NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Optimizing diamond-like carbon coatings - From experimental era to artificial intelligence

Diamond-like carbon (DLC) coatings are widely used for numerous engineering applications due to their superior multi-functional properties. Deposition of good quality DLC is governed by energy per unit carbon atom or ion and plasma kinetics, which are independent parameters. Translating independent parameters to dependent parameters to produce a best DLC is subjected to deposition method, technology, and system configurations which may involve above 50 combinations of bias voltage, chamber pressure, deposition temperature, gas flow rate, etc. Hence DLC coatings are optimized to identify the best parameters which yield superior properties. This article covers DLC introduction, the role of independent parameters, translation of independent parameters to dependent parameters, and discussion of four generations of DLC optimization. The first-generation of DLC optimization experimentally optimizes the parameter-to-property relationship, and the second-generation describes multi-parameter optimization with a hybrid of experimental and statistical-based analytical methods. The third generation covers the optimization of DLC deposition parameters with a hybrid of statistical methods and artificial intelligence (AI) tools. The ongoing fourth generation not only performs multi-parameter and multi-property optimization but also use AI tools to predict DLC properties and performance with higher accuracy. It is expected that AI-driven DLC optimizations and progress in virtual synthesis of DLC will not only assist in resolving DLC challenges specific to emerging markets and complex environments, but will also become a pathway for DLC to enter a digital-twin era

Developing magnetic functionalized multi-walled carbon nanotubes-based buckypaper for the removal of Furazolid

Magnetic f-MWCNTs-based BP/PVA membrane was fabricated and utilized for the elimination of furazolidone (FZD) from aqueous solution. Characterisation and adsorption studies were performed to evaluate the performance and adsorptive efficiency, respectively of the membrane. Furthermore, statistical and machine learning technique were also applied to predict the removal efficiency of FZD on the membrane. The results revealed that magnetic f-MWCNTs-based BP/PVA membrane has the potential to be used as an efficient membrane for practical applications

Identification of chemical species using artificial intelligence to interpret optical emission spectra

The nonlinear modeling capabilities of artificial neural networks (ANN’s) are renowned in the field of artificial intelligence (Al) for capturing knowledge that can be very difficult to understand otherwise. Their ability to be trained on representative data within a particular problem domain and generalise over a set of data make them efficient predictive models. One problem domain that contains complex data that would benefit from the predictive capabilities of ANN’s is that of optical emission spectra (OES). OES is an important diagnostic for monitoring plasma species within plasma processing. Normally, OES spectral interpretation requires significant prior expertise from a spectroscopist. One way of alleviating this intensive demand in order to quickly interpret OES spectra is to interpret the data using an intelligent pattern recognition technique like ANN’s. This thesis investigates and presents MLP ANN models that can successfully classify chemical species within OES spectral patterns. The primary contribution of the thesis is the creation of deployable ANN species models that can predict OES spectral line sizes directly from six controllable input process parameters; and the implementation of a novel rule extraction procedure to relate the real multi-output values of the spectral line sizes to individual input process parameters. Not only are the trained species models excellent in their predictive capability, but they also provide the foundation for extracting comprehensible rules. A secondary contribution made by this thesis is to present an adapted fuzzy rule extraction system that attaches a quantitative measure of confidence to individual rules. The most significant contribution to the field of Al that is generated from the work presented in the thesis is the fact that the rule extraction procedure utilises predictive ANN species models that employ real continuously valued multi-output data. This is an improvement on rule extraction from trained networks that normally focus on discrete binary output

Advances in CAD/CAM/CAE Technologies

CAD/CAM/CAE technologies find more and more applications in today’s industries, e.g., in the automotive, aerospace, and naval sectors. These technologies increase the productivity of engineers and researchers to a great extent, while at the same time allowing their research activities to achieve higher levels of performance. A number of difficult-to-perform design and manufacturing processes can be simulated using more methodologies available, i.e., experimental work combined with statistical tools (regression analysis, analysis of variance, Taguchi methodology, deep learning), finite element analysis applied early enough at the design cycle, CAD-based tools for design optimizations, CAM-based tools for machining optimizations

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

In this study, NiO/Rosa Canina-L seeds activated carbon nanocomposite (NiO/ACNC) was prepared by adding dropwise NaOH solution (2 mol/L) to raise the suspension pH to around 9 at room temperature under ultrasonic irradiation (200 W) as an efficient method and characterized by FE-SEM, FTIR and N2 adsorption-desorption isotherm. The effect of different parameters such as contact time (0–120 min), initial metal ion concentration (25–200 mg/L), temperature (298, 318 and 333 K), amount of adsorbent (0.002–0.007 g) and the solution's initial pH (1–7) on the adsorption of Pb (II) was investigated in batch-scale experiments. The equilibrium data were well fitted by Langmuir model type 1 (R2 > 0.99). The maximum monolayer adsorption capacity (qm) of NiO/ACNC was 1428.57 mg/L. Thermodynamic parameters (¿G°, ¿H° and ¿S°) were also calculated. The results showed that the adsorption of Pb (II) onto NiO/ACNC was feasible, spontaneous and exothermic under studied conditions. In addition, a fuzzy-logic-based model including multiple inputs and one output was developed to predict the removal efficiency of Pb (II) from aqueous solution. Four input variables including pH, contact time (min), dosage (g) and initial concentration of Pb (II) were fuzzified using an artificial intelligence-based approach. The fuzzy subsets consisted of triangular membership functions with eight levels and a total of 26 rules in the IF-THEN approach which was implemented on a Mamdani-type of fuzzy inference system. Fuzzy data exhibited small deviation with satisfactory coefficient of determination (R2 > 0.98) that clearly proved very good performance of fuzzy-logic-based model in prediction of removal efficiency of Pb (II). It was confirmed that NiO/ACNC had a great potential as a novel adsorbent to remove Pb (II) from aqueous solution.Postprint (author's final draft

The Theoretical Overview of the Selected Optimization and Prediction Models Useful in the Design of Aluminum Alloys and Aluminum Matrix Composites

The growing attention regarding aluminum alloy matrix composites within the aerospace, automotive, defense, and transportation industries make the development of new engineering materials with the improved mechanical properties. Currently, materials are selected because of their abilities to satisfy engineering demands high for strength-to-weight ratio, tensile strength, corrosion resistance, and workability. These properties make aluminum alloys and aluminum matrix composites (AMCs) an excellent option for various industrial applications. Soft computing methods such as the artificial neural network (ANN), adaptive-neuro fuzzy inference systems (ANFIS), and Taguchi with ANOVA are the most important approaches to solve the details of the mechanism and structure of materials. The optimal selection of variables has important effects on the final properties of the alloys and composites. The chapter presents original research papers from our works and taken from literature studies dealing with the theory of ANN, ANFIS, and Taguchi, and their applications in engineering design and manufacturing of aluminum alloys and AMCs. Also, the chapter identifies the strengths and limitations of the techniques. The ANFIS and ANN approaches stand out with wide properties, optimization, and prediction, and to solving the complex problems while the Taguchi experimental design technique provides the optimum results with fewer experiments

Analysis of effective mechanical properties of thin films used in microelectromechanical systems

This research aims at analyzing the effective mechanical properties of thin film materials that are used in MEMS. Using the effective mechanical properties, reliable simulations of new or slightly altered designs can be performed successfully. The main reason for investigating effective material properties of MEMS devices is that the existing techniques can not provide consistent prediction of the mechanical properties without time-consuming and costly physical prototyping if the device or the fabrication recipe is slightly altered. To achieve this goal, two approaches were investigated: soft computing and analytical. In the soft computing approach, the effective material properties are empirically modeled and estimated based on experimental data and the relationships between the parameters affecting the mechanical properties of devices are discovered. In this approach, 2D-search, Micro Genetic Algorithms, Neural networks, and Radial Basis Functions Networks were explored for the search of the effective material properties of the thin films with the help of a Finite Element Analysis (FEA) and modeling the mechanical behavior such that the effective material properties can be estimated for a new device. In the analytical approach, the physical behavior of the thin films is modeled analytically using standard elastic theories such as Stoney’s formulae. As a case study, bilayer cantilevers of various dimensions were fabricated for extracting the effective Young’s modulus of thin film materials: Aluminum, TetraEthylOrthoSilicate (TEOS)-based SiO2, and Polyimide. In addition, a Matlab® graphical user interface (GUI), STEAM, is developed which interfaces with Ansys®. In STEAM, a fuzzy confidence factor is also developed to validate the reliability of the estimates based on factors such as facility and recipe-dependent variables. The results obtained from both approaches generated comparable effective material properties which are in accord with the experimental measurements. The results show that effective material properties of thin films can be estimated so that reliable MEMS devices can be designed without timely and costly physical prototyping

Machine learning for property prediction and optimization of polymeric nanocomposites: a state-of-the-art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted