Paint flow simulation method and flow simulation program

Abstract

To provide a coating material fluid flow simulation method and a coating material fluid flow simulation program which can predict, with satisfactory accuracy, a state of a coating material after secondary dripping in a coated object having a plate bonding part.SOLUTION: The present invention is directed to a coating material fluid flow simulation method for predicting a flow of a coating material adhered to a coated object 110 by converting a surface shape of a panel member forming the coated object into an aggregate of planer polygon-shaped elements Ei having nodes ai. The method has the steps of: calculating, in an overlapping part where the panel members overlap with each other, a liquid pool amount of the coating material at each of the nodes from distance d between the node and the element opposed to the node and an occupied area S of the node; calculating an inclined angle α relative to a gravity direction between the adjacent nodes; calculating viscosity μ of the coating material after passage of a predetermined period of time for each of the nodes; and calculating a dripping velocity Ve of the coating material after passage of the predetermined period of time for each of the nodes based on the liquid pool amount, the viscosity, and the inclined angle.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a paint flow simulation method and a flow simulation program, and more particularly to a paint flow simulation method and a flow simulation program for simulating the paint flow in electrodeposition coating. [Background technology]

[0002] In the process of painting an automobile body, an electrodeposition coating process is carried out in which the body is submerged in a treatment tank containing paint, and a voltage is applied between the treatment tank and the body to form a coating film of electrodeposited paint on the body surface. After the electrodeposition-coated body is washed in a water washing process to remove any paint remaining on the surface of the coating film and in any gaps, it is carried into a drying furnace and subjected to high-temperature baking to harden the electrodeposited coating film.

[0003] In the paint baking process, if paint is not sufficiently washed off with water and remains, the paint may lose viscosity due to heating, causing it to drip, which is known as secondary dripping. In overlapping parts of a vehicle body where panel members overlap, paint pools in the gaps between the overlapping panel members, and when this pooled paint is heated, its viscosity decreases, causing it to drip from the gap, which is known as secondary dripping.

[0004] When paint drips from secondary drips and adheres to the surface of the vehicle body, it reduces the appearance quality, so repairs are carried out by polishing. However, polishing can cause problems such as a decrease in rust prevention performance and an increase in manufacturing costs due to the increased labor required.

[0005] To solve these problems, there is a need for a body structure design that can minimize repair work by suppressing the occurrence of secondary sagging as much as possible and directing the secondary sagging to areas that do not affect the paint quality. Conventionally, in order to design such a body structure, flow simulation technology has been developed that can predict the occurrence of secondary sagging from the body structure.

[0006] For example, Patent Document 1 discloses a paint flow simulation method for predicting the state of paint puddles that occur in gaps formed in an object to be painted. In this simulation method, a threshold value for the gap is set, and when the gap distance is equal to or less than the threshold, a narrow fluid region is created in which paint puddles occur in the gap. In addition, the Navier-Stokes equation, which is a momentum conservation equation for fluid, is applied to this narrow fluid region, and a surface tension parameter whose value changes depending on the paint temperature is included in the Navier-Stokes equation, and the direction of gravity is set, thereby predicting the amount and direction in which the puddled paint will drip when the paint is heated and the surface tension is reduced. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] JP 2017-126106 A Summary of the Invention [Problem to be solved by the invention]

[0008] Conventional simulation technology can predict the direction in which paint will drip when surface tension decreases. However, as the paint drips during the baking process, water evaporates and the viscosity increases, changing the dripping speed. Conventional technology was not able to accurately predict the state of paint after secondary dripping occurs, such as whether the dripped paint will remain on the vehicle body and require repair work, or whether it will fall off the vehicle body and no repair work will be necessary.

[0009] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a paint flow simulation method and a paint flow simulation program that are capable of accurately predicting the state of paint after secondary dripping occurs on the object to be painted. [Means for solving the problem]

[0010] In order to achieve the above-mentioned object, one embodiment of the present invention is a paint flow simulation method for predicting the flow of paint attached to an object to be painted by converting the surface shape of a panel member forming the object to be painted into a collection of planar polygonal elements having nodes, and is characterized by including the steps of: calculating the amount of paint puddling for each node at the overlapping portion where the panel members overlap from the distance between the node and the element facing the node and the dominant area of ​​the node; calculating the inclination angle between adjacent nodes with respect to the direction of gravity; calculating the viscosity of the paint after a predetermined time has elapsed for each node; and calculating the dripping speed of the paint after the predetermined time has elapsed for each node based on the amount of puddle, the viscosity, and the inclination angle.

[0011] According to this configuration, the amount of paint pooling at the overlapping parts of the panel members constituting the workpiece can be calculated to simulate the flow of the paint pooled at the overlapping parts. In addition, in a situation where the paint is heated in the baking process and secondary dripping of the paint occurs, the dripping speed of the paint after a predetermined time has elapsed is calculated for each node based on the calculated amount of pooling, the inclination angle with respect to the direction of gravity, and the paint viscosity after a predetermined time has elapsed (i.e., the viscosity of the paint that changes over time as the paint drips), and it is possible to model the dripping speed of the paint that changes over time and predict where the dripped paint will be on the workpiece when baking is completed, and the state of the paint after secondary dripping occurs can be predicted with high accuracy.

[0012] Furthermore, a paint flow simulation method according to one embodiment of the present invention is characterized in that, in the process of calculating the amount of accumulated liquid, the distance between the node and the element opposite the node is set based on actual measurement data of the distance of the gap at the overlapping portion of an existing workpiece that is similar to the workpiece.

[0013] According to this configuration, when determining the distance of the gap that will occur in the overlapping portion, the actual measurement data of the distance of the gap in the overlapping portion of an existing workpiece that is similar to the workpiece to be simulated is used, so that the manufacturing variation that appears in the actual measurement data can be added to the simulation, thereby making it possible to predict secondary sagging with high accuracy.

[0014] Furthermore, in a paint flow simulation method according to one embodiment of the present invention, in the process of calculating the amount of accumulated liquid, the distance between the node and the element opposite the node is set based on a predicted value obtained by a calculation device through machine learning from the actual measurement data and data regarding the processing conditions applied to the overlapping portion of the existing workpiece.

[0015] According to this configuration, when determining the distance of the gap that occurs in the overlapping portion, the manufacturing variation due to the processing conditions applied to the overlapping portion can be added to the simulation, which allows for more accurate prediction of secondary sagging.

[0016] In addition, a paint flow simulation program according to an embodiment of the present invention includes: On the computer, The surface shape of the panel member forming the object to be coated is determined by On the computer Converted into a collection of planar polygonal elements with nodes Let The flow of paint on the object is predicted by the To make, A paint flow simulation program, The computer includes: a step of calculating an amount of paint pooling for each of the nodes from a distance between the node and the element facing the node and a dominating area of ​​the node at an overlapping portion where the panel members overlap; a step of calculating an inclination angle between adjacent nodes with respect to the direction of gravity; a step of calculating a viscosity of the paint after a predetermined time has elapsed for each of the nodes; and a step of calculating a dripping speed of the paint after the predetermined time has elapsed for each of the nodes based on the amount of paint pooling, the viscosity, and the inclination angle. Run it It is characterized by:

[0017] This configuration makes it possible to simulate the flow of paint that has pooled in the overlapping area. In addition, in a situation where the paint is heated in the baking process and secondary dripping occurs, the paint dripping speed after a predetermined time has elapsed is calculated for each node, making it possible to model the paint dripping speed that changes over time and predict where the dripped paint will be on the workpiece when baking is completed, thereby making it possible to accurately predict the state of the paint after secondary dripping occurs. Effect of the Invention

[0018] According to the paint flow simulation method and flow simulation program of the present invention, it is possible to accurately predict the state of paint after the occurrence of secondary dripping on a workpiece having an overlapping portion. [Brief description of the drawings]

[0019] [Figure 1] FIG. 2 is a schematic diagram showing a painting line for a vehicle body. [Diagram 2] 1 is a block diagram showing a flow simulation device for executing a flow simulation method according to an embodiment of the present invention. [Figure 3A] 1 is a flowchart showing the procedure of a flow simulation method. [Figure 3B] 1 is a flowchart showing the procedure of a flow simulation method. [Figure 4] FIG. 2 is a perspective view showing a numerical calculation model of a vehicle body. [Diagram 5] FIG. 2 is a schematic diagram partially illustrating a plurality of elements constituting a numerical calculation model. [Figure 6] FIG. 13 is an explanatory diagram showing the initial gap distance d0 of node a1. [Figure 7] FIG. 11 is an explanatory diagram showing, in a table, actual measurement data of gap distances at representative points of overlapping parts of an existing vehicle body and data related to processing conditions. [Figure 8A] 1 is an explanatory diagram showing the dominating area S of node a1. FIG. [Figure 8B]1 is an explanatory diagram showing the inter-node distance L between nodes a1 and a8. FIG. [Figure 9A] FIG. 2 is an explanatory diagram showing the inclination angle α between nodes a1 and a8. [Figure 9B] FIG. 2 is an explanatory diagram showing the inclination angle α between nodes a1 and a8. [Figure 9C] FIG. 2 is an explanatory diagram showing the inclination angle α between nodes a1 and a8. [Figure 10] 13 is a flowchart showing a procedure for a drip movement path determination process. [Figure 11] FIG. 4 is an explanatory diagram showing a paint movement path. [Figure 12] 10 is a flowchart showing a procedure for a dripping determination process. [Figure 13] FIG. 4 is a schematic diagram showing a lower surface side element. [Figure 14] 13 is a diagram illustrating the movement pattern of paint that has accumulated in the gap d1. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. Fig. 1 is a schematic diagram showing a painting line 100 for a vehicle body 110, and shows an overview of the painting line from an electrocoating process through a water washing process to a baking process. As shown in the figure, the vehicle body 110 moves along the painting line 100 while being hung from a hanger 102 provided on a conveyor 101 for transportation.

[0021] 1, in the electrodeposition coating process, a treatment tank 104 is provided for performing electrodeposition coating treatment on a vehicle body 110, which is an object to be coated. The treatment tank 104 is filled with electrodeposition paint 105, and during the electrodeposition coating treatment, the suspended vehicle body 110 is submerged in the electrodeposition paint 105 in the treatment tank 104. An electrode (not shown) is disposed in the treatment tank 104, and a voltage is applied between this electrode and the vehicle body 110 to form a coating film on the surface of the vehicle body 110.

[0022] The vehicle body 110 that has been lifted out of the treatment tank 104 is guided to the water washing process by the conveyor 101. In the water washing process, excess paint, dirt, and the like adhering to the vehicle body 110 are removed by spraying a cleaning liquid toward the vehicle body 110 from a plurality of spray nozzles 106. The vehicle body 110 that has passed through the water washing process is guided to the baking process and is carried into an oven 108, which is a heating and drying furnace. The vehicle body 110 that passes through this oven 108 is heated to a predetermined baking temperature by applying infrared heating or hot air heating, thereby hardening the coating film and fixing it to the surface of the vehicle body 110. In order to suppress the outflow of heated air and increase energy efficiency, the middle part of the oven 108 in the direction of travel of the conveyor 101 is set at a higher position than the parts on both sides.

[0023] In the baking process, if the paint is not sufficiently washed off in the water washing process and remains on the vehicle body 110, the viscosity of the paint decreases due to heating by the oven 108, and the paint drips down the surface of the vehicle body 110, which is called secondary dripping. The vehicle body 110 is composed of plate-like parts such as an inner panel arranged inside the vehicle body and an outer panel arranged outside the vehicle body. In the vehicle body 110, at a plate joint where two or more plate-like parts are overlapped, paint that has pooled in the gap between the plate joints is heated and drips down from the gap, which is called secondary dripping. The flow simulation method according to this embodiment can predict such secondary dripping of paint. The flow simulation method according to this embodiment will be described below.

[0024] 2 is a block diagram showing a flow simulation device 10 for executing the flow simulation method according to this embodiment. The flow simulation device 10 simulates pools and flow paths of coating liquid remaining on a workpiece when the workpiece is subjected to a coating process. This flow simulation device 10 is composed of a single computer such as a microcomputer or a personal computer, or a plurality of computers connected to each other via a network.

[0025] The flow simulation device 10 of this embodiment includes an input device 12 serving as information input means, an external storage device 14 serving as storage means, a calculation device 16 serving as control means, and a display device 18 serving as display means.

[0026] The input device 12 can be configured using input means such as a keyboard, a mouse, and / or a touch panel.

[0027] The external storage device 14 is an external memory connected to the arithmetic device 16, and can be composed of a magnetic disk, an optical disk, or the like.

[0028] The arithmetic device 16 is a computer including a microcomputer, and includes a central processing unit (CPU) as an information processing unit, internal memories such as RAM and ROM, and an input / output interface for communicating with other devices. The information processing unit of the arithmetic device 16 is not limited to a CPU, and may be, for example, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), an application specific standard product (ASSP), a system on chip (SOC), etc. The arithmetic device 16 can execute, with the CPU, a paint flow simulation program stored in the internal memory or the external storage device 14, a paint flow simulation program recorded on a recording medium readable by the arithmetic device 16, or a paint flow simulation program loaded from the outside via a network or communication device (not shown). With this flow simulation program, the arithmetic device 16 simulates immersing the workpiece to be analyzed, which is designated by the input device 12, in the electrodeposition paint 105 in the treatment tank 104, and simulates the puddles and drips of paint that occur on the workpiece due to the immersion.

[0029] The above-mentioned flow simulation is provided in the calculation device 16 and includes a model construction unit 21, a gap distance calculation unit 22, an artificial intelligence unit 23, a paint viscosity calculation unit 24, an inclination angle calculation unit 25, a paint adhesion amount calculation unit 26, and a dripping determination unit 27.

[0030] As shown in FIGS. 4 and 5, the model construction unit 21 converts shape data of a part constituting an object to be painted (in this embodiment, a panel member constituting a vehicle body 110) into a plurality of planar elements E having nodes ai. i 6, the gap distance calculation unit 22 divides the panel members into two parts, and constructs a numerical calculation model 60. The gap distance calculation unit 22 divides the panel members into two parts, as shown in FIG. 6, into two parts, and constructs a numerical calculation model 60. The gap distance calculation unit 22 divides the panel members into two parts, as shown in FIG. 6, into two parts, and constructs a numerical calculation model 60. i The artificial intelligence unit 23 performs machine learning to learn the amount of change in the distance of the gap between the panel members caused by processing the panel members. The paint viscosity calculation unit 24 calculates the viscosity μ of the paint that changes over time. The inclination angle calculation unit 25 calculates the inclination angle α with respect to the gravity direction G. The paint adhesion amount calculation unit 26 calculates the amount of paint adhesion after a predetermined time for each node. The dripping determination unit 27 determines whether or not dripping paint will fall from the vehicle body 110.

[0031] The simulation results of the object to be coated analyzed by the arithmetic unit 16 are displayed on the display unit 18. The display unit 18 is a device capable of visually displaying information, and can be configured, for example, by a liquid crystal display, an organic EL display, a plasma display, and / or a cathode ray tube display.

[0032] Next, a paint flow simulation method using the flow simulation device 10 of this embodiment will be described with reference to the flowcharts of FIGS. 3A and 3B.

[0033] 3A, in step S1, various calculation condition data are read from the external storage device 14 and / or the internal memory. Here, the calculation condition data includes the execution interval of the flow simulation (e.g., 100-second intervals), the end time Te of the simulation (e.g., 3600 seconds), the temperature condition of the oven 108 (e.g., 60° C. from 0 to 200 seconds, 100° C. from 200 to 600 seconds, and 170° C. from 600 to 3600 seconds), the transport angle condition of the vehicle body 110 (e.g., 30° from the horizontal direction from 0 to 200 seconds, 0° from the horizontal direction from 200 to 3400 seconds, and −30° from the horizontal direction from 3400 to 3600 seconds), and the like.

[0034] In step S2, a numerical calculation model of the vehicle body 110 is read as data of the object to be painted. Fig. 4 is a perspective view showing a numerical calculation model 60 of the vehicle body 110 which is the object to be painted, and Fig. 5 is a schematic diagram partially extracting a plurality of elements constituting the numerical calculation model 60. As shown in Fig. 5, a panel member which is a part constituting the vehicle body 110 is divided into a plurality of elements E by the finite element method. i (i=an integer from 1 to N) to construct a two-dimensional numerical calculation model 60. Specifically, the numerical calculation model 60 divides the vehicle body 110 into a plurality of triangular and / or quadrangular planar elements E i As shown in FIG. 5, each polygonal plane element E included in the numerical calculation model 60 can be converted into mesh data or polygon data. i At the vertices of the vector, nodes ai (i=an integer from 1 to n) are arranged, and an X-coordinate value, a Y-coordinate value, and a Z-coordinate value are specified for each node ai. The numerical calculation model 60 may be a three-dimensional numerical calculation model instead of a two-dimensional one. The XYZ orthogonal coordinate system is a coordinate system fixed to the analysis target, and is set in advance so that the Z-axis corresponds to the direction of gravity G when the vehicle body 110 to be analyzed is in a horizontal state.

[0035] In step S2, adjacent nodes are set for each node ai. Here, the adjacent nodes are nodes included in elements that share the node ai of interest. Specifically, as shown in FIG. 5, when one target node a1 is focused on, the nodes a2 to a9 included in the four elements E1 to E4 that share the node a1 are adjacent nodes. In addition, in an overlapping portion where panel members constituting the car body 110 are overlapped, as shown in FIG. 6, the node b7 that is closest to the node a1 of interest in the planar element F4 of the panel member facing the node a1 of interest is also set as an adjacent node. In the following description, these adjacent nodes are also referred to as "adjacent nodes."

[0036] In step S3, the gap distance d0 between the panel members is calculated at the overlapping portion where the panel members overlap. The gap distance d0 calculated here is based on the value at the time of design of the car body 110 read in step S2. In the following description, this "gap distance d0" is also referred to as "initial gap distance d0." In this embodiment, for each node ai, the planar element F of the panel member facing it is calculated. i (hereinafter, "opposing element F i An initial gap distance d0, which is the shortest distance between the first gap d0 and the second gap d1, is calculated.

[0037] FIG. 6 is an explanatory diagram showing the initial gap distance d0 of the node a1. In FIG. 6, of the overlapping first and second panel members, the first panel member is defined as an element E i and the node ai, and the opposing surface of the second panel member opposing the first panel is represented by the opposing element F i and node bi. Also, node a1 of the first panel member and opposing element F i As shown in the figure, the initial gap distance d0 between the node a1 and the closest opposing element F i Here, it is the distance between the node a1 and the closest opposing element F4 (the distance of a straight line extending in the normal direction of the opposing element F4).

[0038] The calculation of this initial gap distance d0 can be set so as to be performed only for nodes ai in the area where the panel members constituting the car body 110 overlap (that is, the area of ​​the overlapping portion).

[0039] In the next step S4, a distance d (hereinafter also referred to as "gap distance d") is set for the initial gap distance d0 of the representative point of each panel member in the overlapping portion of the panel members, taking into account manufacturing variations. Here, the manufacturing variations refer to, for example, changes in the gap distance d0 between the panel members caused by processing such as pressing or welding on the panel members. For example, in the overlapping portion of the vehicle body 110 where two panel members are joined and overlapped, the designed gap distance (initial gap distance d0) is 0 mm (i.e., no gap), but in reality, a gap occurs in the overlapping portion when pressing or welding is performed. In this manner, paint may accumulate in the gap caused by the manufacturing variations, which may cause secondary dripping. In the simulation of this embodiment, the value of the gap distance d after the change is set in consideration of such changes in the gap d0 caused in the manufacturing process, thereby improving the simulation accuracy of secondary dripping.

[0040] The gap distance d can be set based on actual measurement data obtained by measuring the gap distance at a representative point of the overlapping portion of an existing vehicle body that is similar to the vehicle body 110 to be simulated. In this embodiment, the calculation device 16 performs machine learning of the amount of change from the initial gap distance d0 based on this actual measurement data and data on the processing conditions applied to the overlapping portion of the existing vehicle body, and sets the gap distance d based on this machine-learned predicted value. Specifically, in the similar existing vehicle body, one or more representative points are set at the overlapping portion where the panel members overlap, and the gap distance is actually measured at the representative points. Then, for this representative point, data (e.g., 1 mm) of the gap distance at the time of design (i.e., the initial gap distance d0), actual measurement data (e.g., 1.2 mm) of the initial gap distance d0, and data on the processing conditions applied to the overlapping portion are input to the calculation device 16 by the input device 12, and the artificial intelligence unit 23 of the calculation device 16 is caused to perform machine learning of the amount of change in the initial gap distance d0 caused by processing the panel members. Using this machine learning function, when the computing device 16 calculates the initial gap distance d0 of the vehicle body 110 to be simulated, it calculates and sets the gap distance d for each representative point of the vehicle body 110 that corresponds to each representative point of the existing vehicle body, taking into account changes due to manufacturing variations.

[0041] FIG. 7 is an explanatory diagram showing, in a table, actual measurement data obtained by measuring the gap distance of the representative points of the overlapping parts of an existing vehicle body, and data on the processing conditions applied to the overlapping parts. As in the example shown in FIG. 7, the actual measurement data is associated with data on the processing conditions, such as press processing and welding, applied to the existing vehicle body. In FIG. 7, the vehicle body is divided into a plurality of regions (for example, in front of the front door, behind the front door, in front of the rear door, behind the rear door, etc.), and processing conditions and three representative points P1 to P3 are set for each of the divided regions. Here, the processing condition data for press processing includes data on the press conditions (for example, the magnitude of pressure, the processing speed, the name and model of the press machine, the temperature during processing, the magnitude of vibration, etc.) and data on the process (for example, which factory out of a plurality of predetermined factories that perform press processing was used, which process out of a plurality of predetermined processes related to pressing was performed, etc.). The data on the processing conditions for welding includes data on the welding conditions (e.g., the current value of spot welding, the pressure applied during welding, the current application time, the name and model of the welding equipment, etc.), data on the process (e.g., which factory out of a plurality of predetermined factories performing welding was used, which process was performed, etc.), and data on the jig used (e.g., the name of the jig, the accuracy of the jig, etc.). The artificial intelligence unit 23 can be configured to, for example, digitize these processing conditions based on a preset criterion and numerically predict the amount of change in the initial gap distance d0.

[0042] The computing device 16 uses a built-in artificial intelligence function to machine-learn the amount of change (i.e., the amount of change due to manufacturing variations) from the gap distance at the design time of the representative points P1, P2, and P3 (i.e., the initial gap distance d0) to the actually measured gap distance (i.e., the amount of change due to manufacturing variations) based on the actual measurement data of the gap distances of the representative points P1, P2, and P3 shown in Fig. 7 and data related to processing conditions. After the machine learning, the computing device 16 calculates the initial gap distance d0 (for example, the initial gap distance d0 of the node a1 closest to the representative point P1 = 1 mm) of the overlapping portion of the vehicle body 110 to be simulated in step S3, and the artificial intelligence unit of the computing device 16 sets the gap distance d after the change from the initial gap distance d0 taking into account the manufacturing variations (for example, the gap distance d of the node a1 = 1.2 mm) for the representative point of the overlapping portion.

[0043] The method for setting the gap distance d is not limited to this. For example, the gap distances of multiple representative points of overlapping parts of the bodies of multiple existing vehicles may be actually measured, and the gap distance d may be calculated from the average value of the measured gap distances, taking into account the manufacturing variation of the representative points of the vehicle body 110 to be simulated.

[0044] In the next step S5, the dominating area S is calculated for each node ai. FIG. 8A is an explanatory diagram showing the dominating area S of node a1. The dominating area S of node a1 is defined by connecting the intermediate positions between node a1 and each of the adjacent nodes a2 to a9. The dominating area S may be calculated for each node by other methods. In addition, in step S5, the inter-node distance L between adjacent nodes is calculated. FIG. 8B is an explanatory diagram showing the inter-node distance L between adjacent nodes a1 and a8. The inter-node distance L is calculated based on the coordinate values ​​of each of nodes a1 and a8.

[0045] In the next step S6, the gap distance d taking into account manufacturing variations is calculated for the area other than the representative points of the overlapping portion from the gap distance d of the representative points of the overlapping portion obtained in step S4 using an interpolation method. For example, if three representative points P1, P2, and P3 exist for one overlapping portion, the gap distance d taking into account manufacturing variations is set from the initial gap distance d0 for the three nodes ai closest to each of the representative points P1, P2, and P3 in step S4. The gap distances d of the other nodes ai of the overlapping portion are calculated from the gap distances d of these three nodes ai using an interpolation function such as linear interpolation.

[0046] In the next step S7, an initial amount of paint adhesion V0 at each node ai is calculated. This initial amount of paint adhesion V0 is the amount of paint adhesion remaining on the electrodeposition coating film of the vehicle body 110, and is the amount of paint adhesion that may cause secondary dripping.

[0047] In overlapping areas where liquid pools occur in the gaps between panel components, the initial adhesion amount V0 at node ai is calculated by multiplying the dominating area S of node ai by the gap distance d, as shown in the following equation (1). V0=S*d...Equation (1)

[0048] Node ai is the opposite element F i In the case of a non-overlapping portion having no overlapping portion, the initial adhesion amount V0 of the node ai can be calculated by multiplying the dominating area S of the node ai by the initial coating thickness th of the paint. The initial coating thickness th used in calculating the initial adhesion amount V0 is calculated in advance by an experiment or the like.

[0049] In addition, even in the overlapping portion, if the gap distance d between the panel members is larger than a preset reference distance for remaining paint (for example, 2 mm), the initial adhesion amount V0 of the node ai can be calculated by multiplying the dominating area S and the initial thickness th of the paint. In this way, in step S7, the reference distance for remaining paint is set, and if the gap distance d is equal to or smaller than the reference distance, the initial adhesion amount V0 of the paint can be calculated by formula (1). The value of the reference distance for remaining paint varies depending on factors such as the cleaning ability such as water pressure in the above-mentioned water washing process, the properties such as the viscosity of the paint, the properties of the panel surface, and the temperature of the oven 108. Therefore, the reference distance for remaining paint under various conditions obtained in advance by experiments or the like is stored in the external storage device 14 or the internal memory, and the reference distance for remaining paint that matches the conditions of the embodiment can be read from the database.

[0050] In the next step S8, the calculated initial paint adhesion amount V0 of each node ai is stored as the current paint adhesion amount Vt in the calculation device 16. In the next step S9, a reset process of a timer t that measures the simulation time is performed. In this embodiment, the timer t is started when the temperature of the panel member of the vehicle body 110 in the oven 108 reaches 95 to 100°C.

[0051] In the next step S10, the initial viscosity μ0 of the paint when the timer t is reset is set, and this initial viscosity μ0 is set to the current viscosity μ t The initial viscosity μ0 can be set to the viscosity when the electrodeposition paint 105 is at a temperature of 95 to 100° C., for example.

[0052] 3B, in the next step S11, a counting process of a timer t that measures the flow simulation time is performed. In this counting process, the execution interval of the flow simulation (e.g., 100 seconds) read as the calculation condition data is added to the timer t.

[0053] In the next step S12, the viscosity μ of the paint after a predetermined time is calculated for each node ai. The viscosity μ of each node can be calculated by the following formula (2). μt+1 =μ t *(1+k)...Equation (2)

[0054] Here, k is a viscosity increase coefficient set for each node ai based on an experiment. The viscosity increase coefficient k is set so that it is a large value on the outer surface of the vehicle body 110 where the air flow velocity is high in the oven 108, and a small value on the inner surface of the vehicle body 110 where the air flow velocity is low. In this embodiment, the coefficient k is set to a coefficient k1 of the outer surface and a coefficient k2 (k1>k2) of the inner surface. When the vehicle body 110 passes through the oven 108, the water in the paint evaporates and the paint viscosity μ increases. In this embodiment, by adding the time change of this paint viscosity μ to the calculation of the simulation, a highly accurate flow simulation can be performed for the secondary dripping behavior of the paint. The viscosity μ may be a variable according to the temperature of the oven 108 or the air flow velocity in the oven.

[0055] In the next step S13, an inclination angle α based on the direction of gravity G is calculated for each adjacent node based on the coordinate value of each node ai. Here, Figs. 9A to 9C are explanatory diagrams showing the inclination angle α between nodes a1 and a8, and show the change in the inclination angle α according to the transport angle of the car body 110. As shown in Fig. 1, in the baking process, the transport angle of the car body 110 changes according to the elapsed time, so that the direction of gravity G acting on the numerical calculation model 60 of the car body 110 also changes. Therefore, as shown in Fig. 9, the inclination angle α changes according to the change in the transport angle (direction of gravity). Note that the transport angle of the car body 110 in the baking process is read as the transport angle condition of the calculation condition data in step S1, and in step S13, the inclination angle α is calculated while referring to the transport angle condition for each elapsed time.

[0056] In the next step S14, a process of determining the paint movement path is performed for each node ai. In this process, it is determined whether or not there is an adjacent node located below the node ai in the direction of gravity. Hereinafter, the adjacent node located below the direction of gravity is referred to as a "lower adjacent node". If it is determined that there is one or more lower adjacent nodes for the node ai, the lower adjacent node with the steepest inclination angle with respect to the horizontal plane (i.e., the lower adjacent node with the smallest inclination angle α with an inclination angle α of 90° or less with respect to the direction of gravity) is selected as the lower adjacent node through which the paint moves from the node ai, and is determined as the movement path. In the following description, the one lower adjacent node determined as the movement path is referred to as a "steep adjacent node". In this way, a steep adjacent node is selected from the node ai in the focus, and the next steep adjacent node is selected based on the selected steep adjacent node, so that the path through which the paint moves from the node ai can be determined. In addition, if it is determined that there is no lower adjacent node for node ai, it is determined that paint will drip from the position of the target node ai. In addition, if it is determined that paint will drip, and an element Ei exists at the destination of the drip, it is determined that the paint will adhere to the node ai.

[0057] In step S14, the adjacent node of the node ai of interest is the opposing element F constituting the upper end of the overlapping portion of the panel members. i If the node bi of is included, the paint movement path determination process shown in Fig. 10 is performed. Fig. 10 is a flowchart showing the procedure of the paint movement path determination process, and Fig. 11 is an explanatory diagram showing the paint movement path according to this flowchart.

[0058] As shown in FIG. 10, in step S31, an opposing element F i It is determined whether the node bi of the opposing element F is included. iIf the node bi of the element ai is not included, the process proceeds to step S32, where the steep-gradient adjacent node of the node ai of interest is selected and determined as the movement path, as described above. For example, when the node a51 of Fig. 11 is focused on, the lower adjacent node is only the node a52, and the adjacent nodes do not include a node of the opposite element, so it is determined that the paint moves from the node a51 to the steep-gradient adjacent node, the node a52.

[0059] In step S31, the opposing element F i If the node bi of the element F is included, the nodes to be moved are the steep adjacent nodes and the opposing element F i In step S33, the current timer t is acquired. For example, as shown in FIG. 11, when the node a52 is focused on, the node a53, which is a steep adjacent node, and the node a54, which is an opposite element F, are selected as the nodes to be moved along the path. 50 In the next step S34, the gap distance d of the overlapping portion is obtained. In this embodiment, the selected steep-gradient adjacent node and the opposing element F i The gap distance d between the two is obtained.

[0060] In the next step S35, the acquired gap distance d is compared with a preset gap distance threshold d c It is determined whether this threshold d c is a threshold value for determining whether the gap distance d is a gap large enough for dripping paint to enter. c If the gap is larger than t, that is, if the gap is large enough for the paint to enter, the process proceeds to step S36, and the amount of paint adhesion V at the acquired time t is calculated. t For example, in step S35, if the gap distance d is less than or equal to the threshold value d cEven if the gap is larger than , if there is already paint pooling in the gap or if paint that has solidified at high temperatures is attached, the paint will not be able to enter the gap. In this way, in step S36, it is determined whether or not the situation is such that paint can enter the gap at time t. If it is determined in step S36 that there is a gap through which paint can flow, the process proceeds to step S32, where it is determined to move to the steeply adjacent node of the focused node ai. For example, when the focus is on node a52 in FIG. 11, if the gap distance d of node a53 is greater than or equal to the threshold value d c If the gap is larger than a51 and paint can flow into this gap, it is determined that paint moves from node a51 to node a52.

[0061] In step S35, the gap distance d is set to a threshold value d c If it is equal to or less than the gap, or if it is determined in step S36 that the gap is one into which paint cannot flow, the process proceeds to step S37, in which paint is applied to the nodes of the adjacent panel members, i.e., the opposing element F i For example, when the node a52 is focused on in FIG. 11, the gap distance d of the node a53 is determined to be equal to or smaller than the threshold d c or less than or equal to threshold d c When the gap is larger than the gap, but a large amount of paint has already adhered to the gap and the paint cannot enter, the paint flows from the node a51 to the opposite element F 50 It is determined that the first step is to move to node b54, and then to node a56 adjacent to node b51.

[0062] In the next step S15, the paint dripping speed Ve after a predetermined time is calculated for each adjacent node ai. The dripping speed Ve is calculated by a function F having variables W, T, μ, and α shown in the following equation (3). Ve=F(W, T, μ, α)...Equation (3) Here, W is the paint drip width in the dominating area S of node ai, T is the paint drip thickness at node ai, μ is the paint viscosity at node ai, and α is the inclination angle of the adjacent node with respect to the gravity direction G. Also, here, μ is the paint viscosity calculated in step S12, and α is the inclination angle calculated in step S13; as the paint viscosity μ increases, the drip speed Ve slows down, and as the inclination angle α becomes steeper, the drip speed Ve increases. Also, in formula (3), the larger the values ​​of the drip width W and drip thickness T, the faster the drip speed Ve becomes.

[0063] In the next step S16, a paint dripping distance D is calculated for each adjacent node ai. The dripping distance D can be calculated by the following formula (4). D=Ve*Δt...Equation (4) Here, Ve is the dripping speed calculated in step S14, and t is the timer time.

[0064] In the next step S17, based on the paint movement path determined in step S14 and the calculated dripping speed Ve and dripping distance D, the paint outflow amount (paint movement amount) V after a predetermined time is calculated for each node ai. out and the amount of paint flowing in (amount of paint moving) after a given time V in The following is calculated:

[0065] In the next step S18, for each node ai, the paint adhesion amount V t+1 The paint adhesion amount V is calculated. t+1 is the initial amount of paint remaining at node ai, V t From the above, the paint outflow amount V calculated in step S17 out is subtracted from the paint inflow amount V calculated in step S17. in This adds up the amount of paint remaining at node ai after a given time, V t+1 will be required. V t+1 =V t -V out +V in ...Equation (5)

[0066] In the next step S19, a dripping determination process is performed for each node ai. Fig. 12 is a flowchart showing the procedure for performing the dripping determination process. As shown in Fig. 12, in step S101, a counter i for the node number is reset, and in the following step S102, the paint adhesion amount V t+1 is read. In the next step S103, in order to determine whether or not the paint is falling, it is determined whether or not the node ai to be determined belongs to a bottom-side element. Here, Fig. 13 is a schematic diagram showing a bottom-side element. As shown in Fig. 13, when a vector C extending perpendicularly from an element has a component in the gravity direction G, the element is determined to be a bottom-side element that may cause paint to fall.

[0067] In step S103, if it is determined that the node ai to be judged belongs to the lower surface side element, the process proceeds to step S104, and the paint adhesion amount V t+1 is the critical fall amount V c In step S104, it is determined whether the paint adhesion amount V t+1 is the critical fall amount V c If it is determined that the amount exceeds the predetermined amount V, the process proceeds to step S105, where it is determined that the paint has fallen and the amount V excluding the fallen paint is a is the amount of paint attached, V t On the other hand, in step S103, if it is determined that the node ai to be judged belongs to the upper surface side element, or in step S104, the paint adhesion amount V t+1 is the critical fall amount V c If it is determined that the amount of paint adhesion V t+1 is the amount of paint attached, V t is stored as

[0068] In the next step S107, the node number counter i is counted, and in the following step S108, it is determined whether the counter i exceeds the maximum value n. If it is determined in step S108 that the counter i falls below the maximum value n, the dripping determination process is executed again from step S102. If it is determined that the counter i has reached the maximum value n, the routine is terminated and the flowchart of FIG. 3B is executed. The maximum value n determined in step S108 indicates the number of nodes included in the numerical calculation model 60, and the flowchart of FIG. 12 is executed until the dripping determination process for all nodes is completed.

[0069] When the dripping determination process is completed, the process proceeds to step S20 in the flowchart shown in FIG. 3B. In step S20, it is determined whether or not the timer t exceeds the end time Te (e.g., 3600 seconds) that is read as the calculation condition data. If it is determined in step S20 that the timer t is below the end time Te, the process proceeds to step S11 again to perform the counting process of the timer t, and the subsequent steps are continued. On the other hand, if it is determined in step S20 that the timer t has reached the end time Te, the process proceeds to step S21, and the calculation results (the change in the amount of paint present over time, the trajectory of the dripping paint, the remaining locations of the dripped paint, etc.) are displayed on a display or the like that constitutes the display device 18. It should be noted that the display device 12 may be configured to display not only the simulation results but also the simulation process.

[0070] As described above, in the flow simulation method of this embodiment, in a situation where paint puddles occur at overlapping portions of panel members constituting the vehicle body 110, the amount of paint puddles is calculated from the area of ​​the overlapping portions and the gap distance of the overlapping portions, and secondary dripping of paint can be predicted taking into account the puddles that occur at the overlapping portions. In addition, the paint dripping speed Ve that changes with elapsed time is calculated based on the calculated amount of puddles, the viscosity μ of the paint after a predetermined time has elapsed (i.e., the viscosity μ of the paint that changes with time when the paint drips in the baking process), and the gravity direction G. Therefore, when the paint is heated in the baking process and secondary dripping of the paint occurs, it is possible to model the paint dripping speed Ve that changes with time and predict where the dripped paint will be on the vehicle body 110 at the end of baking, and the state of the paint after the secondary dripping occurs can be predicted with high accuracy.

[0071] In addition, for the overlapping portion, the gap distance d is calculated, and the amount of paint attached to this gap after a specified time V is calculated. t+1 Since the paint movement path is determined by calculating the above, the movement path of the secondary dripping paint can be accurately predicted. FIG. 14 is a diagram for explaining the movement pattern of the paint pooled in the gap d1 of the overlapping portion between the first panel 71 and the second panel 72, and shows an example of a state in which the paint 76 drips down to the overlapping portion between the first panel 71 and the third panel 73 below the gap d1. In FIG. 14, (a) shows an initial state in which the paint 76 pools in the gap d1, and (b) to (c) show a state in which the paint 76 pooled in the gap d1 in (a) has dripped down. In (b), there is a sufficient space in the gap d2 at the destination, and all of the paint 76 has entered therein. In (c), there is a gap in the gap d2 at the destination, but the space is small, and a part of the paint 76 is attached to the outer surface of the third panel 73. In FIG. 1(d), the viscosity μ becomes high and part of the paint 76 hardens while remaining attached within the gap d1, and the gap d2 is small and part of the paint 76 does not enter the gap d2 and instead adheres to the outer surface of the third panel 73. In the flow simulation method of this embodiment, the paint viscosity μ, paint velocity Ve and paint adhesion amount V tThe paint adhesion amount V is calculated by adding the gap distance d to the simulation calculation. t By calculating the paint movement path, it is possible to predict various movement patterns of the paint 76 as shown in FIG.

[0072] In addition, in the flow simulation method of this embodiment, when determining the gap distance d between panel members that occurs at the overlapping portion of the panel members, actual measurement data of the gap distance of the overlapping portion of an existing vehicle body that is similar to the vehicle body 110 to be simulated is used, so that manufacturing variations that appear in the actual measurement data can be added to the simulation. This makes it possible to predict secondary sagging with high accuracy. Furthermore, in the above-mentioned embodiment, when determining the gap distance d, manufacturing variations due to the processing conditions applied to the overlapping portion can be added to the simulation. This makes it possible to predict secondary sagging with high accuracy.

[0073] The present invention is not limited to the above-described embodiment, and various modifications are possible without departing from the spirit of the invention.

[0074] For example, in the above-mentioned flow simulation method, the amount of paint pooling occurring in the overlapping portion may be calculated based on the initial gap distance d0 without setting the gap distance d. In such a case, steps S4 and S6 can be omitted in the flowchart shown in FIG. [Explanation of symbols]

[0075] 10. Flow simulation device 12 Input Devices 14 External storage device 16 Arithmetic unit 18 Display device 100 Painting Line 108 Oven (heating and drying furnace) 110 Car body (painted object) ai node E i element Fi Opposing elements d0 Initial gap distance α Tilt angle

Claims

1. A paint flow simulation method for predicting the flow of paint attached to a workpiece by converting a surface shape of a panel member forming the workpiece into a collection of planar polygonal elements having nodes, the method comprising the steps of: calculating an amount of paint pooling at each of the nodes from a distance between the node and the element facing the node and a dominating area of ​​the node at an overlapping portion where the panel members overlap; calculating an inclination angle between adjacent nodes with respect to the direction of gravity; calculating a viscosity of the paint after a predetermined time has elapsed for each of the nodes; calculating a dripping speed of the paint for each of the nodes after the predetermined time has elapsed based on the amount of accumulated liquid, the viscosity, and the inclination angle; A paint flow simulation method comprising:

2. 2. The paint flow simulation method according to claim 1, wherein in the step of calculating the amount of accumulated liquid, the distance between the node and the element opposite the node is set based on actual measurement data of the distance of the gap at the overlapping portion of an existing workpiece that is similar to the workpiece.

3. The paint flow simulation method according to claim 2, characterized in that in the process of calculating the amount of accumulated liquid, the distance between the node and the element opposite the node is set based on a predicted value obtained by a calculation device through machine learning from the actual measurement data and data regarding the processing conditions applied to the overlapping portion of the existing workpiece.

4. A paint flow simulation program for causing a computer to convert the surface shape of a panel member forming an object to be painted into a collection of planar polygonal elements having nodes, and for predicting the flow of paint adhering to the object to be painted, comprising: The computer includes: calculating an amount of paint pooling at each of the nodes from a distance between the node and the element facing the node and a dominating area of ​​the node at an overlapping portion where the panel members overlap; calculating an inclination angle between adjacent nodes with respect to a gravity direction; calculating a viscosity of the paint after a predetermined time has elapsed for each of the nodes; calculating a dripping speed of the paint for each of the nodes after the predetermined time has elapsed based on the amount of accumulated liquid, the viscosity, and the inclination angle; A paint flow simulation program characterized by executing the above.

Images