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.

ImageNet has become a reputable resource for transfer learning, allowing the development of efficient ML models with reduced training time and data requirements. However, vibration analysis in predictive maintenance, structural health monitoring, and fault diagnosis, lacks a comparable large-scale, annotated dataset to facilitate similar advancements. To address this, a dataset framework is proposed that begins with bearing vibration data as an initial step towards creating a universal dataset for vibration-based spectrogram analysis for all machinery. The initial framework includes a collection of bearing vibration signals from various publicly available datasets. To demonstrate the advantages of this framework, experiments were conducted using a deep learning architecture, showing improvements in model performance when pre-trained on bearing vibration data and fine-tuned on a smaller, domain-specific dataset. These findings highlight the potential to parallel the success of ImageNet in visual computing but for vibration analysis. For future work, this research will include a broader range of vibration signals from multiple types of machinery, emphasizing spectrogram-based representations of the data. Each sample will be labeled according to machinery type, operational status, and the presence or type of faults, ensuring its utility for supervised and unsupervised learning tasks. Additionally, a framework for data preprocessing, feature extraction, and model training specific to vibration data will be developed. This framework will standardize methodologies across the research community, allowing for collaboration and accelerating progress in predictive maintenance, structural health monitoring, and related fields. By mirroring the success of ImageNet in visual computing, this dataset has the potential to improve the development of intelligent systems in industrial applications.

Ball bearings find widespread use in various manufacturing and mechanical domains, and methods based on machine learning have been widely adopted in the field to monitor wear and spot defects before they lead to failures. Few studies, however, have addressed the problem of censored data, in which failure is not observed. In this paper, we propose a novel approach to predict the time to failure in ball bearings using survival analysis. First, we analyze bearing data in the frequency domain and annotate when a bearing fails by comparing the Kullback-Leibler divergence and the standard deviation between its break-in frequency bins and its break-out frequency bins. Second, we train several survival models to estimate the time to failure based on the annotated data and covariates extracted from the time domain, such as skewness, kurtosis and entropy. The models give a probabilistic prediction of risk over time and allow us to compare the survival function between groups of bearings. We demonstrate our approach on the XJTU and PRONOSTIA datasets. On XJTU, the best result is a 0.70 concordance-index and 0.21 integrated Brier score. On PRONOSTIA, the best is a 0.76 concordance-index and 0.19 integrated Brier score. Our work motivates further work on incorporating censored data in models for predictive maintenance.

Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machines. This paper explores the construction of vibration images, a preprocessing technique that has been previously used to train convolutional neural networks for ball bearing joint IFD. The main results demonstrate the robustness of this technique by applying it to a larger dataset than previously used and exploring the hyperparameters used in constructing the vibration images.

IBM and the other vendors who are bidding on the CORAL2 systems for the US Department of Energy can't talk about those bids, which are in flight, and Big Blue and its partners in building the "Summit" supercomputer at Oak Ridge National Laboratory and "Sierra" at Lawrence Livermore National Laboratory – that would be Nvidia for GPUs and Mellanox Technologies for InfiniBand interconnect – are all about publicly focusing on the present, since these two machines are at the top of the flops charts now. We know they are actually working hard to win the next deal for the exascale successors to these two machines, but when we had a chat at the SC18 supercomputer conference with Dave Turek, vice president of technical computing and OpenPower, we didn't even bother to bring CORAL2 up. There were other interesting things to discuss. But as an aside: We did talk to Turek about CORAL2 back in June at ISC18, just after the bids for the systems had been turned in to the Department of Energy, and he couldn't say much then except that IBM should get credit for delivering Summit and Sierra more or less as planned and that this should mean a lot when it comes to the CORAL2 bids. But maybe it wouldn't because with each generation of machines, the major labs have to do an architecture survey and take into account any new developments – or lack thereof – that could offer better performance, wider application support, lower prices, or any combination of the above. In a sense, it is always back to square one on these big systems deals, which is good for driving innovation but perhaps something to make the major suppliers a bit testy until they win the deals. It seems inconceivable that the combination of the IBM Power10 chip and a future Nvidia GPU and possibly 400 Gb/sec NDR or 800 Gb/sec XDR InfiniBand won't win the CORAL2 bids, but with Cray back in the game with its own Slingshot interconnect, there is a chance that it could win at least one of the three CORAL2 machines.

Drones used to attack two Russian military bases in Syria were so high-tech they were designed to offset jamming technology, were capable of launching precision strikes and could not have been made without foreign assistance, the defence ministry in Moscow has said . The ministry's drone department head Gen Alexander Novikov said the drones used in the weekend's raids differed from the rudimentary craft earlier used by rebels in Syria. The attacks required satellite navigation data that are not available on the internet, complex engineering works and elaborate tests, Gen Novikov said. Analysts say the drones present the biggest military challenge so far to Russia's role in Syria'The creation of drones of such class is impossible in makeshift conditions,' Novikov said. 'Their development and use requires the involvement of experts with special training in the countries that manufacture and use drones.' The ministry said Saturday's raid on the Hemeimeem air base in the province of Lattakia and Russia's naval facility in the port of Tartus involved 13 drones.

These days my research focuses on change point detection methods. These are statistical tests and procedures to detect a structural change in a sequence of data. An early example, from quality control, is detecting whether a machine became uncalibrated when producing a widget. There may be some measurement of interest, such as the diameter of a ball bearing, that we observe. The machine produces these widgets in sequence. Under the null hypothesis, the ball bearing's mean diameter does not change, while under the alternative, at some unkown point in the manufacturing process the machine became uncalibrated and the mean diameter of the ball bearings changed.