The research was conducted to achieve the objective of implementing lean manufacturing to reduce production variability in prefabricated items for a restaurant chain in the Huanchaco district of Trujillo by 2024. The Lean Manufacturing methodology was used with the Jidoka tool for process automation while maintaining human intervention. The research findings showed a 47.47% reduction in defective products; a reduction in variability after increasing process capability (Cp) from 0.13, a process with inadequate capability requiring significant modifications, to a final capability of 1.92, resulting in an adequate process; an increase in real process capability (Cpk), achieving a final capability of 1.53, a capable and focused process; and finally, an increase in Taguchi capability (Cpm), achieving a final capability of 1.33, a process that meets specifications.
The sustainability for urban contexts is a very important objective today, and it needs to be linked transversally to all processes of technological change that impact people and the territory. In this study, we compare the nanoparticles measured inside diesel, gas, and electric buses during their normal operation in Bogota, Colombia. We used a miniature diffusion size classifier (DiSCmini) to measure nanoparticles concentrations, average particle size, and lung-deposited surface area. Our results revealed significantly lower levels of this pollutant inside electric buses. The concentration of nanoparticles per cubic centimeter is about 41% and 27% lower in electric buses compared to diesel and gas respectively. The lung-deposited surface area is also lower in electric buses. Nevertheless, average particle size in electric buses is 10% and 18% smaller compared to diesel, and gas respectively. The results of this study are important because they show useful information for future selection processes of bus technologies for public passenger transport in other cities around the world; This research provides information resources that can be used in technical evaluation processes that link the possible effects on the health of users and the environment, and favor concepts for the improvement of sustainability.
For the manufacturing of thin-walled-complex-shaped components used in complex dies and moulds it is important to manufacture these kinds of components with hard, tough, and heat-resistant materials such as Inconel 718. During the end milling of such types of components, there are many challenges related to surface roughness, tool wear, etc., identified in the recent past. Therefore, in this study, a prediction model for surface roughness with respect to the input parameters such as cutting speed, Depth of Cut, Feed rate and nose radius has been developed. In the present work, an attempt has been made to develop a prediction model of SR during milling of Inconel 718 with the help of BBD-RSM-Regression as well as ANN. Besides this, a comparative analysis has been conducted among these techniques, and it has been observed that the AI technique provides better prediction than the BBD-RSM-Regression. The ANOVA technique is also applied to find an estimate of the percentage contribution of each machine parameter with respect to machine time. Further, GA is used for the purpose of effective optimization. Five structural trials have also been conducted to validate this novel strategy, which has been proven to be more successful.
In the past decade, a steady increase has been noticed in the number of mobile users and the use of mobile communication. In wireless communication links are stable. Duration of the link is the most important design by which the performance of the mobile Ad Hoc network (MANET) can be determined. In this research article, a derivative metric of graph the-ory attained from mobility model technique in Ad Hoc network of casually moving object, link duration of probability density function (PDF) been ex-amined by using three mobility models namely random waypoint, random direction, and freeway model. The performance is based on the node speed, transmission range, the density of node, area of simulation, and computa-tional complexity with time. By using Monte Carlo simulations, the reliabil-ity and robustness of models have been analyzed. The main advantage of the proposed scheme is implemented is an easy hardware platform because of the concept in mobility models and the easiness of code.
Among all type of batteries, Lithium Air Batteries (LAB) are considered to be the most effective due to their highest energy density of around 11900 Wh/kg but there are some major issues are being faced by LAB such as large overpotential, poor cycle life, low current density, and decreased energy efficiency. The solution to these issues is primarily dependent on the proper selection of an electrocatalyst. A new approach for using a bi-functional electrocatalyst produced excellent results. Here, Co3O4/α-MnO2 composite has been considered as a bifunctional catalyst because cobalt oxide performed well in the Oxygen Evolution Reaction (OER) process while manganese oxide performed well in the Oxygen Reduction Reaction (ORR) process. A simple two-step hydrothermal process is used in this work to synthesize Co3O4/α-MnO2. This work focuses on the behavior of the composite electrocatalyst when varying percentages of Cobalt oxide (5%, 10%, 15%, and 20%) are deposited on the alpha-Manganese Oxide nanorods. The primary characteristics of each sample with different percentages of Cobalt Oxide are examined, and the performance of each sample is compared to one another. Several testing techniques like Cyclic Voltammetry (CV), Linear Sweep Voltammetry (LSV), X-Ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) are performed on the samples. The combination of cobalt oxide and manganese oxide showed a synergistic effect and work as a bifunctional electrocatalyst. As the percentage of Co3O4 deposited on the α-MnO2 nanorod increased, it behaves more like an OER electrocatalyst leading to a decrease in charging potential. This work will help in finding an optimum amount of Co3O4 that should be deposited on α-MnO2 nanorods to get an efficient (ORR/OER) bifunctional electrocatalyst.
