Volume 4, Issue 11, November 2022


Improvement Productivity and Quality by Using Lean Six Sigma: A Case Study in Mechanical Manufacturing

Minh Duc Ly; Que Nguyen Kieu Viet

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 11, Pages 251-266
DOI: 10.47392/irjash.2022.066

 
Intelligent integrated production systems are always of interest to production planners. However, in order to deploy and improve from a mere processing fac- tory to a processing factory with an integrated intelligent production system, it requires a team of employees, engineers, and managers to always have a spirit of innovation, continuously improving existing semi-automatic equipment into automatic ones, aiming to move towards a smart factory. The result of this research is to reduce waste in the process of assembling mechanical products by applying the DMAIC process (Define, Measure, Analysis, Improve, Con- trol), lean six sigma tools, the test of variance, hypothesis testing and exper- imental design, IBM SPSS 2020, Matlab2019a and Minitab 18 software are used for data analysis and Solid work software is used to design and simulate mechanical parts. This study shows a systematic approach to analysis to find the root cause of defects in the product assembly process, a method of diag- nosing defective products as well as the application of charts to the analysis of waste products, and improving quality by applying basic quality manage- ment tools such as Pareto charts, fishbone diagrams, value stream mapping, man-machine chart, and failure tree analysis (FTA). Clearly identify the types of waste such as components sliding on top of each other without rust, and sur- face roughness of metal products that do not meet the standards. Experimental design models and statistical models, and statistical tests are applied to Lean six sigma’s DMAIC model in the process of analyzing the machining process. The results of analysis and process improvement have improved in a reduc- tion of scrap in the assembly line of mechanical products by 59.66% per year, an increase in assembly-line productivity by 7.8% per year, and a decrease in waste costs incurred by 59.66% per year. The application of the DMAIC cycle to improve the quality of the assembly line of mechanical products, in addition to reducing waste, also reduces the quality cost of the assembly line.

Net Zero Energy Buildings Initiatives - A Review

Ragunath A; Poonam Syal

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 11, Pages 267-271
DOI: 10.47392/irjash.2022.067

Buildings are one of the major energy consumers globally. The climate change and global warming are symptoms of increasing Green House Gases emis- sions because of growing energy consumption. Majority of electricity pro- duced from fossil fuels, coal, which are the source of GHG emission. Here renewable energy and efficient measure of energy consumption reduction can give the opportunity to do save our earth by reducing GHG emission level. The integrated solution of energy demand and environmental impact for build- ing is the net zero energy buildings. This paper reviews the NZEBs initiative around the world in a different perspective such as passive design of building, active approach incudes energy efficient measures and electricity production by renewable energy particularly for buildings. Further this paper highlights, optimizing and modelling using different tools and software used for imple- menting NZEBs. This paper suggests the possible ways of implementation of NZEBs and its benefits in residential, commercial and educational buildings for various climate zones and recent case studies results.

Deep Learning for Covid-19 Identification: A Comparative Analysis

Suresh P; Justin Jayaraj K; Aravintha Prasad VC; Abishek Velavan; Mr Gokulnath

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 11, Pages 272-280
DOI: 10.47392/irjash.2022.068

Covid 19 was an epidemic in 2022. Detection of Covid in X-Ray samples is crucial for diagnosis and treatment. This was also challenging for the identification of covid by radiologists. This study proposes Transfer Learning for detecting Covid-19 from X-Ray images. The proposed Transfer Learning detects the normal x-ray and covid 19 x-ray samples. In addition to this proposed model, different architectures including trained Desnet121, Efficient B4, Resnet 34, and mobilenetv2 were evaluated for the covid dataset. Our suggested model has compared the existing covid-19 detection algorithm in terms of accuracy. The Experimental model detects covid 19 patients with an accuracy of 98 percent. Our proposed work is to analyse the covid19 by the automation with helps of deep learning algorithms which results in high accuracy in detection Covid19 using x-ray samples. This model can assist radiologists and doctors in the diagnosis of covid-19 and make the test more accessible.

Prediction of Concrete Compressive Strength Using Artificial Neural Network

Chirag H B; Darshan M; Rakesh M D; Priyanka D S; Manjunath Aradya

International Research Journal on Advanced Science Hub, 2022, Volume 4, Issue 11, Pages 281-287
DOI: 10.47392/irjash.2022.069

Concrete is the most widely used material by humans after water. Rapid growth in the construction industry, concrete will continue to be the dominant material in the future. Concrete is a composite material like aggregates, water, and admixtures. Destructive testing of concrete to know its strength achieved after the mix design will be an expensive and time-consuming process. With recent advances in soft computing techniques like artificial intelligence, these results can be predicted by feeding the algorithm with a large number of data available to obtain the desired results. In the present research work, it is proposed to use artificial neural networks to predict the strength of different types of concrete. A Multilayer Perceptron has input and output layers, and one or more hidden layers with many neurons stacked together. Data capturing will be done regarding different types of concrete and artificial neural networks are preliminarily trained with various inputs to solve problems with data applica- ble to obtain the desired results. This ANN with captured data helps in minimizing repetitive process and tests involved to obtain the results through experimental procedures which is time, material, and money- consuming with practical difficulties. The advantage of python is that designer can create a customized program for interactive design, Python determination also improve the analytical skill of the student and programs can be converted into executable software. Concrete Cubes are cast to validate the predicted Result of the Software.