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fuzzy system for control of a cnc cutting machine.
by：Luteng CNC Parts
2020-09-19
Abstract: This paper aims to propose a complex system based on fuzzy logic for automatic selection of oxygen pressure CNC in tin plate cutting machine.
The thickness and type of the fuzzy system is analyzed and the required oxygen pressure plate and amperage are automatically adjusted, thus eliminating the error of human operation in the decision-making. Key words: fuzzy system, cutting, control, machine, pressure 1.
Today, working with machine tools is one of the most important activities to support industrial development.
CNC machine tools and machine tools are called CNC (
Computer numerical control).
The numerical control process consists of a specially designed continuous \"supply\" of programmable controllers with a set of instructions (
Composed of letters and numbers)
This will control the movement of the machine. -tool (Mares, 2001). [
Figure 1 slightly]
The Sick OD laser sensor is electric.
An optical sensor that can measure the distance on a flat surface or reflective object, using a specific process that avoids any direct contact.
The distance of the object is detected by the triangulation principle.
The directional laser beam constitutes a point on the object under test.
The image of the point is reflected in the position detector and the position is converted to the distance from the object (Breaz, 2001). 2.
The working principle of the tin plate cutting machine Zinser CNC 2010 we have built a complex system that improves accuracy and cutting quality based on fuzzy logic.
The device replaces the correct selection of oxygen pressure and current for different types of tin plates and their different thicknesses by human operators.
The differential sensor installed on the top track of the car automatically obtains the thickness of the plate.
The second signal on the typing board is determined by the operator and selected from the button on the console in the Zinser 2010 machine.
The fuzzy system is applied to two analog signals with a value of (0 . . . 5)
V, whose value depends on the thickness of the plate type.
Processing program based on two application signals and their implementation in fuzzy controller-
As the type of processing rule--
In the analog output, we obtain two continuous tension signals with a range (0 . . . 5)V (Tirian, 2009).
In a complex system based on fuzzy logic, the processing of two signals is digital and closed.
The set of rules we use is determined by a group of human experts and can be changed at any time (Precup, 1999)
Therefore, make sure that the system adapts to any operating conditions of a real CNC machine. 3.
Design of fuzzy controller
2 The scheme diagram of fuzzy controller is described. [
Figure 2:
We designed the fuzzy controller with Matlab.
We have also established language terms (
For input and output)
, Attribution function and rule base. A.
Information input dimensions we consider the following input dimensions: A1: for tin plate thickness :[
Figure 3 slightly]A2.
Plate material type :【
Figure 4 slightly]B.
Enter the size information (control)
We consider the following output size: b1.
Current control :[
Figure 5 Slightly]B2.
For oxygen pressure control :【
Figure 6 slightly]
C. Control Rules (inference)
After consulting the literature and experts in the field, the rules were established for practical reasons.
Figure 7 describes the reasoning table that connects fuzzy input variables to output variables--Above--using the max-
Minimum inference method.
De-process
Fuzzy control because of its main advantage is based on the weight center of the single case, that is, the small processing necessary condition for the real-time operation of the fuzzy controller.
Therefore, in order to actually apply the review, we have established a singleton type membership function corresponding to the number of output language terms \"control.
In practice, it is very common to use it with max
Min inference method and above
The result is a prominent performance of the control system (Titian, 2009). [
Figure 7 Slightly]
It should be noted that in the case of the \"plate thickness\" input size (small)
And \"type of material \"(steel)
The relay controls the cutting of the oxygen generator and switches it to the output of 0.
573, corresponding to a slightly larger pressure than 3 atmospheric pressures. 4.
Conclusion This paper presents a control board for complex equipment of CNC Zinser cutting machine.
Compared with other existing systems on the market, the system has several advantages.
These advantages are: in decisions involving age adjustment and oxygen pressure, the error of human operators is reduced;
Adapt to any real situation by modifying the rule base at a low price. 5.
Reference Breaz R. (2001).
Research Contribution of technical evaluation of precision machine tools and compensation errors, doctoral thesis, clj Napoca, 2001 Mares F. (2001).
\"Control and Command Elements\", edit Negro, Galati.
Romania, 2001 PreliminariesE & Preitl, St. (1999).
Fuzzy Controller, edit academic \"vision\", pp. 123-128, ISBN: 973-9400-61-
Timimisoara, Romania. O. , Anghel, S. & Pinca C. (2009).
The Fifth International Symposium on Applied Computing intelligence and informatics, May 28, on the process control system for eliminating crack continuous casting-29, pp. 265-269, ISBN: 978-1-4244-4478-
6 Timisoara, Romania, 2009 Tirian G. O. , O. Prostean, S. Rusu-Pinka B angleC, D. Cristea (2009).
Fuzzy system for the implementation of continuous casting crack control, DAAAM chronicles and 20 international seminar Records, Volume 20, No. 1, ISSN 1726-9679, ISBN 978-3-90150970-4, pp. 1661-1662, 25-
The thickness and type of the fuzzy system is analyzed and the required oxygen pressure plate and amperage are automatically adjusted, thus eliminating the error of human operation in the decision-making. Key words: fuzzy system, cutting, control, machine, pressure 1.
Today, working with machine tools is one of the most important activities to support industrial development.
CNC machine tools and machine tools are called CNC (
Computer numerical control).
The numerical control process consists of a specially designed continuous \"supply\" of programmable controllers with a set of instructions (
Composed of letters and numbers)
This will control the movement of the machine. -tool (Mares, 2001). [
Figure 1 slightly]
The Sick OD laser sensor is electric.
An optical sensor that can measure the distance on a flat surface or reflective object, using a specific process that avoids any direct contact.
The distance of the object is detected by the triangulation principle.
The directional laser beam constitutes a point on the object under test.
The image of the point is reflected in the position detector and the position is converted to the distance from the object (Breaz, 2001). 2.
The working principle of the tin plate cutting machine Zinser CNC 2010 we have built a complex system that improves accuracy and cutting quality based on fuzzy logic.
The device replaces the correct selection of oxygen pressure and current for different types of tin plates and their different thicknesses by human operators.
The differential sensor installed on the top track of the car automatically obtains the thickness of the plate.
The second signal on the typing board is determined by the operator and selected from the button on the console in the Zinser 2010 machine.
The fuzzy system is applied to two analog signals with a value of (0 . . . 5)
V, whose value depends on the thickness of the plate type.
Processing program based on two application signals and their implementation in fuzzy controller-
As the type of processing rule--
In the analog output, we obtain two continuous tension signals with a range (0 . . . 5)V (Tirian, 2009).
In a complex system based on fuzzy logic, the processing of two signals is digital and closed.
The set of rules we use is determined by a group of human experts and can be changed at any time (Precup, 1999)
Therefore, make sure that the system adapts to any operating conditions of a real CNC machine. 3.
Design of fuzzy controller
2 The scheme diagram of fuzzy controller is described. [
Figure 2:
We designed the fuzzy controller with Matlab.
We have also established language terms (
For input and output)
, Attribution function and rule base. A.
Information input dimensions we consider the following input dimensions: A1: for tin plate thickness :[
Figure 3 slightly]A2.
Plate material type :【
Figure 4 slightly]B.
Enter the size information (control)
We consider the following output size: b1.
Current control :[
Figure 5 Slightly]B2.
For oxygen pressure control :【
Figure 6 slightly]
C. Control Rules (inference)
After consulting the literature and experts in the field, the rules were established for practical reasons.
Figure 7 describes the reasoning table that connects fuzzy input variables to output variables--Above--using the max-
Minimum inference method.
De-process
Fuzzy control because of its main advantage is based on the weight center of the single case, that is, the small processing necessary condition for the real-time operation of the fuzzy controller.
Therefore, in order to actually apply the review, we have established a singleton type membership function corresponding to the number of output language terms \"control.
In practice, it is very common to use it with max
Min inference method and above
The result is a prominent performance of the control system (Titian, 2009). [
Figure 7 Slightly]
It should be noted that in the case of the \"plate thickness\" input size (small)
And \"type of material \"(steel)
The relay controls the cutting of the oxygen generator and switches it to the output of 0.
573, corresponding to a slightly larger pressure than 3 atmospheric pressures. 4.
Conclusion This paper presents a control board for complex equipment of CNC Zinser cutting machine.
Compared with other existing systems on the market, the system has several advantages.
These advantages are: in decisions involving age adjustment and oxygen pressure, the error of human operators is reduced;
Adapt to any real situation by modifying the rule base at a low price. 5.
Reference Breaz R. (2001).
Research Contribution of technical evaluation of precision machine tools and compensation errors, doctoral thesis, clj Napoca, 2001 Mares F. (2001).
\"Control and Command Elements\", edit Negro, Galati.
Romania, 2001 PreliminariesE & Preitl, St. (1999).
Fuzzy Controller, edit academic \"vision\", pp. 123-128, ISBN: 973-9400-61-
Timimisoara, Romania. O. , Anghel, S. & Pinca C. (2009).
The Fifth International Symposium on Applied Computing intelligence and informatics, May 28, on the process control system for eliminating crack continuous casting-29, pp. 265-269, ISBN: 978-1-4244-4478-
6 Timisoara, Romania, 2009 Tirian G. O. , O. Prostean, S. Rusu-Pinka B angleC, D. Cristea (2009).
Fuzzy system for the implementation of continuous casting crack control, DAAAM chronicles and 20 international seminar Records, Volume 20, No. 1, ISSN 1726-9679, ISBN 978-3-90150970-4, pp. 1661-1662, 25-
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