Digital twinning optimization method for thermo-sensitive paper die cutting process parameters

Abstract

The invention discloses a digital twinning optimization method for thermo-sensitive paper die cutting process parameters, relates to the field of thermo-sensitive paper die cutting, solves the problem of low optimization efficiency of the die cutting process parameters, and comprises the following steps: collecting die cutting machine structure data of a die cutting machine, and constructing a virtual twinning model of the die cutting machine by combining a digital twinning technology; continuously cutting the original thermo-sensitive paper through the virtual twin model of the die-cutting machine, and summarizing a plurality of groups of die-cutting process parameters in the thermo-sensitive paper die-cutting process based on a die-cutting result; analyzing the actual cutting condition of the die-cutting thermo-sensitive paper, and constructing a cutting quality evaluation index and a loss evaluation index of the die-cutting thermo-sensitive paper based on an analysis result; analyzing the process parameters of the die-cutting machine by combining the cutting quality evaluation index and the loss evaluation index with the virtual twin model of the die-cutting machine, and outputting the optimal process parameters; according to the method, fitting is carried out on the two aspects of the cutting quality and the loss condition of the die-cutting machine, and rapid and accurate optimization of the die-cutting process parameters is achieved.

Description

Technical Field

[0001] This invention belongs to the field of thermal paper die-cutting technology, specifically a digital twin optimization method for thermal paper die-cutting process parameters. Background Technology

[0002] Thermal paper is a special coated paper whose surface is chemically treated so that it changes color locally when exposed to heat, thus enabling the printing of text or images without ink or ribbon. Thermal paper die-cutting refers to the process of using die-cutting technology to process large rolls or sheets of thermal paper into the final required shape and size during the production process. Digital twins fully utilize data such as physical models, sensors, and operational history to integrate a simulation process involving multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities, completing the mapping in virtual space to reflect the entire life cycle of the corresponding physical equipment.

[0003] However, due to the various uncertainties in the thermal paper die-cutting process, such as material performance fluctuations and equipment status changes, traditional process parameter optimization methods often rely on experience and trial and error, resulting in low efficiency in process parameter optimization. To address this, the present invention proposes a digital twin optimization method for thermal paper die-cutting process parameters. Summary of the Invention

[0004] The purpose of this invention is to propose a digital twin optimization method for thermal paper die-cutting process parameters to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A digital twin optimization method for thermal paper die-cutting process parameters, including: Step S1: Collect the die-cutting machine structure data and construct a virtual twin model of the die-cutting machine using digital twin technology; Step S2: The original thermal paper is continuously cut using a virtual twin model of the die-cutting machine. Based on the die-cutting results, multiple sets of die-cutting process parameters in the thermal paper die-cutting process are summarized. Step S3: Analyze the actual cutting situation of the die-cut thermal paper, and construct the cutting quality evaluation index and loss evaluation index of the die-cut thermal paper based on the analysis results; Step S4: Analyze the process parameters of the die-cutting machine by combining the cutting quality evaluation index and the loss evaluation index with the virtual twin model of the die-cutting machine, and output the optimal process parameters. Step S5: Adjust the actual process parameters of the die-cutting machine to the optimal parameters to optimize the thermal paper die-cutting process parameters.

[0006] Further, step S1 includes the following sub-steps: Step S11: Scan the die-cutting machine with a 3D scanning device to create the initial geometric model corresponding to the die-cutting machine; Step S12: Collect the dimensions, weight, moment of inertia, density, elastic modulus, Poisson's ratio of moving and fixed parts in the die-cutting machine, as well as the constraint relationships between different parts, and add them to the initial geometric model to obtain the processing geometric model; Step S13: Import the machining geometry model into the dynamics software, set the kinematic and dynamic parameters of the moving parts in the machining geometry model, set the loads of the solid parts in the machining geometry model under different dynamic parameter conditions, and obtain the dynamics model corresponding to the die-cutting machine. Step S14: The control logic of the PLC in the die-cutting machine and the sensor feedback are reproduced by PLC simulation software and imported into the dynamic model of the die-cutting machine to construct a virtual twin model of the die-cutting machine.

[0007] Further, step S2 includes the following sub-steps: Step S21: Set a single-color original thermal paper to be fixed on the virtual twin model of the die-cutting machine, set multiple sets of process parameters, and the die-cutting machine in the virtual twin model of the die-cutting machine continuously cuts the original thermal paper according to different process parameters to obtain die-cut thermal paper corresponding to multiple sets of process parameters. Step S22: Lay the die-cut thermal paper flat on the table, record the cut edge as the die-cut edge, collect the thermal paper image corresponding to the die-cut thermal paper, and extract the die-cut pixel value of each pixel in the thermal paper image. Step S23: Read the original pixel value of the single color corresponding to the original thermal paper, and compare the die-cut pixel value of each pixel in the die-cut thermal paper with the original pixel value in turn. If the die-cut pixel value is different from the original pixel value, the corresponding pixel is recorded as a difference pixel; if the die-cut pixel value is the same as the original pixel value, no operation is performed.

[0008] Furthermore, step S2 also includes the following sub-steps: Step S24: Count the number of different pixels corresponding to the die-cut thermal paper and record it as the total number of pixel differences. If the total number of pixel differences is greater than or equal to the preset threshold for the number of pixel differences, then the corresponding die-cut thermal paper is recorded as unqualified; otherwise, proceed to step S25. Step S25: Divide the die-cut pixel value of each pixel in the die-cut thermal paper to obtain the first component, the second component and the third component of the corresponding pixel. Calculate the die-cut grayscale value of the corresponding pixel based on the first component, the second component and the third component. Specifically, the first component is the R value component of the die-cut pixel value, the second component is the G value component of the die-cut pixel value, and the third component is the B value component of the die-cut pixel value. Step S26: Compare the die-cut grayscale value of all pixels with the grayscale threshold. If the die-cut grayscale value is greater than or equal to the grayscale threshold, no operation is performed. If the die-cut grayscale value is less than the grayscale threshold, the corresponding pixel is recorded as an edge pixel.

[0009] Furthermore, step S2 also includes the following sub-steps: Step S27: Establish a Cartesian coordinate system with one end of the die-cut edge as the origin, and obtain the pixel coordinates of all edge pixels above the die-cut edge; denote the edge pixels at the origin as selected pixels, denote the edge pixels adjacent to the selected pixels as adjacent pixels, and calculate the slope between the selected pixels and the adjacent pixels. Step S28: Treat the current adjacent pixel as the next selected pixel, and then obtain the adjacent pixels of the next selected pixel. Continue iterating to the other end of the die-cut edge to obtain multiple slopes above the edge pixels. Subtract the minimum slope from the maximum slope to obtain the slope fluctuation value. If the slope fluctuation value is greater than or equal to the preset slope fluctuation threshold, the corresponding die-cut thermal paper is marked as unqualified. If the slope fluctuation value is less than the slope fluctuation threshold, no operation is performed. Step S29: Discard all process parameters corresponding to unqualified die-cut thermal paper, and record the remaining process parameters as die-cutting process parameters.

[0010] Further, step S3 includes the following sub-steps: Step S31: Obtain the die-cut thermal paper and set the die-cut thermal paper to the same number as the die-cutting process parameters; Step S32: Place the die-cut thermal paper horizontally, focus the microscopic imaging device on the side where the die-cut edge is located, and acquire a microscopic image of the die-cut side of the corresponding die-cut thermal paper. Step S33: Draw a straight line to the right from the leftmost point at the top of the die-cut side micrograph to obtain the upper surface baseline; draw a straight line to the right from the leftmost point at the bottom of the die-cut side micrograph to obtain the lower surface baseline; read the burr height and burr width of the upper surface burr based on the upper surface baseline; read the burr height and burr width of the lower surface burr based on the lower surface baseline.

[0011] Further, step S3 includes the following sub-steps: Step S34: Discard the upper and lower surface burrs with a burr width less than or equal to k. Traverse the remaining upper and lower surface burrs. Obtain the upper surface fluctuation value by subtracting the minimum value of the upper surface burr height from the maximum value of the upper surface burr height. Obtain the lower surface fluctuation value by subtracting the minimum value of the lower surface burr height from the maximum value of the lower surface burr height. Step S35: Add the burr heights of all remaining upper surface burrs and lower surface burrs together and take the average to obtain the average burr height; combine the average burr height, upper surface fluctuation value and lower surface fluctuation value to construct the corresponding cutting quality evaluation index for die-cut thermal paper.

[0012] Furthermore, step S3 also includes the following sub-steps: Step S36: Calculate the actual die-cutting area of ​​the thermal paper, compare the actual die-cutting area with the estimated die-cutting area. If the actual die-cutting area is greater than or equal to the estimated die-cutting area, the wasted die-cutting area is obtained by subtracting the estimated die-cutting area from the actual die-cutting area. If the actual die-cutting area is less than the estimated die-cutting area, the actual die-cutting area is regarded as the wasted die-cutting area. Step S37: Obtain the initial tool price of the tool on the die-cutting machine, and obtain the tool waste price by multiplying the initial tool price by the tool depreciation rate; Step S38: Combine the actual die-cutting area, wasted die-cutting area, initial tool price, and tool waste price to construct the corresponding loss evaluation index for die-cut thermal paper.

[0013] Further, step S4 includes the following sub-steps: Step S41: Obtain the cutting quality evaluation index and the loss evaluation index; Step S42: Then, obtain the virtual twin model of the die-cutting machine, and establish the cutting quality evaluation function and the loss evaluation function based on the virtual twin model of the die-cutting machine; Step S43: Obtain multiple sets of die-cutting process parameters, and calculate the cutting quality evaluation result and loss evaluation result of each set of die-cutting process parameters based on the cutting quality evaluation index and the loss evaluation index. Step S44: Substitute the cutting quality evaluation results and die-cutting process parameters into the cutting quality evaluation function, and solve in reverse to obtain the correction coefficients in the cutting quality evaluation function corresponding to each set of die-cutting process parameters; add the correction coefficients of multiple sets of die-cutting process parameters together and take the average value to obtain the average correction coefficient of the cutting quality evaluation function. Step S45: Substitute the average correction coefficient of the cutting quality evaluation function into the cutting quality evaluation function to obtain the updated cutting quality evaluation function. Calculate the updated cutting quality evaluation results corresponding to multiple sets of die-cutting process parameters using the updated cutting quality evaluation function.

[0014] Furthermore, step S4 also includes the following sub-steps: Step S46: Substitute the loss evaluation results and die-cutting process parameters into the loss evaluation function, and solve in reverse to obtain the correction coefficient in the loss evaluation function corresponding to each set of die-cutting process parameters; add the correction coefficients in the loss evaluation function corresponding to multiple sets of die-cutting process parameters, and take the average to obtain the average correction coefficient of the loss evaluation function. Step S47: Substitute the average correction coefficient of the loss evaluation function into the loss evaluation function to obtain the updated loss evaluation function, and calculate the updated loss evaluation results corresponding to multiple sets of die-cutting process parameters through the updated loss evaluation function. Step S48: Obtain the comprehensive quality evaluation value by dividing the updated cutting quality evaluation result by the updated loss evaluation result; sort the comprehensive quality evaluation values ​​in descending order, read the die-cutting process parameters corresponding to the first-ranked comprehensive quality evaluation value, and record them as the optimal parameters.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention collects the structural data of the die-cutting machine and constructs a virtual twin model of the die-cutting machine using digital twin technology. The original thermal paper is continuously cut using the virtual twin model of the die-cutting machine, and multiple sets of die-cutting process parameters in the thermal paper die-cutting process are summarized based on the die-cutting results, thereby realizing the acquisition of multiple sets of die-cutting process parameters corresponding to the die-cutting machine.

[0016] 2. This invention analyzes the actual cutting situation of die-cut thermal paper, and constructs cutting quality evaluation indicators and loss evaluation indicators based on the analysis results. By combining the cutting quality evaluation indicators and loss evaluation indicators with a virtual twin model of the die-cutting machine, the process parameters of the die-cutting machine are analyzed, and the optimal process parameters are output. The actual process parameters of the die-cutting machine are adjusted with the optimal parameters to optimize the die-cutting process parameters of thermal paper. By fitting the cutting quality and loss of the die-cutting machine, the die-cutting process parameters can be quickly and accurately optimized. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the coordinate system corresponding to the die-cutting edge in this invention; Figure 3 This is a schematic diagram of burrs on the die-cut edge in this invention; Figure 4 This is a schematic diagram of the electronic device in this invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figures 1-3 As shown, the technical solution provided by this invention is: a digital twin optimization method for thermal paper die-cutting process parameters. This method involves constructing a virtual twin model of the die-cutting machine, obtaining multiple sets of die-cutting process parameters based on the virtual twin model, constructing cutting quality evaluation indicators and loss evaluation indicators for die-cut thermal paper based on the actual die-cutting results, setting cutting quality evaluation functions and loss evaluation functions in conjunction with the virtual twin model of the die-cutting machine, updating the cutting quality evaluation functions and loss evaluation functions based on the process parameter data, prioritizing multiple sets of die-cutting process parameters based on the updated cutting quality evaluation functions and loss evaluation functions, and selecting the optimal process parameters. Die-cutting process parameters during the thermal paper die-cutting process are collected, and a virtual model of thermal paper die-cutting is constructed based on the die-cutting process parameters and digital twin technology. Cutting quality function and material loss rate function are set according to the virtual model of thermal paper die-cutting. Multiple sets of die-cutting process parameters of thermal paper die-cutting process are evaluated based on the cutting quality function and material loss rate function, and the actual thermal paper die-cutting process is adjusted and optimized based on the evaluation results.

[0021] In this embodiment, the method is as follows: Step S1: Collect the die-cutting machine structure data and construct a virtual twin model of the die-cutting machine using digital twin technology; In this invention, step S1 includes the following sub-steps: Step S11: Scan the die-cutting machine with a 3D scanning device to create the initial geometric model corresponding to the die-cutting machine; Specifically, a laser scanner can be used to scan the die-cutting machine, capture the point cloud data of the die-cutting machine, and then create the initial geometric model of the die-cutting machine through point cloud processing, surface reconstruction and other steps. Step S12: Collect the dimensions, weight, moment of inertia, density, elastic modulus, Poisson's ratio, and constraint relationships between different components (such as hinges, guide rails, and bearings) of moving parts (such as crankshafts, connecting rods, pressure plates, and feeding platforms) and fixed parts (such as machine frame, guide rails, and fixed tool holders) in the die-cutting machine, and add them to the initial geometric model to obtain the processing geometric model; the dimensions, weight, moment of inertia, density, elastic modulus, Poisson's ratio, and constraint relationships of different components can be obtained from the corresponding structural specification of the die-cutting machine; Step S13: Import the machining geometry model into the dynamics software, set the kinematic and dynamic parameters of the moving parts in the machining geometry model, set the loads of the solid parts in the machining geometry model under different dynamic parameter conditions, and obtain the dynamics model corresponding to the die-cutting machine. Load refers to the external force that a part or component bears when it is working; Specifically, the kinematic parameters are the real-time displacement, real-time velocity, and real-time acceleration of the moving parts at different speeds, and the dynamic parameters are the real-time forces and real-time torques of the moving parts at different speeds; these can be obtained from the die-cutting machine's structural instruction manual. Step S14: The control logic of the PLC in the die-cutting machine and the sensor feedback are reproduced by PLC simulation software and imported into the dynamic model of the die-cutting machine to construct a virtual twin model of the die-cutting machine. It should be noted that, since the construction of digital twin models is a relatively mature technology at present, this invention only lists the key processes in the construction process and does not describe the specific construction process.

[0022] Step S2: The original thermal paper is continuously cut using a virtual twin model of the die-cutting machine. Based on the die-cutting results, multiple sets of die-cutting process parameters in the thermal paper die-cutting process are summarized. In this invention, step S2 includes the following sub-steps: Step S21: Set a single-color original thermal paper to be fixed on the virtual twin model of the die-cutting machine, set multiple sets of process parameters, and the die-cutting machine in the virtual twin model of the die-cutting machine continuously cuts the original thermal paper according to different process parameters to obtain die-cut thermal paper corresponding to multiple sets of process parameters; wherein, the process parameters include die-cutting pressure, die-cutting speed and cutting depth within the die-cutting time; Specifically, die-cutting pressure refers to the pressure applied by the cutter in the die-cutting machine to the original thermal paper, measured in Newtons; die-cutting speed refers to the speed at which the cutter moves or the original thermal paper is fed into the die-cutting machine, measured in millimeters per second; and cutting depth refers to the depth to which the bottom of the cutter in the die-cutting machine cuts into the original thermal paper, measured in millimeters. Step S22: Lay the die-cut thermal paper flat on the table, record the cut edge as the die-cut edge, collect the thermal paper image corresponding to the die-cut thermal paper, and extract the die-cut pixel value of each pixel in the thermal paper image. Specifically, when acquiring images of the thermal paper corresponding to the die-cut thermal paper, an image acquisition device is fixed at a certain height directly above the geometric center of the die-cut thermal paper, and the thermal paper image is acquired through the image acquisition device. Step S23: Read the original pixel value of the single color corresponding to the original thermal paper, and compare the die-cut pixel value of each pixel in the die-cut thermal paper with the original pixel value in turn. If the die-cut pixel value is different from the original pixel value, the corresponding pixel is recorded as a difference pixel; if the die-cut pixel value is the same as the original pixel value, no operation is performed. Step S24: Count the number of different pixels corresponding to the die-cut thermal paper and record it as the total number of pixel differences. If the total number of pixel differences is greater than or equal to the preset threshold for the number of pixel differences, then the corresponding die-cut thermal paper is recorded as unqualified; otherwise, proceed to step S25. Step S25: Divide the die-cut pixel value of each pixel in the die-cut thermal paper to obtain the first component, the second component and the third component of the corresponding pixel. Calculate the die-cut grayscale value of the corresponding pixel based on the first component, the second component and the third component. Specifically, the first component is the R value component of the die-cut pixel value, the second component is the G value component of the die-cut pixel value, and the third component is the B value component of the die-cut pixel value. Step S26: Compare the die-cut grayscale value of all pixels with the grayscale threshold. If the die-cut grayscale value is greater than or equal to the grayscale threshold, no operation is performed. If the die-cut grayscale value is less than the grayscale threshold, the corresponding pixel is recorded as an edge pixel. Step S27, as follows Figure 2 As shown, a Cartesian coordinate system is established with one end of the die-cut edge as the origin to obtain the pixel coordinates of all edge pixels above the die-cut edge; the edge pixel at the origin is recorded as the selected pixel, and the edge pixel adjacent to the selected pixel is recorded as the adjacent pixel; the slope between the selected pixel and the adjacent pixel is calculated by the formula. Step S28: Treat the current adjacent pixel as the next selected pixel, and then obtain the adjacent pixels of the next selected pixel. Continue iterating to the other end of the die-cut edge to obtain multiple slopes above the edge pixels. Subtract the minimum slope from the maximum slope to obtain the slope fluctuation value. If the slope fluctuation value is greater than or equal to the preset slope fluctuation threshold, the corresponding die-cut thermal paper is marked as unqualified. If the slope fluctuation value is less than the slope fluctuation threshold, no operation is performed. Step S29: Discard all process parameters corresponding to unqualified die-cut thermal paper, record the remaining process parameters as die-cutting process parameters, and label each group of die-cutting process parameters as i, i=1,2,...,z.

[0023] Step S3: Analyze the actual cutting situation of the die-cut thermal paper, and construct the cutting quality evaluation index and loss evaluation index of the die-cut thermal paper based on the analysis results; In this invention, step S31 includes the following sub-steps: Step S31: Obtain the die-cut thermal paper and set the die-cut thermal paper to the same number as the die-cutting process parameters; Step S32: Place the die-cut thermal paper horizontally, focus the microscopic imaging device on the side where the die-cut edge is located, and acquire a microscopic image of the die-cut side of the corresponding die-cut thermal paper. For step S33, please refer to... Figure 3As shown, the upper surface baseline is obtained by drawing a straight line to the right from the leftmost point at the top of the die-cut side micrograph, and the lower surface baseline is obtained by drawing a straight line to the right from the leftmost point at the bottom of the die-cut side micrograph. The burr height and burr width of the upper surface burr are read based on the upper surface baseline; the burr height and burr width of the lower surface burr are read based on the lower surface baseline. Wherein, the burr height is the distance between the highest point of the burr and the baseline, and the burr width is the length through which the baseline passes the burr; Step S34: Discard the upper and lower surface burrs with a burr width less than or equal to k. Traverse the remaining upper and lower surface burrs. Obtain the upper surface fluctuation value BSi by subtracting the minimum value of the upper surface burr height from the maximum value of the upper surface burr height. Obtain the lower surface fluctuation value BXi by subtracting the minimum value of the lower surface burr height from the maximum value of the lower surface burr height. Step S35: Add the burr heights of all remaining upper and lower surface burrs together and average them to obtain the average burr height; combine the average burr height MGi, the upper surface fluctuation value, and the lower surface fluctuation value to construct the corresponding cutting quality evaluation index QGi for die-cut thermal paper. Specifically, the cutting quality evaluation index is as follows: QGi = A1 × (1 - MGi / BMG) + A2 × (1 - (BXi + BSi) / BB); where BMG is the average standard burr height and BB is the standard surface fluctuation value; A1 and A2 are weighting coefficients; specifically, MGi < BMG, BXi + BSi < BB; A1 and A2 are determined based on actual production needs and experience. For example, if the burr height has a more critical impact on product quality in actual production, then the weighting coefficient A1 related to the burr height can be appropriately increased; conversely, if edge smoothness is more important, then A2 should be increased. Step S36: Calculate the actual die-cut area SMMi of the die-cut thermal paper. Compare the actual die-cut area with the estimated die-cut area. If the actual die-cut area is greater than or equal to the estimated die-cut area, the wasted die-cut area LFMi is obtained by subtracting the estimated die-cut area from the actual die-cut area. If the actual die-cut area is less than the estimated die-cut area, the actual die-cut area is regarded as the wasted die-cut area. The estimated die-cut area is the area of ​​the expected die-cut thermal paper. Step S37: Obtain the initial tool price CSDi of the tool on the die-cutting machine, and obtain the tool waste price LFDi by multiplying the initial tool price by the tool depreciation rate; wherein, the tool depreciation rate is a proportional coefficient obtained by combining the tool usage record and the tool wear and depreciation situation. In practice, the annual depreciation rate can be calculated by the workload method or the double-declining balance method, and then the tool depreciation rate for each use of the tool is calculated based on the estimated number of uses in a year. Step S38: Combining the actual die-cutting area, wasted die-cutting area, initial tool price, and tool waste price, a loss evaluation index SPBi for the corresponding die-cut thermal paper is constructed. Specifically, the loss evaluation index is as follows: SPBi = B1 × LFMi / SMMi + B2 × LFDi / CSDi; where B1 and B2 are weighting coefficients; It should be noted that the values ​​of both the loss evaluation index and the cutting quality evaluation index are between zero and one. A higher loss evaluation index indicates greater loss in the die-cutting operation, while a higher cutting quality evaluation index indicates better cutting in the die-cutting operation. Therefore, the smaller the loss evaluation index, the better, and the larger the cutting quality evaluation index, the better.

[0024] Step S4: Analyze the process parameters of the die-cutting machine by combining the cutting quality evaluation index and the loss evaluation index with the virtual twin model of the die-cutting machine, and output the optimal process parameters. In this invention, step S4 includes the following sub-steps: Step S41: Obtain the cutting quality evaluation index and the loss evaluation index; Step S42: Then, obtain the virtual twin model of the die-cutting machine, and establish the cutting quality evaluation function QPH and the loss evaluation function SPH based on the virtual twin model of the die-cutting machine; wherein, the cutting quality evaluation function and the loss evaluation function are both products of correction coefficients and empirical formulas; the empirical formulas are obtained by process engineers through the physical logic and experimental trends of the virtual twin model of the die-cutting machine; For example: The cutting quality evaluation function is QPH: QPH=α×MY×QS / (MS×H; where MY×QS / (MS×H) is an empirical formula, MY is the die-cutting pressure, QS is the cutting depth, MS is the die-cutting speed, H is the paper thickness, and α is the correction coefficient of the cutting quality evaluation function; It should be explained that cutting quality is positively correlated with die-cutting pressure, as higher pressure makes it easier for the blade to cut through the thermal paper, resulting in better quality; however, excessive pressure may also damage the base paper (considered in the loss evaluation function); cutting quality is positively correlated with cutting depth, as a greater cutting depth makes it easier to penetrate the thermal paper; cutting quality is negatively correlated with die-cutting speed, as a faster die-cutting speed results in a shorter time the blade spends at a certain point per unit time, leading to decreased cutting quality; cutting quality is negatively correlated with paper thickness, as thicker paper is more difficult to cut through, resulting in poorer performance under the same die-cutting pressure and cutting depth. The loss evaluation function is SPH: SPH = β1 × (QS - H) 2 +β2×MY 2 / 10000; where (QS-H) 2 and MY 2 / 10000 is an empirical formula, β1 is the first correction coefficient in the loss evaluation function, and β2 is the second correction coefficient in the loss evaluation function; It should be noted that QS-H represents the difference between the cutting depth of the cutter and the thickness of the paper. If QS > H, it means that the bottom paper has been cut through, which may damage the printing layer or the backing paper. The square term is used to describe the non-linear trend of "the more severe the overcut, the greater the loss". This represents the nonlinear losses caused by high pressure (such as indentations, tool wear, paper breakage, etc.). Since the loss is not linearly related to the pressure, a square term is used, and dividing by 10000 is for unit normalization and numerical scaling. Step S43: Obtain multiple sets of die-cutting process parameters, and calculate the cutting quality evaluation result and loss evaluation result of each set of die-cutting process parameters based on the cutting quality evaluation index and the loss evaluation index. Step S44: Substitute the cutting quality evaluation results and die-cutting process parameters into the cutting quality evaluation function, and solve in reverse to obtain the correction coefficients in the cutting quality evaluation function corresponding to each set of die-cutting process parameters; add the correction coefficients of multiple sets of die-cutting process parameters together and take the average value to obtain the average correction coefficient of the cutting quality evaluation function. Step S45: Substitute the average correction coefficient of the cutting quality evaluation function into the cutting quality evaluation function to obtain the updated cutting quality evaluation function. Calculate the updated cutting quality evaluation results corresponding to multiple sets of die-cutting process parameters using the updated cutting quality evaluation function. Step S46: Substitute the loss evaluation results and die-cutting process parameters into the loss evaluation function, and solve in reverse to obtain the correction coefficient in the loss evaluation function corresponding to each set of die-cutting process parameters; add the correction coefficients in the loss evaluation function corresponding to multiple sets of die-cutting process parameters, and take the average to obtain the average correction coefficient of the loss evaluation function. Step S47: Substitute the average correction coefficient of the loss evaluation function into the loss evaluation function to obtain the updated loss evaluation function, and calculate the updated loss evaluation results corresponding to multiple sets of die-cutting process parameters through the updated loss evaluation function. Step S48: Obtain the comprehensive quality evaluation value by dividing the updated cutting quality evaluation result by the updated loss evaluation result; sort the comprehensive quality evaluation values ​​in descending order, read the die-cutting process parameters corresponding to the first-ranked comprehensive quality evaluation value, and record them as the optimal parameters.

[0025] Step S5: Adjust the actual process parameters of the die-cutting machine to the optimal parameters to optimize the thermal paper die-cutting process parameters.

[0026] Example 2: This embodiment of the invention also provides an electronic device for running the digital twin optimization method for the thermal paper die-cutting process parameters; see [link to previous example]. Figure 4The schematic diagram of an electronic device provided by the embodiment of the present invention shown above includes a memory and a processor. The memory is used to store one or more computer instructions, which are executed by the processor to realize the above-mentioned digital twin optimization method for thermal paper die-cutting process parameters. Furthermore, Figure 4 The electronic device shown also includes a communication bus and a communication interface, with the processor, communication interface and memory connected via the communication bus; The memory may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The communication bus can be an ISA bus, PCI bus, or EISA bus, etc. The communication bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by only one double-headed arrow, but this does not mean that there is only one communication bus or one type of communication bus. The processor may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0027] Example 3: This embodiment of the invention also provides a computer storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the digital twin optimization method for the thermal paper die-cutting process parameters described above. For specific implementation details, please refer to the method embodiment, which will not be repeated here. The computer program product of the digital twin optimization method for thermal paper die-cutting process parameters provided in this embodiment of the invention includes a computer storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0028] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system or device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0029] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0030] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital twin optimization method for thermal paper die-cutting process parameters, characterized in that, The methods include: Step S1: Collect the die-cutting machine structure data and construct a virtual twin model of the die-cutting machine using digital twin technology; Step S2: The original thermal paper is continuously cut using a virtual twin model of the die-cutting machine. Based on the die-cutting results, multiple sets of die-cutting process parameters in the thermal paper die-cutting process are summarized. Step S3: Analyze the actual cutting situation of the die-cut thermal paper, and construct the cutting quality evaluation index and loss evaluation index of the die-cut thermal paper based on the analysis results; Step S4: Analyze the process parameters of the die-cutting machine by combining the cutting quality evaluation index and the loss evaluation index with the virtual twin model of the die-cutting machine, and output the optimal process parameters. Step S5: Adjust the actual process parameters of the die-cutting machine to the optimal parameters to optimize the thermal paper die-cutting process parameters.

2. The digital twin optimization method for thermal paper die-cutting process parameters according to claim 1, characterized in that, Step S1 includes the following sub-steps: Step S11: Scan the die-cutting machine with a 3D scanning device to create the initial geometric model corresponding to the die-cutting machine; Step S12: Collect the dimensions, weight, moment of inertia, density, elastic modulus, Poisson's ratio of moving and fixed parts in the die-cutting machine, as well as the constraint relationships between different parts, and add them to the initial geometric model to obtain the processing geometric model; Step S13: Import the machining geometry model into the dynamics software, set the kinematic and dynamic parameters of the moving parts in the machining geometry model, set the loads of the solid parts in the machining geometry model under different dynamic parameter conditions, and obtain the dynamics model corresponding to the die-cutting machine. Step S14: The control logic of the PLC in the die-cutting machine and the sensor feedback are reproduced by PLC simulation software and imported into the dynamic model of the die-cutting machine to construct a virtual twin model of the die-cutting machine.

3. The digital twin optimization method for thermal paper die-cutting process parameters according to claim 1, characterized in that, Step S2 includes the following sub-steps: Step S21: Set a single-color original thermal paper to be fixed on the virtual twin model of the die-cutting machine, set multiple sets of process parameters, and the die-cutting machine in the virtual twin model of the die-cutting machine continuously cuts the original thermal paper according to different process parameters to obtain die-cut thermal paper corresponding to multiple sets of process parameters. Step S22: Lay the die-cut thermal paper flat on the table, record the cut edge as the die-cut edge, collect the thermal paper image corresponding to the die-cut thermal paper, and extract the die-cut pixel value of each pixel in the thermal paper image. Step S23: Read the original pixel value of the single color corresponding to the original thermal paper, and compare the die-cut pixel value of each pixel in the die-cut thermal paper with the original pixel value in turn. If the die-cut pixel value is different from the original pixel value, the corresponding pixel is recorded as a difference pixel; if the die-cut pixel value is the same as the original pixel value, no operation is performed.

4. The digital twin optimization method for thermal paper die-cutting process parameters according to claim 3, characterized in that, Step S2 further includes the following sub-steps: Step S24: Count the number of different pixels corresponding to the die-cut thermal paper and record it as the total number of pixel differences. If the total number of pixel differences is greater than or equal to the preset threshold for the number of pixel differences, then the corresponding die-cut thermal paper is recorded as unqualified; otherwise, proceed to step S25. Step S25: Divide the die-cut pixel value of each pixel in the die-cut thermal paper to obtain the first component, the second component and the third component of the corresponding pixel. Calculate the die-cut grayscale value of the corresponding pixel based on the first component, the second component and the third component; where the first component is the R value component of the die-cut pixel value, the second component is the G value component of the die-cut pixel value, and the third component is the B value component of the die-cut pixel value. Step S26: Compare the die-cut grayscale value of all pixels with the grayscale threshold. If the die-cut grayscale value is greater than or equal to the grayscale threshold, no operation is performed. If the die-cut grayscale value is less than the grayscale threshold, the corresponding pixel is recorded as an edge pixel.

5. The digital twin optimization method for thermal paper die-cutting process parameters according to claim 4, characterized in that, Step S2 further includes the following sub-steps: Step S27: Establish a Cartesian coordinate system with one end of the die-cut edge as the origin, and obtain the pixel coordinates of all edge pixels above the die-cut edge; denote the edge pixels at the origin as selected pixels, denote the edge pixels adjacent to the selected pixels as adjacent pixels, and calculate the slope between the selected pixels and the adjacent pixels. Step S28: Treat the current adjacent pixel as the next selected pixel, and then obtain the adjacent pixels of the next selected pixel. Continue iterating to the other end of the die-cut edge to obtain multiple slopes above the edge pixels. Subtract the minimum slope from the maximum slope to obtain the slope fluctuation value. If the slope fluctuation value is greater than or equal to the preset slope fluctuation threshold, the corresponding die-cut thermal paper is marked as unqualified. If the slope fluctuation value is less than the slope fluctuation threshold, no operation is performed. Step S29: Discard all process parameters corresponding to unqualified die-cut thermal paper, and record the remaining process parameters as die-cutting process parameters.

6. The digital twin optimization method for thermal paper die-cutting process parameters according to claim 1, characterized in that, Step S3 includes the following sub-steps: Step S31: Obtain the die-cut thermal paper and set the die-cut thermal paper to the same number as the die-cutting process parameters; Step S32: Place the die-cut thermal paper horizontally, focus the microscopic imaging device on the side where the die-cut edge is located, and acquire a microscopic image of the die-cut side of the corresponding die-cut thermal paper. Step S33: Draw a straight line to the right from the leftmost point at the top of the die-cut side micrograph to obtain the upper surface baseline; draw a straight line to the right from the leftmost point at the bottom of the die-cut side micrograph to obtain the lower surface baseline; read the burr height and burr width of the upper surface burr based on the upper surface baseline. The burr height and burr width of the lower surface burr are obtained based on the lower surface baseline.

7. The digital twin optimization method for thermal paper die-cutting process parameters according to claim 6, characterized in that, Step S3 includes the following sub-steps: Step S34: Discard the upper and lower surface burrs with a burr width less than or equal to k. Traverse the remaining upper and lower surface burrs. Obtain the upper surface fluctuation value by subtracting the minimum value of the upper surface burr height from the maximum value of the upper surface burr height. Obtain the lower surface fluctuation value by subtracting the minimum value of the lower surface burr height from the maximum value of the lower surface burr height. Step S35: Add the burr heights of all remaining upper surface burrs and lower surface burrs together and take the average to obtain the average burr height; combine the average burr height, upper surface fluctuation value and lower surface fluctuation value to construct the corresponding cutting quality evaluation index for die-cut thermal paper.

8. The digital twin optimization method for thermal paper die-cutting process parameters according to claim 7, characterized in that, Step S3 further includes the following sub-steps: Step S36: Calculate the actual die-cutting area of ​​the thermal paper, compare the actual die-cutting area with the estimated die-cutting area. If the actual die-cutting area is greater than or equal to the estimated die-cutting area, the wasted die-cutting area is obtained by subtracting the estimated die-cutting area from the actual die-cutting area. If the actual die-cutting area is less than the estimated die-cutting area, the actual die-cutting area is regarded as the wasted die-cutting area. Step S37: Obtain the initial tool price of the tool on the die-cutting machine, and obtain the tool waste price by multiplying the initial tool price by the tool depreciation rate; Step S38: Combine the actual die-cutting area, wasted die-cutting area, initial tool price, and tool waste price to construct the corresponding loss evaluation index for die-cut thermal paper.

9. The digital twin optimization method for thermal paper die-cutting process parameters according to claim 1, characterized in that, Step S4 includes the following sub-steps: Step S41: Obtain the cutting quality evaluation index and the loss evaluation index; Step S42: Then, obtain the virtual twin model of the die-cutting machine, and establish the cutting quality evaluation function and the loss evaluation function based on the virtual twin model of the die-cutting machine; Step S43: Obtain multiple sets of die-cutting process parameters, and calculate the cutting quality evaluation result and loss evaluation result of each set of die-cutting process parameters based on the cutting quality evaluation index and the loss evaluation index. Step S44: Substitute the cutting quality evaluation results and die-cutting process parameters into the cutting quality evaluation function, and solve in reverse to obtain the correction coefficients in the cutting quality evaluation function corresponding to each set of die-cutting process parameters; The average correction coefficient of the cutting quality evaluation function is obtained by summing the correction coefficients of multiple sets of die-cutting process parameters and taking the mean value. Step S45: Substitute the average correction coefficient of the cutting quality evaluation function into the cutting quality evaluation function to obtain the updated cutting quality evaluation function. Calculate the updated cutting quality evaluation results corresponding to multiple sets of die-cutting process parameters using the updated cutting quality evaluation function.

10. The digital twin optimization method for thermal paper die-cutting process parameters according to claim 9, characterized in that, Step S4 further includes the following sub-steps: Step S46: Substitute the loss evaluation results and die-cutting process parameters into the loss evaluation function, and solve in reverse to obtain the correction coefficient in the loss evaluation function corresponding to each set of die-cutting process parameters; The average correction coefficient of the loss evaluation function is obtained by summing the correction coefficients in the loss evaluation function corresponding to multiple sets of die-cutting process parameters and taking the average. Step S47: Substitute the average correction coefficient of the loss evaluation function into the loss evaluation function to obtain the updated loss evaluation function, and calculate the updated loss evaluation results corresponding to multiple sets of die-cutting process parameters through the updated loss evaluation function. Step S48: Obtain the comprehensive quality evaluation value by dividing the updated cutting quality evaluation result by the updated loss evaluation result; sort the comprehensive quality evaluation values ​​in descending order, read the die-cutting process parameters corresponding to the first-ranked comprehensive quality evaluation value, and record them as the optimal parameters.

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