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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.

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.

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.

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).

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.

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...

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.

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.

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.