Breakthroughs, discoveries, and DIY tips sent every weekday. Since 3D printers debuted in the 1980s, the devices have been used to build meat, chocolate, human organs, clothing, cars, and houses . It's more mainstream than ever, and you can buy a machine for less than $200.

We build our dataset from triangu-lated meshes derived from Objaverse-XL and Thingi10K datasets.

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Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures. This contribution presents a noise-aware decision-making algorithm that quantifies and models device-specific noise profiles to manage variability adap-tively. It uses distributional analysis and pairwise divergence metrics with clustering to choose between single-device and robust multi-device Bayesian optimization strategies. Unlike conventional methods that assume homogeneous devices or generic robustness, this framework explicitly leverages inter-device differences to enhance performance, reproducibility, and efficiency. An experimental case study involving three nominally identical 3D printers (same brand, model, and close serial numbers) demonstrates reduced redundancy, lower resource usage, and improved reliability. Overall, this framework establishes a paradigm for precision-and resource-aware optimization in scalable, automated experimental platforms. Introduction Recent advances in automation technologies have revolutionized scientific research, particularly in fields that rely on high-throughput experimentation.

Tactile sensing plays a key role in enabling dexterous and reliable robotic manipulation, but realizing this capability requires substantial calibration to convert raw sensor readings into physically meaningful quantities. Despite its near-universal necessity, the calibration process remains ad hoc and labor-intensive. Here, we introduce 3D Cal, an open-source library that transforms a low-cost 3D printer into an automated probing device capable of generating large volumes of labeled training data for tactile sensor calibration. We demonstrate the utility of 3D Cal by calibrating two commercially available vision-based tactile sensors, DIGIT and GelSight Mini, to reconstruct high-quality depth maps using the collected data and a custom convolutional neural network. In addition, we perform a data ablation study to determine how much data is needed for accurate calibration, providing practical guidelines for researchers working with these specific sensors, and we benchmark the trained models on previously unseen objects to evaluate calibration accuracy and generalization performance. By automating tactile sensor calibration, 3D Cal can accelerate tactile sensing research, simplify sensor deployment, and promote the practical integration of tactile sensing in robotic platforms.

Additive manufacturing, or 3D printing, is a complex process that creates free-form geometric objects by sequentially placing material to construct an object, usually in a layer-by-layer process. One of the most widely used methods is Fused Deposition Modeling (FDM). FDM is used in many of the consumer-grade polymer 3D printers available today. While consumer grade machines are cheap and plentiful, they lack many of the features desired in a machine used for research purposes and are often closed-source platforms. Commercial-grade models are more expensive and are also usually closed-source platforms that do not offer flexibility for modifications often needed for research. The authors designed and fabricated a machine to be used as a test bed for research in the field of polymer FDM processes. The goal was to create a platform that tightly controls and/or monitors the FDM build parameters so that experiments can be repeated with a known accuracy. The platform offers closed loop position feedback, control of the hot end and bed temperature, and monitoring of environment temperature and humidity. Additionally, the platform is equipped with cameras and a mechanism for in-situ photogrammetry, creating a geometric record of the printing throughout the printing process. Through photogrammetry, backtracking and linking process parameters to observable geometric defects can be achieved. This paper focuses on the design of a novel mechanism for spinning the heated bed to allow for photogrammetric reconstruction of the printed part using a minimal number of cameras, as implemented on this platform.

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Tactile sensors have a wide range of applications, from utilization in robotic grippers to human motion measurement. If tactile sensors could be fabricated and integrated more easily, their applicability would further expand. In this study, we propose a tactile sensor-M3D-skin-that can be easily fabricated with high versatility by leveraging the infill patterns of a multi-material fused deposition modeling (FDM) 3D printer as the sensing principle. This method employs conductive and non-conductive flexible filaments to create a hierarchical structure with a specific infill pattern. The flexible hierarchical structure deforms under pressure, leading to a change in electrical resistance, enabling the acquisition of tactile information. We measure the changes in characteristics of the proposed tactile sensor caused by modifications to the hierarchical structure. Additionally, we demonstrate the fabrication and use of a multi-tile sensor. Furthermore, as applications, we implement motion pattern measurement on the sole of a foot, integration with a robotic hand, and tactile-based robotic operations. Through these experiments, we validate the effectiveness of the proposed tactile sensor.