A system for analyzing the degree of aging of a slitter knife

Abstract

This invention discloses a tool aging analysis system for slitting machines, belonging to the field of tool analysis technology. It includes a data acquisition module, a database, a status monitoring module, an aging analysis module, and a maintenance and replacement module. Through image recognition and speed parameter calculation, it can effectively assess the sharpness and wear status of the tools, with high accuracy and real-time performance, improving tool utilization efficiency, machining quality, and production cost control. Simultaneously, by analyzing sharpness and wear values, it can promptly replace or maintain severely worn tools, effectively extending tool life and reducing tool failure rates. Analysis of aging values ​​allows for timely identification of tools requiring replacement, avoiding machining quality problems caused by tool aging. Accurate aging prediction can reduce sudden downtime caused by tool aging, maintaining production continuity, and enabling maintenance and replacement before tools completely fail, minimizing production interruption time.

Description

Technical Field

[0001] This invention relates to the field of cutting tool analysis technology, specifically to a system for analyzing the aging degree of cutting tools for slitting machines. Background Technology

[0002] Slicing machines are widely used for processing various materials, such as wood, plastics, metals, and paper. By cutting, they separate large pieces of material into thin slices or smaller pieces of the required size. Overall, slicing machines are a very important tool in the manufacturing and processing industries, improving production efficiency, cutting accuracy, and operational safety. To maintain the performance of the slicing machine and extend its service life, it is crucial to regularly inspect and replace worn-out cutting tools.

[0003] As cutting tools age and wear, they lose their original sharpness, leading to a decline in cutting quality and the potential for burrs, tears, or unevenness on the cut surface. Cutting speed may gradually decrease, affecting production efficiency and resulting in longer processing times and increased material waste. Wear and aging of the blades can also cause tool breakage, increasing safety risks for operators. Therefore, to maintain the performance of the slitting machine and extend its service life, it is crucial to regularly inspect and replace aging tools. Furthermore, proper tool maintenance and use can effectively extend tool life and reduce production costs. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned in the background art by proposing a slitting machine tool aging degree analysis system.

[0005] To achieve the above objectives, this invention provides a slitting machine tool aging degree analysis system, comprising: a data acquisition module, a database, a condition monitoring module, an aging analysis module, and a maintenance and replacement module; the specific steps are as follows:

[0006] The condition monitoring module analyzes the operational information of the slitting machine's cutting tools to obtain the tool's sharpness and wear values, thereby identifying the aging and wear condition of the cutting tools. The specific steps are as follows:

[0007] Retrieve tool images corresponding to each acquisition time; use an image recognizer to identify the acquired tool images and perform grayscale processing; divide the grayscale processed tool images into two regions, from top to bottom, namely the end region and the cutting edge region; further divide the end region and the cutting edge region into several blocks evenly, and identify the grayscale value of each block, where the grayscale value of each block in the end region is denoted as Rd, and the grayscale value of each block in the cutting edge region is denoted as Ri; where d = 1, 2, 3...D, D is a positive integer, D represents the total number of blocks in the end region, and d represents the sequence number of any block in the end region; where i = 1, 2, 3...I, I is a positive integer, I represents the total number of blocks in the cutting edge region, and i represents the sequence number of any block in the cutting edge region; set a standard grayscale value corresponding to the end region and the cutting edge region, respectively, R1 and R2;

[0008] Substitute the gray values ​​Rd of the block in the end region, Ri of the block in the cutting edge region, R1 of the standard gray value of the block in the end region, and R2 of the standard gray value of the block in the cutting edge region into the formula. The sharpness value M is calculated; where a1 and a2 are the set weighting factors; from this, the tool sharpness value Mj corresponding to each acquisition time can be obtained; where j = 1, 2, 3...J, J takes a positive integer value, J represents the total number of acquisition times, and j represents the sequence number of any acquisition time.

[0009] As can be seen from the formula, the closer the gray value of the center point of the tool tip region at each acquisition time is to the standard gray value (i.e., ... The closer the gray value is to zero, the greater the sharpness value of the tool; the closer the gray value of the center point of the tool's cutting edge region at each acquisition time is to the standard gray value (i.e., The closer the value is to zero, the greater the sharpness of the tool. It should be noted that the greater the sharpness of the tool, the less wear occurs in the tip area, middle area and cutting edge area of ​​the tool, and the less need to replace it.

[0010] The feed rate and cutting speed of the tool at each acquisition time are retrieved and denoted as Uj and Cj, respectively. The values ​​of feed rate Uj and cutting speed Cj are substituted into the formula UCj=Uj×b1+Cj×b2 to calculate the running wear value, where b1 and b2 are the set weighting factors. The tool sharpness value Mj and running wear value UCj corresponding to each acquisition time are sent to the aging analysis module.

[0011] The aging analysis module performs aging analysis based on the tool sharpness value and running wear value at each acquisition time, and analyzes whether the tool needs to be replaced accordingly.

[0012] The maintenance and replacement module unloads old tools and installs new tools based on each slitting machine tool and its corresponding aging value.

[0013] As a preferred embodiment of the present invention, the aging analysis of the tool sharpness value and running wear value specifically includes the following steps:

[0014] The tool sharpness value Mj and the running wear value UCj corresponding to each acquisition time are substituted into the formula Kj=c1×Mj+c2×UCj to calculate the wear degree value Kj; where c1 and c2 are the set weight factors; a two-dimensional rectangular coordinate system is constructed with time as the abscissa and wear degree value as the ordinate to obtain a line graph of wear degree value changing with time; and the aging value is obtained by slope analysis based on this.

[0015] An aging threshold is set and compared with the aging value of each tool. When the aging value of a tool is greater than or equal to the set aging threshold, the tool is recorded as an aged tool. Thus, each slitting machine tool and its corresponding aging value can be obtained and sent to the maintenance and replacement module.

[0016] In a preferred embodiment of the present invention, the specific steps of slope analysis are as follows:

[0017] A two-dimensional Cartesian coordinate system is constructed with time as the x-axis and wear level as the y-axis. Wear level values ​​are input into the coordinate system according to their corresponding acquisition times, and the positions of these values ​​in the coordinate system are recorded as wear points. Wear points are connected sequentially by line segments to obtain a line graph showing the change in wear level over time. The slope of the line segment formed by two adjacent wear points is calculated and recorded as the wear slope. The wear slopes greater than zero are summed to calculate the wear aggravation degree, recorded as H1. The wear slopes less than zero are summed to calculate the wear improvement degree, recorded as H2. The wear aggravation degree H1, wear improvement degree H2, and the wear level value K closest to the current acquisition time are substituted into the formula. The aging value Lp is calculated; where w1 and w2 are set weighting factors; p = 1, 2, 3...P, where P is a positive integer, P represents the total number of slitting machine tools, and p represents the serial number of any slitting machine tool.

[0018] In a preferred embodiment of the present invention, the old tool is unloaded and a new tool is installed based on the tool's aging value. The specific steps are as follows:

[0019] Step 1: Sort all the aged cutting tools in descending order of their aging values, and select the slitting machine with the highest aging value as the slitting machine to be unloaded.

[0020] Step 2: Remove the old cutter from the cutter holder or cutting head of the slitting machine and correctly install the new cutter onto the cutter holder or cutting head of the slitting machine.

[0021] Step 3: Repeat steps 1 and 2 above until all the unloading slitting machines have completed the installation of new tools.

[0022] As a preferred embodiment of the present invention, the specific steps for setting the standard grayscale values ​​of the end region and the cutting edge region are as follows:

[0023] The standard grayscale value is the grayscale value of the tool in its optimal state before it has been used and worn. Under normal circumstances, the sharper the cutting edge area, the higher the grayscale value of the area closer to the cutting edge. When the tool ages or wears, the cutting edge becomes less sharp, and the grayscale value of the corresponding area becomes lower. Therefore, the grayscale value of the cutting edge area is greater than the grayscale value of the end area and is greater than zero.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. By using image recognition and speed parameter calculation, the sharpness and wear condition of cutting tools can be effectively assessed with high accuracy and real-time performance, which helps to improve tool utilization efficiency, machining quality, and production cost control. At the same time, by analyzing the sharpness value and running wear value, severely worn tools can be replaced or maintained in a timely manner, which can effectively extend the tool life, reduce the tool failure rate, optimize the production process, and improve overall production efficiency.

[0026] 2. By analyzing aging values, tools that need to be replaced can be identified in a timely manner, avoiding machining quality problems caused by tool aging; accurate aging prediction can reduce sudden downtime caused by tool aging, maintain production continuity, and allow maintenance or replacement before tools completely fail, reducing production interruption time; it can accurately identify the aging status of tools, optimize maintenance decisions, improve production efficiency, save costs, and provide scientific data support for maintenance personnel. Attached Figure Description

[0027] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0028] Figure 1 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0029] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0033] Embodiments of the present invention, such as Figure 1 As shown, a slitting machine tool aging degree analysis system is provided, including a data acquisition module, a database, a status monitoring module, an aging analysis module, and a maintenance and replacement module;

[0034] The data acquisition module collects operation information through sensors mounted on the slitting machine and sends it to the database; the operation information includes the tool image, feed rate and cutting speed of the slitting machine;

[0035] The database stores the received slitting machine operation information and sends it to the status monitoring module;

[0036] The condition monitoring module analyzes the operational information of the slitting machine's cutting tools to obtain the tool's sharpness and wear values, thereby identifying the aging and wear condition of the cutting tools. The specific steps are as follows:

[0037] Retrieve tool images corresponding to each acquisition time; use an image recognizer to identify the acquired tool images and perform grayscale processing; divide the grayscale processed tool images into two regions, from top to bottom, the end region and the cutting edge region (it should be noted that the end region is the part far from the cutting edge, and the cutting edge region is the part close to the cutting edge); further divide the end region and the cutting edge region into several blocks evenly, and identify the grayscale value of each block, where the grayscale value of each block in the end region is denoted as Rd, and the grayscale value of each block in the cutting edge region is denoted as Ri; where d = 1, 2, 3...D, D takes a positive integer value, D represents the total number of blocks in the end region, and d represents... The index of any block within the end region is given by i, where i = 1, 2, 3...I, where I is a positive integer, I represents the total number of blocks within the cutting edge region, and i represents the index of any block within the cutting edge region. A standard grayscale value is assigned to the end region and the cutting edge region, respectively, R1 and R2. It should be noted that the standard grayscale value usually refers to the grayscale value of the tool in its optimal state before wear and tear. Normally, because the cutting edge region is sharper, the area closer to the cutting edge region has a higher grayscale value. When the tool ages or wears, the cutting edge becomes less sharp, and the corresponding area has a lower grayscale value. Therefore, 0 < R1 < R2.

[0038] Substitute the grayscale value Rd of the block in the end region, the grayscale value Ri of the block in the cutting edge region, the standard grayscale value R1 of the block in the end region, and the standard grayscale value R2 of the block in the cutting edge region into the set formula. The sharpness value M is calculated; where a1 and a2 are the set weighting factors, a1 is 1.73 and a2 is 2.62; thus, the tool sharpness value corresponding to each acquisition time is denoted as Mj; where j = 1, 2, 3...J, J is a positive integer, J represents the total number of acquisition times, and j represents the sequence number of any acquisition time; as shown by the formula, the closer the gray value of the center point of the tool tip region at each acquisition time is to the standard gray value (i.e., ... The closer the gray value is to zero, the greater the sharpness value of the tool; the closer the gray value of the center point of the tool's cutting edge region at each acquisition time is to the standard gray value (i.e., The closer the value is to zero, the greater the sharpness of the tool; it should be noted that the sharpness of the tool...

[0039] The higher the sharpness value, the less wear occurs in the tip, middle, and cutting edge areas of the tool, and the less it needs to be replaced.

[0040] The feed rate and cutting speed of the tool at each acquisition moment are retrieved and denoted as Uj and Cj, respectively. Feed rate typically refers to the tool's speed in the cutting direction, while cutting speed is the tool's speed along the cutting path. The values ​​of feed rate Uj and cutting speed Cj are substituted into the set formula UCj = Uj × b1 + Cj × b2 to calculate the running wear value UCj. Here, b1 and b2 are set weighting factors, a1 is set to 1.44, and a2 is set to 1.58. The tool sharpness value Mj and running wear value UCj corresponding to each acquisition moment are then sent to the aging analysis module. It should be noted that increasing feed rate and cutting speed can improve production efficiency but also increases tool wear and reduces machining quality; conversely, decreasing feed rate and cutting speed can improve surface quality and tool life but reduces production efficiency. Correctly setting feed rate and cutting speed can improve machining efficiency, extend tool life, and improve machining quality. Therefore, feed rate and cutting speed are set or adjusted by industry professionals according to the machining environment and workpiece requirements.

[0041] By using image recognition and speed parameter calculation, the sharpness and wear condition of cutting tools can be effectively assessed with high accuracy and real-time performance, which helps to improve tool utilization efficiency, machining quality, and production cost control. At the same time, by analyzing the sharpness value and running wear value, severely worn tools can be replaced or maintained in a timely manner, which can effectively extend the tool life, reduce the tool failure rate, optimize the production process, and improve overall production efficiency.

[0042] The aging analysis module performs aging analysis based on the tool sharpness value and running wear value at each data acquisition time, and analyzes whether the tool needs to be replaced accordingly; the specific steps are as follows:

[0043] The wear level value Kj is calculated by substituting the tool sharpness value Mj and the running wear value UCj corresponding to each acquisition time into the set formula Kj=c1×Mj+c2×UCj; where c1 and c2 are set weight factors, a1 is 0.44 and a2 is 0.63.

[0044] A two-dimensional Cartesian coordinate system is constructed with time as the x-axis and wear level as the y-axis. The wear level value Kj is input into the coordinate system according to the corresponding acquisition time, and the position of the wear level value in the coordinate system is recorded as a wear point. Line segments are used to connect the wear points sequentially to obtain a line graph of the wear level value changing over time. The slope of the line segment formed by two adjacent wear points is calculated and recorded as the wear slope, which represents the changing trend of the wear level value between the two adjacent wear points. When the slope is greater than zero, it indicates that the wear level value of the wear point is increasing, indicating that the tool wear is intensifying from the wear point to the next wear point. When the slope is less than zero, it indicates that the wear level value of the wear point is decreasing, indicating that the tool wear is lessening from the wear point to the next wear point. The wear intensification degree is calculated by summing the wear slopes greater than zero and recorded as H1. The wear improvement degree is calculated by summing the wear slopes less than zero and recorded as H2. The wear intensification degree H1, wear improvement degree H2, and the wear level value K closest to the current acquisition time are substituted into the set formula. The aging value Lp is calculated; where w1 and w2 are set weighting factors; p = 1, 2, 3...P, where P is a positive integer, P represents the total number of slitting machine tools, and p represents the serial number of any slitting machine tool; as can be seen from the formula, the greater the degree of wear aggravation and the smaller the degree of wear improvement, the more serious the aging of the tool.

[0045] An aging threshold is set and compared with the aging value of each tool. When the aging value of a tool is greater than or equal to the set aging threshold, it indicates that the tool has a greater risk of aging and wear, and the tool is recorded as an aged tool. Thus, each slitting machine tool and its corresponding aging value can be obtained and sent to the maintenance and replacement module.

[0046] By analyzing aging values, tools that need to be replaced can be identified in a timely manner, avoiding machining quality problems caused by tool aging; accurate aging prediction can reduce sudden downtime caused by tool aging, maintain production continuity, and allow maintenance or replacement before tools completely fail, reducing production interruption time; it can accurately identify the aging status of tools, optimize maintenance decisions, improve production efficiency, save costs, and provide scientific data support for maintenance personnel.

[0047] The maintenance and replacement module unloads old tools and installs new tools based on each slitting machine tool and its corresponding aging value; the specific steps are as follows:

[0048] Step 1: Sort all the aged cutting tools in descending order of their aging values, and select the slitting machine with the highest aging value as the slitting machine to be unloaded.

[0049] Step 2: Remove the old cutter from the cutter holder or cutting head of the slitting machine and correctly install the new cutter onto the cutter holder or cutting head of the slitting machine.

[0050] Step 3: Repeat steps one and two above until all the unloading slitting machines have completed the installation of new cutting tools;

[0051] By systematically assessing tool aging values, prioritizing the replacement of severely aged tools, and following standardized unloading and installation procedures, the efficiency and accuracy of tool maintenance have been improved. Properly scheduling tool replacements can reduce the risk of equipment damage, extend the overall lifespan of the equipment, and reduce maintenance costs.

[0052] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0053] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A system for analyzing the aging degree of cutting tools in a slitting machine, characterized in that, It includes a data acquisition module and a database; the data acquisition module collects operation information through sensors mounted on the slitting machine and sends it to the database; the operation information includes the tool image, feed rate and cutting speed of the slitting machine; it is characterized by further including a condition monitoring module, an aging analysis module and a maintenance and replacement module; The condition monitoring module analyzes the operational information of the slitting machine's cutting tools to obtain the tool's sharpness and wear values, thereby identifying the aging and wear condition of the cutting tools. The specific steps are as follows: Retrieve tool images corresponding to each acquisition time; use an image recognizer to identify the acquired tool images and perform grayscale processing; divide the grayscale processed tool images into two regions, from top to bottom, namely the end region and the cutting edge region; further divide the end region and the cutting edge region into several blocks evenly, identify the grayscale value of each block in the end region and the grayscale value of each block in the cutting edge region; set a standard grayscale value corresponding to the end region and the cutting edge region respectively. The sharpness value is obtained by normalizing the gray values ​​of the blocks in the end region, the blocks in the cutting edge region, the standard gray values ​​of the blocks in the end region, and the standard gray values ​​of the blocks in the cutting edge region; thus, the tool sharpness value corresponding to each acquisition time can be obtained. Retrieve the tool feed rate and cutting speed at each acquisition time; normalize the values ​​of feed rate and cutting speed to obtain the running wear value; and send the tool sharpness value and running wear value corresponding to each acquisition time to the aging analysis module. The aging analysis module performs aging analysis based on the tool sharpness value and running wear value at each acquisition time, and analyzes whether the tool needs to be replaced accordingly. The maintenance and replacement module unloads old tools and installs new tools based on each slitting machine tool and its corresponding aging value.

2. The aging degree analysis system for a slitting machine tool according to claim 1, characterized in that, The specific steps for aging analysis of tool sharpness and operational wear values ​​are as follows: The wear degree value is obtained by normalizing the tool sharpness value and running wear value at each acquisition time. A two-dimensional rectangular coordinate system was constructed with time as the horizontal axis and wear level as the vertical axis to obtain a line graph of wear level changing over time; and slope analysis was performed to obtain the aging value. An aging threshold is set and compared with the aging value of each tool. When the aging value of a tool is greater than or equal to the set aging threshold, the tool is recorded as an aged tool. Thus, each slitting machine tool and its corresponding aging value can be obtained and sent to the maintenance and replacement module.

3. The aging degree analysis system for a slitting machine tool according to claim 2, characterized in that, The specific steps of slope analysis are as follows: A two-dimensional rectangular coordinate system is constructed with time as the horizontal axis and wear level as the vertical axis. The wear level values ​​are input into the coordinate system according to the corresponding collection time, and the position of the wear level value in the coordinate system is recorded as the wear point. The wear points are connected sequentially by line segments to obtain a line graph of the wear level value changing with time. The slope of the line segment formed by two adjacent wear points is recorded as the wear slope; the wear slopes greater than zero are summed to obtain the wear aggravation degree; the wear slopes less than zero are summed to obtain the wear improvement degree. The aging value is obtained by normalizing the values ​​of wear aggravation, wear improvement, and wear degree K most recent at the current acquisition time.

4. The aging degree analysis system for a slitting machine tool according to claim 1, characterized in that, The steps for unloading old tools and installing new tools based on their aging values ​​are as follows: Step 1: Sort all the aged cutting tools in descending order of their aging values, and select the slitting machine with the highest aging value as the slitting machine to be unloaded. Step 2: Remove the old cutter from the cutter holder or cutting head of the slitting machine and correctly install the new cutter onto the cutter holder or cutting head of the slitting machine. Step 3: Repeat steps 1 and 2 above until all the unloading slitting machines have completed the installation of new tools.

5. The aging degree analysis system for a slitting machine tool according to claim 1, characterized in that, The specific steps for setting the standard grayscale values ​​for the end area and the cutting edge area are as follows: The standard grayscale value is the grayscale value of the tool in its optimal state before it has been used and worn. Under normal circumstances, the sharper the cutting edge area, the higher the grayscale value of the area closer to the cutting edge. When the tool ages or wears, the cutting edge becomes less sharp, and the grayscale value of the corresponding area becomes lower. Therefore, the grayscale value of the cutting edge area is greater than the grayscale value of the end area and is greater than zero.

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