3D-Printing for Analytical Ultracentrifugation

Analytical ultracentrifugation (AUC) is a classical technique of physical biochemistry providing information on size, shape, and interactions of macromolecules from the analysis of their migration in centrifugal fields while free in solution. A key mechanical element in AUC is the centerpiece, a component of the sample cell assembly that is mounted between the optical windows to allow imaging and to seal the sample solution column against high vacuum while exposed to gravitational forces in excess of 300,000 g. For sedimentation velocity it needs to be precisely sector-shaped to allow unimpeded radial macromolecular migration. During the history of AUC a great variety of centerpiece designs have been developed for different types of experiments. Here, we report that centerpieces can now be readily fabricated by 3D printing at low cost, from a variety of materials, and with customized designs. The new centerpieces can exhibit sufficient mechanical stability to withstand the gravitational forces at the highest rotor speeds and be sufficiently precise for sedimentation equilibrium and sedimentation velocity experiments. Sedimentation velocity experiments with bovine serum albumin as a reference molecule in 3D printed centerpieces with standard double-sector design result in sedimentation boundaries virtually indistinguishable from those in commercial double-sector epoxy centerpieces, with sedimentation coefficients well within the range of published values. The statistical error of the measurement is slightly above that obtained with commercial epoxy, but still below 1%. Facilitated by modern open-source design and fabrication paradigms, we believe 3D printed centerpieces and AUC accessories can spawn a variety of improvements in AUC experimental design, efficiency and resource allocation.Comment: 25 pages, 6 figure

AN ALGORITHM FOR RECONSTRUCTING THREE-DIMENSIONAL IMAGES FROM OVERLAPPING TWO-DIMENSIONAL INTENSITY MEASUREMENTS WITH RELAXED CAMERA POSITIONING REQUIREMENTS, WITH APPLICATION TO ADDITIVE MANUFACTURING

Cameras are everywhere for security purposes and there are often many cameras installed close to each other to cover areas of interest, such as airport passenger terminals. These systems are often designed to have overlapping fields of view to provide different aspects of the scene to review when, for example, law enforcement issues arise. However, these cameras are rarely, if ever positioned in a way that would be conducive to conventional stereo image processing. To address this, issue an algorithm was developed to rectify images measured under such conditions, and then perform stereo image reconstruction. The initial experiments described here were set up using two scientific cameras to capture overlapping images in various cameras positons. The results showed that the algorithm was accurately reconstructing the three-dimensional (3-D) surface locations of the input objects. During the research an opportunity arose to further develop and test the algorithms for the problem of monitoring the fabrication process inside a 3-D printer. The geometry of 3-D printers prevents the location of cameras in the conventional stereo imaging geometry, making the algorithms described above seem like an attractive solution to this problem. The emphasis in 3-D printing on using extremely low cost components and open source software, and the need to develop the means of comparing observed progress in the fabrication process to a model of the device being fabricated posed additional development challenges. Inside the 3-D printer the algorithm was applied using two scientific cameras to detect the errors during the printing of the low-cost open-source RepRap style 3-D printer developed by the Michigan Tech’s Open Sustainability Technology Lab. An algorithm to detect errors in the shape of a device being fabricated using only one camera was also developed. The results show that a 3-D reconstruction algorithm can be used to accurately detect the 3-D printing errors. The initial development of the algorithm was in MATLAB. The cost of the MATLAB software might prevent it from being used by open-source communities. Thus, the algorithm was ported to Python and made open-source for everyone to use and customize. To reduce the cost, the commonly used and widely available inexpensive webcams were also used instead of the expensive scientific cameras. In order to detect errors around the printed part, six webcams were used, so there were 3 pairs of webcams and each pair were 120 degrees apart. The results indicated that the algorithms are precisely detect the 3-D printing errors around the printed part in shape and size aspects. With this low-cost and open-source approach, the algorithms are ready for wide range of use and applications

A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing

This work introduces an innovative parallel, fully-distributed finite element framework for growing geometries and its application to metal additive manufacturing. It is well-known that virtual part design and qualification in additive manufacturing requires highly-accurate multiscale and multiphysics analyses. Only high performance computing tools are able to handle such complexity in time frames compatible with time-to-market. However, efficiency, without loss of accuracy, has rarely held the centre stage in the numerical community. Here, in contrast, the framework is designed to adequately exploit the resources of high-end distributed-memory machines. It is grounded on three building blocks: (1) Hierarchical adaptive mesh refinement with octree-based meshes; (2) a parallel strategy to model the growth of the geometry; (3) state-of-the-art parallel iterative linear solvers. Computational experiments consider the heat transfer analysis at the part scale of the printing process by powder-bed technologies. After verification against a 3D benchmark, a strong-scaling analysis assesses performance and identifies major sources of parallel overhead. A third numerical example examines the efficiency and robustness of (2) in a curved 3D shape. Unprecedented parallelism and scalability were achieved in this work. Hence, this framework contributes to take on higher complexity and/or accuracy, not only of part-scale simulations of metal or polymer additive manufacturing, but also in welding, sedimentation, atherosclerosis, or any other physical problem where the physical domain of interest grows in time

Improving precision of material extrusion 3D printing by in-situ monitoring and predicting 3D geometric deviation using Conditional Adversarial Networks

The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D geometric deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the geometric deviation of the predicted profiles by making compensation to the CAD model

The Wiltshire Wills Feasibility Study

The Wiltshire and Swindon Record Office has nearly ninety thousand wills in its care. These records are neither adequately catalogued nor secured against loss by facsimile microfilm copies. With support from the Heritage Lottery Fund the Record Office has begun to produce suitable finding aids for the material. Beginning with this feasibility study the Record Office is developing a strategy to ensure the that facsimiles to protect the collection against risk of loss or damage and to improve public access are created.<p></p> This feasibility study explores the different methodologies that can be used to assist the preservation and conservation of the collection and improve public access to it. The study aims to produce a strategy that will enable the Record Office to create digital facsimiles of the Wills in its care for access purposes and to also create preservation quality microfilms. The strategy aims to seek the most cost effective and time efficient approach to the problem and identifies ways to optimise the processes by drawing on the experience of other similar projects. This report provides a set of guidelines and recommendations to ensure the best use of the resources available for to provide the most robust preservation strategy and to ensure that future access to the Wills as an information resource can be flexible, both local and remote, and sustainable