Cutting Wheels
Fiberglass Nets is punched from fiberglass cloth for grinding wheel after coating with phenolic and epoxy resin and curing by heating ..
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There have two kind of weave: plain weave and leno weave.
Surface: with black paper(P) or wax paper or non-woven fabric.
Item: Cutting Disc,Grinding Disc
Warranty: 3 years
Customized support: OEM, ODM, OBM
Place of Origin: China
Model Number: Various as per customer requirements.
Type: Cutting Disc,Abrasive Disc
Application: Cutting tool for stainless steel and metal
Material: Aluminum Oxide
Bond: Resin
Color: Black/Green/Brown/Customized
OEM: Yes
Diameter: 4-16 Inch
Style: Abrasive Disc
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ã€Abstract】 The main methods of motion control are summarized in this paper. According to the characteristics of water supply system and the problems in control, the optimal control strategy of water supply pump station is selected. Key words water supply system; water supply pump station; PID control; artificial neural network; fuzzy control; expert control; control strategy I. INTRODUCTION With the continuous improvement of the automation, the motion control system can adopt complex algorithms, Control performance has also been greatly improved. The intelligent controller in motion control system provides an effective theory and method to solve the control of complex system which is difficult to solve by traditional methods. Motion control methods are more mature: PID control algorithm, artificial neural network control, fuzzy control, expert control, humanoid intelligent control. Second, the main method of motion control 1. PID control. PID control is one of the earliest developed and widely used control methods. It is a method based on mathematical model of object, especially suitable for deterministic control system that can establish accurate mathematical model. However, for nonlinear and time-varying uncertain systems, it is difficult to achieve the desired control effect with the conventional PID controller. Moreover, in actual production, due to the complicated parameter tuning method, conventional PID parameters tend to be poorly set and poor in performance. 2. Artificial neural network control. Artificial neural network originated in the 1940s, it reflects the basic characteristics of the human brain from some aspects, but it is not the true description of the human brain, but only its abstraction, simplification and simulation, network information processing by neurons To achieve the interaction. The key of neural network control is to select a suitable neural network model and train and learn it until it reaches the requirement, that is to find the optimal neural network structure and weight. However, neural network learning requires a certain amount of experimental samples, which must also be obtained from known and prior experiments. At the same time, the neural network's training and learning processes are sometimes complex and require thousands of runs to get the best possible structure. Sometimes obtained is a local optimal solution, rather than the global optimal solution, due to the limitations of the method, it is also difficult to achieve effective control of the object in question. 3. Fuzzy control. In the actual project, a very skilled operator, with his rich practical experience, can obtain more satisfactory control effects by judging the various phenomena in the field. Controlling complex industrial processes can also be achieved by translating empirically-based measures into appropriate control rules and developing a controller instead of these rules. Practice has proved that fuzzy controller based on fuzzy control theory (FC) can accomplish this task. Fuzzy control is based on the fuzzy reasoning and imitation of human thinking methods, difficult to establish a mathematical model of the object to implement a control. It uses fuzzy sets in fuzzy mathematics to characterize these vague languages ​​and uses production rules, that is, "if the conditions are true, then execute" statements. The application of fuzzy control technology in China has achieved remarkable results. 4. Expert control. Expert control is an important part of intelligent control. Based on the combination of the theory and technology of expert system with the theories and methods of control theory, the expert control is to imitate the intelligence of experts in the unknown environment and realize the effective control of the system. At the core of expert control is the expert system, which has the ability to deal with various non-structural problems, especially dealing with qualitative, heuristic or uncertain knowledge and information, and achieve various control objectives through various reasoning processes. 5. Humanoid intelligent control. After 20 years of hard work, humanoid intelligent control (HSIC) has formed a basic theoretical system and a systematic design method, and has achieved success in a large number of practical applications. Its main content is to summarize the experience of human control, imitating human control ideas and behaviors, and using generative rules to describe its control and intuitive reasoning behavior. Because the basic characteristic of HSIC is to imitate the control expert's control behavior, its control algorithm is multi-modal control, and it is used interchangeably among many modal control. The algorithm can perfectly coordinate the many conflicting control quality requirements in the control system. For example, robustness and accuracy, speed and stability. $$ Div $$ Third, the characteristics of water supply pump station and its control requirements In the development of urban construction, intelligent building has become a standard for people to pursue good living conditions, and water pump station is an indispensable link in the intelligent building complex, reasonable Select the pump control, not only can reduce project costs, but also energy. 1. Water supply system characteristics. For specific objects, the most prominent feature of the user's water is random, which user has water, how much water, when the water, etc., have great uncertainty. From a macro point of view, the characteristics of water supply system mainly in the following areas: (1) the unknown system parameters, time-varying, randomness and dispersion; (2) the system lag unknown and time-varying; (3) Serious non-linearity; (4) the correlation between system variables; and (5) the unknown, diversity and randomness of environmental interference. 2. Problems in control. The above characteristics belong to the control problem of the complex object (or process) with uncertainty. The traditional control is powerless, mainly in the following aspects: (1) Uncertainty problem. Many of the control problems in water systems are uncertain, difficult to model using traditional methods, and therefore fail to achieve effective control. (2) highly nonlinear. There are a lot of nonlinear problems in the water supply system. In the traditional control theory, the nonlinear theory is much less mature than the linear theory, because the method is too complicated and difficult to apply. (3) semi-structured and unstructured issues. Traditional control theory can not solve the problem of semi-structured and unstructured water supply system. (4) Water supply system complexity issues. Complex relationships between the various subsystems intricate, highly coupled between the various elements, mutual restraint, the external environment is extremely complex, the traditional control of the lack of effective solutions. (5) reliability issues. The conventional mathematical model-based control problem tends to be an interdependent one, and the reliability of the control of simple systems is not conspicuous. For the water supply system, if the above method is used, then (page 18) (on the next page 16) may be due to changes in the conditions of the entire control system collapse. Thus it can be seen that the traditional methods can not effectively control such systems and more effective control methods must be explored. 3. Control requirements. No matter what kind of control measures, we must meet the user's water needs (that is, to maintain a certain pressure), to protect the environment from noise pollution, in addition to consider energy saving. Therefore, the control requirements can be determined to meet the user's water supply requirements under the premise of minimizing environmental pollution and energy conservation. Fourth, the selection of control strategies Control strategy selection and the characteristics of the controlled object are closely related to the wrong or inappropriate control strategies often lead to very poor control, or even lead to out of control system. At present, in the modern water supply pump station in order to save energy are generally used in the inverter, to improve the quality of control to create a good condition. There are PID control modules in the inverter, but the PID algorithm is not suitable for the complicated system of water supply with uncertainty. Artificial neural networks, due to the limitations of the method, also have difficulty controlling the subject in question. The expert control system (ECS) is difficult to implement due to the collection of characteristic information, the expression of characteristic information and the establishment of a complete knowledge base. It is not always a good choice to adopt an expert control system. The fuzzy controller (FC) based on fuzzy control theory can realize the control of complex industrial processes. Its control of quality and effectiveness is still satisfactory, is an alternative strategy. Humanoid intelligent control, experts use HISC and FC control strategies to simulate the complex object (or process) with uncertainty respectively. Although both of them calculate control quantities based on error and error rate of change, HISC and FC can implement effective control, but the control quality and robustness of the former is better, so the use of HSIC to complete the uncertainty of the strong (strong coupling) characteristics of the field test, HISC and FC can implement effective control, The control of the water system is - a more sensible choice. V. CONCLUSION Intelligent control has been widely used in many fields such as industry, agriculture and military. It has solved a large number of practical control applications that can not be solved by traditional control and has shown great vitality and development prospects. With the development of basic theory and practice Application of the expansion of intelligent control will achieve a big leap in the field of control.