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Results for “Machining”
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Machine learning improves predictions of agricultural nitrous oxide (N 2 O) emissions from intensively managed cropping systems
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Machine-learning-based inversion of nuclear responses
A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the correct interpretation of neutrino oscillation experiments. Nuclear quantum Monte Carlo methods infer the nuclear electroweak response functions from their Laplace transforms. Inverting the Laplace transform is a notoriously ill-posed problem; and Bayesian techniques, such as maximum entropy, are typically used to reconstruct the original response functions in the quasielastic region. In this work, we present a physics-informed artificial neural network architecture suitable for approximating the inverse of the Laplace transform. Utilizing simulated, albeit realistic, electromagnetic response functions, we show that this physics-informed artificial neural network outperforms maximum entropy in both the low-energy transfer and the quasielastic regions, thereby allowing for robust calculations of electron scattering and neutrino scattering on nuclei and inclusive muon capture rates.
Supervised Learning in Physical Networks: From Machine Learning to Learning Machines
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Geothermal play development using machine learning and magnetotellurics data
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OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data
Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.
Machine Learning Tutorial - Introduction to Machine Learning [Slides]
Abstract not provided.
Part-to-part and machine-to-machine variability in corrosion and roughness properties of additively manufactured stainless steel.
Abstract not provided.
Part-to-part and machine-to-machine variability in corrosion and roughness properties of additively manufactured stainless steel.
Abstract not provided.
Machine Learning Acceleration of Molecular Dynamics Simulation: Gaussian propagator from machine-learned Hessian
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Machines Learning about Machines - ML for Analysis and Control of HPC Infrastructure.
Abstract not provided.
Monitoring Health of Large Machine Diagnostics using Machine Learning
Presentation will be given at university level.
The Logic of Scientific Discovery by Machines: Intersections of Philosophy, Machine Learning, & Ocean Mixing
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Advanced Machine Learning and Computational Methods: SMART (Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications) Phase 2
For presentation at the 2023 FECM/Carbon Management Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
From Machine Learning to Machine Reasoning: A Model-Based System Engineering Perspective
Presentation for DICE conference
Evaluation of Torque-Dense Electric Machine Technology for Off-Highway Vehicle Electrification
Electrification of off-highway vehicles offers the benefits of improved energy efficiency, enhanced control, and reduction in greenhouse gas emissions. However, progress towards electrification has been limited by the low torque density (30 kNm/m 3 ) of conventional electric machines compared to mobile hydraulic machines (up to approximately 1000 kNm/m 3 ). This paper reviews emerging variants of electric machines that offer a step improvement in torque density and potential pathway to enable electrified off-highway vehicles. First, sizing approaches for electric and hydraulic machines are developed, and torque-dense electric machines reviewed in literature are compared to commercial hydraulic machines to identify design trends in terms of speed, torque density, and power density. Next, key metrics are identified for the electric machine, based on which the following four emerging electric machine variants that promise to improve torque density are reviewed: i) multi-harmonic machines injection, ii) combined radial-axial flux machines, iii) magnetic gears, and iv) magnetically-geared machines. Here, the findings from this review show that these new electric machines can achieve upwards of 300% improvement in electric machine torque density, with several designs exceeding 100 kNm/m 3 , making them worthy candidates for further research to bridge the torque density gap with hydraulic machines.
Predicting chatter using machine learning and acoustic signals from low-cost microphones
Machining chatter is a phenomenon resulting from self-oscillation between a machining tool and workpiece. This self-oscillation results in variation on the machined product that reduces the ability to meet desired specifications. Chatter is a widely studied topic as it directly relates to the quality of machined products. Here, this study details the application of a Random Forest (RF) classifier with Recursive Feature Elimination (RFE) to machining audio collected by a single microphone during down-milling operations. This approach allows straightforward feature elimination that results in an easily understood set of analyzed dimensions. Stability is predicted solely based on the classification output of the RF classifier. Our approach proves highly predictive with consistent machining setup and a small sample set. We also review transferability between machining setups and present key findings. Our RF approach demonstrates the ability to analyze and classify chatter through a low-cost approach with limited training data required. The motivation for using a single microphone is to enable detection on machines without other sensors, such as accelerometers, present in the machining setup. The value of the in-process sensor and chatter classifier is highlighted because the machining setup included asymmetric dynamics that reduced the accuracy of the traditional analytical stability solution. We see a natural progression to deploying this audio-only methodology with real-time processing and classification using either a laptop or smartphone. This progression will allow visual indicators during the machining process that can alert machinists of progression into unstable machining processes.