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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2023

Extreme-scale stochastic optimization and simulation via learning-enhanced decomposition and parallelization (Final Technical Report)

Stochastic optimization and simulation models ubiquitously arise in designing and operating complex service/engineering systems. They can be extreme in scale due to high-dimensional data and decisions, and can also involve decisions made sequentially in response to newly revealed data, both causing significant computational challenge. The objective of this research is to explore a unified framework that integrates machine learning with discrete optimization and risk-averse modeling, to improve the efficiency of decomposition paradigms for stochastic optimization and simulations at extreme scale. The models we consider represent a broad class of complex decision-making problems, where 0-1 or continuous decisions are made before and/or after knowing multiple sources of uncertainties that could be correlated. We will employ machine learning methods to dynamically decide and prioritize computational procedures, including cut generation, branching, and bounding of the optimal objective. Furthermore, the research will shed new lights on the traditional decomposition algorithms for extreme-scale computing. Deliverables of the research include new modeling and computational methods for advancing the state-of-the-art research in optimization and simulation, bringing many relevant risk-averse, data-driven optimization problems in practice within the range of tractability. Examples include distributed computing server scheduling and sensor deployment for monitoring critical infrastructures. Success in this effort will enable progress in solving multiple extreme-scale problems in the complex system design and operations arising from DoE missions in energy, environment, and national security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Analyze the real-time performance of the optimized fiber-PTR instrument

An instrument to perform in-reactor, real-time thermal property measurements of nuclear fuels and materials was recently designed, optimized, and tested at Massachusetts Institute of Technology Research Reactor. It was the second insertion experiments of a total of two rounds. Here we report the collection and analysis of the real-time captured thermal diffusivity data on the preloaded SiC. With the optimized design, the instrument temperature had been successfully raised as we expected. Only one probe (the top one) returned real-time thermal diffusivity in a time span of approximately two days. Measured thermal diffusivity of the preloaded SiC was significantly lower than the literature value at the same temperature, indicating a significant influence from the irradiation induced microstructure damage. Meanwhile, the real-time thermal diffusivity value continuously reduced with time, indicating the dynamic microstructure evolution. The possible reasons of the probes that did not return data were hypothesized. The further optimization of the instrument with the objective to improve the measurement accuracy and instrument survivability is also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Department of Energy/IDEA: More best-practices research

This article continues the overview of the U.S. Department of Energy and IDEA collaboration by highlighting additional case studies that demonstrate innovative engineering approaches in district energy systems. It focuses on how campuses are improving efficiency, integrating advanced technologies, and reducing energy use and emissions through data-driven strategies. The University of Cincinnati is featured for its highly efficient chilled water system, using advanced algorithms and upgraded “smart chillers” to optimize performance, reduce energy consumption, and lower costs. Wake Forest University emphasizes a data-informed modernization process, using system optimization and targeted building improvements to significantly boost efficiency and reduce overall energy use. Arizona Western College showcases creative solutions to extreme cooling demands, including system redesign, smart controls, and thermal storage to improve reliability and cut peak energy costs. Overall, the article demonstrates that combining innovative engineering, real-time data analysis, and system optimization can significantly enhance performance, reduce emissions, and deliver cost savings in district energy systems.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Beyond Ion Migration in Metal Halide Perovskites: Toward a Broader Photoelectrochemistry Perspective

Ion migration is a broad term used to account for the degradation of halide perovskite materials and devices. However, ion mobility is only one piece of the full picture-mobile ions/defects are first created, then transported, and eventually annihilated or immobilized. In this Perspective, we summarize emerging work that shows how tractable photochemical and Faradaic reactions provide a continuous source of ions to migrate. Furthermore, we discuss strategies to fundamentally manipulate ion migration by targeting specific electrochemical and reduction/oxidation mechanisms. This highlights the important role of defect photoelectrochemistry, as well as the soft nature of the perovskite lattice, in ion migration and self-healing. We conclude that distinguishing more detailed processes involved in “ion migration”, with an emerging focus on the reactions that form mobile ionic defects, is necessary to greatly improve the stability of devices and open up more technological applications.

defects↗
DOE OSTI2025

Maximizing long-term biohydrogen production with Clostridium thermocellum for high solids conversion of lignocellulosic biomass

Biological hydrogen production from lignocellulosic biomass sustainably couples organic waste reduction with renewable energy generation. Efficient conversion is challenged by the structural complexity of lignocellulose and resulting recalcitrance to enzymatic degradation. Clostridium thermocellum natively breaks down biomass with highly effective hemi-/cellulases systems (i.e., cellulosomes) and generates hydrogen in anaerobic cultivation, creating a compelling platform for lignocellulosic biohydrogen production. Achieving commercially viable production rates requires balancing high biomass loading and throughput against uniform mixing conditions required for enzyme dispersion, pH and temperature control, and efficient hydrogen and metabolite removal in continuous operation. To address these barriers to process intensification, we implemented novel reactor and process designs for high-solids lignocellulosic biomass fermentations using the C. thermocellum KJC19-9 strain, genetically engineered for co-utilization of cellulose and hemicellulose sugars (i.e., xylose). Via computational fluid dynamics (CFD) modeling and experimental validation, we achieved a >50% improvement in biohydrogen production with an improved anchor-type impeller morphology, coupled to a threefold reduction in agitation rate. To further reduce rheological constraints and accumulation of toxic metabolites, we then transitioned the process to sequencing fed-batch operation. The resulting process generated 24.87 L H 2 L −1 from 160 g L −1 of deacetylated and mechanically refined (DMR)-pretreated corn stover biomass over 16 days while solubilizing >95% of influent cellulose and hemicellulose, setting a new performance benchmark for continuous production of biohydrogen from lignocellulose.

08 HYDROGEN↗
DOE OSTI2020

Thermoeconomic cost optimization of superconducting magnets for proton therapy gantries

A compact gantry delivering 70-220 MeV protons with fixed field in the superconducting magnets could reduce the cost and improve the adoption of proton therapy. While a number of magnet and cryogenics designs have been proposed, the combined capital and operating costs of state-of-the-art superconducting materials have not been analyzed. In response, we develop a thermoeconomic model of a multi-stage, conduction cooled gantry lattice and analyze the cryocooler operating cost, cryocooler capital cost and conductor capital cost for Nb-Ti, Nb 3 Sn, REBCO and Bi-2223 over a continuous range of magnet temperatures, and a differential evolution algorithm is used to identify the optimal combination of thermal intercept temperatures. Although Nb3Sn yields the lowest Net Present Value (NPV) of $111.7k at a magnet temperature of 9.4 K, the optimized Bi-2223 design at 12.8 K approaches the realm of commercial feasibility by offering improved thermal stability and forgoing the need for costly conductor heat treatment and magnet quench training. Furthermore, it was found that Nb3Sn was more cost effective than Nb-Ti and that REBCO was not economically viable for the parameters of this investigation. Overall, the thermoeconomic model developed herein can optimize conductor choices, magnet temperatures and thermal staging which has value for any conduction-cooled superconducting magnet.

BSCCO↗
DOE OSTI2020

Opinion: Lighting Research During and After the Pandemic

For many lighting researchers, the recent restrictions related to COVID-19 have paused most in-person laboratory or field experiments. In certain countries, regulatory institutions have allowed such experiments to resume but issued safety recommendations like pre-checking participants for disease symptoms and excluding those that are especially vulnerable. To avoid potential complications and delays, researchers are likely to resort to other research designs that enable remote research — eliminating the need for the experimenter and subjects to be in the same room. While unanticipated, this shift to remote research methods is an opportunity to revisit and further develop remote experimental techniques, which can have beneficial long-lasting implications on lighting research during and after the pandemic. Perhaps the most common type of remote research is online experiments; which can greatly benefit from improved techniques to allow for greater control or characterization of experimental conditions, like those proposed by Villa and Labayrade . These techniques require validation specific to the type of response collected and context, e.g. space type and attributes. New research techniques continue to emerge and will enable remote collection of objective measures such as gaze direction and high dynamic range images (HDRIs) without specialized equipment. In fact, many of these methods and techniques are commonly used in other disciplines, like vision science and psychology, and can be adapted for lighting research. Not all lighting studies can be conducted online, but in the age of ubiquitous and connected technologies, there is a largely missed opportunity to utilize crowdsourced responses to lighting conditions in real environments where people live and go about their daily lives without the research being limited to a certain time and laboratory locations. For instance, outdoor street luminaires can be rated by passersby using ecological momentary assessments on mobile phones. While this approach does not provide laboratory-level control over experimental conditions, it can provide more contextual responses resulting from ‘normal’ dynamic gaze, behavior, and interactions. Further, it can also reduce bias related to the experimental setting. Further development of remote research methods and their use in lighting research can improve the quality of lighting research. Given that sample sizes in most lighting research are relatively small, remote research can substantially increase sample sizes, reduce research costs, and shorten study duration, all while reducing interaction between experimenter and subjects. Another benefit is reaching a wider audience and including under-represented individuals who typically are not reachable using common recruitment methods. The pandemic has set back our plans for in-lab experiments, but can it inspire us to think of new and creative ways to expand lighting research?

Abboushi, Belal K.↗
DOE OSTI2021

Substitution of virgin carbon fiber with low-cost recycled fiber in automotive grade injection molding polyamide 66 for equivalent composite mechanical performance with improved sustainability

Recycled carbon fiber has historically proven challenging to integrate into composite manufacturing due in no small part to the low-density, randomly oriented, discontinuous fiber format that results from typical recycling. Discontinuous fiber requires the use of alternative technologies than those traditionally applied to continuous fiber (e.g., hand lay-up, winding). Extrusion compounding is one such applicable technology, but material transfer into the system requires alternative feeding equipment or the use of an altered procedure as trialed in this research. In this study, an injection molding compound for automotive applications was prepared with recycled carbon fiber and compared against an existing commercial compound. Input fibers and molded compound were evaluated for mechanical performance, while relevant variables such as compounded fiber alignment and aspect ratio were compared to the existing baseline material to confirm a like-for-like composite material structure. Overall, analysis indicates that recycled fiber performs similarly to virgin fiber reinforcement, demonstrating that recycled fiber may be a viable drop-in replacement for short-fiber discontinuous applications.

42 ENGINEERING↗
DOE OSTI2022

Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines

In recent years, as engine control strategies have grown increasingly sophisticated in a continued drive for increasing efficiency and reducing emissions, engine operation has been pushed into regimes that are limited by combustion instabilities. These instabilities produce undesirable abnormal combustion events, which pose barriers to further improvement in engine efficiency. Many of the phenomena involved are difficult to model or control using traditional, purely physics-based models and reactive control approaches. Artificial intelligence (AI) techniques offer some promise for achieving more effective combustion stability control, especially when appropriately applied within a physics-informed framework. This chapter illustrates the current state-of-the-art in applying AI to combustion stability control and examines three case studies with application to the dilute stability limit in spark-ignition engines to illustrate the utility and limitations of AI in these applications.

Maldonado Puente, Bryan↗
DOE OSTI2025

Friction surface layer deposition of triple-phase Al 10 Cr 12 Fe 35 Mn 23 Ni 20 high entropy alloy: Process optimization and microstructural evolution

A high-strength Co-free triple-phase Al 10 Cr 12 Fe 35 Mn 23 Ni 20 high-entropy alloy (HEA) was successfully fabricated using Friction Surface Layer Deposition (FSLD), a bulk manufacturing method. Multiple single-layer deposits were produced by varying forging force (F) and traverse speeds (T r ) to optimize the process parameters. The optimized conditions (F = 40 kN & T r = 200 mm/min) were then applied to manufacture a scaled-up multi-layer specimen. The initial microstructure of the HEA consisted of coarse grains of the soft FCC-phase, long columnar dendrites of the hard BCC-phase, and small precipitates of the harder B2-phase within the BCC-dendrites. During FSLD, the FCC-matrix underwent continuous dynamic recrystallization due to high-temperature severe plastic deformation, forming finer equiaxed grains. Simultaneously, the BCC-dendrites fractured into smaller fragments, some of which experienced partial growth and coarsening under applied stress, resulting in an hourglass morphology. In contrast, the small B2-precipitates within the BCC-fragments dissolved during the elevated temperatures of FSLD and reprecipitated as substantially finer precipitates during continuous cooling post-FSLD. Additionally, the orientation relationships between the FCC and BCC/B2 phases were completely destroyed by the severe thermoplastic deformation inherent to FSLD. The microstructural refinements led to a substantial improvement in hardness from 177 HV to 283 HV, driven by Hall-Petch strengthening. The increased number of interfaces, including coherent BCC-B2 interfaces, potentially enhances the sink strength and radiation tolerance of the HEA, making it a promising candidate for nuclear applications. In conclusion, this study also highlights FSLD as a versatile technique for achieving tunable properties in HEAs, with detailed schematics illustrating the complex mechanisms of phase transformations during processing.

Additive Manufacturing↗
DOE OSTI2023

A Survey of Thunderstorms That Produce Megaflashes Across the Americas

We previously observed that long-horizontal lightning flashes exceeding 100 km in length, known as “megaflashes,” occur preferentially in certain thunderstorms. In this study, we develop a cluster feature approach for automatically documenting the evolutions of thunderstorm systems from continuous lightning observations provided by the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites (GOES). We apply this methodology to GOES-16 GLM observations from 2018 to mid-2022 to improve our understanding of megaflash-producing storms. We find that megaflashes occur in long-lived (median: 14 hr) storms that grow to exceptional sizes (median: 11,984 km 2 ) while they propagate across long distances (median: 622 km) compared to ordinary storms. The first megaflashes are typically produced within 15 min of the storm reaching its peak intensity and extent. However, most megaflashes occur ≥13 hr after the initial megaflash activity, and are sufficiently close to convection to suggest initiation in the thunderstorm core (where GLM has difficulty detecting faint early light sources from megaflashes). Megaflashes generated outside of convection are rare, accounting for 2.7% of the sample using a 50 km convective distance threshold, but also tend to be larger than normal megaflashes, possibly due to having direct access to electrified stratiform clouds through which megaflashes propagate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An Advanced Cooling Device for Concentrated Photovoltaic Systems

Concentrated photovoltaics (CPV) have the potential to significantly enhance the energy conversion utilization of solar panels and reduce solar generation costs, making them a crucial area of advancement in solar power generation technology. However, the concentration of sunlight can lead to overheating of solar panels, resulting in a notable reduction in both the efficiency of solar power generation and the lifespan of the panels. This challenge remains the predominant technical hurdle that hinders the application of concentrated photovoltaic power generation technology. In this study, we propose a new cooling method for concentrated photovoltaic power generation systems via an integrated approach of incorporating Phase-Change Thermal Storage (PCTS) and Thermoelectric Generator (TEG) technology. This new method not only enhances the overall system's electricity generation efficiency but also effectively resolves the technical challenge of concentrated photovoltaic panel overheating issues, ensuring the continuity of concentrated photovoltaic power generation and extending the lifespan of solar panels and their components. In order to make full use of the wasted heat generated by photovoltaic power generation and effectively improve the power generation efficiency of the system, this work developed a phase change heat storage device based on a phase change material. This device uses the temperature difference between day and night to recover wasted heat from photovoltaic power generation. Through integration with the thermoelectric power generation system, thermal energy can be converted into electrical energy. In addition, the Peltier effect of thermoelectric materials is used to construct a photovoltaic panel overheating protection system, which significantly improves the reliability and service life of the system.

14 SOLAR ENERGY↗
DOE OSTI2020

Highly Reversible Plating/Stripping of Porous Zinc Anodes for Multivalent Zinc Batteries

Zinc continues to garner immense interest due to its versatility as an anode material in several configurations utilizing either alkaline or mild-pH electrolytes. Current research on using mild-pH electrolytes has improved the rechargeability aspect of Zn-based batteries since Zn2+ is solely utilized for plating/stripping of Zn. Several studies have incorporated Zn metal foils, yet, dramatic improvements can be achieved by expressing Zn as a porous structure. Herein, we use a quasi-pulsed electrodeposition process to prepare a conformal Zn coating onto 3D porous copper foam. By tuning the electrodeposition parameters, we achieved an optimal Zn coating that undergoes reversible plating/stripping when tested in symmetric Zn cells, which supported a low overpotential of ~60 mV for up to 100 cycles. We further investigated changes in the surface morphology by studying the Zn surface of both foil and 3D structure using scanning electron microscopy and X-ray micro-computed tomography. Both techniques showed that the Zn foil undergoes dramatic alterations at the surface, which results in inhomogeneous deposition of Zn, whereas the 3D form exhibited minimal changes. Lastly, we paired both Zn foil and 3D Zn with vanadium oxide and demonstrated that the porous structure supports high rate capability and high specific capacity.

25 ENERGY STORAGE↗
DOE OSTI2021

Estimating the Rebound Effect of the U.S. Road Freight Transport

The United States (U.S.) road freight sector has continued to grow over recent decades. Growth in road freight could result in more fuel consumption and hence increased greenhouse gas emissions. Policymakers have attempted to manage the growth of energy usage through improved fuel economy based on technological advances. However, such improvements may not lead to anticipated goals because of the rebound effect, where improvements in energy efficiency trigger more travel and energy consumption that offsets energy savings. Thus, this study aims to determine the potential rebound effect from improved energy efficiency in the U.S. road freight sector. Eight fuel cost models are applied and asymmetric price response is incorporated in estimating the U.S. road freight sector’s rebound effect from 1980 to 2016. In addition, a recently developed data envelopment analysis is applied to determine the annual rebound effect in the road freight sector. The results suggest that, after accounting for the asymmetric price response, the average rebound effect of the U.S. road freight sector ranges from 6.9% to 8.8%, a level considerably less than that found for several industrialized countries and emerging economies. However, a considerable increase in the rebound effect has been seen in more recent years. Here, the findings suggest that overlooking the rebound effect in environmental policies could impede the goal of reducing total energy consumption and accompanying emissions. Policymakers should incorporate the rebound effect from efficiency enhancement in policy development and utilize some potential programs to reduce the adverse influence of rebound effect in related policies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

E-PVT: enhanced position-velocity-time scheduler for computer-controlled optical finishing with comprehensive considerations of dynamics constraints, continuity and efficiency

Deterministic computer-controlled optical finishing is an essential approach for achieving high-quality optical surfaces. Its determinism and convergence rely heavily on precise and smooth motion control to guide the machine tool over an optical surface to correct residual errors. One widely supported and smooth motion control model is position-velocity-time (PVT), which employs piecewise cubic polynomials to describe positions. Our prior research introduced a PVT-based velocity scheduling method, demonstrating sub-nanometer level convergence in ion beam figuring (IBF) processes. However, three challenges remained. Firstly, this method relies on quadratic programming, resulting in computational intensiveness for dense tool paths. Secondly, the dynamics constraints and velocity and acceleration continuities are not comprehensively considered, limiting the full potential of PVT-based control. Thirdly, no compensation mechanism existed when dynamics constraints are exceeded. In this study, in response to these challenges, we proposed the Enhanced PVT (E-PVT) method, reducing the time complexity from O ( n 3 ) to O ( n ) while fully addressing dynamics constraints and continuities. A novel compensation method utilizing particle swarm optimization was proposed to address situations where dynamics constraints might be exceeded while maintaining the overall processing efficiency. Validation through simulation and experimentation confirmed the improved performance of E-PVT.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Plasma Ignition and Combustion Stabilization Technology to Improve Flexible Operation, Reliability and Economics of an Existing Coal-Fired Boiler

GE Steam Power, Inc. (GE) proposed to improve reliability, flexibility, and economics of an existing coal-fired power plant by applying a new advanced technology developed by GE, a plasma-assisted pulverized fuel firing system. The objective of this program is to demonstrate the achievement of lower load by improved flame stabilization and therefore lower operating costs in a full-scale field installation at coal-fired electric utility. GE’s Plasma Ignition and Combustion Stabilization System is designed to operate continuously to support low load operation. With the plasma on, the flame will be attached and stable, removing the firing system as a limitation to low load operation. In addition, GE’s exclusively from ABENZ company licensed AC based technology has a 90+% system efficiency compared to all other systems at which are DC and operate with ~75% efficiency. Plant operating costs are lowered by eliminating use of expensive support fuel as well as the ability to operate at lower loads. The utilities’ ability to better match the demand curve will result in significant savings. Maintenance is lower for an AC system than a DC system as it operates at lower current. This eliminates the need for a demineralized cooling water system and provides longer electrode life which translates into both material and labor savings. It is the objective of GE to not only demonstrate the additional low load achievable with a plasma system after best achievable tuning, sensor and software approach has been exhausted, but also the increased stability of the flame at all loads with plasma assistance as well as cost savings at all low loads using plasma instead of oil. Upon successful completion of this project, GE will have sufficient field experience to rapidly deploy the Plasma Technology. The project objectives were achieved through the implementation of plasma ignitor technology at PacifiCorp Hunter Station Unit 3. A plasma ignitor system was retrofitted on ten wall-fired burners, Mill 3-4 combustion system. The Plasma Ignitors installed at Hunter proved that this GE technology is a direct and complete replacement for the original oil ignitors. The Hunter Unit 3 burner management system allows the plasma system to be used in all applications that originally required oil to be burned. This includes any time the Mill 3-4 is started or stopped for any reason including boiler starts, load changes, and low load support.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Data Visualization and Analytics for Optimal Process Parameter Selection in Turning

The objective of this project is to research physics-guided machine learning methods to recommend optimal tools and machining process parameters for turning applications using the MSC test database. For a given turning application, the MSC metalworking specialist needs to make decisions on tools and the associated process parameters for the MSC customer. For a given material, there are many alternatives for tools and a wide range of process parameters to consider. MSC has built a database of tools and parameters for different applications from the historical turning tests completed at various customer sites. The research project aims to use machine learning methods to predict optimal tools and process parameters for the MSC metalworking specialists using the MSC test database. This enables continuous learning of optimal tool and process parameters for different applications as new information is collected from testing. Through MSC, this information can be shared with machining shops across the US leading to improved productivity and efficiency.

42 ENGINEERING↗