Modeling and control of flatness in cold rolling mill using fuzzy petri nets

Today, having a good flatness control in steel industry is essential to ensure an overall product quality, productivity and successful processing. Flatness error, given as difference between measured strip flatness and target curve, can be minimized by modifying roll gap with various control functions. In most practical systems, knowing the definition of the model in order to have an acceptable control is essential. In this paper, a fuzzy Petri net method for modeling and control of flatness in cold rolling mill is developed. The method combines the concepts of Petri net and fuzzy control theories. It focuses on the fuzzy decision making problems of the fuzzy rule tree structures. The method is able to detect and recover possible errors that can occur in the fuzzy rule of the knowledge-based system. The method is implemented and simulated. The results show that its error is less than that of a PI conventional controller.<br /

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

State of the Art of Laser Hardening and Cladding

In this paper an overview is given about laser surface modification processes, which are developed especially with the aim of hardness improvement for an enhanced fatigue and wear behaviour. The processes can be divided into such with and without filler material and in solid-state and melting processes. Actual work on shock hardening, transformation hardening, remelting, alloying and cladding is reviewed, where the main focus was on scientific work from the 21st century

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

Supervisory-plus-regulatory control design for efficient operation of industrial furnaces

A two-level system engineering design approach to integrated control and supervision of industrial multi-zone furnaces has been elaborated and tested. The application case study is the three-zone 25 MW RZS furnace plant at Skopje Steelworks. The integrated control and supervision design is based on combined use of general predictive control optimization of set-points and steady-state decoupling,at the upper level, and classical two-term laws with stady-state decouling, at the executive control level. This design technique exploits the intrinsic stability of thermal processes and makes use of constrained optimization, standard non-parametric time-domain process models, identified under operating conditions, using truncated k-time sequence matrices, controlled autoregressive moving average models. Digital implementations are sought within standard computer process control platform for practical engineering and maintenance reasons

MODELING AND SIMULATION OF PM MOTOR TESTING ENVIRONMENT TOWARDS EV APPLICATION CONSIDERING ROAD CONDITIONS

The electric vehicle (EV) performance testing is an indispensable aspect of the design study and marketing of electric vehicle. The development of a suitable electric motor testing environment for EVs is very significant. On the one hand, it provides a relatively realistic testing environment for the study of the key technologies of electric vehicles, and it also plays an essential role in finding a reasonable and reliable optimization scheme. On the other hand, it provides a reference to the evaluation criteria for the products on the market. This thesis is based on such requirements to model and simulate the PM motor testing environment towards EV applications considering road conditions. Firstly, the requirements of the electric motor drive as a propulsion system for EV applications are investigated by comparing to that of the traditional engine as a propulsion system. Then, as the studying objective of this work, the mathematical model of PMSM is discussed according to three different coordinate systems, and the control strategy for EV application is developed. In order to test the PM motor in the context of an EV, a specific target vehicle model is needed as the virtual load of the tested motor with the dyno system to emulate the real operating environment of the vehicle. A slippery road is one of the severe driving conditions for EVs and should be considered during the traction motor testing process. Fuzzy logic based wheel slip control is adopted in this thesis to evaluate the PM motor performance under slippery road conditions. Through the proposed testing environment, the PM motor can be tested in virtual vehicle driving conditions, which is significant for improving the PM motor design and control

Closed-loop control of product properties in metal forming

Metal forming processes operate in conditions of uncertainty due to parameter variation and imperfect understanding. This uncertainty leads to a degradation of product properties from customer specifications, which can be reduced by the use of closed-loop control. A framework of analysis is presented for understanding closed-loop control in metal forming, allowing an assessment of current and future developments in actuators, sensors and models. This leads to a survey of current and emerging applications across a broad spectrum of metal forming processes, and a discussion of likely developments.Engineering and Physical Sciences Research Council (Grant ID: EP/K018108/1)This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.cirp.2016.06.00