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Over the years, machinists have been exploring the hard part turning of AISI D2 steels. Initially, cylindrical grinding was used for this purpose, but it was later replaced by single-point turning due to its advantages such as high material removal, low cost, and greater flexibility. Conventional inserts are used in single-point turning, but they have been reported to have large radial forces, high notch wear, and poor surface finish. Therefore, multi-radii wiper inserts were designed to overcome these machining issues, but their use was restricted to shallower depths of cut and moderate feed rates because of the thick chips produced by the high entry angle. Prime inserts, on the other hand, were designed with a modest entry angle, making them ideal for evaluating tool wear/life, material removal and surface roughness at greater cutting speeds, depths of cut, and feed rates. It was observed that cutting speed has a significant effect on tool wear/life with a contribution of 55.38% followed by feed rate (13.72%) and depth of cut (11.43%). Cutting speed (84.87%) and feed rate (13.01%) are observed to be the most significant parameters controlling material removed. It was also observed that feed rate has a significant effect on workpiece surface roughness with a contribution of 67.30% followed by depth of cut (20.60%), whereas cutting speed had no significant effect on surface roughness. Moreover, it is found that prime insert outperformed wiper and conventional inserts in terms of tool life/wear and surface roughness.

Modern manufacturing operations always aim toward sustainable production through sustainable operations. Lean Six Sigma manufacturing is one of the leading models to increase operational efficiency and productivity and reduce product manufacturing costs. The lean Six Sigma problem-solving methodology DMAIC has been one of the several techniques organizations use to improve their productivity and the quality of their product and services. This paper aims to apply Lean Six Sigma and DMAIC to enhance production capacity and reduce per-unit cost. Furthermore, this research work has been carried out to analyze the impact of stakeholders on Lean Six Sigma projects. The research follows the DMAIC methodology to investigate and analyze the root cause of the problems and give possible solutions for eliminating or reducing the issues. Particularly, fishbone and 5-Whys techniques were used to determine whether the two key processes, AC Outdoor unit testing with the help of reusable power cords and the un-efficient use of expanding machine, had an impact on low productivity and high per-unit cost. The analysis indicated the importance of stakeholders in lean Six Sigma projects. It has been found that key stakeholders can affect the result of lean Six Sigma projects, e.g., in the power cord modification project, a total of USD 7738 has been lost, while in expanding machine modification project total of USD 1339 has been lost due to ignorance of key stakeholders in both projects. This paper provides practical guidance to lean Six Sigma project team leaders to develop and define the key stakeholders at the beginning of the project and clearly identify the stakeholders’ responsibilities. Furthermore, the project leader must analyze and identify internal and external stakeholders b/c stakeholders may be internal or external. This paper provides theoretical guidance to lean Six Sigma project team leaders since ignoring stakeholders could give a misleading picture in terms of project cost, savings, and duration of the project. The project leader must consider key stakeholders’ costs and future strategies before starting the project. Although some project managers and experts have conducted analyses of stakeholders’ impact on projects, lean Six Sigma literature lacks solid examples of stakeholders’ impact on LSS project results. This study tries to address this research gap by analyzing the impact of key stakeholders on LSS projects.

Dry machining of aluminum (Al) 2024 alloy has been performed with four different cutting inserts (cemented carbide, titanium nitride (TiN) coated, titanium aluminum nitride (TiAlN) coated, and polycrystalline diamond (PCD) coated), and their performance is assessed for tool wear and workpiece surface roughness. Design of experiments and response surface methodology (RSM) was performed to optimize the cutting parameters. TiAlN coated inserts presented an average ≈ 21%, ≈ 36%, and ≈ 58% less tool wear than the uncoated cemented carbide, TiN and PCD coated inserts, respectively. While TiN coated inserts exhibited an average ≈ 17%, ≈ 37%, and ≈ 42% less workpiece surface roughness than the uncoated cemented carbide, TiAlN, and PCD coated inserts, respectively. PCD coated inserts have greater mechanical properties, but due to the poor adhesion strength of the coating, it performed worst regarding tool wear and workpiece surface roughness. Energy dispersive X-ray spectroscopy (EDX) analysis of the chips validated our findings that the adhesion of coated tools is also very important for the evaluation of machining performance other than mechanical properties. It is concluded that the mechanical properties and adhesion of the coated tools are both important in assessing the tool wear and workpiece surface roughness. Also, the research community and industry need to consider adhesion strength of the coated tools for better machining performance.

Friction and wear are very crucial aspects of the performance, service life, and the operational costs for a mechanical component or equipment. To reduce the friction and wear at the interface of the sliding or mating parts, different conventional binary coatings like TiN, CrN, TiC, etc., have been used in the last two decades. But ternary nitride coatings have replaced the binary coatings due to better tribo-mechanical properties. Now, ternary nitride coatings are being extensively used in several fields such as cutting tools, machinery parts, orthopedic implants, microelectronics, marine equipment, decorative purposes, automotive, aerospace industry, etc. Many researchers have developed and investigated the ternary nitride coatings for different applications. Nonetheless, there is a huge research potential in the development and optimization of the tribo-mechanical properties of the ternary nitride coatings. Therefore, tribo-mechanical studies of the ternary nitride coatings are needed for fostering the new industrial applications. This paper is focused to summarize and compare the tribo-mechanical properties of the ternary nitride coatings comprehensively and aims to explore the novel research directions in the development of the ternary nitride coatings.

The computational aero-acoustic study of an isolated passenger car tire is carried out to understand the effect of dimensions of longitudinal tire grooves and operational parameters (velocity and temperature) on tire noise. The computational fluid dynamics and acoustic models are used to obtain aero-acoustic tire noise at near-field and far-field receivers around the tire and artificial neural networks-based regression are used to study the highly non-linear and interactive causal relationships in the system. Unsteady Reynolds-Averaged Navier-Stokes based realizable k-epsilon model is used to solve the flow field in the computational domain. The Ffowcs Williams and Hawkings model is used to obtain aero-acoustic tire noise at far-field positions. Spectral analysis is used to convert the output time domain to frequency domain and to obtain A-weighted sound pressure level. Artificial neural network–based response surface regression is conducted to understand casual relationships between A-weighted sound pressure level and control variables (Groove depth, Groove width, Temperature and velocity). Maximum A-weighted sound pressure level is observed in the wake region of the tire model. The interaction study indicates that ∼10% reduction in the aero-acoustic emissions is possible by selecting appropriate combinations of groove width and groove depth. The interaction of velocity with width is found to be most significant with respect to A-weighted sound pressure level at all receivers surrounding the tire. The interaction of operational parameters, that is, velocity and temperature are found to be significant with respect to A-weighted sound pressure level at wake and front receivers. Therefore, the regional speed limits and seasonal temperatures need to be considered while designing the tire to achieve minimum aero-acoustic emissions.

Titanium nitride (TiN) is a hard-ceramic material having excellent wear and corrosion resistance properties. In this study, TiN coating has been deposited on carbide cutting inserts by a low-temper...

The numerical study investigates the effect of various comparison criteria on the forced convective heat transfer of water-based cuprous oxide nanofluids in a horizontal mini tube under uniform heat flux conditions. The forced convective flow regime was validated, and it was ensured that the free convection effects were not present. The temperature-dependent thermophysical properties were considered for the numerical simulation. The volume concentrations of the nanofluids were kept below 2% (i.e., 0.6%, 1.3% and 1.9%) as higher concentrations lead to agglomeration and sedimentation of nanoparticles. The various comparison criteria employed for the assessment of heat transfer enhancement were equal pumping power, equal velocity, equal mass flow rate and equal Reynolds number criterion. The enhancements in heat transfer coefficient (at ϕv = 1.9% and base fluid Re = 1380) were 7.7%, 4.9%, 3.6% and 3.3% when assessed on the basis of equal Reynolds number, equal velocity, equal mass flow rate and equal pumping power, respectively. It was found that the enhanced heat transfer of the nanofluids should preferably be examined on the basis of equal pumping power criterion instead of above-mentioned other three criteria. The discussion on the invalidity of three evaluation criteria based on Reynolds number, velocity and mass flow rate has also been made.

The most critical energy and environmental challenge that our planet is facing today is to minimize the dependence on fossil fuels. Carbon dioxide may be of utmost significance as a solution of this issue through realization of carbon neutral energy cycle. Potentially, this could be achieved through the carbon dioxide capture as the urgent response to ongoing climate change. Activated carbon (AC) adsorption is one the most effective, environment friendly and techno-economic process for the carbon capture. In the current research, an electro-conductive-activated carbon was prepared by mixing powdered activated carbon (PAC) with an electro-conductive polymer (ECP). Different ratios of 0, 25, 50, 75 and 100 wt% of ECP with PAC were used for the different analyses of activated carbons in a gas mixture of CO2/N2 using a physical adsorption system. Adsorption and desorption analyses, capacities of the process and desorption effects were examined. Electro-conductive polymers (ECP) were mixed with AC samples, where breakthrough time was increased up to 400% when mixed with the PAC for CO2 adsorption. Following adsorption analysis, desorption of activated carbons was conducted with different potentials. It was revealed that mixing could help the PAC sample to overcome the packing issue to increase the breakthrough capacity and the volumes before and after the breakthrough adsorption in the packed bed systems. The desorption rates of the PAC sample were also enhanced, and fast desorption was observed when mixed with ECP. It is envisioned that this method is very much promising carbon capture method for the techno-economic feasibility and sustainable development of the environment.

In the current work, the effects of design (groove depth and groove width) and operational (temperature and velocity) parameters on aerodynamic performance parameters (coefficient of drag and coefficient of lift) of an isolated passenger car tire have been investigated. The study is conducted by using neural network-based Monte-Carlo analysis on computational fluid dynamics (CFD). The computer experiments are designed to obtain the causal relationship between tire design, operational, and aerodynamic performance parameters. The Reynolds-averaged Navier–Stokes equations-based RealizableK-εmodel has been employed to analyze the variations in flow patterns around an isolated tire. The design parameters are varied over wide range and full factorial design, while considering temperature and velocity is completely explored to draw conclusive results. The multi-layer perceptron type neural network with the back-propagation algorithm is trained to map any non-linearity in causal relationships. The sensitivity analysis is performed to find the relationship between control variables and performance indicators. The importance of control variable is determined by both sensitivity and significance analyses and the paired interaction analysis is performed between selected control variables to find the interactive behavior of corresponding variables. The design parameter of groove width with 6.8% and 41% reduction in drag and lift coefficient, respectively, and conventionally overlooked operational parameter of velocity with 4% and 35% impact on drag and lift coefficient, respectively, are found to be the most significant variables. The air trapped between the longitudinal grooves and the road is found to follow the beam theory. The interaction of the groove depth and width is found to be significant with respect to coefficient of lift based on the air beam concept. The interaction of groove width and velocity is found to be significant with respect to both coefficients of lifts and drag.

Pakistan is a developing country that faces severe energy crises due to the increased use of energy. The purpose of this study is energy transition by designing a strategy for the adoption of renewable energy policies in the entire energy system by using all renewable energy resources to forecast future energy needs and carbon emission mitigation potential. This research study aims to evaluate the renewable energy policies of Pakistan and to analyze the ways to secure energy sources in the future using LEAP. The study established a path for the transformation of the Pakistan energy system by considering the potential of renewable resources, the cost of the energy system, and the primary energy supply. The highest value of energy demand is noted for the 1st scenario, while the lowest emissions are noted for the 16th scenario for each renewable source (WIN16, SOL16, and BIO16). The lowest values of energy demand and emissions (192.1 TWh and 37.7 MMT, respectively) are shown using the green solution compared to other scenarios (hydro, nuclear, BAU), concluding that the green solution is the most suitable scenario. The analysis shows, that from a technological and economic perspective, it is possible to carry out transformation with the necessary steps to effectively achieve a renewable energy system. The findings of this study show that the green scenario in Pakistan which has the lowest operational and externality costs is the best choice for the future.

A large power generation facility is a complex multi-criteria system associated with multivariate couplings, high dependency, and non-linearity among the operating variables which present a major challenge to ensure efficient power production. In this research, an integrated artificial intelligence (AI) and response surface methodology (AI-RSM) framework to achieve the efficient power production operation of a 660 MW coal power plant is presented. Two AI algorithms, i.e., extreme learning machine (ELM) and support vector machine (SVM) are trained comprehensively on the power plant's operational data and are validated as well. Full factorial design of experiments on the three levels of the operating parameters are constructed and simulated from the better performing AI model which is an effective non-linear representation of the complex power plant operation. RSM analysis is carried out under three power generation scenarios to simulate the effective values of the operating variables which are tested on the power plant's operation and a reasonable agreement is found with the experimental observations. The notable improvement in fuel consumption rate, thermal efficiency, and heat rate of the power plant under Half Load, Mid Load, and Full Load capacity of the power plant is achieved by the AI-RSM framework enabled analyses. It is estimated that annual reduction in CO2, CH4 and Hg emissions measuring 210 kg tons per year (kt/y), 23.8 t/y and 2.7 kg/y, respectively can be obtained corresponding to Mid Load operating state of the power plant. The research presents the reliable and robust utilization of AI-RSM framework for simulating the effective operating conditions for the fossil-based power plants’ operation with an eventual goal to improve the techno-environmental performance which is expected to contribute to net-zero emissions goal from the energy sector.

Constructing the power curve of a power generation facility integrated with complex and large-scale industrial processes is a difficult task but can be accomplished using Industry 4.0 data analytics tools. This research attempts to construct the data-driven power curve of the generator installed at a 660 MW power plant by incorporating artificial intelligence (AI)-based modeling tools. The power produced from the generator is modeled by an artificial neural network (ANN)—a reliable data analytical technique of deep learning. Similarly, the R2.ai application, which belongs to the automated machine learning (AutoML) platform, is employed to show the alternative modeling methods in using the AI approach. Comparatively, the ANN performed well in the external validation test and was deployed to construct the generator’s power curve. Monte Carlo experiments comprising the power plant’s thermo-electric operating parameters and the Gaussian noise are simulated with the ANN, and thus the power curve of the generator is constructed with a 95% confidence interval. The performance curves of industrial systems and machinery based on their operational data can be constructed using ANNs, and the decisions driven by these performance curves could contribute to the Industry 4.0 vision of effective operation management.

Tribological, mechanical, and chemical properties of the TiN coatings on Ti substrate were experimentally investigated for implant applications. X-ray diffraction (XRD) demonstrated that the principal crystal structure of TiN coating was (111) preferred orientation with FCC structure. Experimental evaluation was conducted at two substrate surface roughness, i.e., 0.1 μm and 0.4 μm. TiN coatings having 0.4-μm substrate surface roughness and approximately 3.3-μm coating thickness demonstrated optimum results of adhesion strength, hardness, coefficient of friction, wear rate, and corrosion rate in simulation body fluid (SBF). The selected TiN-coated sample exhibited maximum of 16.585 GPa hardness, 238.7 GPa elastic modulus, approximately 20 N adhesion, and 0.088 coefficient of friction. TiN coating showed approximately 8 times more corrosion resistance and 4 times more wear resistance than the bare titanium substrate. Energy dispersive spectroscopy (EDS) analysis of the wear tracks of TiN coating in SBF showed no presence of any harmful ingredients and confirmed its biocompatibility over the usage time in SBF. TiN-coated sample with higher substrate surface roughness (0.4 μm) demonstrated better tribo-mechanical properties and could reduce the cost of production than the conventionally used TiN-coated Ti implants of lower substrate surface roughness (0.1 μm).

The emissions from coal power plants have serious implication on the environment protection, and there is an increasing effort around the globe to control these emissions by the flue gas cleaning technologies. This research was carried out on the limestone forced oxidation (LSFO) flue gas desulfurization (FGD) system installed at the 2*660 MW supercritical coal-fired power plant. Nine input variables of the FGD system: pH, inlet sulfur dioxide (SO2), inlet temperature, inlet nitrogen oxide (NOx), inlet O2, oxidation air, absorber slurry density, inlet humidity, and inlet dust were used for the development of effective neural network process models for a comprehensive emission analysis constituting outlet SO2, outlet Hg, outlet NOx, and outlet dust emissions from the LSFO FGD system. Monte Carlo experiments were conducted on the artificial neural network process models to investigate the relationships between the input control variables and output variables. Accordingly, optimum operating ranges of all input control variables were recommended. Operating the LSFO FGD system under optimum conditions, nearly 35% and 24% reduction in SO2 emissions are possible at inlet SO2 values of 1500 mg/m3 and 1800 mg/m3, respectively, as compared to general operating conditions. Similarly, nearly 42% and 28% reduction in Hg emissions are possible at inlet SO2 values of 1500 mg/m3 and 1800 mg/m3, respectively, as compared to general operating conditions. The findings are useful for minimizing the emissions from coal power plants and the development of optimum operating strategies for the LSFO FGD system.

Power plant heat rate is a plant level performance parameter that indicates the economy of power production, equipment’s safety, and availability. In this paper, seven operating parameters, including the performance indices of integrated energy devices and the environmental conditions are incorporated for modeling the power plant heat rate by Artificial Neural Network (ANN), Support Vector Machine (SVM), and automated machine learning (AutoML) approach. The parametric significance order is determined by ANN and SVM-based Monte Carlo analytics and other machine learning-driven algorithms. Subsequently, the best-performing model is selected based on the external validation test and deployed for knowledge mining purposes. The improvement in the power plant heat rate by the parametric adjustment is achieved and subsequently, up to 3.12 percentage point (pp) increase in the thermal efficiency of the power plant is confirmed. Moreover, the fuel savings corresponding to the improved power plant heat rate are also calculated at three power generation modes. Their equivalence to an annual reduction in emissions is quantified. It is estimated that the accumulated reduction in CO2, SO2, CH4, N2O, and Hg emissions, i.e., 288.2 kilo tons / year (kt/y), can be achieved under 3.15% improvement in the power plant heat rate, corresponding to 75% power generation mode.

Energies 13(21), 5592 (2020). doi:10.3390/en13215592, Published by MDPI, Basel

Magnesium oxide (MgO) is a good candidate for an interface layer in multifunctional metal-oxide nanoscale thin-film heterostructures due to its high breakdown field and compatibility with complex oxides through O bonding. In this research, molecular beam epitaxy (MBE) is used to deposit 10 nm to 15 nm MgO single-crystal films on silicon carbide with hexagonal polytype 6H (6H-SiC) to serve as an interface layer for effective integration of functional oxides. In this work, the effect of MBE process control variables on the growth rate of the MgO film measured in nanometers per minute is investigated. Experiments are conducted at various process conditions and the resulting MgO film growth rate at each combination of process conditions is measured. The process control variables studied are the substrate temperature (100 °C – 300 °C), magnesium source temperature (328 °C – 350 °C), plasma intensity (0 mV – 550 mV), and percentage oxygen on the starting surface of 6H-SiC substrate (9 % – 13 %) after the substrate is prepared by high-temperature hydrogen etching. The film thickness is computed using the effective attenuation length (EAL) of silicon photoelectron peak intensity as measured by x-ray photoelectron spectroscopy (XPS). The film thickness is converted to growth rate by dividing it with the duration of film growth. Using the experimental data, a neural network model is developed to estimate growth rate for any given process variable combination. From this neural network model, multiple replications of data were generated to conduct a 3-level full factorial design of experiments and response surface-based analysis. The study reveals that the plasma intensity has the most significant influence on growth rate. The results indicate that growth rate is relatively low on high-quality substrates with √3 × √3 R30° reconstructed 6H-SiC (0001) surface with optimum oxygen content (approximately 10 %); in contrast, the growth rate is relatively high on substrates with high surface roughness and excessive oxygen on the starting substrate surface.

The building insulation is one of the needed element or component in building construction; it protects the construction against heat leakage for saving energy. The current research work aims to utilize natural fibers in concrete and develop such type of environmentally friendly material that has better thermal insulation properties. Thermal conductivity test, scanning electronic microscope tomography (SEM) and microscopic images for interfacial bonding of fibers with concrete were performed for plain concrete and natural fibres light weight reinforced concrete (RC) samples. Thermo-gravimetric analysis (TGA) was performed to check the thermal stability of natural fibres RC. The compressive strength was also measured to investigate the mechanical cost at which thermal insulation is achieved. Thermal conductivity results showed that by increasing the percentage of natural fibres (Jute, Coconut, Sugar cane, Sisal, Basalt) in concrete the thermal insulation characteristics increased while compressive strength decreased. TGA indicated that Jute, Sugarcane and Basalt fibres RC have better thermal stability as compared with plain concrete up to 50 °C which is suitable for South Asian countries climate but Sisal and Coconut fibres reinforced samples have comparatively a bit lower thermal stability. However, at 2.5% mixing of coconut and jute we were able to achieve improvement in both compressive strength and thermal insulation.

Titanium dioxide (TiO2) has been widely used as a biomaterial due to its excellent mechanical and wear resistance properties. In this study, we have deposited TiO2 thin films on stainless steel substrate at low temperature (90 ℃) by middle frequency (MF) magnetron sputtering and cathodic arc PVD techniques at two different substrate surface roughness. Scanning Electron Microscope (SEM) and Energy Dispersive X-ray (EDX) techniques have been utilized for the study of surface morphology and stoichiometric elemental chemical composition of the TiO2 thin films. Scratch adhesion and nano indentation experiments were conducted for the assessment of the film adhesion and mechanical properties of the TiO2 thin films respectively. Results proved that the TiO2 thin film deposited on higher substrate surface roughness (0.7 µm) samples by MF magnetron sputtering PVD process have superior adhesion and mechanical properties. Correlation comparative analysis of the TiO2 thin films deposited by the MF magnetron sputtering PVD process gave the optimum results at 1238 nm film thickness and 0.7 µm substrate surface roughness. The maximum of 14 N adhesion strength, 13.8 GPa hardness and 345.6 GPa elastic modulus values have been recorded.

This article presents a Monte Carlo-based factor-wise sensitivity analysis conducted on the performance variables of a Molecular Beam Epitaxy (MBE) process. Using lab-scale MBE equipment, magnesium oxide (MgO 111) films are grown on a hexagonal silicon carbide 6H-SiC (0001) substrate. The thin film surface chemistry in terms of O‒Mg and OH‒Mg bonding states is examined using X-ray photoelectron spectroscopy. A multi-layer perceptron is used to model the process. Monte Carlo experiments are conducted on the process model to study the causal relationship between the critical process control variables and the key performance indicators. The sensitivity of O‒Mg and OH‒Mg bonding states in MgO films to each of the four process control variables (growth time, substrate temperature, magnesium source temperature, and percentage starting oxygen) is examined. Each control variable is varied individually while keeping other control variables constant at their mid values in one case and randomly varying in another ca...

Ba M-type ferrite films of composition BaFe11Zn0.5Zr0.5O19 were grown on MgO (111) substrates by pulsed laser deposition. Both Zn and Zr divalent ions were introduced to reduce the magnetic anisotropy field to ~7.5 kOe, or 46% lower than the parent Ba M-type compound. Temperature dependence of magnetization over 25 to 350°C was investigated, indicating a Curie temperature of 320°C. Ferromagnetic resonance linewidth, as the peak-to-peak power absorption derivative at 9.55 GHz, was 120 Oe. The substituted sample had a K1 of ~0.920 × 106 erg/cm3. Such sample properties provide an attractive option for self-biased microwave devices applications operating at X (8-12 GHz) and Ku bands (12-18 GHz) as circulators and isolators.

Chemical vapor deposition is one of the several viable methods for growing vertically aligned single-walled carbon nanotubes (VA-SWNTs). Utilizing cobalt (Co) catalyst supported on multilayer Al/SiO2 and a hydrocarbon feedstock, VA-SWNTs are grown in excess of a millimeter height. To control VA-SWNTs length, one has to use the right combination of process control variables such as hydrocarbon gas flow rate, chamber temperature, and chamber pressure. This paper presents a process meta-model-based full factorial experimental design and analysis to study the yield of tall VA-SWNTs. All of the process variables under the study play a role in influencing VA-SWNTs length; the current study investigates main effects and their interactions. The meta-model-based analysis demonstrates that the hydrocarbon flow rate and the chamber pressure are the most statistically significant control variables that influence the length of VA-SWNTs. In addition, the response surface graph confirms that a higher gas flow rate at lower chamber pressure will consistently yield tall VA-SWNTs. We found that gas flow rate is the most significant of the control variables and only the optimum gas flow rate can ensure the growth of tall VA-SWNTs. We noticed that the interaction of gas flow rate with chamber temperature is also significant to the length of VA-SWNTs grown. All these observations together indicate that the dynamic pressure of the gas in the chamber plays an important role in the assurance of the length of VA-SWNTs. Outcomes of this investigation are beneficial for moving us closer towards producing VA-SWNTs on a mass scale.

Magnesium oxide (MgO) is a candidate for interface layer application in multifunctional heterostructures. Molecular beam epitaxy (MBE) of MgO on 6H-SiC is studied for effective integration of functional oxides. In this research we present the neural network assisted design of experiment (DOE) analysis of the crystalline structure of MgO films grown over 6H-SiC substrates. Based on reflection high energy electron diffraction (RHEED) characterization of the MgO (111) films grown we present the DOE analyses of the strain propagation in two different orientations (110 and 112) during growth process with respect to growth time, substrate temperature, magnesium source temperature, plasma intensity and percentage oxygen on the starting surface of 6H-SiC substrates. We conducted constant substrate temperature (150°C) and constant plasma intensity (350mV) analyses to quantify the relation between lattice strains in and directions. The constant plasma intensity analysis reveals that the growth propagates in a tensile strain mode for well reconstructed SiC surface. However, excessive oxygen on the SiC surface causes the lattice of MgO twist due to tensile strain as seen from the direction and compressive strain in direction. In constant substrate temperature analysis we found that the principal tensile strain inducing variable in direction is growth time and the strain continues to increase as the film grows thicker. However, in direction the optimum plasma intensity is critical to maintain the optimum strain in the film structure.

Functional properties of thin film metal oxides depend upon their stoichiometric and structural uniformity. Controlling the film deposition process can help tune the functionality of these films by ensuring the control over chemistry and structure of the films. The high volume manufacturing of functional devices will benefit from the development of reliable control models developed from research efforts in designing robust manufacturing processes. The use of neural networks as computer models to simulate the molecular beam epitaxy (MBE) of iron oxide thin films is presented in this work. Monte Carlo experiments are used to study the sensitivities and significances of process control variables to the stoichiometric performance indicators. Moreover, we also explore the relationship between growth dynamics of iron oxide (Fe2 O3 , Fe3 O4 , and mixed Fex Oy ) and magnesium oxide (MgO) thin films. The common metal adsorption controlled growth mechanism of two films with different structural and stoichiometric complexities were observed and the similarities among the trends of analogous stoichiometric indicators at comparable metal arrival rates of the two films are presented. The dependence of undesirable bonding states of iron and magnesium metals with the film thicknesses was also observed in both processes. The commonalities suggest the potential to use of neural network assisted Monte Carlo analysis to link common atomic-level mechanisms to processing variables in one nano-scale system and use them to predict some level of behavior in other nanoscale processes with similar atomic-level mechanisms.Copyright © 2011 by ASME

Like most nanomanufacturing processes, molecular beam epitaxy (MBE) processes are based on atomic-level control of growing films and thus are sensitive to subtle changes that make repeatability and reproducibility of desired performance indicators a nontrivial task. The gamut of challenges include insufficient understanding of atomic-level interactions, involvement of a large number of candidate process variables, lack of direct observation and measurement techniques for key performance indicators, and significant cost and time requirements for conducting experiments. A conventional design of experiment-based analysis becomes an unrealistic option due to its demand on extensive experimentation. In this paper, we present a hybrid approach that combines current process knowledge, artificial neural networks, and design of experiments (DOE) to make use of preliminary experimental data to analyze the process behavior, enhance process knowledge, and lay down foundations for cost effective systematic experimentation. Based on preliminary experimental data generated while exploring the MBE process for growing a MgO interface layer on 6H-SiC substrate, we developed a neural-network-based meta model that can interpolate and estimate the process responses to any combination of process variable settings within the input space. Using the neural-network model trained on preliminary experimental data, we estimate the process responses for a three-level full-factorial DOE runs. Based on these runs, the DOE based analysis is carried out. The results help explain the MgO film growth dynamics with respect to process variables such as substrate temperature, growth time, magnesium source temperature, and trace oxygen on the initial substrate surface. This approach can be expanded to statistically analyze the dynamics of other complex nanoprocesses when only the exploratory preliminary experimental data are available. This approach can also lay the foundation for efficient and systematic experimentation to further analyze and optimize the processes to address issues such as process repeatability and reliability.

A highly two-dimensional and chemically pure MgO (111) thin film grown epitaxially under ultra-high vacuum on a (√3 × √3) R30° reconstructed wideband gap 6H-SiC (0001) substrate surface can serve as a promising minimal mismatch interfacial layer between the substrate surface and a crystalline structure of functional oxide film. However, the reliability and repeatability of the growth process is challenging. In this paper the dynamic termination of the MgO (111) polar oxide during layer-by-layer growth is explained by OH group chemical bonding state of MgO structure. The analysis of causal relationships is conducted at three different thickness levels to account for the sensitivity of film chemistry to the mismatching strain related inherent twist of crystalline structure along the film thickness. The contribution of sources of the undesired bonding states is quantified by examining the process sensitivity trends to the most critical process variable (percentage starting oxygen) at different thickness levels.

We show that the magnetoresistance of Co2FexMn1?xSi-based spin valves, over 70% at low temperature, is directly related to the structural ordering in the electrodes and at the electrodes/spacer (Co2FexMn1?xSi/Ag) interfaces. Aberration-corrected atomic resolution Z-contrast scanning transmission electron microscopy of device structures reveals that annealing at 350??C and 500??C creates partial B2/L21 and fully L21 ordering of electrodes, respectively. Interface structural studies show that the Ag/Co2FexMn1?xSi interface is more ordered compared to the Co2FexMn1?xSi/Ag interface. The release of interface strain is mediated by misfit dislocations that localize the strain around the dislocation cores, and the effect of this strain is assessed by first principles electronic structure calculations. This study suggests that by improving the atomic ordering and strain at the interfaces, further enhancement of the magnetoresistance of CFMS-based current-perpendicular-to-plane spin valves is possible.

Surface preparation of silicon carbide (SiC) is critical to producing silicon carbide based wide bandgap semiconductor for electronic devices operating in high temperatures and/or harsh environments. In this research we study the impact of cleaning time and cleaning temperature on reflection high energy electron diffraction-based structural and X-ray photoelectron spectroscopy based stoichiometric performance indicators. We used a neural network assisted Monte Carlo analysis of the high temperature hydrogen etching procedure. We observed that the optimum cleaning time and temperature ranges for structural and stoichiometric analyses corroborate generally. The detailed examination of the stoichiometric analysis points to narrow parameter ranges where both optimum surface chemistry and (√3 × √3) R30° reconstruction of the structure of the surface can be achieved. A range of 1,400–1,500°C cleaning temperature for a time period of 14–20 minutes was found to consistently result in (√3 × √3) R30° reconstruction with an optimum oxygen content.