A Web Smart Space Framework for Intelligent Search Engines

A web smart space is an intelligent environment which has additional capability of searching the information smartly and efficiently. New advancements like dynamic web contents generation has increased the size of web repositories. Among so many modern software analysis requirements, one is to search information from the given repository. But useful information extraction is a troublesome hitch due to the multi-lingual; base of the web data collection. The issue of semantic based information searching has become a standoff due to the inconsistencies and variations in the characteristics of the data. In the accomplished research, a web smart space framework has been proposed which introduces front end processing for a search engine to make the information retrieval process more intelligent and accurate. In orthodox searching anatomies, searching is performed only by using pattern matching technique and consequently a large number of irrelevant results are generated. The projected framework has insightful ability to improve this drawback and returns efficient outcomes. Designed framework gets text input from the user in the form complete question, understands the input and generates the meanings. Search engine searches on the basis of the information provided

Auction-based approach to resolve the scheduling problem in the steel making process

Steel production is an extremely complex process and determining coherent schedules for the wide variety of production steps in a dynamic environment, where disturbances frequently occur, is a challenging task. In the steel production process, the blast furnace continuously produces liquid iron, which is transformed into liquid steel in the melt shop. The majority of the molten steel passes through a continuous caster to form large steel slabs, which are rolled into coils in the hot strip mill. The scheduling system of these processes has very different objectives and constraints, and operates in an environment where there is a substantial quantity of real-time information concerning production failures and customer requests. The steel making process, which includes steel making followed by continuous casting, is generally the main bottleneck in steel production. Therefore, comprehensive scheduling of this process is critical to improve the quality and productivity of the entire production system. This paper addresses the scheduling problem in the steel making process. The methodology of winner determination using the combinatorial auction process is employed to solve the aforementioned problem. In the combinatorial auction, allowing bidding on a combination of assets offers a way of enhancing the efficiency of allocating the assets. In this paper, the scheduling problem in steel making has been formulated as a linear integer program to determine the scheduling sequence for different charges. Bids are then obtained for sequencing the charges. Next, a heuristic approach is used to evaluate the bids. The computational results show that our algorithm can obtain optimal or near-optimal solutions for combinatorial problems in a reasonable computation time. The proposed algorithm has been verified by a case study

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

AI based state observer for optimal process control: application to digital twins of manufacturing plants

Les plantes de fabricació estan subjectes a restriccions dinàmiques que requereixen una optimització robusta per millorar el rendiment i l' eficiència del sistema. En aquest projecte es presenta un nou sistema de control òptim basat en IA per a un bessó digital d' una planta de fabricació. El sistema proposat implementa un observador d' estat basat en IA per predir l' estat intern d' un model de procés altament incert i no lineal, tal com seria un sistema de producció real. Una funció d' optimització multi-objectiu es utilitzada per controlar els paràmetres de producció i mantenir el procés funcionant en condicions òptimes. El mètode d'Optimització del Control basat en AI es va implementar en un cas d'estudi d'una planta de fabricació d'acer. El rendiment del sistema es va avaluar utilitzant els KPIs de fabricació rellevants, com ara les taxes d'utilització i productivitat de l'equip del procés. L'ús de sistema de control optimitzat via AI millora amb èxit els KPIs de procés i potencialment podria reduir els costos de producció.Las plantas de fabricación están sujetas a restricciones dinámicas que requieren una optimización robusta para mejorar el rendimiento y la eficiencia. En este informe se presenta un nuevo sistema de control óptimo basado en IA para un gemelo digital de una planta de fabricación. El sistema propuesto implementa un observador de estado basado en IA para predecir el estado interno de un modelo de proceso altamente incierto y no lineal, tal y como sería un sistema de producción real. Una función de optimización multiobjetivo es utilizada para controlar los parámetros de producción y mantener el proceso funcionando en condiciones óptimas. El método de Optimización del Control basado en AI se implementó en un caso de estudio de una planta de fabricación de acero. El rendimiento del sistema se evaluó utilizando los KPIs de fabricación relevantes, como la utilización del equipo y las tasas de productividad del proceso. El uso del sistema de control óptimo de IA mejora los KPIs del proceso y podría reducir potencialmente los costos de producción.Manufacturing plants are subject to dynamic constrains requiring robust optimization methods for improved performance and efficiency. A novel AI based optimal control system for a Digital Twin of a manufacturing plant is presented in this report. The proposed system implements an AI based state observer to predict the internal state of a highly uncertain and non-linear process model, such as a real production system. A multi-objective optimization function is used to control production parameters and keeps the process running at an optimal condition. The AI Optimization Control method was implemented on a study case on a steel manufacturing plant. The performance of the system was evaluated using the relevant manufacturing KPIs such as the equipment utilization and productivity rates of the process. The use of the AI optimal control system successfully improves the process KPIs and could potentially reduce production costs

A review and classification of computer-based manufacturing scheduling tools

Computer-based manufacturing scheduling tools can play a key role in the management of industrial operations, as obtaining economic and reliable schedules is at the core of excellence in customer service and of efficiency in manufacturing companies. As a consequence, this topic has been receiving an increasing interest in the last decades, resulting in a number of case studies and descriptions of implementation of these tools. However, to the best of our knowledge, there is no review of these cases in order to classify existing references and to identify relevant issues still not properly addressed. Therefore, in this paper we carry out a systematic review of case studies of manufacturing scheduling tools. In order to provide a coherent taxonomy for the analysis of these tools, we develop a classification based on the functionalities of the manufacturing scheduling tools. Using this framework, existing contributions are classified and discussed, and a number of conclusions and open issues are identified. We hope that our work can establish a coherent picture of the topic so it serves as a starting point for future research