10 research outputs found
Deep Reinforcement Learning based Power Split Strategy for Fuel Cell Hybrid Electric Trucks
No full textAuthor Pratheesh Sivaraman NairMasterarbeit Johannes Kepler Universität Linz 2024Arbeit gesperr
Deep Reinforcement Learning based Power Split Strategy for Fuel Cell Hybrid Electric Trucks
No full textAuthor Pratheesh Sivaraman NairMasterarbeit Johannes Kepler Universität Linz 2024Arbeit gesperr
The contribution of microbial biotechnology to sustainable development in agriculture and allied sectors
No full textCloning, Sequencing and In Silico Characterization of OmpF Protein of Salmonella typhimurium for its Immune-Potential
No full textOptimization in single point incremental forming of Inconel 718 through response surface methodology
No full textIncremental sheet forming is a flexible and versatile process with a promising future in the batch production and prototyping sectors. With decreased design time and negligible production time, incremental sheet forming provides reliability, flexibility, and quality, while being an economical option in contrast to the traditional forming process. In this paper, Inconel 718, a material that has extensive use in aircraft engines, is considered for experimental work to obtain the optimum combination of process parameters. Response surface methodology is used to optimize the process parameters, in particular feed rate, step depth, and lubricant viscosity. The output responses are surface roughness, profile accuracy, and wall thickness. Analysis of variance (ANOVA) is performed using the experimental results to predict the statistical influence of the process parameters. The optimal combination of process parameters is further predicted using a numerical optimization technique to achieve better profile accuracy and surface finish. The results obtained are experimentally validated and are in good agreement with the predicted values.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
Experimental study on single point incremental forming of Inconel 718
No full textThe contemporary sheet metal industry employs forming methods that use a precise die and punch to form components with precise tolerances. In mass production, the high cost of manufacturing dies and punches is absorbed by the number of components formed, whereas this is not the case for low volume production where it increases the manufacturing cost of the products. To overcome the demand for the development of manufacturing technologies that are both agile and meet industrial requirements, an incremental sheet metal forming experiment was carried out on Inconel 718. The incremental sheet metal forming process in the research stage needs to be improved both in terms of accuracy and product quality. The main process parameters involved in the study are incremental depth, rotational speed, and feed rate of the forming tool. Based on the process parameters, experiments are conducted using the Taguchi L9 orthogonal array design and responses such as geometrical accuracy, surface roughness, and thinning are studied. The forming limit and micro-structural study allow us to understand the forming behavior of Inconel 718, which will enhance the applicability of the material.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
