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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

In this paper, an experimental soil moisture drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity model (VIC) over southwestern China (SW) is presented. Satellite precipitation data are used to force VIC for a near-real-time estimate of land surface hydrologic conditions. Initialized with satellite-aided monitoring (MONIT), the climate model (CFSv2)-based forecast (MONIT+CFSv2) and ensemble streamflow prediction (ESP)-based forecast (MONIT+ESP) are both performed. One dry season drought and one wet season drought are employed to test the ability of this framework in terms of real-time tracking and predicting the evolution of soil moisture (SM) drought, respectively. The results show that the skillful CFSv2 climate forecasts (CFs) are only found at the first month. The satellite-aided monitoring is able to provide a reasonable estimate of forecast initial conditions (ICs) in real-time mode. In the presented cases, MONIT+CFSv2 forecast exhibits comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1 month. Compared to MONIT+ESP, MONIT+CFSv2 ensembles give more skillful SM drought forecast during the dry season, as indicated by a smaller ensemble range, while the added value of MONIT+CFSv2 is marginal during the wet season. Furthermore, a quantitative attribution analysis of SM forecast uncertainty demonstrates that SM forecast skill is mostly controlled by ICs at the first month and that uncertainties in CFs have the largest contribution to SM forecast errors at longer lead times. This study highlights a value of this framework in generating near-real-time ICs and providing a reliable SM drought prediction with 1 month ahead, which may greatly benefit drought diagnosis, assessment, and early warning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

The Information Adaptive System - A demonstration of real-time onboard image processing

The Information Adaptive System (IAS) program has the objective to develop and demonstrate, at the brassboard level, an architecture which can be used to perform advanced signal procesing functions on board the spacecraft. Particular attention is given to the processing of high-speed multispectral imaging data in real-time, and the development of advanced technology which could be employed for future space applications. An IAS functional description is provided, and questions of radiometric correction are examined. Problems of data packetization are considered along with data selection, a distortion coefficient processor, an adaptive system controller, an image processing demonstration system, a sensor simulator and output data buffer, a test support and demonstration controller, and IAS demonstration operating modes.

Thomas, G. L.↗
NASA NTRS2014

LADEE Multi-Domain Simulation

The Lunar Atmosphere Dust Environment Explorer (LADEE) was a small explorer class spacecraft that was launched on Sept 7, 2013 and that was de-orbited and successfully impacted the Moons surface on April 17, 2014 after completing all of the mission objectives. The low-cost rapidly prototyped hardware design used for the spacecraft was extend to the development of the software base. To achieve this goal, a Model Based Design approach was utilized to develop the onboard flight software, and out of this development a model based multipurpose simulator was created of the LADEE spacecraft and its mission environment. This simulator extended the traditional function of propagating the vehicle's kinematic and rotational states and included the electrical and thermal states propagation. Traditionally, these domains are handled by domain specific high fidelity simulations that use the states histories from other domains as input. By reducing the fidelity and abstracting the relevant features being monitored and controlled by the flight software, it was possible to model the coupling across these domains resulting in more accurate overall system behavior. A faster than real-time workstation (WSIM) version of the LADEE simulator was used to develop and test the software control algorithms in the Simulink environment. To maximize the performance of the simulation, modeling knobs were introduced to reduce the resolution of the some of the domains models when the effects of that domain were not significant for the scope of that simulation. The automatic code generation feature in Simulink was used to port the simulation to several real-time environments to support Processor-in-the-Loop (PIL) and Hardware-in-the-Loop (HIL) testing, verification and validation. The real-time environment required that the design of each of the domain models be deterministic as possible in the time required to perform all of the calculations to update its states. The simulation interface was designed to be compatible with the command interface employed by the LADEE mission operation team. The WSIM, PIL, and HIL simulators thus used a common interface and thus were used for flight software testing, for mission operations personnel training (nominal and off-nominal operations) prior to the mission and to perform command sequence verification during the mission. This presentation will look at the modeling strategies used to create a common interface to the simulator and to model and couple multiple domains within the simulation, the results of those strategies, and the lessons learned.

Multi-Domain↗
NASA NTRS2007

Myotonometry as a Surrogate Measure of Muscle Strength

Space flight-induced muscle atrophy/neuromuscular degradation and the consequent decrements in crew-member performance are of increasing concern as mission duration lengthens, and planetary exploration after extended space flight is planned. Pre- to post-flight strength measures have demonstrated that specific countermeasures, such as resistive exercise, are effective at countering microgravity-induced muscle atrophy and preventing decrements in muscle strength. However, in-flight assessment/monitoring of exercise countermeasure effectiveness will be essential during exploration class missions due to their duration. The ability to modify an exercise countermeasure prescription based on such real-time information will allow each individual crew member to perform the optimal amount and type of exercise countermeasure to maintain performance. In addition, such measures can be used to determine if a crew member is physically capable of performing a particular mission-related task during exploration class missions. The challenges faced in acquiring such data are those common to all space operations, namely the requirement for light-weight, low power, mechanically reliable technologies that make valid measurements in microgravity, in this case of muscle strength/neuromuscular function. Here we describe a simple, light-weight, low power, non-invasive device, known as the Myotonometer, that measures tissue stiffness as an indirect measure of muscle contractile state and muscle force production. Repeat myotonometer measurements made at the same location on the surface of the rectis femoris muscle (as determined using a 3D locator device, SEM plus or minus 0.34 mm) were shown to be reproducible over time at both maximal voluntary contraction (MVC) and at rest in a total of 17 sedentary subjects assessed three times over a period of seven days. In addition, graded voluntary isometric force production (i.e. 20%, 40%, 60%, 80% & 100% of MVC) during knee extension was shown to be significantly (p less than 0.01) correlated with contemporaneous myotonometer measurements made on the rectis femoris muscle in a total of 16 healthy subjects (8 males, 8 females). Further-more, this device has been operationally tested during parabolic flight demonstrating its suitability for use in a microgravity environment. Our data indicates that the Myotonometer is a viable surrogate measure of muscle contractile state/tone and of muscle strength/force production. Additional studies are required to assess the suitability of this technique for assessing these measures in de-conditioned subjects such as crew-members.

Ang, B. S.↗
DOE OSTI2019

On the Effectiveness of Recurrent Neural Networks for Live Modeling of Cyber-Physical Systems

Attention to cyber security of cyber-physical systems (CPS) has led to the development of innovative cyber-resilient methodologies to ensure early detection and mitigation of cyber anomalies and threats. The concept of Digital Twin (DT) has recently emerged as one of the approaches to achieve the objective of resilience. In the approach using DT, a software-based live model of a target CPS is used to continuously monitor, surveil and verify the correctness of the target CPS operation. In this paper, we empirically study the effectiveness of Recurrent Neural Network (RNN)-based models as the basis of DT-based resilience. We uncover the important characteristics of an RNN-based solution with experimentation on a lab-scale Canal Lock CPS emulator with live validations and attack scenarios. For the first time, we demonstrate actual, real-time use of a RNN-based model as a DT for performing live analysis on an operational CPS. Based on the observed results, we highlight the importance of a DT model's training interval, prediction interval and CPS polling interval in the process of anomaly detection. We uncover the limitations in anomaly detection due to real-time synchronization needs of the RNN-based DT. We highlight this uncovered tug of war between synchronization and anomaly detection is inherent in any complex CPS that is monitored and synchronized by relying on the same sensor streams of ground truth for both synchronization as well as anomaly detection.

Yoginath, Srikanth↗
DOE OSTI2016

Real-Time Considerations for Rugged Embedded Systems

This chapter introduces the characterizing aspects of embedded systems, and discusses the specific features that a designer should address to an embedded system “rugged”, i.e., able to operate reliably in harsh environments. The chapter addresses both the hardware and the less obvious software aspect. After presenting a current list of certifications for ruggedization, the chapters present a case study that focuses on the interaction of the hardware and software layers in reactive real-time system. In particular, it shows how the use of fast FPGA prototyping could provide insights on unexpected factors that influence the performance and thus responsiveness to events of a scheduling algorithm for multiprocessor systems that manages both periodic, hard real-time task, and aperiodic tasks. The main lesson is that to make the system “rugged”, a designer should consider these issues by, for example, overprovisioning resources and/or computation capabilities.

FPGA↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗
DOE OSTI2022

Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management

Transportation accounts for a significant portion of total global energy consumption. Excessive energy consumption usually occurs in urban traffic environments with congestion and travel delays. With the advancement of remote sensing and computer vision technologies, real-time traffic conditions can be monitored. Therefore, sustainable transportation management strategies can be developed to optimize the overall energy and environment performance and reduce congestion and emissions. This work presents a smart traffic monitoring system based on remote camera sensors. Real-time and historical traffic conditions at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) were monitored and analyzed to develop optimal transportation management strategies for sustainability. Computer vision algorithms were developed and applied to process the real-time camera data to obtain complete traffic information across the ORNL campus. Weeks of historical data were collected and processed to analyze the traffic and identify bottlenecks. The proposed traffic monitoring and management approach can be applied and extended to benefit other campuses or urban areas.

Shao, Yunli↗
DOE OSTI2019

Closed-Loop Error Learning Control for Uncertain Nonlinear Systems With Experimental Validation on a Mobile Robot

This article develops a closed-loop error learning control (CLELC) algorithm for feedback linearizable systems with experimental validation on a mobile robot. Traditional feedback and feedforward controllers are designed based on the nominal model by using the feedback linearization control (FLC) method. Then, an intelligent controller is designed based on the sliding mode learning algorithm that utilizes closed-loop error dynamics to learn the system behavior. The controllers are working in parallel, and the intelligent controller can gradually replace the feedback controller from the control of the system. In addition to the stability of the sliding mode learning algorithm, the closed-loop stability of an nth-order feedback linearizable system is proven. The simulation results demonstrate that the CLELC algorithm can improve control performance (e.g., smaller rise time, settling time, and overshoot) in the absence of uncertainties, and also provides robust control performance in the presence of uncertainties as compared to traditional FLC method. To test the efficiency and efficacy of the CLELC algorithm, the trajectory tracking problem of a tracked mobile robot is studied in real time. The experimental results demonstrate that the CLELC algorithm ensures highly accurate trajectory tracking performance than traditional FLC method.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Communication and control in an integrated manufacturing system

Typically, components in a manufacturing system are all centrally controlled. Due to possible communication bottlenecking, unreliability, and inflexibility caused by using a centralized controller, a new concept of system integration called an Integrated Multi-Robot System (IMRS) was developed. The IMRS can be viewed as a distributed real time system. Some of the current research issues being examined to extend the framework of the IMRS to meet its performance goals are presented. These issues include the use of communication coprocessors to enhance performance, the distribution of tasks and the methods of providing fault tolerance in the IMRS. An application example of real time collision detection, as it relates to the IMRS concept, is also presented and discussed.

Shin, Kang G.↗
NASA NTRS1994

A high temperature testing system for ceramic composites

Ceramic composites are presently being developed for high temperature use in heat engine and space power system applications. The operating temperature range is expected to be 1090 to 1650 C (2000 F to 3000 F). Very little material data is available at these temperatures and, therefore, it is desirable to thoroughly characterize the basic unidirectional fiber reinforced ceramic composite. This includes testing mainly for mechanical material properties at high temperatures. The proper conduct of such characterization tests requires the development of a tensile testing system includes unique gripping, heating, and strain measuring devices which require special considerations. The system also requires an optimized specimen shape. The purpose of this paper is to review various techniques for measuring displacements or strains, preferably at elevated temperatures. Due to current equipment limitations it is assumed that the specimen is to be tested at a temperature of 1430 C (2600F) in an oxidizing atmosphere. For the most part, previous high temperature material characterization tests, such as flexure and tensile tests, have been performed in inert atmospheres. Due to the harsh environment in which the ceramic specimen is to be tested, many conventional strain measuring techniques can not be applied. Initially a brief description of the more commonly used mechanical strain measuring techniques is given. Major advantages and disadvantages with their application to high temperature tensile testing of ceramic composites are discussed. Next, a general overview is given for various optical techniques. Advantages and disadvantages which are common to these techniques are noted. The optical methods for measuring strain or displacement are categorized into two sections. These include real-time techniques. Finally, an optical technique which offers optimum performance with the high temperature tensile testing of ceramic composites is recommended.

Hemann, John↗
DOE OSTI2016

TH-AB-202-08: A Robust Real-Time Surface Reconstruction Method On Point Clouds Captured From a 3D Surface Photogrammetry System

Purpose: Surface photogrammetry (e.g. VisionRT, C-Rad) provides a noninvasive way to obtain high-frequency measurement for patient motion monitoring in radiotherapy. This work aims to develop a real-time surface reconstruction method on the acquired point clouds, whose acquisitions are subject to noise and missing measurements. In contrast to existing surface reconstruction methods that are usually computationally expensive, the proposed method reconstructs continuous surfaces with comparable accuracy in real-time. Methods: The key idea in our method is to solve and propagate a sparse linear relationship from the point cloud (measurement) manifold to the surface (reconstruction) manifold, taking advantage of the similarity in local geometric topology in both manifolds. With consistent point cloud acquisition, we propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, building the point correspondences by the iterative closest point (ICP) method. To accommodate changing noise levels and/or presence of inconsistent occlusions, we further propose a modified sparse regression (MSR) model to account for the large and sparse error built by ICP, with a Laplacian prior. We evaluated our method on both clinical acquired point clouds under consistent conditions and simulated point clouds with inconsistent occlusions. The reconstruction accuracy was evaluated w.r.t. root-mean-squared-error, by comparing the reconstructed surfaces against those from the variational reconstruction method. Results: On clinical point clouds, both the SR and MSR models achieved sub-millimeter accuracy, with mean reconstruction time reduced from 82.23 seconds to 0.52 seconds and 0.94 seconds, respectively. On simulated point cloud with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent performance despite the introduced occlusions. Conclusion: We have developed a real-time and robust surface reconstruction method on point clouds acquired by photogrammetry systems. It serves an important enabling step for real-time motion tracking in radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Datascope to Enable Earth Independent Medical Operations (EIMO)

BACKGROUND: NASA has amassed sixty years of knowledge and experience relevant to maintenance of crew health and performance in low earth orbit. The Apollo Program introduced the importance of ensuring progressively autonomous operational capability. Earth Independent Medical Operations (EIMO) will require a gradual shift in the balance of medical responsibility, management, and authority from terrestrial to space-based assets. Terrestrial assets will continue to be essential for pre-mission screening and planning in addition to maintenance of crew health and performance. However, new capabilities are needed to enable EIMO and the amount of data required to support these systems, and mitigate the impacts of data transmission delays and reduced bandwidth coupled with lack of cloud-like resources and on-board computing capacity that is currently unclear or operationally insufficient. OVERVIEW: The overall goal of EIMO is to develop artificial intelligence (AI)-based solutions to analyze crew health and performance data utilizing a clinical decision support system (CDSS) to provide crew medical officers (CMO) with the equivalent of real-time, on-board medical consults. The EIMO ecosystem is envisioned as a “system of systems” where embedded reference databases and real-time data streams from multiple input vectors continuously and seamlessly assess crew health and performance. EIMO will be designed to make recommendations to the CMO using multi-modal AI-based natural language processing and machine learning methods with interoperability to push/pull data within and between multiple vehicle and habitat architectures. DISCUSSION: Data flows and storage/retrieval capacity are severely constrained during space missions and the challenges will become even greater during exploration missions. Just as each past program from Mercury to the International Space Station (ISS) required rethinking the interaction between ground-based controllers and space-based crew, so too will future missions to the Moon and Mars. While the NASA High-Performance Spaceflight Computing Processor project aims to increase computational capacity by 100 times over current spaceflight computers, the projected deliverable still lags considerably behind what will be needed to enable an AI-driven CDSS. Restrictions in processing speed and data storage capacity, coupled with transmission bottlenecks and delays, necessitate definition and optimization of an integrated data architecture to enable a progressively autonomous medical capability.

Medical operations↗
NASA NTRS1990

Automated eddy current analysis of materials

This research effort focused on the use of eddy current techniques for characterizing flaws in graphite-based filament-wound cylindrical structures. A major emphasis was on incorporating artificial intelligence techniques into the signal analysis portion of the inspection process. Developing an eddy current scanning system using a commercial robot for inspecting graphite structures (and others) has been a goal in the overall concept and is essential for the final implementation for expert system interpretation. Manual scans, as performed in the preliminary work here, do not provide sufficiently reproducible eddy current signatures to be easily built into a real time expert system. The expert systems approach to eddy current signal analysis requires that a suitable knowledge base exist in which correct decisions as to the nature of the flaw can be performed. In eddy current or any other expert systems used to analyze signals in real time in a production environment, it is important to simplify computational procedures as much as possible. For that reason, we have chosen to use the measured resistance and reactance values for the preliminary aspects of this work. A simple computation, such as phase angle of the signal, is certainly within the real time processing capability of the computer system. In the work described here, there is a balance between physical measurements and finite element calculations of those measurements. The goal is to evolve into the most cost effective procedures for maintaining the correctness of the knowledge base.

Workman, Gary L.↗
DOE OSTI2019

BTO 3.1.2.55 Milestone Report – Report of Literature Review for Maldistribution in Heat Exchangers

In this quarter, a comprehensive review of the literature was performed to understand the effects of flow maldistribution and current efforts to improve performance through flow control. Nearly all systems have some degree of maldistribution on either the air-side, refrigerant-side, or both. Air-side maldistribution was found to have a greater impact on heat exchanger performance compared to refrigerant-side, but both sides of the system are important to consider when developing solutions. Up to a maximum of 65% capacity loss was reported in large commercial systems due to air-side maldistribution. Microchannel heat exchangers are becoming more widespread due to their efficiency and size but are even more prone to maldistribution than conventional fin-tube heat exchangers. Current solutions tend to focus on optimizing header design and refrigerant circuitry to improve performance. Several active control methods, such as hybrid control, have been explored to control refrigerant flow in real time, but there is a need for an adaptive yet cost effective system which can optimize performance in changing conditions.

42 ENGINEERING↗
DOE OSTI2024

Analog Computing for Science

Conventional digital computing faces fundamental physical limits: large scale computing systems already con sume tens of Megawatts of power, Dennard scaling has ended, and data movement costs dominate application performance. Next generation experimental facilities generate data at rates that overwhelm conventional pro cessing and demand real-time analysis at the source. Analog computing, which exploits the continuous dynamics of physical systems to perform computation, promises a transformative path toward orders-of-magnitude gains in energy efficiency and time-to-solution for scientific workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Polymer-Based Thermally Stable Chemiresistive Sensor for Real-Time Monitoring of NO 2 Gas Emission

Here, we present a thermally stable, mechanically compliant, and sensitive polymer-based NO 2 gas sensor design. Interconnected nanoscale morphology driven from spinodal decomposition between conjugated polymers tethered with polar side chains and thermally stable matrix polymers offers judicious design of NO 2 -sensitive and thermally tolerant thin films. The resulting chemiresitive sensors exhibit stable NO 2 sensing even at 170 °C over 6 h. Controlling the density of polar side chains along conjugated polymer backbone enables optimal design for coupling high NO 2 sensitivity, selectivity, and thermal stability of polymer sensors. Lastly, thermally stable films are used to implement chemiresistive sensors onto flexible and heat-resistant substrates and demonstrate a reliable gas sensing response even after 500 bending cycles at 170 °C. Such unprecedented sensor performance as well as environmental stability are promising for real-time monitoring of gas emission from vehicles and industrial chemical processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Novel Approach for Real-Time Quality Monitoring in Machining of Aerospace Alloy through Acoustic Emission Signal Transformation for DNN

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement for nickel-based superalloys in the aerospace industry due to its high specific strength, which is retained at temperatures above 800 °C. However, low damage tolerance, i.e., brittle material behavior with a propensity to rapid crack propagation, has limited the application of γ-TiAl. Any cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of γ-TiAl components, making the workpiece surface’s quality from finish machining a critical component to product quality and performance. To address this issue and enable more widespread use of γ-TiAl, this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN). Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature extraction and classification, which face challenges such as the extraction of good/relevant features and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying the AE signature based on pedigreed experimental data and finally predicting the process-induced surface quality. The results of the present work show good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing the significant potential for real-time quality monitoring in manufacturing processes.

36 MATERIALS SCIENCE↗