Ssensor installation position analysis method, sensor installation position analysis program, and recording medium that records program

Abstract

The present invention provides a sensor installation position analysis method and a sensor installation position analysis program for more efficiently monitoring and managing the state of an object requiring monitoring or the like, such as equipment and machinery. A sensor installation position analysis method according to one aspect of the present invention comprises: a step for recording workpiece data, jig and tool data, and machining condition data; a step for acquiring state value data on the basis of the workpiece data, the jig and tool data, and the machining condition data; a step for recording reference value data; and a step for acquiring correlation coefficient data and intensity coefficient data on the basis of the reference value data and the state value data.

Description

Sensor installation position analysis method, sensor installation position analysis program, and recording medium on which this program is recorded

[0001] The present invention relates to a sensor installation position analysis method, a sensor installation position analysis program, and a recording medium having the program recorded thereon. Specifically, the method is useful for analyzing and selecting sensor installation positions for objects such as jigs and tools that require monitoring and management.

[0002] In order to monitor and manage the operational status of company-owned facilities and machinery, activities such as the Internet of Things (IoT) and Digital Transformation (DX) have become important initiatives at manufacturing sites. For example, measuring devices or sensors such as laser displacement meters and load cells are attached to press machines used for parts production to reduce problems such as machine stoppages and the mixing of defective products due to molding defects such as misalignment of molded products and burrs, and status monitoring and management systems are being established as a means of avoiding these problems.

[0003] In order to monitor and manage the condition of equipment and machinery, it is important to install devices that convert the condition of the tools used there into electrical signals, in other words, sensors that can respond in correlation with changes in conditions such as strain, pressure, and temperature, collect these signals, and use signal processing to separate and convert the characteristics that correspond to the changes in condition into data that can be used to monitor and manage the condition.

[0004] In relation to the above, a technology for monitoring an object using a sensor or the like is described in Patent Document 1 below.

[0005] Japanese Patent Application Laid-Open No. 2019-013976

[0006] Incidentally, in jigs and tools, the events and locations that require monitoring and management are usually locations that are exposed to high or low temperatures, high pressure, or sliding, and are prone to damage such as wear, chipping, and cracking. Therefore, it is desirable for the sensor to be located in a position that coincides with or is in contact with the location that is at risk of such damage.

[0007] However, as mentioned above, in high-stress environments, sensors may have a short lifespan or malfunction, making it impossible to directly detect events that require monitoring. It may also be difficult to place sensors directly at the locations that require monitoring. Furthermore, there are concerns that installing sensors may induce or accelerate damage to the object, such as wear, chipping, or cracking. Therefore, the current method of avoiding this situation involves installing sensors in an indirect manner, either by installing them at a distance or through another object.

[0008] Furthermore, because the monitoring is performed using sensors, there is a problem in that the physical quantities that can actually be measured are limited to those that can be measured by the sensors. In other words, it is difficult to quantitatively evaluate the degree to which the sensor signal at the measurement location correlates with changes in the monitored phenomenon. To give a more specific example, while the impact of workpiece materials such as plastics, metals, or minerals on tools is a state of damage such as wear, chipping, and cracking, there is a problem in that it is not possible to evaluate whether the response (e.g., strain) correlated with changes in these conditions is appropriate at the measurement location.

[0009] In view of the above problems, the present invention aims to provide a sensor installation location analysis method and a program for analyzing sensor installation locations, which allow for more efficient monitoring and management of the status of objects that require monitoring, such as equipment and machinery, as well as a recording medium on which this program is recorded.

[0010] A sensor installation position analysis method according to one aspect of the present invention that solves the above-mentioned problems includes the steps of recording workpiece data, tool data, and machining condition data, acquiring status value data based on the workpiece data, tool data, and machining condition data, recording reference value data, and acquiring correlation coefficient data and strength coefficient data based on the reference value data and the status value data.

[0011] In addition, in this respect, although not limited thereto, it is preferable that the reference value data is acquired based on the state value data.

[0012] Furthermore, in this respect, although not limited thereto, it is preferable that the step of acquiring state value data is acquired by at least one of the finite element method, the finite volume method, the difference method, the boundary element method, the particle method, and the meshless method.

[0013] In addition, in this aspect, it is preferable, but not limited to, to have a step of acquiring distribution map data based on the correlation coefficient data and the intensity coefficient data.

[0014] In addition, in this aspect, it is preferable, but not limited to, to have a step of acquiring recommended sensor installation position data based on the correlation coefficient data and the intensity coefficient data.

[0015] Furthermore, in this respect, although not limited thereto, in the step of acquiring correlation coefficient data and intensity coefficient data based on reference value data and state value data, it is preferable to acquire recommended area data determined by correlation coefficient data and intensity coefficient data that are equal to or greater than a predetermined value.

[0016] Furthermore, in this respect, although not limited thereto, it is preferable that the recommended sensor installation position data includes recommended sensor installation direction data.

[0017] In addition, in this respect, although not limited thereto, it is preferable that the correlation coefficient data and the intensity coefficient data are obtained according to the following formulas. In the above formula, Rj is the value of the correlation coefficient data, Sj is the value of the strength coefficient data, M is the total number of elements or the total number of nodes, N is the number of all reference value data, τ ｍａｘ is the maximum value among the recorded reference data, τ ｍｉｎ indicates the minimum value among the recorded reference data. ｍａｘ is the maximum value of the state value data, and α ｍｉｎ indicates the minimum value of the state value data.

[0018] Furthermore, in this respect, although not limited thereto, it is preferable to define as recommended region data a region where the absolute value of the correlation coefficient data is 0.6 or more and the intensity coefficient data is −20 or more.

[0019] In addition, a program for analyzing sensor installation positions according to another aspect of the present invention causes a computer to execute the steps of recording workpiece data, tool data, and machining condition data, acquiring status value data based on the workpiece data, tool data, and machining condition data, recording reference value data, and acquiring correlation coefficient data and strength coefficient data based on the reference value data and the status value data.

[0020] In addition, a recording medium according to another aspect of the present invention has recorded thereon a program for analyzing sensor installation positions for executing the steps of recording workpiece data, tool data, and machining condition data, acquiring status value data based on the workpiece data, tool data, and machining condition data, recording reference value data, and acquiring correlation coefficient data and strength coefficient data based on the reference value data and status value data.

[0021] As described above, the present invention can provide a sensor installation location analysis method and a sensor installation location analysis program for more efficiently monitoring and managing the status of objects that require monitoring, such as equipment and machinery, as well as a recording medium on which this program is recorded.

[0022] 1 is a diagram showing an external appearance of a workpiece according to an embodiment; FIG. 2 is a diagram showing an external appearance and cross-sectional image of a jig / tool ​​according to an embodiment; FIG. 3 is a cross-sectional image showing the relationship between the workpiece and the jig / tool ​​according to an embodiment; FIG. 4 is a diagram showing an example of principal stress distribution in the workpiece and the jig / tool ​​according to an embodiment; FIG. 5 is a distribution diagram in which the correlation coefficient Rj and strength coefficient Sj according to an embodiment are substituted into a calculation model constituting the jig / tool; FIG. 6 is a distribution diagram in which a region is selected in which the correlation coefficient Rj is 0.7 or more and the strength coefficient Sj is 2 or more according to an embodiment; FIG. 7 is a diagram showing two selected locations according to an embodiment; FIG. 8 is a diagram showing the transition of maximum principal stress with respect to stroke at a damaged portion of a die according to an embodiment; FIG. 9 is a distribution diagram of correlation coefficients and strength coefficients with respect to maximum principal stress at a corner rounded portion of a die according to an embodiment; FIG. 10 is a diagram showing the external appearance of a device that digitizes and transmits signals according to an embodiment; FIG. 11 is an image diagram showing dedicated software that transfers strain and temperature data measured by a sensor according to an embodiment to an external cloud server; FIG. 12 is a diagram showing the mounting position and external appearance of a sensor according to an embodiment; FIG. 13 is a diagram showing a typical signal waveform in one forging operation according to an embodiment; FIG. 14 is a diagram showing the state of material filling into a die according to an embodiment; FIG. 15 is a diagram showing the correlation between the range of change in strain and the state of material filling into a die according to an embodiment;

[0023] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, the present invention can be embodied in many different forms and is not limited to the specific examples described in the following embodiments and examples.

[0024] (Sensor installation position analysis program) The sensor installation position analysis program (hereinafter referred to as "this program") according to this embodiment is intended to cause a computer to execute the following steps: (S1) recording workpiece data, tool data, and machining condition data; (S2) acquiring status value data based on the workpiece data, tool data, and machining condition data; (S3) recording reference value data; and (S4) acquiring correlation coefficient data and strength coefficient data based on the reference value data and status value data.

[0025] (Sensor installation position analysis method) This program is executed by an information processing device, i.e., a computer, and specifically is stored in a recording medium such as a computer hard disk, and is read into a volatile recording medium such as a memory and executed as necessary. That is, when this program is executed by a computer, it becomes a sensor installation position analysis method (hereinafter referred to as "this method") that includes the steps of: (S1) recording workpiece data, tool data, and machining condition data; (S2) acquiring status value data based on the workpiece data, tool data, and machining condition data; (S3) recording reference value data; and (S4) acquiring correlation coefficient data and strength coefficient data based on the reference value data and the status value data.

[0026] The computer used to execute this program is not limited as long as it has the above functions, but it is preferable that it includes, but is not limited to, components of a typical computer, such as a central processing unit (CPU), a non-volatile recording medium such as a hard disk or flash memory, a volatile recording medium such as memory, a bus connecting these, input devices such as a keyboard or mouse, and a display device such as a monitor.

[0027] The computer may be a so-called notebook computer or desktop computer, or may be a portable information terminal, which has become increasingly popular in recent years, specifically a so-called smartphone or tablet terminal.

[0028] Next, each step of the method executed by the program will be described in detail. As is clear from the above description, the method can be used to analyze the position and orientation in which a sensor should be attached to an object to be monitored or managed, such as a tool or mold (hereinafter simply referred to as "object")

[0029] First, this method includes a step (S1) of recording workpiece data, tool data, and machining condition data.

[0030] Here, "workpiece data" refers to data containing information about the workpiece, and specifically, it preferably includes material data and shape data of the workpiece. Furthermore, it is preferable that the shape data includes at least one of node data and element data containing information about nodes and elements, depending on the calculation method.

[0031] Here, "material data" refers to data containing information about the material of the workpiece, and specifically, it is preferable that the data contain at least one of the following information: Young's modulus, Poisson's ratio, yield stress, yield condition equation, deformation resistance, creep characteristics, hardness, thermal expansion coefficient, heat capacity, thermal conductivity, emissivity, dielectric constant, magnetic permeability, and electrical resistivity.

[0032] Furthermore, "shape data" refers to data containing information about the shape of the workpiece, and specifically includes at least one of surface data drawn using dimensions defining its initial appearance or obtained using a 3D digitizer or 3D scanner, and data defining the front and back surfaces of the surface or the space separated by the surface as inside and outside. Here, "workpiece" refers to a material to be processed using a tool, such as, but not limited to, a metal piece that is deformed using a tool such as a mold. The shape data will be appropriately modified in shape by processing in subsequent steps.

[0033] Furthermore, the shape data preferably includes the node data and element data described above. "Node data" refers to data containing information about nodes, and "element data" refers to data containing information about elements. As will become clear from the description below, "node" and "element" are useful in computational processes such as the finite element method. For example, in the finite element method, shape data information after deformation due to processing is updated, and state value data is recorded and updated, so the analytical accuracy varies depending on the number of nodes and elements. Therefore, including information about nodes and elements has the advantage of allowing accuracy adjustment. Furthermore, it is preferable that each of the node data and element data includes identification number data and position data. Because the positions of nodes and elements change as appropriate depending on subsequent processing, it is useful to identify individual nodes and elements by identification numbers and include position information corresponding to those identification numbers in order to identify them. As is clear from this description, the identification number data is data containing information regarding the identification number for identifying a node or element, and the position data is data containing information regarding the position information of the node or element (for example, the respective position information in a coordinate system such as the X coordinate, Y coordinate, and Z coordinate) that is provided corresponding to the identification number.

[0034] Furthermore, "tool data" refers to data containing information about tools, specifically including material data and shape data of the tools. The shape data preferably includes at least one of node data and element data containing information about nodes and elements, depending on the procedure of the calculation method.

[0035] Here, "material data" refers to data containing information about the material of the tool, and its format is the same as that of the material data in the workpiece data. Specifically, the material data of the tool preferably includes at least one of the following information: Young's modulus, Poisson's ratio, yield stress, yield condition equation, deformation resistance, creep characteristics, hardness, thermal expansion coefficient, heat capacity, thermal conductivity, emissivity, dielectric constant, magnetic permeability, and electrical resistivity.

[0036] Furthermore, "shape data" refers to data containing information about the shape of a tool or jig. Specifically, it preferably includes at least one of surface data drawn using dimensions defining its initial appearance or obtained using a 3D digitizer or 3D scanner, and data defining the front and back surfaces of the surface or the space separated by the surface as internal and external. The format, etc., is the same as that of the shape data of the workpiece. Here, "tools" refers to tools used to process the workpiece, including, but not limited to, forging dies for processing metal pieces, press dies, die-casting dies, casting dies, injection molding dies, open-die forging anvils, rolling rolls, cutting tips, grinding wheels, etc. Note that the shape data of tools or jigs will be subject to appropriate shape changes through processing in later steps, although not as frequently as the workpiece.

[0037] Furthermore, the shape data preferably includes the node data and element data described above. "Node data" refers to data containing information about nodes, and "element data" refers to data containing information about elements. As will become clear from the following description, "node" and "element" are useful in computational processes such as the finite element method. For example, in the finite element method, the analysis accuracy varies depending on the number of nodes and elements because the shape data after deformation due to processing is updated and state value data is recorded and updated. Therefore, including information about nodes and elements has the advantage of allowing the accuracy to be adjusted. Furthermore, although not limited to these, each piece of node data preferably includes node number data, position data, and initial value data. Furthermore, although not limited to these, element data preferably includes at least data on the node number combinations that make up the element, element number data, and initial value data. Because the positions of nodes and elements vary depending on subsequent processing, it is useful to identify individual nodes and elements by identification numbers and include position information corresponding to the identification numbers. As is clear from this description, the identification number data is data containing information about identification numbers for specifying nodes and elements, and the position data is data containing information about the position information of the nodes and elements (e.g., position information in a coordinate system such as X coordinate, Y coordinate, and Z coordinate) that is provided corresponding to the identification number. As is clear from this description, the format of this node data and element data is the same as that of the node data and element data of the workpiece described above. Furthermore, in this method, one or more jigs and tools may be used. When multiple jigs and tools are used, multiple sets of tool data will be prepared.

[0038] The "machining condition data" herein refers to data containing information about the conditions for machining the workpiece using the tool. The machining condition data preferably includes, for example, process type data, temperature data, friction coefficient data, operation type data, etc.

[0039] Furthermore, the "process type data" is data containing information regarding the type of process, and specifically, it is preferable that the data contains information for characterizing processes such as casting, forging, rolling, extrusion, forging, shape rolling, press forming, joining, cutting, grinding, etc.

[0040] Furthermore, the "temperature data" is data including information on the temperature in the process, and is preferably data including information on control from the start to the end of the process, indicated by, for example, the heating temperature, heating time, holding temperature, holding time, cooling temperature, cooling time, etc. of the process.

[0041] Furthermore, the "friction coefficient data" is data containing information regarding the friction coefficient in the process, and it is preferable that the friction coefficient data be data containing a coefficient corresponding to the state between the tool and the workpiece, such as no lubrication, solid lubrication, liquid lubrication, mist lubrication, etc.

[0042] Furthermore, the "motion type data" is data containing information regarding the type of motion in the process, and is preferably data containing information specifying the basic motion of the above-mentioned tool or workpiece, such as link motion, crank motion, speed control motion, load control motion, or angular velocity control motion.

[0043] This method also includes a step (S2) of acquiring status value data based on workpiece data, tool data, and machining condition data. Here, "status value data" refers to data containing information about the status manifested by the workpiece data and tool data, specifically, data containing values ​​related to displacement, strain, stress, surface pressure, load, temperature, etc. Furthermore, if the workpiece data and tool data contain node data and element data, the status value data is preferably recorded corresponding to each node and element of the node data and element data. Furthermore, as is clear from the above description, because changes in the status value data over time are important for highly accurate analysis, it is also preferable to include time data and record each status value data corresponding to this time data. That is, for each of the multiple node data and element data, multiple time data and status value data corresponding to each of the multiple time data are recorded. Moreover, it is preferable that the status value data is not limited to one type of physical quantity, but that multiple status values ​​are recorded.

[0044] In this step, it is also preferable to add contact data to each of the node data and element data in addition to the state value data. "Contact data" refers to data containing information about the contact between the workpiece and the tool. Since the friction coefficient differs depending on whether each node or element is in a contact or non-contact state, it is very useful to distinguish between these states.

[0045] Furthermore, although the method for calculating the state value data in this step is not limited, it is preferable to use the finite element method. Here, the "finite element method" refers to a method in which, regardless of whether it is an object or space, a target region is constructed using the smallest unit known as an "element" and approximated to a finite number of degrees of freedom, thereby enabling numerical calculations using a finite number of mathematical expressions. In the case of one dimension, an element is composed of at least two "nodes," in the case of two dimensions, at least three nodes, and in the case of three dimensions, at least four nodes. As described above, a node contains at least information on node number data, node coordinate data, and initial value data, and element data preferably contains at least information on the node number pair data, element number data, and initial value data that constitute the element.

[0046] Also, although the state value data may maintain the initial values ​​given as information to the nodes and elements, they are updated to values ​​obtained by numerical calculation and recorded. In view of the above, this is a very effective numerical analysis method when analyzing structures with complex shapes such as jigs and tools, workpieces, etc. Changes in the state value data, such as changes in workpiece data, tool data, and processing condition data, or changes in the state over time during the process, can be retrieved arbitrarily by specifying the node number and element number of the object to be analyzed.

[0047] In this step, in addition to the finite element method, the finite volume method, the difference method, the boundary element method, the particle method, the meshless method, etc. can be used as long as the state value data can be obtained, but there are no limitations. When solving numerically, the difference method is a method of discretizing and handling equations, while the finite volume method and the boundary element method are methods of dividing the equation into a finite number of volume elements and boundary elements, respectively, and discretizing and handling the equations. In addition, in the particle method and meshless method, particles corresponding to a finite number of nodes are responsible for the state value data.

[0048] It should be noted that, to reiterate, in this step, it is preferable that the state value data be recorded in correspondence with time data. Since the state (specifically, the positional relationship between the workpiece and the tool) differs at each stage of the processing, i.e., at each instant of time, the forces applied to the workpiece and the tool at each time and in each positional relationship will differ. Therefore, obtaining the time data and the corresponding state value data as a set of data has the advantage of enabling more detailed analysis.

[0049] This method also includes a step (S3) of recording reference value data. Here, "reference value data" refers to data containing information about a physical quantity corresponding to a defect to be monitored during the machining process. Specific examples of the physical quantity include a change in contact pressure in the case of a workpiece seizure defect, a change in stress at the damaged location in the case of a jig / tool ​​breakage defect, and a change in friction coefficient in the case of poor lubrication. It is also possible to use the condition value data itself as the reference value data. Specifically, a change in load in the condition value data in the case of a forging overload defect, or a change in an integral value calculated from the product of stress and strain increment in the condition value data in the case of a workpiece ductile fracture defect, are examples of such data.

[0050] Furthermore, although the reference value is a physical quantity, as mentioned above, there are situations where it is difficult to measure directly or where there is no corresponding sensor. As mentioned above, the condition value data is data containing information about the condition appearing in the workpiece or tool, specifically values ​​related to displacement, strain, stress, surface pressure, load, temperature, etc., which can be measured by attaching a corresponding sensor to the tool. In other words, this method aims to grasp the reference value, which is difficult to measure directly, as a measurable value by obtaining both the reference value and the condition value and determining the correlation between them.

[0051] Here, the reference value data acquisition pattern may be calculated based on the state value data, or may be the state value itself, or may be defined as it is without calculation. For example, when calculated based on the state value data, the reference value data is the seizure of the workpiece to the mold, which can be calculated using the surface pressure at the same position, and when it is the state value itself, a crack in the molding part of the mold can be the stress at the same position. Furthermore, data including information such as smoke, light emission, gloss, sound, and vibration can be used as the reference value data defined as it is without calculation.

[0052] Furthermore, like the above-mentioned state value data, it is preferable that this reference value data is also recorded corresponding to time data. Since the physical quantity corresponding to the defect to be monitored during the machining process, which is the reference value data, has the property of appearing and changing at a certain stage in the machining process, i.e., within a certain time interval, there is an advantage that more detailed analysis can be performed by dividing the period from the start to the end of machining into time intervals and obtaining sets of reference value data corresponding to the divided time intervals.

[0053] The method also includes (S4) a step of obtaining correlation coefficient data and intensity coefficient data based on the reference value data and the state value data.

[0054] In this step, "correlation coefficient data" refers to data containing information about correlation coefficients, specifically, data containing information about coefficients relating to the correlation between reference values ​​and condition values. By calculating these correlations as numerical, objective values, it becomes possible to use the strength of the relationship between the recorded reference values ​​and condition values ​​measurable by sensors inside or on the surface of the tool as a measure of suitability, thereby enabling analysis of efficient sensor installation positions.

[0055] However, in this step, the correlation coefficient data may include, but is not limited to, data including the absolute value of the correlation coefficient. Correlations can be positive or negative. Even if the correlation is negative, since it is clear that changes in the reference value occur as changes in the condition values ​​inside or on the surface of the tool, the absolute value of the correlation coefficient can be used to analyze an efficient sensor installation position. The correlation coefficient can be calculated using various known methods and is not limited to these methods. Generally, the correlation coefficient is calculated as a value between 1 and -1. In this case, the absolute value is preferably 0.6 or greater, more preferably 0.7, and even more preferably 0.8 or greater.

[0056] In this step, "intensity coefficient data" refers to data containing information about the intensity coefficient, and the "intensity coefficient" refers to a coefficient that allows the amount of change in the state value to be compared with the amount of change in the reference value. In other words, when the correlation coefficient is large, it is clear that there is a strong correlation between the change in the reference value and the change in the state value. However, when the intensity coefficient is small, there is a problem that analytical accuracy will be low unless the sensor sensitivity is set high. Therefore, by comparing the magnitude of this intensity coefficient, it is possible to ensure sufficient analytical accuracy even if the sensor sensitivity is low. The intensity coefficient can be calculated using various known methods and is not limited to these. However, generally, a value of -20 corresponds to a magnification of 0.01, so the value is preferably -20 or greater, and more preferably -10 or greater. A value of -10 corresponds to a magnification of 0.1, so the value is more preferably -10 or greater. Furthermore, the intensity is preferably 1.0, and even more preferably 2.0 or greater.

[0057] Furthermore, in this step, as described above, the correlation coefficient data and intensity coefficient data are preferably obtained according to the following formulas, although there are no limitations thereon.

[0058] In the above formula, Rj is the value of the correlation coefficient data, Sj is the value of the strength coefficient data, M is the total number of elements or the total number of nodes, N is the number of all reference value data, τｍａｘ is the maximum value among the recorded reference data, τ ｍｉｎ indicates the minimum value among the recorded reference data. ｍａｘ is the maximum value of the state value data, and α ｍｉｎ indicates the minimum value of the state value data.

[0059] Furthermore, in this step, even when the specific formula is used as described above, it is preferable to define the region where the absolute value of the correlation coefficient data is 0.6 or more and the strength coefficient data is -20 or more as the recommended region data, and more preferably -10 or more. Here, "recommended region data" refers to data containing information related to the recommended region, and "recommended region" refers to the region of the correlation coefficient and strength coefficient that meets the desired conditions for the tool described above. This range is the preferred region for installing the sensor. This region may be a two-dimensional region or a three-dimensional region.

[0060] That is, in this step, it is preferable that the recommended region data be determined by correlation coefficient data and intensity coefficient data that are equal to or greater than predetermined values.

[0061] Furthermore, this step preferably includes, but is not limited to, a step of acquiring distribution map data based on the correlation coefficient data and the intensity coefficient data. Here, "distribution map data" refers to data containing information about the distribution map, and a "distribution map" refers to a two-dimensional or three-dimensional diagram representing the distribution of correlation coefficient or intensity coefficient values ​​within a specified region. When a recommended region is determined as described above, it is preferable to display this recommended region. Furthermore, even within this recommended region, the values ​​of the correlation coefficient and intensity coefficient often vary depending on the position. Displaying these as a distribution map allows users to determine where and how to install sensors.

[0062] In addition, in this method, it is preferable, but not limited to, to (S5) acquire recommended sensor installation position data based on the correlation coefficient data and intensity coefficient data. Here, "recommended sensor installation position data" refers to data containing information regarding recommended locations for installing sensors. Identifying recommended locations for installing specific sensors based on the correlation coefficient and intensity coefficient provides the advantage of enabling more accurate monitoring of jigs and tools. Note that the recommended sensor installation position data here includes information regarding location, including two-dimensional or three-dimensional coordinate information. However, this position data may include not only point coordinate information but also predetermined area information.

[0063] Furthermore, in this respect, although not limited thereto, it is preferable that the recommended sensor installation position data include recommended sensor installation direction data. By determining the recommended sensor installation position data, the accuracy of the analysis using the sensor can be improved. However, for example, with a sensor such as a strain sensor, there are many cases where the sensitivity of the sensor to the direction of strain varies greatly in the analysis, or where the analysis is premised on detecting strain in a specific direction. In such cases, it is preferable to include information on the installation direction of the sensor in addition to information on the installation position. This has the advantage of further improving the accuracy of the analysis.

[0064] As described above, this embodiment can provide a sensor installation location analysis method and a sensor installation location analysis program for more efficiently monitoring and managing the status of objects that require monitoring, such as equipment and machinery (hereinafter simply referred to as "objects").

[0065] Here, the sensor installation position analysis method according to the above embodiment was implemented using an actual example to confirm the usefulness of this method and this program. Specific examples of application to a shaft-drawing process using forward extrusion are described below.

[0066] (Example 1) First, Fig. 1 shows an external view of the workpiece to be processed in this example. The workpiece was assumed to be a cylindrical metal object. Specifically, the workpiece was made of steel, with a diameter of 9.6 mm and a height of 10 mm. The number of elements was 1012.

[0067] FIG. 2 shows the appearance and cross-sectional image of the tool used in this example. In this figure, a shaft-drawing die and an extrusion die (punch) are prepared as the tool. The workpiece is inserted from the top of the die, and then pushed out by the punch, thereby drawing (reducing the diameter) the tip of the workpiece. The die is made of steel, has an outer diameter of 30 mm, a height of 40 mm, and a conical shape extending from an inner diameter of 10 mm to an inner diameter of 7.2 mm at the drawn portion. The die was configured as an analytical model with 4,069 elements and 4,224 nodes.

[0068] In this example, the punch was made of steel and had a cylindrical shape with a diameter of 9.9 mm and a height of 25 mm, and the analytical model consisted of 2002 elements and 2123 nodes.

[0069] 3 shows a cross section of the relationship between the workpiece and the tool when the workpiece is inserted into the tool. In this example, the workpiece is rotationally symmetrical with respect to the central axis O. That is, as is clear from the above explanation, this example is an example of axial reduction processing.

[0070] The processing conditions in this example were a constant temperature of 20° C. and a coefficient of friction between the workpiece and the tool of 0.05.

[0071] Then, based on the above conditions, the state value data was calculated using the finite element method. In this example, the maximum principal stress at the start point of the lead angle of the die bore, which is one of the state value data, was selected as the reference value data, and extracted from the calculation results together with the time data. The results of the change in the maximum principal stress shown in this figure are shown in the table below. Also, as shown in Table 1 below, the minimum value τ of the reference value ｍｉｎ is 606.35 MPa at t = 0.1 seconds (i = 1), and the maximum value τ ｍａｘThe principal stress distribution in the workpiece and the tool in this case is shown in Figure 4.

[0072] Then, based on the above calculation results, the state values ​​αij at the element positions or node positions of each tool were calculated. The results are shown in Table 2 below.

[0073] Then, based on the state value and the reference value obtained above, the correlation coefficient Rj and the intensity coefficient Sj were obtained using the following equations. In the above formula, Rj is the value of the correlation coefficient data, Sj is the value of the strength coefficient data, M is the total number of elements or the total number of nodes, N is the number of all reference value data, τ ｍａｘ is the maximum value among the recorded reference data, τ ｍｉｎ indicates the minimum value among the recorded reference data. ｍａｘ is the maximum value of the state value data, and α ｍｉｎ indicates the minimum value of the state value data.

[0074] Furthermore, the correlation coefficient Rj and strength coefficient Sj thus obtained were substituted into a calculation model for constructing the tool, and a distribution diagram was created. This distribution diagram is shown in FIG.

[0075] Then, from the distribution diagram, we selected areas where the correlation coefficient Rj was 0.7 or more and the strength coefficient Sj was 2 or more. An image of this selected area is shown in Figure 6. As a result, we confirmed that two locations could be selected, as shown in Figure 7, for example.

[0076] Example 2 Next, a strain sensor was attached to an actual mold, and a system was created that measures minute strains and temperatures during molding and converts the results into data.

[0077] In order to digitize the stress load at the damaged part of the die, it is necessary to clarify the relationship between the strain on the outside of the die measured with a strain sensor and the stress load at the damaged part. In this example, the stress load at the damaged part of the die was calculated from the results of stress analysis using three-dimensional finite element method simulation software. Figure 8 shows the change in the maximum principal stress at the damaged part of the die versus stroke.

[0078] Then, to determine the mounting position of the sensor, the strain components of the mold surface and the maximum principal stress at the damaged part of the mold, as well as the correlation coefficient for their changes and the strength coefficient for the changes were calculated. The correlation coefficient and strength coefficient for each element in the finite element model were calculated and plotted as a distribution diagram. The correlation coefficient Rj and strength coefficient Sj were calculated based on the above formula. As mentioned above, τ is the maximum principal stress at the damaged part, and αij is the strain component of the mold.

[0079] Here, a distribution diagram of the correlation coefficient and strength coefficient for the axial strain component with respect to the maximum principal stress at the corner R of the mold is shown in Figure 9. Then, within the region with a correlation coefficient of 0.98 or more in absolute value, a location showing a high strength coefficient was identified and selected as the sensor installation position.

[0080] We also developed a device that converts the sensor signals into digital data and transmits them via a Wi-Fi network. The appearance of the developed device is shown in Figure 10. It consists of a connector box that houses the sensor and connects it to a USB-Type C cable, and a signal unit that converts the measured signals into digital data and transmits them.

[0081] The strain and temperature data measured by the sensors were transferred to an external cloud server via a Wi-Fi network. The data stored on the cloud server was compiled into data on the strain change range and surface temperature of the die for each forging process using separately developed dedicated software (see Figure 11).

[0082] Then, sensors were attached to the target mold at two locations selected based on the results of Figure 9, and data was collected on the changes in mold strain and surface temperature. The sensor attachment locations and appearance are shown in Figure 12.

[0083] The die height was changed in 0.1 mm increments from 979.1 mm to 980.1 mm in 11 patterns, and 20 forgings were carried out per pattern, producing a total of 220 forged products. All other conditions, except for the die height, were the same as those for mass production. The correlation between the change in the material filling state in the die due to changes in die height and the change in strain in the die was confirmed.

[0084] Typical signal waveforms obtained from circumferentially and axially mounted sensors during one forging pass are shown in FIG.

[0085] The circumferential strain shows a waveform in the tensile direction, capturing the tendency of the mold to expand during molding. On the other hand, the axial strain shows a waveform in the compressive direction, capturing the compressive load applied to the mold. The morphological characteristics of both specimens, except for the suction direction, were similar.

[0086] Meanwhile, Figure 14 shows the state of material filling in the die as a function of die height. Average values, maximum values, and minimum values ​​for 20 forging runs under each die height condition are plotted. The effect of die height changes on the state of material filling is clearly evident.

[0087] The correlation between the change in strain measured by the sensor and the state of material filling into the mold is shown in Figure 15. As a result, a high correlation of about R2 = 0.95 was confirmed for both sensors.

[0088] As described above, this example confirmed that the load status of the mold can be effectively visualized, and it was found that it can be used as an effective tool for managing the load history of mass production and understanding the state of the mold when an abnormality occurs. As a result, the effects of the present invention were confirmed.

[0089] The present invention has industrial applicability as a sensor installation position analysis method and a sensor installation position analysis program.

Claims

1. A sensor installation position analysis method comprising the steps of: recording workpiece data, tool data, and machining condition data; acquiring status value data based on the workpiece data, tool data, and machining condition data; recording reference value data; and acquiring correlation coefficient data and strength coefficient data based on the reference value data and the status value data.

2. The sensor installation position analysis method according to claim 1, wherein the reference value data is acquired based on the state value data.

3. The sensor installation position analysis method according to claim 1, wherein the step of acquiring the state value data is performed by at least one of the finite element method, the finite volume method, the difference method, the boundary element method, the particle method, and the meshless method.

4. The sensor installation location analysis method according to claim 1, further comprising the step of obtaining distribution map data based on said correlation coefficient data and said intensity coefficient data.

5. The sensor installation location analysis method according to claim 1, further comprising a step of obtaining recommended sensor installation location data based on said correlation coefficient data and said intensity coefficient data.

6. A sensor installation position analysis method as described in claim 1, wherein in the step of acquiring the correlation coefficient data and the intensity coefficient data based on the reference value data and the state value data, recommended area data determined by the correlation coefficient data and the intensity coefficient data that are equal to or greater than a predetermined value is acquired.

7. The sensor installation position analysis method according to claim 5, wherein the recommended sensor installation position data includes recommended sensor installation direction data.

8. The sensor installation position analysis method according to claim 1, wherein the correlation coefficient data and the intensity coefficient data are obtained according to the following formulas: In the above formula, Rj is the value of the correlation coefficient data, Sj is the value of the strength coefficient data, M is the total number of elements or the total number of nodes, N is the number of all reference value data, τ ｍａｘ is the maximum value among the recorded reference data, τ ｍｉｎ indicates the minimum value among the recorded reference data. ｍａｘ is the maximum value of the state value data, and α ｍｉｎ indicates the minimum value of the state value data.

9. The sensor installation position analysis method according to claim 6, wherein the recommended area data is defined as an area where the absolute value of the correlation coefficient data is 0.6 or more and the intensity coefficient data is -20 or more.

10. A program for analyzing sensor installation positions that causes a computer to execute the steps of: recording workpiece data, tool data, and processing condition data; acquiring status value data based on the workpiece data, tool data, and processing condition data; recording reference value data; and acquiring correlation coefficient data and strength coefficient data based on the reference value data and the status value data.

11. A recording medium having recorded thereon a program for analyzing sensor installation positions for executing the steps of: recording workpiece data, tool data, and processing condition data; acquiring status value data based on the workpiece data, tool data, and processing condition data; recording reference value data; and acquiring correlation coefficient data and strength coefficient data based on the reference value data and the status value data.

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