Autorotation cutter setting method based on digital die-cutting machine

Abstract

The invention relates to the technical field of tool setting, in particular to an autorotation tool setting method based on a digital die-cutting machine, which comprises the following steps of: performing trial cutting in a blank area of a material to be cut, and guiding a blade of an autorotation tool to rotate to a fixed direction by using cutting force; the rotation cutter blade is controlled to move in a multi-section-speed control mode, so that the rotation cutter blade makes contact with the tool setting sensor at the preset position and track; n times of tool setting values are collected, a mean square deviation value is calculated, abnormal tool setting values with the mean square deviation value larger than a set threshold value are removed, and the remaining tool setting values are averaged to obtain a stable tool setting value; a sample tool setting value formed by K times of stable tool setting values is collected, a target function is calculated through a weighted least square formula, an error compensation value is calculated according to the difference value between the ideal tool setting value and the optimal cutting depth, and the cutting depths of all tools are automatically adjusted through the compensation value in actual cutting. The problem that the automatic tool setting precision is poor is solved, automatic adjustment and compensation of the cutting depth of the self-rotating tool are achieved, and the debugging efficiency is greatly improved.

Description

Technical Field

[0001] The invention 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 Art

[0002] In the actual application scenarios of digital die-cutting machines, the self-rotating knife, as the main cutting tool, undertakes the important task of accurately cutting various materials. However, there are many problems in the current self-rotating knife setting technology based on digital die-cutting machines, which seriously restricts production efficiency and cutting accuracy. Due to the unique working characteristics of the self-rotating knife itself, during the process of cutting materials, the blade will automatically rotate to the cutting direction as it contacts the material. This causes the direction of the blade to be in an uncertain state, and its tip point may appear at any position on the arc with the knife axis as the center and the distance from the knife tip to the axis as the radius. The existing contact-type knife setting sensor, whose contact head is usually a circular plane with a diameter of about 5mm, is only a point on the tip of the knife when setting the knife. In the process of contact between the tip of the knife and the sensor, even a very small difference in the contact position will directly affect the accuracy of the knife setting, making the actual knife setting accuracy unable to reach the high-precision standard of the sensor's nominal plus or minus 1 micron. In addition, the cutting operation will inevitably cause the blade to wear, which requires frequent adjustment of the knife depth. In the multi-blade working mode, each blade needs to be individually adjusted for cutting depth, which is not only extremely inefficient, but also complicated and difficult. This inefficient blade alignment and frequent blade depth adjustment seriously affect the overall working efficiency of the digital die-cutting machine and increase production costs. An effective self-rotating blade alignment method is urgently needed to solve these problems and improve the working performance of the digital die-cutting machine. Summary of the invention

[0003] The present invention 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 technology, the technical solution adopted by the present invention to solve the technical problem is: a self-rotating knife alignment method based on a digital die-cutting machine, the specific steps are as follows:

[0005] S1: Test cutting is performed in a blank area of ​​the material to be cut, and the cutting force is used to guide the blade of the self-rotating knife to rotate to a fixed direction;

[0006] S2: Use a multi-speed control method to control the movement of the self-rotating knife blade so that the self-rotating knife blade contacts the tool setting sensor at a predetermined position and trajectory;

[0007] S3: Collect tool setting values ​​N times and calculate the mean square error σ through the tool setting values i , remove the mean square error σ iThe abnormal tool setting value greater than the set threshold realizes the sliding window function, and the remaining tool setting values ​​are averaged to obtain the stable tool setting value;

[0008] S4: Collect K stable tool setting values ​​to form a sample tool setting value d. The sample tool setting value d is calculated using the weighted least squares formula to obtain the objective function Q. The weighted least squares formula is:

[0009]

[0010] Among them, Q is the objective function, K is the total number of tests, and W i is the weight value of each item, d i is the i-th stable tool setting value; dl is the ideal tool setting value, and the weight is W i When it is 0 or 1, after eliminating the abnormal points through the abnormal point elimination algorithm interval, the ideal tool setting value dl that minimizes Q is solved;

[0011] S5: The optimal cutting depth d' can be adjusted through repeated trial cutting. The error compensation value is calculated based on the difference between the ideal tool setting value dl and the optimal cutting depth d': d err =d'-dl. In actual cutting, the cutting depth of all tools is automatically adjusted by the compensation value.

[0012] Further improvements include controlling the multi-speed in step S2 to control the blade on the tool to move at a speed of V1, decelerate to a stop immediately after moving to the tool setting position above the tool setting sensor, and then move the blade in the opposite direction at a speed of V2 to move out of the tool setting point position away from the tool setting sensor, and then accurately position it to the tool setting position at a speed of V3 until it contacts the tool setting sensor, speed V1>speed V2>speed V3.

[0013] A further improvement includes the mean square error σ in step S3 i , specifically:

[0014]

[0015] Among them, σ i is the mean square error of the tool setting values ​​from the ith to the last time; N is the total number of times from the ith to the last time; d i is the i-th tool setting value; It is the average of the tool setting values ​​from the i-th to the last time.

[0016] A further improvement includes that the number K in step S4 is 20 times.

[0017] Further improvements, including the outlier removal algorithm interval described in step S4, are:

[0018]

[0019] in: Arithmetic mean of sample tool setting values, d i : The i-th stable tool setting value, K: The total number of tool setting tests.

[0020] A further improvement includes the weights W of the weighted least squares method in step S4 i Assigned by the following rules:

[0021] If d i If it is within the interval of the outlier removal algorithm, then W i =1;

[0022] Otherwise W i =0, the corresponding tool value is eliminated.

[0023] The beneficial effects of the present invention are as follows: the present design adopts a multi-speed control method, which can accurately control the contact process between the blade and the tool setting sensor, and reduce the tool setting error caused by the difference in contact position. At the same time, by collecting N tool setting values ​​and calculating the mean square error value σi, and eliminating abnormal tool setting values ​​whose σi is greater than the set threshold, the interference factors in the tool setting process can be effectively eliminated, and a more accurate tool setting result can be estimated, avoiding the problem of low tool setting accuracy caused by the uncertainty of the tool tip position and the difference in contact position, so that the actual tool setting accuracy is controlled within 6μm;

[0024] This design obtains the stable tool setting value of the rotating tool, forms a sample tool setting value, and then uses the weighted least squares formula to calculate the objective function Q. The outliers are eliminated through the outlier elimination algorithm interval to solve the ideal tool setting value dl that minimizes Q. The error compensation value is calculated based on the difference between the ideal tool setting value dl and the optimal cutting depth d', and then the cutting depth of all tools is automatically adjusted. This method realizes batch adjustment of the tool cutting depth, greatly improves the debugging efficiency, and reduces the workload and complexity of manual debugging. The efficient tool setting and automatic adjustment of cutting depth of this design reduce the time waste and material loss caused by low tool setting accuracy and low debugging efficiency, and reduce production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0026] Figure 1 This is the blade alignment step in the present invention;

[0027] Figure 2 This is the step of obtaining a stable tool setting value in the present invention;

[0028] Figure 3 This is the multi-speed control method in the present invention; DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] As a precision processing equipment, the accuracy of the cutting tool system of the digital die-cutting machine plays a decisive role in the processing quality. The digital die-cutting machine is equipped with multiple cutting tools, and the common configuration is 8 knives. Each knife needs to independently and accurately adjust the cutting depth in actual work. This is the key to ensure that it can adapt to the cut materials of different thicknesses and materials, thereby achieving high-quality and high-precision cutting effects. To achieve this goal, the digital die-cutting machine mainly completes the tool setting operation by installing a high-precision tool setting instrument on the platform, such as a contact sensor. When the tool setting process is started, the control system will issue instructions to each knife in turn to drive the knife to descend and approach the tool setting instrument. At the moment when the knife contacts the tool setting instrument, the tool setting instrument will quickly capture and record the relevant data. Through a series of complex and precise calculations, the knife depth value of each knife is measured. These knife depth values ​​will be fed back to the control system in real time. After further data processing and analysis, the control system converts the knife depth value into an accurate cutting depth parameter.

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

[0032] S1: Test cutting is performed in a blank area of ​​the material to be cut, and the cutting force is used to guide the blade of the self-rotating knife to rotate 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 in the position of the blade tip will cause a large tool setting error. Therefore, the blade needs to be guided so that the blade tip can be set at a fixed position.

[0033] S2: Use multi-speed control 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 and stop, reverse movement out of the tool setting point and precise positioning of the tool setting position;

[0034] S3: Reference Figure 2 , collect N tool setting values ​​{d 1 ,d 2 ,...,d N}, calculate the mean square error value σ through the tool setting value i , remove the mean square error σ i The abnormal tool setting value greater than the set threshold realizes the sliding window function, and the remaining tool setting values ​​are averaged to obtain the stable tool setting value;

[0035] S4: Collect K stable tool setting values ​​to form a sample tool setting value d. The sample tool setting value d is calculated using the weighted least squares formula to obtain the objective function Q. The weighted least squares formula is:

[0036]

[0037] Among them, Q is the objective function, K is the total number of tests, and W i is the weight value of each item,

[0038] d i is the i-th stable tool setting value; dl is the ideal tool setting value, and the weight is W i When it is 0 or 1, after the abnormal points are eliminated through the abnormal point elimination algorithm interval, the ideal tool setting value dl that minimizes Q is solved.

[0039] The examples are shown in Table 1 below:

[0040]

[0041]

[0042] Table 1

[0043] The valid data range is: (6.351881, 6.352619), which is approximated to (6.351, 6.353). Therefore, there are 14 valid data. The final valid data is averaged to get 6.352, which is the ideal tool setting value.

[0044] S5: The optimal cutting depth d' can be adjusted through repeated trial cutting. The error compensation value is calculated based on the difference between the ideal tool setting value dl and the optimal cutting depth d': d err =d'-dl. In actual cutting, the cutting depth of all tools is automatically adjusted by the compensation value, which is saved in the machine as a mechanical parameter.

[0045] refer to Figure 1 In step S2, the multi-speed control method is to control the blade on the tool to move at a speed of V1, and immediately decelerate to a stop after moving to the tool setting position above the tool setting sensor. Then, the blade moves in the opposite direction at a speed of V2 to move away from the tool setting point position away from the tool setting sensor, and then accurately locates to the tool setting position at a speed of V3 until it contacts the tool setting sensor. Figure 3 , speed V1>speed V2>speed V3.

[0046] In step S3, the mean square error σ i , specifically:

[0047]

[0048] Among them, σ iis the mean square error of the tool setting values ​​from the ith to the last time; N is the total number of times from the ith to the last time; d i is the i-th tool setting value; d is the average of the tool setting values ​​from the i-th to the last time. This step can eliminate the error in tool setting accuracy and estimate the most accurate tool setting result.

[0049] The embodiment is shown in Table 2 below: In order to ensure efficiency during the cutting process, the number of knife adjustments is 3 to 5 times:

[0050] Serial number 1 2 3 4 5 Tool setting value 6.349 6.355 6.356 6.354 6.353 Mean 6.349 6.352 6.3555 6.355 6.3545 variance No treatment 0.000018 0.0000005 0.000002 0.000005 Mean square error No treatment 0.000009 0.00000025 0.00000066 0.00000125 result No treatment Need to discard the first efficient efficient efficient

[0051] Table 2

[0052] According to the set mean square error threshold of 0.000005 obtained from the test, the first error can be filtered out, and then a stable tool setting value of 6.355 can be obtained.

[0053] In step S4, the number K is preferably 20, and the number K may be more than 20 times.

[0054] The outlier elimination algorithm interval described in step S4 is specifically:

[0055]

[0056] in: Arithmetic mean of sample tool setting values, d i : The i-th tool setting value, N: The total number of tool setting tests.

[0057] The weight W of the weighted least squares method in step S4 is i Assigned by the following rules:

[0058] If d i If it is within the interval of the outlier removal algorithm, then W i =1;

[0059] Otherwise W i =0, indicating d i The points are judged as abnormal points, and the corresponding tool values ​​are removed and do not participate in the minimization process of Q. i It is essentially an identifier of data validity, which is used to dynamically filter abnormal data in the calculation. Step 3 is used to calculate the actual tool cutting depth.

[0060] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A self-rotating knife alignment method based on a digital die-cutting machine, characterized in that: The specific steps are as follows: S1: Test cutting is performed in a blank area of ​​the material to be cut, and the cutting force is used to guide the blade of the self-rotating knife to rotate to a fixed direction; S2: Use a multi-speed control method to control the movement of the self-rotating knife blade so that the self-rotating knife blade contacts the tool setting sensor at a predetermined position and trajectory; S3: Collect tool setting values ​​N times and calculate the mean square error σ through the tool setting values i , remove the mean square error σ i The abnormal tool setting value greater than the set threshold realizes the sliding window function, and the remaining tool setting values ​​are averaged to obtain the stable tool setting value; S4: Collect K stable tool setting values ​​to form a sample tool setting value d. The sample tool setting value d is calculated using the weighted least squares formula to obtain the objective function Q. The weighted least squares formula is: Among them, Q is the objective function, K is the total number of tests, and W i is the weight value of each item, d i is the i-th stable tool setting value; dl is the ideal tool setting value, and the weight is W i When it is 0 or 1, after eliminating the abnormal points through the abnormal point elimination algorithm interval, the ideal tool setting value dl that minimizes Q is solved; S5: The optimal cutting depth d' can be adjusted through repeated trial cutting. The error compensation value is calculated based on the difference between the ideal tool setting value dl and the optimal cutting depth d': d err =d'-dl. In actual cutting, the cutting depth of all tools is automatically adjusted by the compensation value.

2. A self-rotating knife alignment method based on a digital die-cutting machine as claimed in claim 1, characterized in that: In step S2, the multi-speed control method is to control the blade on the tool to move at a speed of V1, and immediately decelerate to a stop after moving to the tool setting position above the tool setting sensor. Thereafter, the blade moves in the opposite direction at a speed of V2 to move out of the tool setting point position away from the tool setting sensor, and then accurately positions at a speed of V3 to the tool setting position until it contacts the tool setting sensor. Speed ​​V1>speed V2>speed V3.

3. A self-rotating knife alignment method based on a digital die-cutting machine as claimed in claim 1, characterized in that: In step S3, the mean square error σ i , specifically: Among them, σ i is the mean square error of the tool setting values ​​from the ith to the last time; N is the total number of times from the ith to the last time; d i is the i-th tool setting value; It is the average of the tool setting values ​​from the i-th to the last time.

4. A self-rotating knife alignment method based on a digital die-cutting machine as claimed in claim 1, characterized in that: In step S4, the number K is 20 times.

5. The self-rotating knife alignment method based on a digital die-cutting machine according to claim 1, characterized in that: The outlier elimination algorithm interval described in step S4 is specifically: in: : The arithmetic mean of the sample tool setting values, d i : The i-th stable tool setting value, K: The total number of tool setting tests.

6. A self-rotating knife alignment method based on a digital die-cutting machine as claimed in claim 5, characterized in that: The weight W of the weighted least squares method in step S4 is i Assigned by the following rules: If d i If it is within the interval of the outlier removal algorithm, then W i =1; Otherwise W i =0, the corresponding tool value is eliminated.

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