Self-rotating knife-to-knife method based on a digital die-cutting machine

Abstract

The present application relates to the technical field of knife alignment, and particularly relates to a self-rotating knife alignment method based on a digital die-cutting machine, which performs trial cutting on a blank area of a material to be cut, uses cutting force to guide the rotation of a blade of the self-rotating knife to a fixed direction, controls the movement of the blade of the self-rotating knife by using a multi-section speed control mode, so that the blade of the self-rotating knife contacts a knife alignment sensor at a predetermined position and trajectory, collects N times of knife alignment values to calculate mean square error values, eliminates abnormal knife alignment values whose mean square error values are greater than a set threshold, calculates a mean value of the remaining knife alignment values to obtain a stable knife alignment value, calculates a target function by using a weighted least square formula on sample knife alignment values formed by K times of stable knife alignment values, calculates an error compensation value according to the difference between an ideal knife alignment value and an optimal cutting depth, and automatically adjusts the cutting depth of all knives in actual cutting by using the compensation value. The present application solves the problem of poor automatic knife alignment accuracy, realizes automatic adjustment and compensation of the cutting depth of the self-rotating knife, and greatly improves the debugging efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of knife alignment, and in particular to a self-rotating knife alignment method based on a digital die-cutting machine. BACKGROUND

[0002] In the actual application scenario of a digital die-cutting machine, a self-rotating knife serves as the main cutting tool and undertakes the important task of accurately cutting various materials. However, the current self-rotating knife alignment technology based on a digital die-cutting machine has many problems, which seriously restricts the production efficiency and cutting accuracy. Due to the unique working characteristics of the self-rotating knife, the blade will automatically rotate to the cutting direction during the cutting process due to the contact force with the material. This results in the direction of the blade being in an uncertain state, and the blade tip point may appear at any position on the circular arc with the knife shaft as the center and the distance from the blade tip to the shaft as the radius. The existing contact type alignment sensor has a circular plane with a diameter of about 5 mm, and the contact point between the sensor and the blade tip is only a point on the blade tip. During the contact between the blade tip and the sensor, even a very small difference in the contact position will directly affect the alignment accuracy, making it impossible to achieve the high-precision standard of plus or minus 1 micrometer of the sensor. In addition, the cutting operation will inevitably cause the blade to wear, which requires frequent adjustment of the cutting depth. In the multi-blade working mode, each blade needs to be adjusted separately, which not only has extremely low efficiency, but also has a complex and difficult adjustment process. This inefficient alignment and frequent cutting depth adjustment seriously affect the overall working efficiency of the digital die-cutting machine, increase the production cost, and an effective self-rotating knife alignment method is urgently needed to solve these problems and improve the working performance of the digital die-cutting machine. SUMMARY

[0003] The present application aims to solve the above-mentioned defects and provides a self-rotating knife alignment method based on a digital die-cutting machine.

[0004] In order to overcome the defects in the background art, the technical solution adopted by the present application to solve its technical problems is as follows: a self-rotating knife alignment method based on a digital die-cutting machine, the specific steps are as follows:

[0005] S1: performing trial cutting in the blank area of the material to be cut, and using the cutting force to guide the blade of the self-rotating knife to rotate to a fixed direction;

[0006] S2: controlling the movement of the blade of the self-rotating knife using a multi-section speed control method, so that the blade of the self-rotating knife contacts the alignment sensor at a predetermined position and trajectory;

[0007] S3: collecting N times of alignment values, and calculating the mean square error value σ i , and eliminating the mean square error value σ iThe abnormal tool setting value greater than the set threshold realizes a sliding window function, and the remaining tool setting values are averaged to obtain a stable tool setting value.

[0008] S4: Collect K stable tool setting values to form a sample tool setting value d, and calculate a target function Q of the sample tool setting value d by a weighted least squares formula, the weighted least squares formula being:

[0009]

[0010] Wherein, Q is a target function, K is the number of stable tool setting tests, w i is the weight value of each term, d i is the i-th stable tool setting value; dl is an ideal tool setting value, the weight w i is 0 or 1, and the ideal tool setting value dl that minimizes Q is solved after removing abnormal points in the interval by an abnormal point removal algorithm.

[0011] S5: The optimal cutting depth d' is obtained by repeated trial cutting, and the error compensation value d err is calculated according to the difference between the ideal tool setting value dl and the optimal cutting depth d', and the cutting depth of all tools is automatically adjusted by the compensation value in actual cutting.

[0012] Further improvement, including in step S2, the control mode of multiple speeds is to control the blade on the tool to move at a speed of V1, and after moving to the tool setting position above the tool setting sensor, immediately decelerate to stop, and then the blade moves away from the tool setting sensor at a speed of V2 in the opposite direction, and then accurately positions to the tool setting position at a speed of V3 until it contacts the tool setting sensor, V1>V2>V3.

[0013] Further improvement, including in step S3, the mean square deviation σ i , specifically:

[0014]

[0015] Wherein, σ i is the mean square deviation of the i-th to the last tool setting value; N is the total number of i-th to the last times; A i is the i-th tool setting value; is the average of the i-th to the last tool setting values.

[0016] Further improvement, including in step S4, the number K is 20 times.

[0017] Further improvement, including in step S4, the abnormal point removal algorithm interval described, specifically:

[0018]

[0019] Wherein: d: the arithmetic mean of sample alignment values, d i : the i-th stable alignment value, K: the number of stable alignment tests.

[0020] Further improvement, including the weight W of the weighted least squares method in step S4 i Distributed by the following rules:

[0021] If d i is in the abnormal point rejection algorithm interval, then w i = 1;

[0022] Otherwise w i = 0, and the corresponding alignment value is rejected.

[0023] The beneficial effects of the present application are: the design adopts a multi-section speed control mode, which can accurately control the contact process of the blade and the alignment sensor, and reduce the alignment error caused by the difference in contact position. At the same time, by collecting N times of alignment values and calculating the mean square deviation σi, and rejecting the abnormal alignment value whose σi is greater than the set threshold, the interference factors in the alignment process can be effectively eliminated, a more accurate alignment result can be estimated, and the problem of low alignment accuracy caused by uncertain blade tip position and contact position difference is avoided, so that the actual alignment accuracy is controlled within 6 μm;

[0024] The design obtains stable alignment values of the rotating cutter, calculates the target function Q by the weighted least squares formula after forming the sample alignment values, and rejects the abnormal points by the abnormal point rejection algorithm interval, to solve the ideal alignment value dl that makes Q minimum. According to the error compensation value calculated by the difference between the ideal alignment value dl and the optimal cutting depth d', the cutting depth of all cutters is automatically adjusted. This way realizes batch adjustment of the cutting depth of the cutters, greatly improves the debugging efficiency, and reduces the workload and complexity of manual debugging. The efficient alignment and automatic adjustment of the cutting depth of the design reduces the time waste and material loss caused by low alignment accuracy and low debugging efficiency, and reduces the production cost. BRIEF DESCRIPTION OF DRAWINGS

[0025] The present application will be further described below in conjunction with the drawings and examples.

[0026] Figure 1 is the alignment step of the blade in the present application;

[0027] Figure 2 is the step of obtaining stable alignment values in the present application;

[0028] Figure 3 is the multi-section speed control mode in the present application; DETAILED DESCRIPTION

[0029] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art without creative work on the basis of the embodiments in the basic application also belong to the scope of protection of the present application.

[0030] As a kind of precision machining equipment, the precision of cutting tool system of digital die-cutting machine plays a decisive role in processing quality, digital die-cutting machine is equipped with multiple cutting tools, commonly configured 8 knives, each knife needs to be independently and accurately adjusted cutting depth in actual work, which is the key to ensure that it can adapt to different thickness, material of cutting material, to realize high quality, high precision cutting effect. To achieve this purpose, digital die-cutting machine mainly completes tool setting operation by installing a high-precision tool setting instrument on the platform, for example, contact sensor, when starting tool setting process, control system will send instructions to each knife in turn, drive tool to descend close to tool setting instrument. At the moment when tool contacts tool setting instrument, tool setting instrument will quickly capture and record relevant data, through a series of complex and accurate operations, the tool depth value of each knife is measured, these tool depth values are fed back to control system in real time, and control system further processes and analyzes data, and converts tool depth value into accurate cutting depth parameter.

[0031] A self-rotating knife tool setting method based on digital die-cutting machine, the specific steps are as follows:

[0032] S1: trial cutting is carried out in the blank area of the material to be cut, the cutting force is used to guide the rotation of the blade of the self-rotating knife to a fixed direction, due to the characteristics of the self-rotating knife, it will rotate to the cutting direction after contacting the cutting material, and the change of the position of the blade tip will cause a large tool setting error, therefore, the blade needs to be guided to set the blade tip at a fixed position for tool setting;

[0033] S2: a multi-section speed control mode is used to control the movement of the blade on the tool, so that the blade on the tool contacts the tool setting sensor at a predetermined position and trajectory, including high-speed approach to the tool setting point, deceleration stop, reverse movement out of the tool setting point and accurate positioning of the tool setting position;

[0034] S3: reference Figure 2 , collect N times of tool setting values {A1, A2,..., A N}, calculate mean square error value σ i of tool setting value, eliminate abnormal tool setting values with mean square error value σ i Greater than the set threshold value to realize sliding window function, and the mean value of the remaining tool setting values is obtained to obtain stable tool setting value;

[0035] S4: Collect K stable tool setting values to form a sample tool setting value d, and calculate a target function Q of the sample tool setting value d by a weighted least square formula, the weighted least square formula being:

[0036]

[0037] wherein Q is the target function, K is the number of stable tool setting tests, w i is the weight value of each term, d i is the i-th stable tool setting value; dl is the ideal tool setting value, the weight w i is 0 or 1, and the ideal tool setting value dl that minimizes Q is solved after removing abnormal points in the interval by an abnormal point removal algorithm.

[0038] The implementation is as shown in Table 1 below:

[0039]

[0040]

[0041] Table 1

[0042] The effective data range is (6.351881, 6.352619), and the approximate value is (6.351, 6.353), so there are 14 effective data, and the average value of the final effective data is 6.352, which is the ideal tool setting value.

[0043] S5: The optimal cutting depth d' can be obtained by repeated trial cutting, and the error compensation value is calculated according to the difference between the ideal tool setting value dl and the optimal cutting depth d': d err = d'-dl, and the cutting depth of all tools is automatically adjusted by the compensation value in actual cutting, and the compensation value is saved as a mechanical parameter in the machine.

[0044] Reference Figure 1 In step S2, the multi-section speed control mode is to control the blade on the tool to move at a speed V1, and immediately slow down to stop after moving to the tool setting position above the tool setting sensor, and then the blade moves away from the tool setting sensor in the opposite direction at a speed V2, and then accurately positions to the tool setting position at a speed V3 until it contacts the tool setting sensor, reference Figure 3 , speed V1> speed V2> speed V3.

[0045] In step S3, the mean square deviation σ i , specifically:

[0046]

[0047] wherein σ i is the mean square deviation of the i-th to the last tool setting value; N is the total number of times from the i-th to the last; Ai is the i-th time tool setting value; is the average of the i-th to the last time tool setting value, through this step, the error of tool setting accuracy can be eliminated, and the most accurate tool setting result is estimated.

[0048] The following table 2 is implemented: in order to ensure efficiency during cutting, the number of tool settings is 3-5 times:

[0049] Serial number 1 2 3 4 5 Tool setting value 6.349 6.355 6.356 6.354 6.353 Mean value 6.349 6.352 6.3555 6.355 6.3545 Variance Untreated 0.000018 0.0000005 0.000002 0.000005 Mean square deviation Untreated 0.000009 0.00000025 0.00000066 0.00000125 Result Untreated Need to discard the first Effective Effective Effective

[0050] Table 2

[0051] According to the test, the set mean square error threshold 0.000005 can filter out the first time, and then the stable tool setting value 6.355 is obtained.

[0052] In step S4, the number K is preferably 20, and the number K can also be more than 20.

[0053] The outlier rejection algorithm interval described in step S4 is as follows:

[0054]

[0055] Among them: : the arithmetic mean of the sample tool setting value, d i : the i-th stable tool setting value, K: the number of stable tool setting tests.

[0056] The weight w of the weighted least squares method described in step S4 i is allocated by the following rules:

[0057] If d i is in the outlier rejection algorithm interval, then w i =1;

[0058] Otherwise, w i =0, indicating that d i is determined as an outlier, the corresponding tool setting value is rejected, and does not participate in the minimization process of Q, w i The essence is an identifier of data validity, which is used for dynamically filtering abnormal data in calculation, and step 3 is used to calculate the true tool cutting depth.

[0059] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. A self-turning knife-to-knife method based on a digital die cutting machine, characterized by, The specific steps are as follows: S1: test cutting is performed on a blank area of the material to be cut, and a cutting force is used to guide the rotation of the blade of the self-rotating cutter to a fixed direction; S2: a multi-section speed control mode is used to control the movement of the blade of the self-rotating cutter, so that the blade of the self-rotating cutter contacts the tool setting sensor at a predetermined position and trajectory; S3: Collect N tool setting values ​​and calculate the root mean square error σ based on the tool setting values. i Remove the mean square error σ i For abnormal tool setting values ​​exceeding a set threshold, a sliding window function is implemented to obtain a stable tool setting value by averaging the remaining tool setting values. S4: K stable tool setting values are collected to form a sample tool setting value d, and the sample tool setting value d is calculated into a target function Q by a weighted least square formula, and the weighted least square formula is: wherein Q is the objective function, K is the number of stable tool setting tests, w i is the weight value of each term, d i is the ith stable tool setting value; dl is the ideal tool setting value, and the weight w i When w is 0 or 1, the ideal tool setting value dl that minimizes Q is solved after the abnormal points are removed by the interval removal algorithm. S5: The optimal cutting depth d' can be obtained by repeatedly trying cutting, and the error compensation value d is calculated according to the difference between the ideal tool setting value dl and the optimal cutting depth d'. err = d'-dl, and the cutting depth of all tools is automatically adjusted by the compensation value in actual cutting.

2. A self-rotating knife-to-knife method based on a digital die cutting machine as claimed in claim 1, characterized in that: In step S2, the multi-section speed control mode is to control the blade on the cutter to move at a speed V1, immediately decelerate to stop after moving to the tool setting position above the tool setting sensor, then move away from the tool setting sensor at a speed V2 in the opposite direction, and then accurately position to the tool setting position at a speed V3 until contact with the tool setting sensor, and V1>V2>V3.

3. A self-rotating knife-to-knife method based on a digital die cutting machine as claimed in claim 1, characterized in that: In step S3 the mean square deviation σ i , in particular: wherein σ i is the mean square deviation of the i-th to last tool setting value; N is the total number of i-th to last; A i is the i-th tool setting value; is the average of the i-th to last tool setting value.

4. A self-knife method for rotary knives based on a digital die cutting machine according to claim 1, characterized in that: In step S4, the number K is 20 times.

5. A self-rotating knife-to-knife method based on a digital die cutting machine as claimed in claim 1, characterized in that: In step S4, the abnormal point elimination algorithm interval is described as follows: wherein: Arithmetic average of sample values of the knife setting, d i : i-th stable knife setting value, K: number of stable knife setting tests.

6. A self-knife method for a rotary knife based digital die cutting machine as claimed in claim 5, characterized in that: The weights w of the weighted least squares in step S4 i are assigned by the following rules: If d i w = 1 if the point is within the range of the outlier rejection algorithm i = 1 Otherwise w i = 0, reject the corresponding pair of values.

Images