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We introduce a computational geometry method for which classification cost becomes roughly proportional to the difficulty of each example.
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Presentation will be given at university level.
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For presentation at the 2023 FECM/Carbon Management Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
Presentation for DICE conference
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.
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.
Traditionally, off-road mobile machines such as excavators and wheel loaders are primarily powered by hydraulics, and throttling valves are used to control their work circuits. In recent years, two general trends are towards more energy efficient systems and electrification. With electrification, both efficiency and control performance can be improved by the elimination of throttling losses and the use of high-bandwidth inverter control. Electrification is generally accomplished with Electro-hydraulic actuators (EHA) but they are limited to lower powered systems due to the high cost of electric machines capable of high power or high torque. This project proposes a new system architecture for off-road vehicles - Hybrid Hydraulic Electric Architecture (HHEA) to improve efficiency and control performance without requiring large electric machines. The widely applicable architecture combines hydraulic power and electric power in such a way that the majority of power is provided hydraulically while electric drives are used to modulate this power. In particular, HHEA utilizes multiple common pressure rails to transmit the majority of power and small electric machines to modulate the power. The energy-saving potential of the the HHEA has been validated for the work circuits of a variety of mobile machines, from small 5-ton excavators to medium sized 20-ton excavators and wheel loaders, and representative duty cycles to reduce energy input by 50-80% compared to the commercial state-of-art load-sensing systems. In addition, the corner power requirements of the electrical machines can be downsized by 85% compared to the EHA approach. Various tradeoff studies have also been conducted, including sensitivities to individual components performances, controllers, accumulator sizes, and variations of the system architecture etc. A control strategy has been developed to maintain or exceed the motion control precision compared to current systems. The motion control strategy consists of a nominal controller, based on a passivity-based backstepping design, and a transition controller, based on least-norn feedforward design. The nominal controller is used in between common pressure rail switches whereas the transition controller compensates for any disturbance that common pressure rail switchings inflict on the system. The control strategy has been experimentally validated on both a medium pressure (200bar) hardware-in-the-loop (HIL) testbed and a high pressure (300+bar) HIL testbed. An efficient and power-dense integrated electric-hydraulic machine consisting of an axial flux electric machine and a radial hydrostatic piston hydraulic machine has been designed, constructed and tested. The machine has an active material power density of 6.1kW/kg, a rated speed of 12500 RPM, and a design efficiency of 85%. This is among the highest power density electric machines using conventional materials. While the integrated machine was designed for modulating the hydraulic power within the HHEA, it can also be used in other applications.
This project focused on the development of wound field synchronous machines (WFSMs) and hybrid excitation synchronous machines (HESMs) with brushless capacitive power transfer for the field excitation. The target application for the machines developed is the main traction motor in electric vehicles. The magnetization in these types of machines is provided by a field winding on the rotor which is excited with DC current. The magnetization level in the machine can be varied by changing the magnitude of the field current. The variable magnetization or field is one of the key features of WFSMs. WFSMs are commonly used as generators however they have several attractive features for automotive traction applications. 1) No use of permanent magnet: Rare earth permanent magnets are primarily mined and processed in China. They have been subject to large price and supply variations and their export may be restricted during times of geopolitical tension. 2) Easy field weakening: Wound field synchronous machines have complete control of their field excitation. With proper design, this type of machine can electromagnetically have an infinite constant power speed range. 3) High power factor: With proper choice of the field excitation, WFSMs may be operated with high or even unity power factor. This potentially allows for the inverter connected to the stator winding to be downsized. In comparison induction machines and interior permanent magnet synchronous machines must supply reactive power to the stator increasing the kVA rating and cost of the inverter. 4) Reduced iron losses at high speed: By reducing the field excitation, the iron loss in the stator can be reduced. This is in comparison to interior permanent magnet synchronous machines which must use stator current to buck or reduce the flux produced by the permanent magnets. Generally, WFSMs have their highest efficiency at high speed. 5) Torque output at high temperatures: The magnetization provided by the field winding only depends on the field current and not on the field winding temperature. This is in contrast to permanent magnet machines where the permanent magnet flux decreases as the magnet temperature increases. Historically a number of approaches have been developed to provide DC current to the rotating field winding in WFSM’s including brushes and slip rings, low frequency brushless exciters, and high frequency rotary transformers and rectifiers. In this project, a different approach was used: brushless capacitive power transfer. Brushless capacitive power transfer uses two sets of rotating capacitors or electrodes in which an AC electric field is established by a high frequency inverter. A displacement current can flow through the airgap in the rotating capacitors which is rectified on the rotor using a diode bridge. The potential advantage of capacitive power transfer is that there is no need for heavy iron to guide magnetic flux. The electric flux lines terminate on the charges on the rotary capacitor surfaces. This should also limit the electric field outside the rotary capacitor airgaps. The main challenge with capacitive power transfer is that the capacitance and surface area of the rotating capacitors is small. Because capacitive power transfer systems are essentially a dual of a magnetic system, an Ampere per Hertz relationship is characteristic versus a Volts per Hertz relationship. A very high frequency power inverter must be used to provide sufficient excitation to the field winding. The concept of using capacitive power transfer to excite a high performance WFSM was initially developed in a previous U.S.A. Dept. of Energy project, DE-EE0006829. This project focused on increasing the power density of the WFSMs and reducing the cost and manufacturing complexity of the capacitive power transfer system. This project has demonstrated that WFSMs with brushless capacitive field power transfer can provide a high-power density and low-cost automotive powertrain technology.
Hybrid manufacturing machine tools have the potential to be a disruptive technology as they can leverage the benefits of both additive and subtractive manufacturing by incorporating both processes on the same machine while limiting the downsides of the individual processes. Since these machines use two very disparate manufacturing processes and hybrid manufacturing is an emerging technology, it will be useful to monitor data coming from the machine and apply it to improve the manufacturing process, the operation of the machine, and to integrate the machine into the larger digital framework of Industrial Internet of Things (IoT). The present work discusses IoT devices that would be beneficial to add to a hybrid machine tool as well as applications for those devices. The proposed methods discussed in this work have not been experimentally implemented on a hybrid machine tool and so there are no performance data available yet. The hybrid machine tool used as a basis to generate these IoT applications is the Mazak VC-500A/5x AM Hot Wire Deposition, which is a 5-axis machine tool incorporated with a wire feedstock 4kW laser deposition system. Methodologies and applications will be outlined for machine health and process monitoring. Other areas covered include process benchmarking, secure networking options for the proposed IoT framework, and hybrid process improvement. Limitations of these methods and future work for new sensor devices and application areas is also discussed.
Electrification of off-highway vehicles is notoriously challenging due to extreme power density requirements. This paper proposes and develops an axial flux machine integrated with a hydraulic pump to realize a single modular, electro-hydraulic machine to electrify off-highway vehicle implements. This integrated machine eliminates redundant bearings, couplings, and shaft seals, re-uses surfaces, and enables direct cooling of the electric machine with the hydraulic fluid, to significantly increase power density. Three popular axial flux machine variants are first compared using an FEA-based design optimization approach. The single rotor, single stator variant is identified to be the most promising for integration with the hydraulic pump. Next, a multi-physics framework of the complete integrated hydraulic pump and axial flux machine is developed to characterize the design space. The results indicate promising potential for this concept to realize efficiency over 85% and power density over 5kW/kg for the complete machine (electric machine, hydraulic pump, and thermal management system), while utilizing conventional materials (thin gauge silicon steel, N45 magnets, and enamelled copper wire). Furthermore, a prototype axial flux machine has been experimentally characterized and integrated with a hydraulic pump to demonstrate the integrated electro-hydraulic machine concept.
Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.
This paper presents the results of aligning the frame to concrete floor of a large 5-axis machine tool by leveling element adjustments using servomotors instead of manual operation. The machine tool leveling elements used in this study are mechanical devices that attach the machine tool frame to the concrete foundation. These leveling elements are also adjustable so that each location along the machine tool can be uniquely adjusted such that the geometric errors of the frame are minimized, and the accuracy of the machine tool is improved. Off-the-shelf servomotors and connection equipment were installed at each of 12 leveling element locations for a precision 5-axis machine tool at Oak Ridge National Laboratory. The servomotors allow a technician to align the machine tool frame remotely, instead of crawling under the machine tool to iteratively manually adjust each of the 12 leveling elements. The open-loop servomotor-leveling element system design is presented in this paper as well as machine tool frame alignment results. The servomotor system significantly reduces leveling element adjustment time, eliminates the need for under-machine access, reduces technician physical strain, and significantly improves the adjustment repeatability.
Event-driven contexts in manufacturing occur pervasively as a result of interactions among involved entities such as machines, workers, materials, and environment. One of the primary tasks in smart manufacturing is to derive a context-aware system conveniently incorporating worker knowledge for generating timely actionable intelligence for workers on factory floor and supervisors to respond. In this paper, we propose to design a human-and-machine interaction recognition framework by using a causality concept to collect contextual data for classifications of normal and abnormal machine operations. The causes and effects are between workers and machines for this initial research. To apply the causality to recognize worker interactions, initially a reliable way to identify the states of machines is necessary. The proposed contextual sensor system, consisting of a power meter for measuring machine operation conditions, a visual camera for capturing worker and machine interactions via a finite state machine model, and an algorithm for determining power signatures of individual components via energy disaggregation is implemented on semiconductor fabrication machines (manual or PLC controlled) each with multiple components. The experiment results demonstrate its context extraction capability such as components states and their corresponding energy usage in real time as well as its ability to identify anomalous operation conditions.
Axial flux machines offer an inherent torque density advantage over conventional radial flux machines and are increasingly being considered for transportation electrification applications. Electrification of off-highway vehicles is notoriously challenging due to extreme power density requirements. This paper investigates the use of axial flux machines for off-highway vehicle electrification. To maximize the power density, a hydraulic pump is integrated with the axial flux machine, resulting in a single modular, electric/hydraulic machine. This paper first compares three popular axial flux machine variants using an FEA-based design optimization approach. The single rotor, single stator variant is identified to be the most promising for integration with the hydraulic pump. Next, a multi-physics framework of the complete integrated hydraulic pump and axial flux machine is developed to search the design space. The results indicate promising potential for this concept to realize efficiency over 85% and power density over 5kW/kg for the complete machine (electric machine, hydraulic pump, and thermal management system), while utilizing conventional materials (thin gauge silicon steel, N45 magnets, and magnet wire)
The extrapolation of wind speeds measured at a meteorological mast to wind turbine rotor heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power-law and logarithmic profiles. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power-law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the rotor-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power-law and log profiles, which, by contrast, are not highly tuned to rotor-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where winds at the heights relevant for wind energy production are known; rather, its performance at nearby locations (i.e., across a wind farm site) without rotor-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random-forest machine-learning model is trained and evaluated at different sites and then compared against the power-law and logarithmic profiles. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50 and 100 km apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power-law and logarithmic profiles, respectively. Using the round-robin approach proposed here, this improvement drops to 20 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based wind speed extrapolation methods.