The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The objective of this paper is to study the relationships among the significant parameters impacting CO2 production. An enhanced understanding of the intricate relationships among the process parameters enables prediction and optimization, thereby improving efficiency of the CO2 capture process. Our modeling study used the operational data collected over a 3-year period from the amine-based post combustion...
Powder Metallurgy (P/M) involves multiple input and output which are non-linearly related for which statistical optimization methods are not suitable. These considerations lead to adoption of neural network (NN) for proper selection of P/M process parameter. In the present work, white cast iron powder is taken as the work material and NN approach is employed which allows specification of multiple...
The purpose of this study is to produce algorithms that are able to predict the intramuscular fat (IMF) percentage of live cattle. Two algorithms based on linear regression analysis and neural network models are developed. These two algorithms extract feature information from live cattle ultrasound images and calculate the predicted IMF percentage values. The results show that these algorithms perform...
This paper presents a method of textile flaw detection and classification based on wavelet reconstruction and BP neural network. The common two types of textile flaws, namely oil stain and hole, can be detected and classified. The method can handle two types of texture fabrics: statistical textures with isotropic patterns and structural textures with oriented patterns. For the extraction of flaw features,...
Any product design goes through many phases. One of the main considerations is the specific mix of design parameters that affect the acceptance of the product in the market. The acceptance of the product, in turn, depends on some of the key factors such as reliability, safety, quality, performance, cost, ease of maintenance, ease of assembly and so on. The designer's perspective, therefore, is wide...
This paper presents a novel approach to model and predict cutting tool wear using statistical signal analysis, pattern recognition and sensor fusion. The data are acquired from two sources: an acoustic emission sensor (AE) and a tool post dynamometer. The pattern recognition used here is based on two methods: artificial neural networks (ANN), and polynomial classifiers (PC). In this work we compare...
A concept to improve post-rolling flatness and offer flat products to the end customer would decrease substantially run-around scrap. This would mean lower energy consumption and lower environmental load per rolled strip. Part of the concept is advanced prediction tools. This paper reports current work in post-rolling flatness prediction of cold-rolled metal strip. The work was tested in an aluminium...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.