Bottle neck position x / y axis tangential precision control method for artificial intelligence based bottle making machine

Abstract

The application discloses an X / Y axis tangential precision control method for a bottle neck position of a bottle making machine based on artificial intelligence and relates to the technical field of automatic control. The application takes vibration intensity data as a first dependent variable, takes processing process parameters and processing material parameters as independent variables, constructs a processing process vibration model of the bottle neck position, obtains a vibration intensity prediction value, extracts material removal rate data completed in a unit time in a historical processing process as a quantitative representation value of processing efficiency, takes processing efficiency as a second dependent variable, takes processing process parameters and processing material parameters as independent variables, constructs a processing efficiency evaluation model of the bottle neck position, obtains a processing efficiency prediction value, takes minimum vibration intensity and maximum processing efficiency at any moment in the processing process as control targets, constructs a processing process optimization model, obtains optimal values of the processing process parameters, and realizes accurate control of X / Y axis movement states of the bottle neck position.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automation control technology, in particular to a method for controlling the tangential precision of the X / Y axis of a bottle neck station of a bottle making machine based on artificial intelligence. BACKGROUND

[0002] In a bottle making machine, the X / Y axis of the bottle neck station generally refers to the motion axis used for processing the bottle neck part. The X axis is usually used to control the feed motion of the processing tool (such as a cutter, a milling cutter, etc.) in the horizontal direction, mainly responsible for the radial processing of the bottle neck part; the Y axis is usually used to control the motion of the processing tool in the vertical direction, responsible for the axial processing of the bottle neck part. Through the coordinated motion of the X axis and the Y axis, complex shape processing of the bottle neck part can be achieved, such as the taper, thread, chamfer, etc. of the bottle neck. The motion precision and control ability of the X axis and the Y axis also directly affect the processing quality of the bottle neck part, including dimensional accuracy, shape accuracy, and surface quality, etc.

[0003] Currently, the main factors affecting the tangential precision of the X / Y axis of the bottle neck station of the bottle making machine include the following aspects:

[0004] 1) Mechanical system factors: Generally, the X axis and the Y axis adopt guide rail transmission mode, and the straightness, parallelism and surface quality of the guide rail directly affect the motion precision of the axis. When the guide rail is worn or deformed, it will cause the tangential precision to decrease; transmission system error: the pitch error of the ball screw, the reverse clearance and the precision of the transmission chain will affect the tangential precision of the X / Y axis; bearing precision: the manufacturing precision, assembly precision and wear during use of the bearing will affect the motion precision of the X / Y axis.

[0005] 2) Control system factors: numerical control system error: the software algorithm, parameter setting and feedback system precision in the control system will affect the tangential precision; servo motor performance: the response speed, torque fluctuation and encoder precision of the servo motor will affect the motion precision of the X / Y axis.

[0006] 3) Process system factors: process system stiffness: insufficient stiffness of the X / Y axis may cause deformation during processing, thereby affecting the tangential precision; influence of processing force: cutting force, clamping force and other forces may cause deformation of the mechanical system, thereby affecting the processing precision.

[0007] 4) Environmental factors: thermal deformation of mechanical parts: machine tools, guide rails, screws and other parts may expand due to heat during processing, which may cause the tangential precision of the X / Y axis to decrease; environmental temperature changes: in addition, fluctuations in the temperature of the workshop environment may also cause thermal deformation of mechanical parts.

[0008] 5) Other factors: assembly and adjustment errors: the assembly accuracy of X / Y axis, the adjustment accuracy and the installation accuracy of the tool will affect the tangential accuracy; tool wear: the wear of the tool during use will cause the change of the processing size, thereby affecting the tangential accuracy of the bottleneck part.

[0009] For the main influencing factors of the X / Y axis tangential accuracy of the bottle neck part station of the bottle making machine, the following solutions are proposed in the prior art: for the optimization measures of the mechanical system, the straightness and parallelism of the guide rail can be checked regularly, and the worn guide rail parts can be replaced in time. The accuracy and wear of the ball screw and bearing in the transmission system are detected regularly, and are replaced if necessary. The structure design of X / Y axis is optimized, the rigidity of mechanical parts is increased, and the deformation in the processing process is reduced. For the adjustment measures of the control system, the parameters of the servo motor such as gain, resonance suppression and load inertia can be adjusted to improve the response speed and stability of the system. By adopting a full closed loop control system, high precision linear grating ruler is installed to feedback the position information of the shaft in real time, and the tangential accuracy is further improved. For the solution measures of thermal deformation control, the equipment and environmental temperature is monitored during processing, the thermal deformation compensation is carried out through the control system, the influence of temperature change on processing accuracy is reduced, and the stability of workshop temperature is maintained to avoid the deformation of mechanical parts caused by environmental temperature fluctuation. The above-mentioned solutions for the decrease of X / Y axis tangential accuracy caused by mechanical system factors, control system factors and environmental factors have achieved good application effect in actual production process, and the X / Y axis tangential accuracy is improved to a certain extent.

[0010] However, in the prior art, the optimization measures for the process are generally to optimize the cutting speed, feed rate and cutting depth according to the processing requirements of the bottleneck part, so as to reduce the vibration in the processing process and improve the tangential accuracy, and there is a lack of process optimization control method considering the comprehensive multi-factor influence mechanism. Since the determination of processing parameters depends on many factors such as processing material characteristics, processing requirements, processing position and tool parameters, the processing parameters obtained by analyzing only a single processing requirement factor are difficult to achieve the expected precision control effect. Therefore, we propose a bottle neck part station X / Y axis tangential accuracy control method based on artificial intelligence. SUMMARY

[0011] The main purpose of the present application is to provide a bottle neck part station X / Y axis tangential accuracy control method based on artificial intelligence, which can effectively solve the problems in the background art.

[0012] To achieve the above purpose, the technical solution adopted by the present application is,

[0013] The bottle neck part station X / Y axis tangential accuracy control method based on artificial intelligence comprises:

[0014] Step one: obtaining historical data of machining process parameters and machining material parameters of the bottleneck work station.

[0015] The machining process parameters include cutting speed, feed rate and cutting depth; the machining material parameters include material hardness, material elastic modulus and material thermal conductivity.

[0016] Step two: extracting vibration intensity data in the historical machining process, taking the vibration intensity data as the first dependent variable, taking the machining process parameters and machining material parameters as the independent variables, constructing a machining process vibration model of the bottleneck work station, and obtaining a vibration intensity prediction value V vib from the machining process vibration model.

[0017] The expression of the machining process vibration model is:

[0018]

[0019] In the formula, Vc is the cutting speed, the unit is m / min; f is the feed rate, the unit is mm / r; a p is the cutting depth, the unit is mm; H is the hardness value of the machining material, the unit is N / mm 2 ; E is the elastic modulus of the machining material, the unit is GPa; k is the thermal conductivity of the machining material, the unit is W / m·K; θ1 is the vibration correction coefficient of the cutting speed; θ2 is the vibration correction coefficient of the feed rate; θ3 is the vibration correction coefficient of the cutting depth; θ4 is the vibration correction coefficient of the material hardness; θ5 is the vibration correction coefficient of the material elastic modulus; θ6 is the vibration correction coefficient of the material thermal conductivity.

[0020] Step three: extracting the material removal rate data completed per unit time in the historical machining process as the quantitative representation value of the machining efficiency, taking the machining efficiency as the second dependent variable, taking the machining process parameters and machining material parameters as the independent variables, constructing a machining efficiency evaluation model of the bottleneck work station, and obtaining a machining efficiency prediction value η from the machining efficiency evaluation model.

[0021] The expression of the machining efficiency evaluation model is:

[0022] η = Vc·f·a p ·α(H)·γ(E)·μ(material type)

[0023] In the formula, Vc is the cutting speed, the unit is m / min; f is the feed rate, the unit is mm / r; a p is the cutting depth, the unit is mm; μ(material type) is the material type correction coefficient; α(H) is the efficiency correction coefficient of the material hardness; γ(E) is the efficiency correction coefficient of the material elastic modulus;

[0024] The calculation formula of the efficiency correction coefficient α(H) of the material hardness is: wherein, β is a constant coefficient; H is the hardness value of the processed material, in units of N / mm 2 ;

[0025] The calculation formula of the efficiency correction coefficient γ(E) of the material elastic modulus is: wherein, δ is a constant coefficient; E is the elastic modulus of the processed material, in units of GPa;

[0026] The material type correction coefficient μ(material type) is determined according to the material type, wherein the material type includes glass, plastic, metal and ceramic;

[0027] When the material type is plastic, the material type correction coefficient μ(material type) takes [0.75, 1];

[0028] When the material type is glass, the material type correction coefficient μ(material type) takes [0.5, 0.8];

[0029] When the material type is metal, the material type correction coefficient μ(material type) takes [0.35, 0.65];

[0030] When the material type is ceramic, the material type correction coefficient μ(material type) takes [0.25, 0.5].

[0031] Step four: taking obtaining the minimum vibration intensity and the maximum processing efficiency at any time during the processing process as the control target, a processing process optimization model is constructed;

[0032] The expression of the processing process optimization model is:

[0033]

[0034] In the formula, F is the objective function; st. is the constraint condition of the objective function; is the weight coefficient of the vibration intensity in the objective function F, is the weight coefficient of the processing efficiency in the objective function F, and is the minimum value of the calculation result; is the minimum value of the calculation result; is the vibration intensity in the processing process, under the conditions that the cutting speed is Vc, the feed amount is f, the cutting depth is a p , the hardness value of the processed material is H, the elastic modulus of the processed material is E, and the thermal conductivity of the processed material is k. is the machining efficiency under the condition that the cutting speed is Vc, the feed amount is f, the cutting depth is a p , the hardness value of the machining material is H, the elastic modulus of the machining material is E, and the thermal conductivity of the machining material is k; Vc min,i is the lower limit of the cutting speed of the i-th machining material; Vc max,i is the upper limit of the cutting speed of the i-th machining material; f min,i is the lower limit of the single machining feed amount of the i-th machining material; f max,i is the upper limit of the single machining feed amount of the i-th machining material; a pmin,i is the lower limit of the single machining cutting depth of the i-th machining material; a pmax,i is the upper limit of the single machining cutting depth of the i-th machining material; E i is the elastic modulus of the i-th machining material; E max is the maximum value of the elastic modulus of the machining material that can be machined by the bottle neck position of the bottle making machine; H i is the hardness value of the i-th machining material; H max is the maximum value of the hardness value of the machining material that can be machined by the bottle neck position of the bottle making machine; k i is the thermal conductivity of the i-th machining material; k max is the maximum value of the thermal conductivity of the machining material that can be machined by the bottle neck position of the bottle making machine.

[0035] Step five: obtaining the optimal value of the machining process parameter by using the machining process optimization model, and controlling the X / Y axis movement state of the bottle neck position according to the obtained optimal value of the machining process parameter.

[0036] The present application has the following beneficial effects,

[0037] Compared with the prior art, the historical data of the machining process parameters and the machining material parameters of the bottleneck part station are acquired, the vibration intensity data in the historical machining process is extracted, the vibration intensity data is taken as the first dependent variable, the machining process parameters and the machining material parameters are taken as the independent variables, the machining process vibration model of the bottleneck part station is constructed, the vibration intensity prediction value is acquired by using the machining process vibration model, the material removal rate data completed per unit time in the historical machining process is extracted as the quantitative representation value of the machining efficiency, the machining efficiency is taken as the second dependent variable, the machining process parameters and the machining material parameters are taken as the independent variables, the machining efficiency evaluation model of the bottleneck part station is constructed, the machining efficiency prediction value is acquired by using the machining efficiency evaluation model, the minimum vibration intensity and the maximum machining efficiency at any moment in the machining process are taken as the control targets, the machining process optimization model is constructed, the optimal value of the machining process parameter is acquired by using the machining process optimization model, and the X / Y axis movement state of the bottleneck part station is controlled according to the acquired optimal value of the machining process parameter, the technical scheme of the present application is based on artificial intelligence technology, the historical machining data is learned, and the optimization model is constructed according to the learning result, the influence mechanism of multiple factors of tangential accuracy can be analyzed comprehensively, so that the machining parameters such as cutting speed, feed rate and cutting depth are optimized, and then the vibration in the machining process is reduced, so that the machining process reaches the expected precision control effect, and the machining efficiency is improved as much as possible on the basis of ensuring the machining effect. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The figure is a flowchart of the bottle neck part station X / Y axis tangential accuracy control method based on artificial intelligence of the present application. DETAILED DESCRIPTION

[0039] The present application will be further described below in combination with specific embodiments, wherein the drawings are only used for exemplary description, and the representation is only a schematic diagram, not a physical diagram, and cannot be understood as a limitation on the present application, in order to better illustrate the specific embodiments of the present application, some components of the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0040] The specific implementation process of the technical scheme of the present application includes the following steps:

[0041] Step 1: Acquire the historical data of the machining process parameters and the machining material parameters of the bottleneck part station. The machining process parameters include cutting speed, feed rate and cutting depth; the machining material parameters include material hardness, material elastic modulus and material thermal conductivity.

[0042] Step 2: Extract the vibration intensity data in the historical processing process, take the vibration intensity data as the first dependent variable, and take the processing process parameters and processing material parameters as the independent variables to construct a processing process vibration model of the bottleneck position, and use the processing process vibration model to obtain a vibration intensity prediction value V vib ; wherein the expression of the processing process vibration model is:

[0043]

[0044] In the formula, Vc is the cutting speed, the unit is m / min; f is the feed amount, the unit is mm / r; a p is the cutting depth, the unit is mm; H is the hardness value of the processing material, the unit is N / mm 2 ; E is the elastic modulus of the processing material, the unit is GPa; k is the thermal conductivity of the processing material, the unit is W / m·K; θ1 is the vibration correction coefficient of the cutting speed; θ2 is the vibration correction coefficient of the feed amount; θ3 is the vibration correction coefficient of the cutting depth; θ4 is the vibration correction coefficient of the material hardness; θ5 is the vibration correction coefficient of the material elastic modulus; θ6 is the vibration correction coefficient of the material thermal conductivity.

[0045] It should be noted that for each correction coefficient in the processing process vibration model, the specific value can be obtained by fitting the historical data. Specifically, by collecting historical data, including cutting parameters, material characteristics and corresponding vibration intensity. The data can be obtained by experimental measurement or production record, and then the collected data is arranged and recorded in the following table, as shown in Table 1:

[0046]

[0047] Table 1: Statistical table of vibration model fitting data

[0048] In Table 1, Vc k , f k , a pk , H k , E k and V vibk represent the cutting speed, feed amount, cutting depth, hardness value of the processing material, elastic modulus of the processing material and vibration intensity data collected in the same processing process k times.

[0049] After the data is arranged, data processing software such as MATLAB software can be used to fit the obtained data, so as to obtain a mathematical model for describing the relationship between vibration intensity and cutting parameters and material characteristics, that is, the processing process vibration model in the present scheme.

[0050] Through the above steps, the vibration intensity generated in the machining process can be quantitatively calculated, and then the vibration intensity can be reduced by controlling various machining process parameters and machining material parameters, so that the machining process is more stable, and the machining precision can be improved.

[0051] Step 3: Extract the material removal rate data completed per unit time in the historical machining process as a quantitative representation value of machining efficiency, take the machining efficiency as the second dependent variable, and take the machining process parameters and machining material parameters as the independent variables to construct a machining efficiency evaluation model of the bottleneck station, and obtain the machining efficiency prediction value η by using the machining efficiency evaluation model. The expression of the machining efficiency evaluation model is:

[0052] η=Vc·f·a p ·α(H)·γ(E)·μ(material type)

[0053] In the formula, Vc is the cutting speed, the unit is m / min; f is the feed rate, the unit is mm / r; a p is the cutting depth, the unit is mm; μ(material type) is the material type correction coefficient; α(H) is the efficiency correction coefficient of material hardness; γ(E) is the efficiency correction coefficient of material elastic modulus;

[0054] It should be noted that the machining efficiency is not only affected by the machining process parameters, i.e. cutting speed, feed rate and cutting depth, but also affected by the material type, material hardness, material elastic modulus and other factors. The higher the material hardness, the lower the cutting speed or feed rate is required to avoid tool wear and vibration, so the machining efficiency is lower. The material with higher elastic modulus usually requires higher cutting force, which may require lower cutting depth or feed rate, so the machining efficiency is lower. In addition, for different machining materials, due to the difference in material quality, the hardness and elastic modulus are different, so they also affect the machining efficiency. Therefore, the efficiency correction coefficient α(H) of material hardness, the efficiency correction coefficient γ(E) of material elastic modulus and the material type correction coefficient μ(material type) are introduced for correction. Specifically:

[0055] The calculation formula of the efficiency correction coefficient α(H) of material hardness is: Wherein, β is a constant coefficient; H is the hardness value of the machining material, the unit is N / mm 2 ;

[0056] The calculation formula of the efficiency correction coefficient γ(E) of material elastic modulus is: Wherein, δ is a constant coefficient; E is the elastic modulus of the machining material, the unit is GPa;

[0057] The material type correction coefficient μ(material type) is determined according to the material type, wherein the material type includes glass, plastic, metal and ceramic; when the material type is plastic, the material type correction coefficient μ(material type) takes [0.75, 1]; when the material type is glass, the material type correction coefficient μ(material type) takes [0.5, 0.8]; when the material type is metal, the material type correction coefficient μ(material type) takes [0.35, 0.65]; and when the material type is ceramic, the material type correction coefficient μ(material type) takes [0.25, 0.5].

[0058] Through the above steps, the machining efficiency in the machining process can be quantitatively evaluated, so as to improve the machining efficiency on the basis of ensuring the machining precision, and more beneficial to improve the enterprise benefit.

[0059] Step 4: Taking the minimum vibration intensity and the maximum machining efficiency at any time in the machining process as the control target, a machining process optimization model is constructed; wherein the expression of the machining process optimization model is:

[0060]

[0061] In the formula, F is a target function; st. is a constraint condition of the target function; is a weight coefficient of the vibration intensity in the target function F, is a weight coefficient of the machining efficiency in the target function F, and is the minimum value of the calculation result; is the vibration intensity in the machining process under the conditions that the cutting speed is Vc, the feed amount is f, the cutting depth is a p , the hardness value of the machining material is H, the elastic modulus of the machining material is E, and the thermal conductivity of the machining material is k; is the machining efficiency in the machining process under the conditions that the cutting speed is Vc, the feed amount is f, the cutting depth is a p , the hardness value of the machining material is H, the elastic modulus of the machining material is E, and the thermal conductivity of the machining material is k; Vc min,i is the lower limit of the cutting speed of the i-th machining material; Vc max,i is the upper limit of the cutting speed of the i-th machining material; f min,i is the lower limit of the single machining feed amount of the i-th machining material; f max,i is the upper limit of the single machining feed amount of the i-th machining material; a pmin,i is the lower limit of the single machining cutting depth of the i-th machining material; a pmax,i ​is the upper limit of single machining cutting depth of the i th machining material; E i is the elastic modulus of the i th machining material; E max is the maximum value of the elastic modulus of the machining material that can be processed by the bottle neck part station of the bottle making machine; H i is the hardness value of the i th machining material; H max is the maximum value of the hardness value of the machining material that can be processed by the bottle neck part station of the bottle making machine; k i is the thermal conductivity of the i th machining material; k max is the maximum value of the thermal conductivity of the machining material that can be processed by the bottle neck part station of the bottle making machine.

[0062] By constructing the machining process optimization model in the improvement step, the multi-factor influence mechanism of tangential accuracy can be analyzed comprehensively, so that the machining parameters such as cutting speed, feed rate and cutting depth can be optimized, thereby reducing the vibration in the machining process, making the machining process achieve the expected accuracy control effect, and at the same time, on the basis of ensuring the machining effect, the machining efficiency is improved as much as possible.

[0063] Step 5: Obtain the optimal value of the machining process parameters by using the machining process optimization model, control the X / Y axis motion state of the bottle neck part station according to the obtained optimal value of the machining process parameters, and obtain the optimal machining effect.

[0064] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based method for controlling the X / Y axis tangential precision of the bottleneck station of a bottle making machine, characterized in that: include: Step 1: Obtain historical data of the processing parameters and processing material parameters of the bottleneck station; Step 2: Extract the vibration intensity data from the historical processing process, use the vibration intensity data as the first dependent variable, and use the processing parameters and processing material parameters as independent variables to build a processing vibration model for the bottleneck workstation, and use the processing vibration model to obtain the vibration intensity prediction value V vib ; Step 3: Extracting the material removal rate data per unit time during the historical processing as a quantitative representation of the processing efficiency, using the processing efficiency as the second dependent variable and the processing parameters and processing material parameters as independent variables, constructing a processing efficiency evaluation model for the bottleneck workstation, and using the processing efficiency evaluation model to obtain a processing efficiency prediction value η; Step 4: Taking the minimum vibration intensity and maximum processing efficiency at any time during the processing as the control target, a processing process optimization model is constructed. The expression of the machining process optimization model is: Where F is the objective function; st. is the constraint condition of the objective function; is the weight coefficient of vibration intensity in the objective function F, is the weight coefficient of processing efficiency in the objective function F, and To obtain The minimum value of the calculation result; In the machining process, the cutting speed is Vc, the feed rate is f, and the cutting depth is a p , the vibration intensity when the hardness value of the processing material is H, the elastic modulus of the processing material is E, and the thermal conductivity of the processing material is k; In the machining process, the cutting speed is Vc, the feed rate is f, and the cutting depth is a p , the processing efficiency when the hardness value of the processing material is H, the elastic modulus of the processing material is E, and the thermal conductivity of the processing material is k; Vc min,i Vc is the lower limit of cutting speed for the i-th processing material; max,i is the upper limit of cutting speed for the i-th processing material; f min,i is the lower limit of the single processing feed of the i-th processing material; f max,i is the upper limit of the single processing feed rate of the i-th processing material; is the lower limit of the cutting depth of a single machining of the i-th machining material; is the upper limit of the cutting depth of a single machining of the i-th machining material; E i is the elastic modulus of the i-th processed material; E max H is the maximum elastic modulus of the material that can be processed by the bottle neck station of the bottle making machine; i is the hardness value of the i-th processed material; H max k is the maximum hardness value of the material that can be processed by the bottle neck station of the bottle making machine; i is the thermal conductivity of the i-th processed material; k max is the maximum thermal conductivity of the material that can be processed by the bottle neck station of the bottle making machine; Step 5: Utilize the processing optimization model to obtain optimal values ​​of processing parameters, and control the X / Y axis motion state of the bottleneck station according to the obtained optimal values ​​of processing parameters.

2. The artificial intelligence-based X / Y-axis tangential precision control method for the bottle neck station of a bottle making machine according to claim 1 is characterized in that: The processing parameters include cutting speed, feed rate and cutting depth; the processing material parameters include material hardness, material elastic modulus and material thermal conductivity.

3. The artificial intelligence-based X / Y-axis tangential precision control method for the bottle neck station of a bottle making machine according to claim 1 is characterized in that: The expression of the vibration model of the machining process is: Where Vc is the cutting speed in m / min; f is the feed rate in mm / r; a p is the cutting depth in mm; H is the hardness of the processed material in N / mm 2 ; E is the elastic modulus of the processing material, unit is GPa; k is the thermal conductivity of the processing material, unit is W / m·K; θ1 is the vibration correction coefficient of cutting speed; θ2 is the vibration correction coefficient of feed rate; θ3 is the vibration correction coefficient of cutting depth; θ4 is the vibration correction coefficient of material hardness; θ5 is the vibration correction coefficient of material elastic modulus; θ6 is the vibration correction coefficient of material thermal conductivity.

4. The artificial intelligence-based X / Y-axis tangential precision control method for the bottle neck station of a bottle making machine according to claim 1 is characterized in that: The expression of the processing efficiency evaluation model is: η=Vc·f·a p ·α(H)·γ(E)·μ(material type) Where Vc is the cutting speed in m / min; f is the feed rate in mm / r; a p is the cutting depth in mm; μ(material type) is the material type correction factor; α(H) is the efficiency correction factor of material hardness; γ(E) is the efficiency correction factor of material elastic modulus.

5. The artificial intelligence-based X / Y-axis tangential precision control method for the bottle neck station of a bottle making machine according to claim 4 is characterized in that: The calculation formula of the efficiency correction coefficient α(H) of material hardness is: Where β is the constant coefficient; H is the hardness value of the processed material, the unit is N / mm 2 ; The calculation formula of the efficiency correction coefficient γ(E) of the material elastic modulus is: Where δ is a constant coefficient and E is the elastic modulus of the processed material, in GPa.

6. The artificial intelligence-based X / Y-axis tangential precision control method for the bottle neck station of a bottle making machine according to claim 4 is characterized in that: The material type correction coefficient μ (material type) is determined according to the material type, wherein the material type includes glass, plastic, metal and ceramic.

7. The artificial intelligence-based X / Y-axis tangential precision control method for the bottle neck station of a bottle making machine according to claim 4, characterized in that: When the material type is plastic, the material type correction coefficient μ(material type) is [0.75, 1]; When the material type is glass, the material type correction coefficient μ(material type) is [0.5, 0.8]; When the material type is metal, the material type correction coefficient μ(material type) is [0.35, 0.65]; When the material type is ceramic, the material type correction coefficient μ(material type) is [0.25, 0.5].

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