352 research outputs found

    Tool condition monitoring of diamond-coated burrs with acoustic emission utilising machine learning methods

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    Within manufacturing there is a growing need for autonomous Tool Condition Monitoring (TCM) systems, with the ability to predict tool wear and failure. This need is increased, when using specialised tools such as Diamond-Coated Burrs (DCBs), in which the random nature of the tool and inconsistent manufacturing methods create large variance in tool life. This unpredictable nature leads to a significant fraction of a DCB tool’s life being underutilised due to premature replacement. Acoustic Emission (AE) in conjunction with Machine Learning (ML) models presents a possible on-machine monitoring technique which could be used as a prediction method for DCB wear. Four wear life tests were conducted with a ∅1.3 mm #1000 DCB until failure, in which AE was continuously acquired during grinding passes, followed by surface measurements of the DCB. Three ML model architectures were trained on AE features to predict DCB mean radius, an indicator of overall tool wear. All architectures showed potential of learning from the dataset, with Long Short-Term Memory (LSTM) models performing the best, resulting in prediction error of MSE = 0.559 μm2 after optimisation. Additionally, links between AE kurtosis and the tool’s run-out/form error were identified during an initial review of the data, showing potential for future work to focus on grinding effectiveness as well as overall wear. This paper has shown that AE contains sufficient information to enable on-machine monitoring of DCBs during the grinding process. ML models have been shown to be sufficiently precise in predicting overall DCB wear and have the potential of interpreting grinding condition

    Deep learning methods applied to digital elevation models: state of the art

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    Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of SpainPID2019-106195RB- I00/AEI/10.13039/50110001103

    Segment Anything Model (SAM) for Radiation Oncology

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    In this study, we evaluate the performance of the Segment Anything Model (SAM) model in clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic: prostate, lung, gastrointestinal, and head \& neck, which are typical treatment sites in radiation oncology. For each case, we selected the OARs of concern in radiotherapy planning and compared the Dice and Jaccard outcomes between clinical manual delineation, automatic segmentation using SAM's "segment anything" mode, and automatic segmentation using SAM with box prompt. Our results indicate that SAM performs better in automatic segmentation for the prostate and lung regions, while its performance in the gastrointestinal and head \& neck regions was relatively inferior. When considering the size of the organ and the clarity of its boundary, SAM displays better performance for larger organs with clear boundaries, such as the lung and liver, and worse for smaller organs with unclear boundaries, like the parotid and cochlea. These findings align with the generally accepted variations in difficulty level associated with manual delineation of different organs at different sites in clinical radiotherapy. Given that SAM, a single trained model, could handle the delineation of OARs in four regions, these results also demonstrate SAM's robust generalization capabilities in automatic segmentation for radiotherapy, i.e., achieving delineation of different radiotherapy OARs using a generic automatic segmentation model. SAM's generalization capabilities across different regions make it technically feasible to develop a generic model for automatic segmentation in radiotherapy

    COMPREHENSIVE ANALYSIS OF SEISMIC SIGNALS FROM PACAYA VOLCANO USING DEEP LEARNING EVENT DETECTION

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    Pacaya volcano located 30 km SW of Guatemala City, Guatemala, has been erupting intermittently since 1961. Monitoring of seismicity is crucial to understanding current activity levels within Pacaya. Traditional methods of picking these small earthquakes in this noisy environment are imprecise. Pacaya produces many small events that can easily blend in with the background noise. A possible solution for this problem is a machine learning program to pick first arrivals for these earthquakes. We tested a deep learning algorithm (Mousavi et al., 2020) for fast and reliable seismic signal detection within a volcanic system. Data from multiple deployments were used, including permanent and temporary arrays from 2015 to 2022. Initially over 12,000 independent events were detected although most were unlocatable. A predetermined 1D velocity model calculated by Lanza & Waite (2018) was initially used to locate the earthquakes. This velocity model was updated using VELEST and the locations were calculated using new 1D P-wave and S-wave velocity models. This resulted in 512 events after a quality control filtering process. These events ranged in depths from -2.5 km (summit of Pacaya) to 0 km (sea level) all located directly beneath the vent. The detection process took about 2-3 hours per 15 days on each 3-component broadband seismometer. The method shows promise in providing an efficient and effective method to pick volcano tectonic seismic events, and it did well identifying the emergent arrivals in these datasets; however, it has shortcomings in detecting some low-frequency event types. This could be addressed through additional training of the algorithm. The very low speeds in our new P-wave and S-wave velocity models highlight the poor consolidation of the young MacKenney cone. Further study is encouraged to better understand the accuracy and type of earthquakes picked, especially the increased level of activity during or leading up to an eruption at Pacaya volcano

    Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning

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    In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 68% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream

    Biomaterials for Bone Tissue Engineering 2020

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    This book presents recent advances in the field of bone tissue engineering, including molecular insights, innovative biomaterials with regenerative properties (e.g., osteoinduction and osteoconduction), and physical stimuli to enhance bone regeneration

    Astrophysics with the Laser Interferometer Space Antenna

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    The Laser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy, and, as such, it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and space-born instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed; ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or interme-diate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help making progress in the different areas. New research avenues that LISA itself, or its joint exploitation with upcoming studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe

    Ensemble Machine Learning Model Generalizability and its Application to Indirect Tool Condition Monitoring

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    A practical, accurate, robust, and generalizable system for monitoring tool condition during a machining process would enable advancements in manufacturing process automation, cost reduction, and efficiency improvement. Previously proposed systems using various individual machine learning (ML) models and other analysis techniques have struggled with low generalizability to new machining and environmental conditions, as well as a common reliance on expensive or intrusive sensory equipment which hinders their industry adoption. While ensemble ML techniques offer significant advantages over individual models in terms of performance, overfitting reduction, and generalizability improvement, they have only begun to see limited applications within the field of tool condition monitoring (TCM). To address the research gaps which currently surround TCM system generalizability and optimal ensemble model configuration for this application, nine ML model types, including five heterogeneous and homogeneous ensemble models, are employed for tool wear classification. Sound, spindle power, and axial load signals are utilized through the sensor fusion of practical external and internal machine sensors. This original experimental process data is collected through tool wear experiments using a variety of machining conditions. Four feature selection methods and multiple tool wear classification resolution values are compared for this application, and the performance of the ML models is compared across metrics including k-fold cross validation and leave-one-group-out cross validation. The generalizability of the models to data from unseen experiments and machining conditions is evaluated, and a method of improving the generalizability levels using noisy training data is examined. T-tests are used to measure the significance of model performance differences. The extra-trees ensemble ML method, which had never before been applied to signal-based TCM, shows the best performance of the nine models.M.S
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