Cleaning system and method for cleaning the working area of ​​a machine tool

Abstract

The present invention provides a cleaning system and method for cleaning the working area of ​​a machine tool. In the past, the working efficiency of the cleaning system of the machine tool can be improved. The cleaning system for cleaning the working area of ​​the machine tool comprises: a cleaning nozzle, which is used to load and unload a loading and unloading device provided on the machine tool, and the cleaning nozzle sprays a fluid; a robot, which is capable of holding the cleaning nozzle; and a cleaning execution unit, which moves the robot to perform a dismantling action and a cleaning action, wherein the dismantling action is an action of causing the robot to hold the cleaning nozzle installed on the loading and unloading device and dismantle it from the loading and unloading device, and the cleaning action is an action of using the robot to move the cleaning nozzle to the working area, and spraying a fluid from the cleaning nozzle to clean the working area.

Description

Technical Field

[0001] The invention relates to a cleaning system and a method for cleaning an operating area of ​​a machine tool. Background Art

[0002] A system for cleaning a working area of ​​a machine tool is known (for example, Japanese Patent Application Laid-Open No. 10-11884). Conventionally, there has been a desire to improve the working efficiency of a cleaning system for a machine tool. Summary of the invention

[0003] In one embodiment of the present invention, a cleaning system for cleaning an operating area of ​​a machine tool is provided, comprising: a cleaning nozzle, which is used to load and unload a loading and unloading device installed on the machine tool, and the cleaning nozzle sprays a fluid; a robot, which is capable of holding the cleaning nozzle; and a cleaning execution unit, which moves the robot to perform a removal action and a cleaning action, wherein the removal action is an action in which the robot holds the cleaning nozzle installed on the loading and unloading device and removes it from the loading and unloading device, and the cleaning action is an action in which the robot is used to move the cleaning nozzle to the operating area and spray fluid from the cleaning nozzle to clean the operating area.

[0004] In other embodiments of the present disclosure, a method for cleaning the working area of ​​a machine tool is provided, in which a robot is moved to perform a dismantling action and a cleaning action. The dismantling action is an action in which the robot grasps a cleaning nozzle installed on a loading and unloading device provided on the machine tool and removes the cleaning nozzle from the loading and unloading device. The cleaning action is to use the robot to move the cleaning nozzle to the working area and spray fluid from the cleaning nozzle to clean the working area.

[0005] According to the present disclosure, a robot can be used to operate a cleaning nozzle to clean a working area of ​​a machine tool, thereby improving the efficiency of the cleaning operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 It is a block diagram of a cleaning system according to one embodiment.

[0007] Figure 2 yes Figure 1 A schematic diagram of the cleaning system is shown.

[0008] Figure 3 Yes means Figure 1 Flowchart of an example of the operation flow of the cleaning system shown.

[0009] Figure 4 Shows the Figure 3 This is an example in which the first image data captured in step S1 in FIG. 5 is converted into an image.

[0010] Figure 5 Shows the Figure 3 An example of imaging the second image data captured in step S3 in .

[0011] Figure 6 This is a diagram schematically showing an example in which the brightness of each pixel of the first image data is digitized.

[0012] Figure 7 FIG. 2 is a diagram schematically showing an example in which the brightness of each pixel of the second image data is quantified.

[0013] Figure 8 This is a diagram in which the brightness of each pixel of the third image data according to an example is quantified.

[0014] Fig. 9 This is a diagram in which the brightness of each pixel of the third image data according to another example is quantified.

[0015] Fig.10 This is a diagram in which the brightness of each pixel of the third image data according to still another example is quantified.

[0016] Fig.11 An example in which the histogram of the third image data is visualized is shown.

[0017] Fig.12 It is a block diagram of a cleaning system according to another embodiment.

[0018] Fig.13 yes Fig.12 A schematic diagram of the cleaning system is shown.

[0019] Fig.14 yes Fig.13 An enlarged view of the loading and unloading device is shown.

[0020] Fig.15 express Fig.13 The loading and unloading device shown is holding the cleaning nozzle.

[0021] Fig.16 Yes means Fig.12 Flowchart of an example of the operation flow of the cleaning system shown.

[0022] Fig.17 Expressing Fig.16 This is an example in which the first image data captured in step S11 in FIG. 1 is converted into an image.

[0023] Fig.18 Expressing Fig.16 This is an example of imaging the second image data captured in step S15 in .

[0024] Fig.19 Yes means Fig.12 A block diagram of another example of the functionality of the cleaning system is shown.

[0025] Fig. 20 Yes means Fig.19 A flowchart of an example of the operation flow of the cleaning system shown.

[0026] Fig.21 Yes means Fig. 20 A flowchart of an example of the process of step S31 in .

[0027] Fig. 22 Yes means Fig. 20 A flowchart of an example of the process of step S32 in .

[0028] Fig.23 Yes means Fig. 20 A flowchart of an example of the process of step S33 in .

[0029] Fig.24 A schematic diagram showing a cleaning system according to still another embodiment. DETAILED DESCRIPTION

[0030] Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In addition, in the various embodiments described below, the same elements are marked with the same reference numerals, and repeated descriptions are omitted. Figure 1 and Figure 2 Next, a cleaning system 10 according to an embodiment will be described. The cleaning system 10 is used to clean a working area 62 of a machine tool 50 .

[0031] like Figure 2 As shown, the machine tool 50 includes a splash guard 54, a processing head 56, a telescoping cover 58, and a processing table 60. The splash guard 54 is a hollow component that delimits the internal space A, and prevents foreign matter such as cutting fluid or chips generated in the internal space A from leaking to the outside. The splash guard 54 includes a bottom wall 54a and a side wall 54b that rises upward from the bottom wall 54a. An opening 54c is formed on the side wall 54b. The opening 54c is opened and closed by an automatic door (not shown) as needed.

[0032] The processing head 56 is disposed in the internal space A, and a tool 64 is installed at its front end. The processing head 56 rotates and drives the tool 64 to process the workpiece. The telescopic cover 58 is a hollow component that can be telescoped freely, and is disposed on the bottom wall 54a of the baffle 54. The telescopic cover 58 prevents the parts of the machine tool 50 from being exposed to foreign objects. The processing table 60 is configured to be movable in the horizontal direction in the internal space A, and is arranged at a position closer to the upper side than the telescopic cover 58. A clamp (not shown) can be loaded and unloaded on the processing table 60, and the clamp can load and unload the workpiece.

[0033] In this embodiment, the working area 62 of the machine tool 50 is an area within the internal space A that is affected by the work on the workpiece (attachment of foreign matter, etc.), for example, defined as an area including the baffle 54 (bottom wall 54a), the telescopic cover 58 and the processing table 60 (clamp).

[0034] like Figure 1 As shown, the cleaning system 10 includes a control device 12, a camera 14, a cleaning nozzle 16, and a fluid supply device 18. The control device 12 controls the operation of the camera 14 and the fluid supply device 18. Specifically, the control device 12 is a computer having a processor 20 (CPU, GPU, etc.) and a memory 22 (ROM, RAM, etc.). The processor 20 is connected to the memory 22 via a bus 24 so as to be communicable, communicates with the memory 22, and performs operations for executing various functions described later.

[0035] In addition, the control device 12 may also be configured to control the machining operation performed by the machine tool 50 by controlling the operation of the machining head 56 and the machining table. Alternatively, a second control device (not shown) different from the control device 12 may be provided, and the second control device controls the machining operation performed by the machine tool 50. In this case, the control device 12 is connected to the second control device in a communicable manner. The memory 22 temporarily or permanently stores various data.

[0036] The imaging device 14 captures an image of the working area 62 of the machine tool 50. As an example, the imaging device 14 is a camera having an imaging sensor such as a CCD or a CMOS, an optical lens such as a focusing lens, and an image processing processor. As another example, the imaging device 14 may be a laser scanner type imaging device having a laser emitting unit for emitting laser light, a light receiving unit for receiving the laser light reflected by an object, and an image generating unit for generating image data from the laser light received by the light receiving unit.

[0037] As another example, the camera 14 may be a three-dimensional visual sensor that captures an image of a subject and can measure the distance to the image of the subject. In addition, the camera 14 may be fixed to the internal space A of the machine tool 50, or, when a portion of the wall of the baffle 54 of the machine tool 50 is open (or is a transparent material), the camera 14 may be disposed outside the baffle 54. Alternatively, the camera 14 may be moved to any position and posture by means of a robot described later. Under the instruction of the control device 12, the camera 14 captures the working area 62 of the machine tool 50 and sends the captured image data to the control device 12.

[0038] The cleaning nozzle 16 is hollow and has a jet port 16a at its front end. The cleaning nozzle 16 ejects the fluid supplied to the inside from the jet port 16a in a predetermined ejection direction. In addition, the cleaning nozzle 16 may also be fixed to the internal space A. In this case, the cleaning nozzle 16 is positioned so that its ejection direction is toward the working area 62 (for example, the processing table 60) to be cleaned. Alternatively, the cleaning nozzle 16 may also be moved to any position and posture by means of a robot described later.

[0039] The fluid supply device 18 supplies fluid to the cleaning nozzle 16 under the command of the control device 12. Specifically, the fluid supply device 18 is fluidically connected to the cleaning nozzle 16 via a fluid supply pipe 26 (e.g., an elastic pipe), and supplies fluid (e.g., compressed gas or compressed liquid) to the inside of the cleaning nozzle 16 through the fluid supply pipe 26. The cleaning nozzle 16 sprays the fluid supplied from the fluid supply pipe 26 to the working area 62 (e.g., the processing table 60), thereby cleaning the working area 62.

[0040] Next, refer to Figure 3 , explaining the operation of the cleaning system 10. When the processor 20 receives a job start instruction from an operator, a host controller, or a computer program, it starts Figure 3 The process shown. Figure 3 At the time point of the illustrated flow, the workpiece is not placed on the processing table 60 , and substantially no foreign matter exists in the working area 62 of the machine tool 50 .

[0041] In step S1, the processor 20 uses the camera 14 to photograph the working area 62. In the present embodiment, the processor 20 performs a simulated processing process before photographing the working area 62. Specifically, the operator (or the workpiece loading robot) sets a fixture on the upper surface 60a of the processing table 60, and then sets a dummy workpiece on the fixture. The dummy workpiece has the same size as the workpiece that becomes the processing object in step S2 described later after processing.

[0042] Furthermore, the processor 20 (or the second control device) operates the machining head 56 and the machining table 60 according to the machining program. The machining program includes instructions for operating the machining head 56 and the machining table 60 and instructions for ejecting machining fluid (cutting fluid, coolant, etc.) from a machining fluid ejection device (not shown), and is stored in the memory 22 in advance.

[0043] The processor 20 executes the machining program, causing the machining head 56 and the machining table 60 to perform the same action as in step S2 described later, and ejects the machining fluid from the machining fluid ejection device (not shown) at the same timing and flow rate as in step S2 described later. When the machining program is finished, the machining head 56 and the machining table 60 return to their initial positions.

[0044] Furthermore, the processor 20 controls the imaging device 14 to image the working area 62 at a time point t2 (= t1 + τ) after a preset time τ has passed from the time point t1 when the machining fluid is finally ejected from the machining fluid ejection device. Here, the time τ can be set so that the time point t2 becomes the time point after the processor 20 ends the machining program in the simulation machining process.

[0045] For example, the imaging device 14 captures the upper surface 60a of the processing table 60 in the working area 62. Alternatively, the imaging device 14 may capture the inner surface of the bottom wall 54a of the baffle 54, the upper surface 58a of the telescopic cover 58, and the upper surface 60a of the processing table 60 in the working area 62.

[0046] The imaging device 14 sends the captured image data ID1 (first image data) to the processor 20, and the processor 20 stores the image data ID1 in the memory 22. The image data ID1 is image data of the working area 62 (for example, the upper surface 60a) captured by the imaging device 14 before machining the workpiece in the subsequent step S2. Figure 4 An example of image data ID1 obtained when the imaging device 14 captures an image of the upper surface 60 a of the processing table 60 is shown.

[0047] In step S2, the machine tool 50 processes the workpiece in the working area 62. Specifically, the operator (or the workpiece loading robot) attaches the tool 64 to the processing head 56, sets a jig on the upper surface 60a of the processing table 60, and then sets the workpiece on the jig.

[0048] Then, the processor 20 (or the second control device) operates the machining head 56 and the machining table 60 according to the machining program, and the machining fluid is ejected from the machining fluid ejection device while the workpiece is machined by the tool 64. As a result, foreign matter such as chips is accumulated in the working area 62 of the machine tool 50. When the machining program ends in step S2, the machining head 56 and the machining table 60 return to the same initial position as when the above-mentioned simulation machining process ends.

[0049] In step S3, the processor 20 controls the camera 14 to photograph the working area 62. Specifically, the processor 20 executes step S3 at a time point t2 after a preset time τ has passed since the time point t1 when the processing fluid was last ejected from the processing fluid ejection device in step S2, and causes the camera 14 to photograph the working area 62. For example, the camera 14 photographs the upper surface 60a of the processing table 60 along the same line of sight as in step S1. The camera 14 sends the image data ID2 (second image data) of the photographed working area 62 to the processor 20, and the processor 20 stores the image data ID2 in the memory 22.

[0050] The image data ID2 is image data of the working area 62 (for example, the upper surface 60 a ) captured by the imaging device 14 after the workpiece is machined in step S2 . Figure 5 An example of image data ID2 obtained when the upper surface 60a is imaged by the imaging device 14 is shown. In the image data ID2 imaged after processing, foreign matter B such as cutting chips is reflected on the upper surface 60a.

[0051] In step S4, the processor 20 generates image data ID3 (third image data) indicating the degree of brightness change between the image data ID1 captured in step S1 and the image data ID2 captured in the most recent step S3. The image data ID3 has the same number of pixels N as the image data ID1 and ID2. T Below, refer to Figure 6 to Figure 10 , a method for generating image data ID3 is explained.

[0052] Figure 6 2 is a diagram schematically showing an example of digitizing the brightness of each pixel of the image data ID1 captured before processing. Figure 7 : is a diagram schematically showing an example of digitizing the brightness of each pixel of the processed image data ID2. Figure 6 and Figure 7 In FIG. 1 , for easy understanding, pixels of 5 rows×5 columns among the pixels of the image data ID1 and ID2 are shown.

[0053] The processor 20 generates the image data ID3 based on the image data ID1 and ID2. The brightness BR3 of each pixel of the image data ID3 is calculated by the following method as the value of the degree of change between the brightness BR1 of the pixel corresponding to the image data ID1 and the brightness BR2 of the pixel of the image data ID2 corresponding to the pixel of the image data ID1.

[0054] As an example, the processor 20 obtains the brightness BR3 of each pixel of the image data ID3 from the mathematical formula (1): BR3=BR1-BR2. Figure 8 The figure shows a schematic diagram in which the brightness BR3 of each pixel of the image data ID3 is numerically converted when the brightness BR3 is calculated from the mathematical formula (1).

[0055] For example, for the yth image data ID3 n row, x n The brightness of the pixel in the column BR3 is due to the yth n row, x n The brightness of the pixel in the column BR1 is 100 ( Figure 6 ), the yth image data ID2n row, x n The brightness BR2 of the pixels in the column is the same as the brightness BR1, 100 ( Figure 7 ), therefore, according to mathematical formula (1), BR3 = BR2-BR1 = 0. That is, when using mathematical formula (1), when the brightness of the corresponding pixels in the image data ID1 before processing and the image data ID2 after processing does not change, the brightness BR3 of the pixels corresponding to the image data ID3 is zero.

[0056] On the other hand, for the yth image data ID3 n+2 row, x n+4 The pixel of the column, the yth n+2 row, x n+4 The brightness BR1 of the pixel in the column is 1 ( Figure 6 ), and the yth n+2 row, x n+4 The brightness BR2 of the pixel in the column is 255 which is different from the brightness BR1 ( Figure 7 ). Such a change in brightness between BR1 and BR2 may be caused by Figure 5 In this case, the yth image data ID3 n+2 row, x n+4 The brightness BR3 of the pixels in the column is expressed by BR3=BR2-BR1=254 according to the mathematical formula (1).

[0057] Thus, in equation (1), the brightness BR3 of each pixel of the image data ID3 is calculated as the difference between the brightness BR1 and BR2, as Figure 8 As shown, when the brightness of the pixel does not change between the image data ID1 and ID2, it is zero. On the other hand, when the brightness of the pixel changes between the image data ID1 and ID2, it is a value other than zero. Figure 8 For easy understanding, pixels having a brightness BR3 other than zero are highlighted.

[0058] As another example, the processor 20 obtains the brightness BR3 of each pixel of the image data ID3 from the mathematical formula (2): BR3=(BR1-BR2) / 2+128. Fig. 9 The figure shows a numerical representation of the brightness of each pixel of the image data ID3 when the brightness BR3 of each pixel of the image data ID3 is obtained from the mathematical formula (2).

[0059] For example, for the yth n row, x nFor the pixels of the row, since the brightness BR1 of the image data ID1 is 100 and the brightness BR2 of the image data ID2 is 100, according to the mathematical formula (2), BR3=(BR1-BR2) / 2+128=128. That is, when the mathematical formula (2) is used, when the brightness of the corresponding pixels in the image data ID1 before processing and the image data ID2 after processing does not change, the brightness BR3 of the pixels corresponding to the image data ID3 is 128.

[0060] On the other hand, for the yth n+2 row, x n+4 For the pixels in the row, since the brightness BR1 of the image data ID1 is 1 and the BR2 of the image data ID2 is 255, BR3 = (BR1-BR2) / 2+128 = 255. Thus, in mathematical formula (2), the brightness BR3 of each pixel of the image data ID3 is calculated based on the difference between the brightness BR1 and BR2, as shown in Fig. 9 As shown, when the brightness of the pixel does not change between the image data ID1 and ID2, the value is 128, and when the brightness of the pixel changes between the image data ID1 and ID2, the value is other than 128.

[0061] As another example, the processor 20 obtains the brightness BR3 of each pixel of the image data ID3 from the mathematical formula (3): BR3=(BR2+1) / (BR1+1). Fig.10 The figure shows a numerical representation of the brightness of each pixel of the image data ID3 when the brightness BR3 of each pixel of the image data ID3 is obtained from the mathematical formula (3).

[0062] For example, for the yth n row, x n For the pixels of the row, since the brightness BR1 of the image data ID1 is 100 and the brightness BR2 of the image data ID2 is 100, according to the mathematical formula (3), BR3 = BR2 / BR1 = 1. That is, when the mathematical formula (3) is used, when the brightness of the corresponding pixels in the image data ID1 before processing and the image data ID2 after processing does not change, the brightness BR3 of the pixels corresponding to the image data ID3 is always 1.

[0063] On the other hand, for the yth n+2 row, x n+4 For the pixels in the row, since the brightness BR1 of the image data ID1 is 1 and the BR2 of the image data ID2 is 255, BR3 = BR2 / BR1 = 128. Thus, in equation (3), the brightness BR3 of each pixel of the image data ID3 is calculated based on the ratio of the brightness BR1 to BR2 ((BR2+1) / (BR1+1)), as shown in Fig.10As shown, when the brightness of the pixel does not change between the image data ID1 and ID2, it is 1, and when the brightness of the pixel changes between the image data ID1 and ID2, it is a value other than 1.

[0064] By the above method, the processor 20 generates the image data ID3 indicating the degree of change between the brightness BR1 of the image data ID1 and the brightness BR2 of the image data ID2. Therefore, the processor 20 as the image data generating unit 28 ( Figure 1 The processor 20 stores the generated image data ID3 in the memory 22.

[0065] in addition, Figure 8 to Figure 10 In order to facilitate understanding, the image data ID3 is represented as grid-shaped data of y columns×x rows, but the image data ID3 generated by the processor 20 does not necessarily have to be grid-shaped data, for example, it can also be data storing pixels and brightness BR3 in a list form.

[0066] Refer again Figure 3 In step S5, the processor 20 obtains the histogram HG of the image data ID3 generated in step S4. The histogram HG is data indicating the relationship between the brightness BR3 of each pixel of the image data ID3 and the number of pixels N. Fig.11 FIG. 2 shows an example of a histogram HG being visualized. In addition, the processor 20 may obtain the histogram HG only in the form of numerical data, or generate Fig.11 The image of the histogram HG shown is displayed on a display (not shown) provided in the control device 12 .

[0067] Here, generally, the brightness of each pixel in the image data is displayed in a total of 256 stages from 0 to 255. When the histogram HG using the mathematical formula (2) is visualized, the brightness BR3 can be represented in 256 stages, and the position of BR3=128 can be set as the median value of the brightness. Therefore, according to the mathematical formula (2), the image of the histogram HG can be displayed using an existing image processing program.

[0068] Furthermore, when the brightness of the pixel is displayed in 256 steps from 0 to 255, by using the above-mentioned mathematical formula (3), even if the brightness BR1 = 0, the brightness BR3 can be prevented from being infinite. In addition, when the brightness of the pixel is displayed in 256 steps from 1 to 256, the mathematical formula (3) can be defined as the mathematical formula of BR3 = BR2 / BR1.

[0069] Regarding this histogram HG, it is assumed that the brightness of the corresponding pixels does not change between the image data ID1 before processing and the image data ID2 after processing (that is, there is no Figure 5When the foreign object B is shown, in the histogram HG, the number of pixels N whose brightness BR3 becomes the reference value α0 and the total number of pixels N of the image data ID3 T Roughly consistent (N≈N T )(That is, since the brightness BR3 = α0, the number of pixels N≈N T When the mathematical formula (1) is used, the reference value α0 is 0; when the mathematical formula (2) is used, the reference value α0 is 128; and when the mathematical formula (3) is used, the reference value α0 is 1.

[0070] On the other hand, when there is a change in the brightness of the corresponding pixels between the image data ID1 before processing and the image data ID2 after processing (that is, there is a change in the brightness of the corresponding pixels). Figure 5 In the case of the foreign matter B shown in FIG. 1 , in the histogram HG, there are also pixels N in the range of brightness BR3 outside the reference value α0. Thus, the histogram HG is data that statistically represents the brightness change between the image data ID1 before processing and the image data ID2 after processing.

[0071] In step S6, the processor 20 determines whether to clean the work area 62 based on the histogram HG. As an example, the processor 20 determines the number of pixels N in the range [α1, α2] of the preset brightness BR3 in the histogram HG. X Relative to the total number of pixels N T The ratio R1 = N X / N T is the predetermined threshold R th1 Below (R1 = N X / N T ≤R th1 ), it is determined that the operation area 62 needs to be cleaned.

[0072] To describe this aspect in detail, as described above, when there is a change in brightness between the image data ID1 before processing and the image data ID2 after processing, the number of pixels N replacing the reference value α0 becomes smaller, and the number of pixels N is also widely distributed within the range of brightness BR3 outside the reference value α0.

[0073] Therefore, if Fig.11 As shown in FIG. 1 , when the threshold values ​​α1 and α2 are set to include the reference value α0, the greater the change in brightness between the image data ID1 and the image data ID2 (that is, Figure 5 The more foreign matter B there is, the number of pixels N in the range of α1≤BR3≤α2 X Therefore, the number of pixels N X Relative to the total number of pixels N T The ratio R1 = N X / N TThe data quantitatively represents the magnitude of the brightness change between the image data ID1 and the image data ID2, that is, the amount of foreign matter B included in the processed image data ID2.

[0074] The processor 20 calculates a ratio R1 from the data of the histogram HG, where the ratio R1 is a threshold value R th1 If the ratio R1 is less than the threshold value R, the processor 20 determines that the work area 62 needs to be cleaned (ie, "Yes"), and the process moves to step S7. th1 If it is large, it is determined as "No", and the process moves to step S8.

[0075] As another example, the processor 20 counts the number of pixels N whose brightness BR3 is outside the range [α1, α2] in the histogram HG. Y Relative to the total number of pixels N T The ratio R2 = N Y / N T is the predetermined threshold R th2 Above (R2 = N Y / N T ≥R th2 ), it is determined that the operation area 62 needs to be cleaned.

[0076] Here, the greater the brightness change between the image data ID1 and the image data ID2 (that is, Figure 5 The more foreign matter B there is, the number of pixels N in the range of α1≤BR3≤α2 X On the other hand, the number of pixels N in the range of BR3<α1 or α2<BR3 is Y Therefore, the number of pixels N Y Relative to the total number of pixels N T The ratio R2 = N Y / N T The processor 20 calculates the ratio R2 from the data of the histogram HG, and the ratio R2 is the threshold value R. th2 If the above, it is determined that the work area 62 needs to be cleaned ("Yes").

[0077] As another example, the processor 20 extracts the curve of the histogram HG obtained in step S5 (see Fig.11 ), the trajectory of the curve of the calculated histogram HG and the reference histogram HG R The consistency of the shape of the trajectory of the curve. The reference histogram HG R This is a histogram when there is no change in brightness between the image data ID1 and the image data ID2.

[0078] Benchmark Histogram HG RFor example, the following method can be used to obtain the image data ID1. Specifically, the processor 20 captures the image data ID1 twice before processing (step S1). Then, the processor 20 uses the method described in step S4 to generate a reference image data ID1 indicating the degree of brightness change of the two image data ID1 before processing. R Then, the processor 20 obtains the reference image data ID R Get the benchmark histogram HG R .

[0079] Instead, the base histogram HG R It can also be manually created by an operator. In step S6, the processor 20 generates a curve when the trajectory of the histogram HG is consistent with the reference histogram HG. R If the degree of consistency of the shape of the trajectory of the curve is smaller than a preset threshold, it is determined to be "yes".

[0080] As another example, the processor 20 calculates the standard deviation of the histogram HG obtained in step S5. In step S6, the processor 20 determines "yes" when the standard deviation of the histogram HG is greater than a preset threshold. By the above method, the processor 20 determines whether to clean the working area 62 (for example, the upper surface 60a) based on the histogram HG. Therefore, the processor 20 serves as the determination unit 30 ( Figure 1 ) to perform its function.

[0081] In step S7, the processor 20 performs cleaning of the working area 62. Specifically, the processor 20 operates the fluid supply device 18 to supply fluid to the cleaning nozzle 16. The cleaning nozzle 16 sprays the fluid supplied from the fluid supply pipe 26 to the working area 62 (the upper surface 60a of the processing table 60) to clean the working area 62. After step S7, the processor 20 returns to step S3, and repeats the cycle of steps S3 to S7 until step S6 is determined to be "no".

[0082] In addition, the processor 20 counts the number of times step S7 is executed (or the number of times the determination result is "yes" in step S6) m, and when the number m reaches the predetermined number m, MAX (For example, m MAX =3), a warning signal in the form of sound or image "the number of cleaning times has reached a predetermined number of times" is issued, and the process can be transferred to step S8 (or the process can also be ended). Figure 3 Thus, it is possible to prevent step S7 from being executed too many times.

[0083] In step S8, the processor 20 analyzes the computer program, for example, to determine whether there are other workpieces to be processed. If the processor 20 determines that there are workpieces to be processed (i.e., "yes"), it returns to step S2. On the other hand, if the processor 20 determines that there are no workpieces to be processed (i.e., "no"), it ends the process. Figure 3 The process shown.

[0084] As described above, in this embodiment, the imaging device 14 captures the image data ID1 and ID2 before and after processing, the image data generating unit 28 generates the image data ID3, and the determination unit 30 determines whether to clean the working area 62 based on the histogram HG. Therefore, the imaging device 14, the image data generating unit 28 and the determination unit 30 constitute a device 70 ( Figure 1 ).

[0085] In this embodiment, processor 20 determines whether to clean work area 62 based on histogram HG statistically showing changes in brightness of image data ID1 and ID2 captured before and after processing. With this configuration, it is possible to determine with high accuracy whether work area 62 needs to be cleaned using a statistical method.

[0086] In addition, in the present embodiment, the processor 20 generates a value in the histogram HG when the ratio R1 is the threshold value R th1 When the ratio R2 is below the threshold R th2 When the above conditions are met, it is determined that it is necessary to clean the work area 62. According to this configuration, it is possible to automatically determine whether the work area 62 needs to be cleaned by a relatively simple algorithm.

[0087] In addition, in the present embodiment, the processor 20 causes the camera 14 to capture the image data ID1 after executing the simulated processing process in step S1. According to this structure, the configuration of the elements of the working area 62 of the processing table 60 and the like and the processing fluid shown in the image data ID1 captured in step S1 and the processed image data ID2 captured in step S3 can be made substantially the same. Therefore, it is possible to prevent the brightness BR3 of each pixel of the image data ID3 from including the value caused by the configuration of the elements of the working area 62 and the processing fluid.

[0088] Next, refer to Fig.12 and Fig.13 , a cleaning system 100 according to another embodiment will be described. The cleaning system 100 is a system for cleaning the working area 62 of the machine tool 50, and includes: an imaging device 14, a cleaning nozzle 16, a fluid supply device 18, a control device 102, a robot 104, and a loading and unloading device 106.

[0089] The control device 102 controls the operation of the imaging device 14, the fluid supply device 18, the robot 104, and the loading and unloading device 106. Specifically, the control device 102 is a computer having a processor 108 (CPU, GPU, etc.) and a memory 22 (ROM, RAM, etc.). The processor 108 is connected to the memory 22 via a bus 24 so as to be communicable, communicates with the memory 22, and performs operations for executing various functions described later.

[0090] like Fig.13 As shown, in this embodiment, the robot 104 is a vertical multi-joint robot, which includes: a robot base 110, a rotating body 112, a robot arm 114, a wrist 116, and robot manipulators 118 and 120. The robot base 110 is fixed to the floor of the workpiece unit. The rotating body 112 is rotatably arranged on the robot base 110 around a vertical axis.

[0091] The robot arm 114 includes a lower arm 122 rotatably provided on the rotating body 112 and an upper arm 124 rotatably provided at the front end of the lower arm 122. The wrist 116 is provided at the front end of the upper arm 124 and rotatably supports the robot hands 118 and 120.

[0092] The robot base 110, the rotating body 112, the robot arm 114 and the wrist 116 are each equipped with a servo motor (not shown). These servo motors drive the rotating body 112, the robot arm 114 and the wrist 116 around the drive axis under the command of the control device 102, thereby moving the robot 104.

[0093] The robot manipulator 118 includes: a manipulator base 128 fixed to an adapter 126, the adapter 126 being provided at the front end of the wrist 116; and a plurality of fingers 130, which are provided on the manipulator base 128 so as to be openable and closable. The manipulator base 128 has a finger driving unit (not shown) having a cylinder or a motor built therein, and the finger driving unit opens and closes the finger 130 under the command from the control device 102. Thus, the robot manipulator 118 uses the finger 130 to hold or release the cleaning nozzle 16. In addition, the finger 130 of the robot manipulator 118 can be configured to hold the workpiece held by the robot manipulator 120 in addition to the cleaning nozzle 16.

[0094] On the other hand, the robot manipulator 120 has a manipulator base 132 fixed to the adapter 126 and a plurality of fingers 134 provided on the manipulator base 132 so as to be openable and closable. The manipulator base 132 has a second finger driving unit (not shown) having an air cylinder or a motor built therein, and the second finger driving unit opens and closes the fingers 134 under a command from the control device 102. Thus, the robot manipulator 120 uses the fingers 134 to hold or release an object such as a workpiece.

[0095] The loading and unloading device 106 is arranged at a predetermined position in the internal space A of the machine tool 50 and is installed on the side wall 54b of the baffle plate 54. Fig.14 As shown, the attachment and detachment device 106 includes a base 136 fixed to the side wall 54 b , a plurality of claws 138 provided on the base 136 so as to be openable and closable, and a claw driving unit 140 for opening and closing the claws 138 .

[0096] The claw driving unit 140 has a cylinder or a motor, and automatically opens and closes the claw unit 138 under the command from the control device 102. The loading and unloading device 106 closes the claw unit 138, such as Fig.15 As shown, the cleaning nozzle 16 is held between the claws 138. Fig.14 As shown, the held cleaning nozzle 16 is released by opening the claw 138 .

[0097] In addition, in the present embodiment, a plane portion 138a is formed on the inner surface of the claw portion 138, and a plane portion 16b that is in surface contact with the plane portion 138a is formed on the side surface of the cleaning nozzle 16. The claw portion 138 can stably hold the cleaning nozzle 16 by virtue of the surface contact between the plane portions 138a and 16b. In addition, a high friction portion (concave-convex portion, rubber layer, high friction resin layer, etc.) that increases the friction coefficient with the cleaning nozzle 16 may be provided on the plane portion 138a of the claw portion 138.

[0098] In addition, the cleaning system 100 may also include a blower (not shown) that blows fluid (e.g., compressed gas) to the inner surface of the claw 138 to blow away foreign matter attached to the inner surface. In this case, the blower is built into the loading and unloading device 106 (e.g., the base 136), and the fluid injection port of the blower can be set on the inner surface of the claw 138 (e.g., the flat surface 138a). In this way, foreign matter can be prevented from being attached to the inner surface of the claw 138, so that the loading and unloading device 106 can always reliably hold the cleaning nozzle 16 in the same position and posture.

[0099] like Fig.13As shown, the imaging device 14 is fixed to the adapter 126 via a bracket 142, and is moved to an arbitrary position and posture by the robot 104. In this embodiment, the imaging device 14 is a three-dimensional vision sensor that captures a subject image and can measure the distance to the subject image.

[0100] A robot coordinate system C is set for the robot 104. R . Robot coordinate system C R The robot coordinate system C is a reference coordinate system for automatically controlling the movable components (rotating body 112, robot arm 114, wrist 116) of the robot 104. R The robot coordinate system C is set such that its origin is located at the center of the robot base 110 and its z-axis coincides with the rotation axis of the rotating body 112. R The robot 104 generates commands for the servo motors to move the movable components of the robot 104, and the camera 14 and the robot manipulators 118 and 120 are respectively arranged in the robot coordinate system C R Any position and posture in .

[0101] The robot base 110 and the rotating body 112 of the robot 104 are arranged outside the baffle 54 of the machine tool 50. The processor 108 operates the robot 104 to move the imaging device 14 and the robot manipulators 118 and 120 into and out of the internal space A of the machine tool 50 through the opening provided in the side wall 54b of the baffle 54.

[0102] Next, refer to Fig.16 , the operation of the cleaning system 100 is described. When the processor 108 receives a job start instruction from an operator, a host controller, or a computer program, the cleaning system 100 starts Fig.16 The process shown. Fig.16 At the time point of the process shown, the workpiece is not set on the processing table 60, and there is substantially no foreign matter in the working area 62 of the machine tool 50. Fig.16 At the time point of the process shown, the cleaning nozzle 16 is mounted on the loading and unloading device 106 ( Fig.15 ).

[0103] In step S11, the processor 108 uses the camera 14 to photograph the working area 62. In the present embodiment, the processor 108 performs a simulated processing process before photographing the working area 62. Specifically, the operator (or the robot 104) sets a fixture on the upper surface 60a of the processing table 60. Next, the processor 108 causes the robot 104 to operate, and uses the robot manipulator 120 to grasp the virtual workpiece in a predetermined storage place outside the machine tool 50, and after transporting the virtual workpiece to the internal space A of the machine tool 50 through the opening 54c of the baffle 54, sets it in the fixture. The virtual workpiece has the same size as the workpiece that becomes the processing object in step S14 described later after processing.

[0104] Furthermore, the processor 108 operates the machining head 56 and the machining table 60 according to the machining program. By executing the machining program, the processor 108 causes the machining head 56 and the machining table 60 to perform the same operation as step S14 described later, and ejects the machining fluid from the machining fluid ejection device at the same timing and flow rate as step S14 described later. When the machining program is finished, the machining head 56 and the machining table 60 return to the initial position.

[0105] Furthermore, the processor 108 starts the photographing operation of the photographing device 14 at a time point t2 after a preset time τ has elapsed from the time point t1 at which the machining fluid was last ejected from the machining fluid ejecting device. Specifically, the processor 108 causes the robot 104 to operate and place the photographing device 14 at a predetermined photographing position. For example, when the photographing device 14 is placed at the photographing position, the photographing device 14 is placed above the working area 62 (i.e., the robot coordinate system C R The positive direction of the z-axis), the sight direction of the camera 14 and the robot coordinate system C R The bottom wall 54a of the baffle 54 of the working area 62, the upper surface 58a of the telescopic cover 58 and the upper surface 60a of the processing table 60 are parallel to the z-axis (ie, the plumb direction) of the working area 62 and enter the field of view of the shooting device 14.

[0106] Robot coordinate system C R The position data of the shooting position in the image processing unit 100 is pre-stored in the memory 22. When the image capturing device 14 is arranged at the shooting position, the processor 108 operates the image capturing device 14 to capture the work area 62. The image capturing device 14 sends the captured image data ID1 (first image data) to the processor 108, and the processor 108 stores the image data ID1 in the memory 22. The image data ID1 is the image data of the work area 62 captured by the image capturing device 14 before the workpiece is processed in the subsequent step S14. Fig.17 An example of image data ID1 obtained when the image pickup device 14 picks up the image of the work area 62 in step S11 is shown.

[0107] In step S12, the processor 108 activates the imaging device 14 to measure the height h of the working area 62. Here, as described above, the working area 62 includes the bottom wall 54a, the telescopic cover 58, and the processing table 60. Fig.13 As shown in FIG. 1 , the upper surface 58a of the telescopic cover 58 is located at a position higher than the inner surface of the bottom wall 54a by a height h2, and the upper surface 60a of the processing table 60 is located at a position higher than the bottom wall 54a by a height h3 (> h2). That is, the working area 62 includes a plurality of areas 54a (the inner surface of the bottom wall 54a), an area 58a (the upper surface 58a), and an area 60a (the upper surface 60a) having different heights h from each other.

[0108] In step S11, the imaging device 14 captures the image data ID1 and measures the height h of each area (54a, 58a, 60a) of the work area 62 included in the image data ID1. For example, the imaging device 14 includes a laser emitting unit that emits laser light and a light receiving unit that receives the laser light reflected by an object in the work area 62.

[0109] The imaging device 14 measures the distance from the imaging device 14 to the object in the working area 62 by using a triangulation method. Alternatively, the imaging device 14 has two cameras and can measure the distance to the object in the working area 62 from the two images captured by the two cameras. By using such a method, the imaging device 14 can measure the distance d3 to the area 60a existing in the working area 62, the distance d2 to the area 58a, and the distance d1 to the area 54a.

[0110] These distances d1, d2, and d3 are information indicating the height h of the regions 54a, 58a, and 60a. That is, when the region 54a is used as the reference for the height h, the height h2 of the region 58a can be obtained by subtracting the distance d2 from the distance d1, and the height h3 of the region 60a can be obtained by subtracting the distance d3 from the distance d1.

[0111] As the information on the height h of the areas 54a, 58a, and 60a, the imaging device 14 may measure the distances d1, d2, and d3, or the heights h2 and h3. The processor 108 obtains the information on the height h measured by the imaging device 14 from the imaging device 14, and stores it in the memory 22.

[0112] In step S13, the processor 108 sets a plurality of image areas according to the height h of the work area 62 in the image data ID1 captured by the imaging device 14 in step S11. Specifically, the processor 108 extracts each area in the work area 62 according to the height h from the image data ID1 based on the information of the height h acquired in step S12.

[0113] For example, when the distances d1, d2, and d3 are acquired as the information of the height h in step S12, the processor 108 extracts the distance d from the image data ID1 within the predetermined range: d th1 ≤d<d th2 For example, assume that the distance d3 of area 60a satisfies d th1 ≤d3<d th2 In this case, the processor 108 extracts the image area of ​​the projection area 60a from the image data ID1 and sets it as the image area 60a' of the height level 3.

[0114] In addition, the processor 108 extracts from the image data ID1 a distance d in a predetermined range: d th2 ≤d<d th3 For example, assume that the distance d2 of area 58a satisfies d th2 ≤d2<d th3 In this case, the processor 108 extracts the image area of ​​the projection area 58a from the image data ID1 and sets it as the image area 58a' of the height level 2.

[0115] In addition, the processor 108 extracts the distance d from the image data ID1 as a predetermined range: d th3 For example, assume that the distance d3 of region 54a satisfies d th3 ≤d3. In this case, the processor 108 extracts the image area of ​​the projection area 54a from the image data ID1, and sets it as the image area 54a' of the height level 1.

[0116] Fig.17 In FIG. 1 , for easy understanding, image region 54a′ (region 54a) is represented by white, image region 58a′ (region 58a) is represented by light gray, and image region 60a′ (region 60a) is represented by dark gray. The threshold value d defining the range of the above distance d is th1 d th2 and d th3 The photographing position of the photographing device 14 is set in advance by the operator and stored in the memory 22 .

[0117] Thus, the processor 108 sets the plurality of image areas 54a', 58a', and 60a' in the image data ID1 according to the height h of the work area 62 based on the information of the height h acquired in step S12 (i.e., the distance d). Therefore, the processor 108 functions as the image area setting unit 144 ( Fig.12 ) to perform its function.

[0118] In addition, in step S12, even if the processor 108 obtains heights h2 and h3 as information on height h, it sets a predetermined range for height h in the same way as the method of distance d, thereby extracting each area within the working area 62 according to the height h from the image data ID1 and setting the image areas 60a', 58a' and 54a'.

[0119] In step S14, the processor 108 processes the workpiece. Specifically, the operator (or the robot 104) mounts the tool 64 on the processing head 56 and sets a fixture on the upper surface 60a of the processing table 60. Next, the processor 108 operates the robot 104, and uses the robot manipulator 120 to grasp the workpiece in a predetermined storage location outside the machine tool 50, and transports the workpiece to the internal space A of the machine tool 50 through the opening 54c of the baffle 54, and sets it on the fixture.

[0120] Next, the processor 108 (or the second control device) operates the machining head 56 and the machining table 60 according to the machining program, ejects the machining fluid from the machining fluid ejection device, and uses the tool 64 to machine the workpiece. As a result, foreign matter is accumulated in the working area 62 of the machine tool 50. When the machining program ends, the machining head 56 and the machining table 60 return to the same initial position as when the simulation machining process of step S11 ends. The workpiece position of the storage place of the workpiece and the position on the machining table 60 where the workpiece should be placed are in the robot coordinate system C R The position data in is stored in the memory 22 in advance.

[0121] In step S15, the processor 108 uses the imaging device 14 to image the working area 62. Here, the processor 108 starts step S15 at time t2 when a predetermined time τ has passed since time t1 when the machining fluid was last ejected from the machining fluid ejection device in step S14.

[0122] Specifically, the processor 108 operates the robot 104, disposes the imaging device 14 at the same imaging position as step S11, and operates the imaging device 14 to image the work area 62 along the same line of sight direction as step S11. The imaging device 14 sends the imaged image data ID2 (second image data) to the processor 108, and the processor 108 stores the image data ID2 in the memory 22.

[0123] The image data ID2 is image data of the working area 62 captured by the imaging device 14 after the workpiece is machined in step S14. Fig.18An example of image data ID2 obtained when the image capturing device 14 captures the image of the work area 62 in step S15 is shown. In the image data ID2 captured after processing, foreign matter B is reflected in the work area 62 (areas 54a, 58a, 60a).

[0124] In step S16, the processor 108 sets the image areas 54a', 58a', and 60a' for the image data ID2 captured in the most recent step S15. Specifically, the processor 108 sets the image areas 54a', 58a', and 60a' for the image data ID2 in the same manner as in step S13, based on the setting information of the image areas 54a', 58a', and 60a' set in step S13 (the position data of the boundary lines of the image areas 54a', 58a', and 60a' in the image data).

[0125] Thus, the positions of the image areas 54a', 58a' and 60a' set for the image data ID1 in step S13 and the positions of the image areas 54a', 58a' and 60a' set for the image data ID2 in step S16 are the same as those in the image data ID2.

[0126] In step S17, the processor 108 determines whether the area 60a at level 3 needs to be cleaned. Specifically, the processor 108 determines whether the area 60a at level 3 needs to be cleaned based on the image data ID1 of the image area 60a' at level 3 captured in step S11. 1_3 and the image data ID of the image area 60a' at the height level 3 in the image data ID2 captured in the most recent step S15. 2_3 , to determine whether area 60a needs to be cleaned.

[0127] Specifically, the processor 108 converts the image data ID 1_3 The brightness of the pixel and the processed image data ID 2_3 By comparing the brightness of the pixels of the image processing unit 108 with those of the image processing unit 106, it is possible to detect whether there is foreign matter in the area 60a from the difference between the pixels. In step S17, when the processor 108 detects foreign matter in the area 60a, it determines that the area 60a needs to be cleaned (ie, "yes").

[0128] When the processor 108 determines that the result is "yes", the process proceeds to step S18, and when the processor 108 determines that the result is "no", the process proceeds to step S19. Fig.12 ) functions, the determination unit 146 performs the function according to the image data ID1, ID2 (specifically, the image data ID 1_3 、ID 2_3 ) to determine whether the operation area 62 (area 60a) needs to be cleaned.

[0129] In step S18, the processor 108 sets the cleaning target area. Specifically, the processor 108 automatically sets the area 60a determined to be required to be cleaned in step S17 and the areas 58a and 54a whose height h is lower than that of the area 60a as the cleaning target areas. Thus, the areas 60a, 58a and 54a are set as the cleaning target areas. In this way, in the present embodiment, the processor 108 as the cleaning target area setting unit 148 ( Fig.12 ) to perform its function.

[0130] In step S19, the processor 108 determines whether the area 58a at level 2 needs to be cleaned. Specifically, the processor 108 determines whether the area 58a at level 2 needs to be cleaned based on the image data ID1 captured in step S11. 1_2 and the image data ID of the image area 58a' at the height level 2 in the image data ID2 captured in the most recent step S15. 2_2 , to determine whether area 58a needs to be cleaned.

[0131] Specifically, the processor 108 converts the image data ID 1_2 The brightness of the pixel and the processed image data ID 2_2 By comparing the brightness of the pixels of the image processing unit 108 with those of the image processing unit 109, it is possible to detect whether there is a foreign object in the area 58a from the difference between the two. In step S19, when the processor 108 detects a foreign object in the area 58a, it determines that the area 58a needs to be cleaned (i.e., "yes"). When the processor 108 determines "yes", it transfers to step S20, and when it determines "no", it transfers to step S21.

[0132] In step S20, the processor 108 sets the cleaning target area. Specifically, the processor 108 automatically sets the area 58a determined to be required to be cleaned in step S19 and the area 54a whose height h is lower than that of the area 58a as the cleaning target area. Thus, the areas 58a and 54a are set as the cleaning target areas.

[0133] In this way, when the processor 108 determines in step S17 (or S19) that an area 60a (or 58a) needs to be cleaned, in step S18 (or S20), areas 58a and 54a (or 54a) whose height h is lower than that of the area 60a (or 58a) are automatically set as cleaning object areas together with the area 60a (or 58a).

[0134] In step S21, the processor 108 determines whether the area 54a at level 1 needs to be cleaned. Specifically, the processor 108 determines whether the area 54a at level 1 needs to be cleaned based on the image data ID1 of the image area 54a' at level 1 captured in step S1. 1_1 The image data ID2 of the image area 54a' at the height level 1 in the image data ID2 captured in the most recent step S15 is the same as the image data ID2 of the image area 54a' at the height level 1 in the image data ID2 captured in the most recent step S15. 2_1 , to determine whether area 54a needs to be cleaned.

[0135] Specifically, the processor 108 converts the image data ID 1_1 The brightness of the pixel and the processed image data ID 2_1 By comparing the brightness of the pixels of the image processing unit 108 with those of the image processing unit 109, it is possible to detect whether there is a foreign object in the area 54a from the difference between them. In step S21, when the processor 108 detects a foreign object in the area 54a, it determines that the area 54a needs to be cleaned (i.e., "yes"). When the processor 108 determines "yes", it transfers to step S22, and when it determines "no", it transfers to step S24. In step S22, the processor 108 sets the area 54a determined to need cleaning in step S21 as the cleaning target area.

[0136] In step S23, the processor 108 performs a cleaning operation. Specifically, first, the processor 108 performs a removal operation to cause the robot 104 to grip the cleaning nozzle 16 mounted on the loading and unloading device 106 and remove it from the loading and unloading device 106. In this removal operation, the processor 108 causes the robot 104 to move the robot manipulator 118 (TCP) to a gripping position for gripping the cleaning nozzle 16 gripped by the claw 138 of the loading and unloading device 106 while the finger 130 is opened.

[0137] When the robot manipulator 118 is arranged at the holding position, the cleaning nozzle 16 held by the claw 138 of the loading and unloading device 106 is arranged between the fingers 130 of the robot manipulator 118, and the flat surface 16b of the cleaning nozzle 16 faces the inner surface of the fingers 130. The holding position is in the robot coordinate system C R The position data in is stored in the memory 22 in advance.

[0138] Next, the processor 108 closes the fingers 130 and grips the flat surface 16b of the cleaning nozzle 16 with the fingers 130. Next, the processor 108 drives the claw driving unit 140 of the loading and unloading device 106 to open the claws 138. In this way, the robot 104 removes the cleaning nozzle 16 from the loading and unloading device 106.

[0139] After the removal of the cleaning nozzle 16, the processor 108 performs a cleaning operation on the cleaning target area set in step S18, S20, or S22. For example, when step S23 is performed after step S18, the processor 108 performs a cleaning operation on the areas 60a, 58a, and 54a set as the cleaning target areas in the order of height h from high to low, that is, the order of area 60a→area 58a→area 54a.

[0140] Specifically, the processor 108 operates the robot 104 to move the cleaning nozzle 16 held by the robot manipulator 118 toward the area 60a, and operates the fluid supply device 18 to eject the fluid from the cleaning nozzle 16, and the entire area 60a is cleaned with the ejected fluid. Next, the processor 108 uses the robot 104 to move the cleaning nozzle 16 toward the area 58a, and ejects the fluid from the cleaning nozzle 16 to clean the entire area 58a.

[0141] Next, the processor 108 uses the robot 104 to move the cleaning nozzle 16 toward the area 54a while ejecting fluid from the cleaning nozzle 16 to clean the entire area 54a. In addition, the movement path (or cleaning position) that the robot 104 moves the cleaning nozzle 16 (or TCP) when cleaning the areas 60a, 58a, and 54a, respectively, can be predetermined by a computer program.

[0142] On the other hand, when step S23 is executed after step S20, processor 108 performs cleaning operation for regions 58a and 54a set as cleaning target regions in the order of height h from high to low, that is, region 58a→region 54a. In addition, when step S23 is executed after step S22, processor 108 performs cleaning operation for region 54a.

[0143] In this way, the processor 108 performs the following cleaning operation: while the robot 104 moves the cleaning nozzle 16 toward the working area 62 (areas 60a, 58a, 54a), the fluid is ejected from the cleaning nozzle 16 to clean the working area 62. Therefore, the processor 108 serves as the cleaning execution unit 150 ( Fig.12 After step S23, the processor 108 returns to step S15, and repeats the loop of steps S15 to S23 until the determination result in step S21 is "No".

[0144] In addition, the processor 108 counts the number of times step S23 is executed (or the number of times the determination result is "yes" in step S17, S19 or S21) m, and when the number m reaches the predetermined number m, the processor 108 counts the number of times step S23 is executed. MAX (For example, m MAX=3), a warning signal in the form of sound or image "the number of cleaning times has reached the predetermined number of times" is issued, and the process can be transferred to step S24 (or the process can also be ended). Fig.16 Thus, it is prevented that step S23 is executed too many times.

[0145] When the determination result is "No" in step S21, the processor 108 performs an assembly operation of assembling the cleaning nozzle 16 to the loading and unloading device. Specifically, the processor 108 causes the robot 104 to operate and place the robot manipulator 118 (TCP) holding the cleaning nozzle 16 at the assembly position. At this time, the claw 138 of the loading and unloading device 106 is in an open state.

[0146] When the robot manipulator 118 is disposed at the assembly position, the flat surface 138a of the claw 138 of the loading and unloading device 106 faces the flat surface 16b of the cleaning nozzle 16 held by the robot manipulator 118. Next, the processor 108 drives the claw driving unit 140 of the loading and unloading device 106 to close the claw 138 to hold the cleaning nozzle 16, and then opens the finger 130 of the robot manipulator 118. In this way, the processor 108 uses the robot 104 to assemble the cleaning nozzle 16 to the loading and unloading device 106.

[0147] In step S24, the processor 108 determines whether there are other workpieces to be processed, similarly to the above step S8. If the determination is "yes", the process returns to step S14, and the process of steps S14 to 24 is repeated until the determination is "no" in step S24. On the other hand, if the determination is "no" in step S24, the process ends. Fig.16 The process shown.

[0148] As described above, in this embodiment, processor 108 causes robot 104 to remove cleaning nozzle 16 and clean work area 62. According to this configuration, robot 104 can operate cleaning nozzle 16 to clean work area 62 of machine tool 50, thereby improving the efficiency of the cleaning operation.

[0149] Furthermore, in the present embodiment, the cleaning nozzle 16 is disposed in the internal space A of the machine tool 50. According to this structure, since it is not necessary to carry the cleaning nozzle 16 and the fluid supply pipe 26 into and out of the machine tool 50, the efficiency of the cleaning operation can be improved, and the cleaning fluid can be prevented from leaking from the cleaning nozzle 16 or the fluid supply pipe 26 to the outside of the machine tool 50. Furthermore, the piping of the fluid supply pipe 26 in the internal space A of the machine tool 50 can be simplified.

[0150] In addition, in this embodiment, when the processor 108 determines that an area 60a (or 58a) needs to be cleaned, it automatically sets areas 58a and 54a (or 54a) whose height h is lower than that of the area 60a (or 58a) as cleaning object areas together with the area 60a (or 58a) (steps S18, S20).

[0151] Furthermore, the processor 108 performs cleaning operations on the areas 60a, 58a, and 54a set as the cleaning target areas in descending order of height h. According to this structure, the processor 108 can optimize the number of cleaning operations on the working area 62. Specifically, when a region is cleaned by the fluid ejected from the cleaning nozzle 16, the foreign matter B blown away may eventually fly to a region with a lower height than the region due to gravity.

[0152] Therefore, if the area 60a is cleaned after the area 58a, the foreign matter B blown off from the area 60a may accumulate in the cleaned area 58a. As in the present embodiment, the cleaning operation is performed on the plurality of areas 60a, 58a, 54a in descending order of height h, and the plurality of areas 60a, 58a, 54a can be effectively cleaned.

[0153] In addition, in this embodiment, the robot 104 has a robot manipulator 118 for holding the cleaning nozzle and a robot manipulator 120 for loading the workpiece. Therefore, various operations can be performed by one robot 104, so the operation efficiency can be achieved and the manufacturing cost can be reduced. Fig.16 In the illustrated flow, the processor 108 may execute a loop of steps S15 to S23 each time n (eg, n=20) workpieces are processed.

[0154] In addition, the above-mentioned device 70 can also be applied to the cleaning system 100. Fig.19 , other functions of the cleaning system 100 will be described. In the present embodiment, the processor 108 functions as the image data generating unit 28. Therefore, the imaging device 14, the image data generating unit 28, and the determining unit 146 constitute the device 70.

[0155] Next, refer to Fig. 20 , other examples of the operation of the cleaning system 100 are described. Fig. 20 The process shown is similar to Fig.16 The difference of the process shown is in steps S31, S32 and S33. Specifically, after step S16, in step S31, the processor 108 executes the high level 3 cleaning determination scheme. Fig.21 This step S31 will be described.

[0156] In step S41, the processor 108 functions as the image data generating unit 28 to generate image data ID 3_3 (3rd image data), the image data ID 3_3 The image data ID represents the image area 60a' of the height level 3 in the image data ID1 captured in step S11. 1_3 and the image data ID of the image area 60a' at the height level 3 in the image data ID2 captured in the most recent step S15. 2_3 The degree of brightness change between.

[0157] Specifically, the processor 108 uses the mathematical formula (1), the mathematical formula (2), or the mathematical formula (3) in the same manner as in step S4 above to obtain the image data ID 3_3 The brightness BR3 of each pixel is generated, thereby generating a pixel having the same brightness as the image data ID 1_3 and ID 2_3 Image data IDs with the same number of pixels 3_3 Image data ID 3_3 The brightness of each pixel BR3 corresponds to the image data ID 1_3 The pixel brightness BR1 and the image data ID 1_3 The image data ID corresponding to the pixel 2_3 The value of the degree of change between the brightness of the pixels BR2.

[0158] In step S42, processor 108 obtains the image data ID generated in step S41. 3_3 The histogram HG3 represents the image data ID 3_3 The brightness of each pixel BR3 and the image data ID 3_3 In step S43, the processor 108 functions as the determination unit 146, and determines whether the area 60a of level 3 needs to be cleaned based on the histogram HG3 using the same method as in step S6 described above.

[0159] As an example, the processor 108 performs the same operation as in step S6 above, and generates a value in the range [α 1_3 , α 2_3 ] The number of pixels N X_3 Relative to the total number of pixels N T_3 The ratio R 1_3 =N X_3 / N T_3 is the predetermined threshold R th1_3 When the brightness BR3 is within the range [α1_3 , α 2_3 ]Number of pixels outside Y_3 Relative to the total number of pixels N T_3 The ratio R 2_3 =N Y_3 / N T_3 is the predetermined threshold R th2_3 If the above, it is judged as "yes".

[0160] As another example, the processor 108 determines whether the trajectory of the curve of the histogram HG3 is consistent with the reference histogram HG R_3 When the consistency of the trajectory of the curve is smaller than a preset threshold, the processor 108 determines that it is "yes". As another example, the processor 108 determines that it is "yes" when the standard deviation of the histogram HG3 is larger than a preset threshold. When the processor 108 determines that it is "yes" in step S43, it sends a Fig. 20 If the result is "No", the Fig. 20 Transfer to step S32 in.

[0161] In step S32, the processor 108 executes the high level 2 cleaning determination scheme. Fig. 22 Step S32 will be described below. In step S51, the processor 108 functions as the image data generating unit 28 to generate image data ID 3_2 (3rd image data), the image data ID 3_2 The image data ID represents the image area 58a' of the height level 2 in the image data ID1 captured in step S11. 1_2 The image data ID2 of the image area 58a' at the height level 2 in the image data ID2 captured in the most recent step S15 is the same as the image data ID2 of the image area 58a' at the height level 2. 2_2 The degree of brightness change between.

[0162] Specifically, the processor 108 uses the mathematical formula (1), the mathematical formula (2), or the mathematical formula (3) to obtain the image data ID 3_2 The brightness BR3 of each pixel is generated, thereby generating a pixel having the same brightness as the image data ID 1_2 and ID 2_2 Image data IDs with the same number of pixels 3_2 Image data ID 3_2 The brightness of each pixel BR3 corresponds to the image data ID 1_2 The pixel brightness BR1 and the image data ID 1_2 The image data ID corresponding to the pixel 2_2 The value of the degree of change between the brightness of the pixels BR2.

[0163] In step S52, processor 108 obtains the image data ID generated in step S51. 3_2 The histogram HG2 represents the image data ID 3_2 The brightness of each pixel BR3 and the image data ID 3_2 In step S53, the processor 108 functions as the determination unit 146 to determine whether the area 58a of the height level 2 needs to be cleaned based on the histogram HG2.

[0164] As an example, the processor 108 performs the same operation as in step S6 above, and generates a value in the range [α 1_2 , α 2_2 ] The number of pixels N X_2 Relative to the total number of pixels N T_2 The ratio R 1_2 =N X_2 / N T_2 is the predetermined threshold R th1_2 When the brightness BR3 is within the range [α 1_2 , α 2_2 ]Number of pixels outside Y_2 Relative to the total number of pixels N T_2 The ratio R 2_2 =N Y_2 / N T_2 is the predetermined threshold R th2_2 If the above, it is judged as "yes".

[0165] As another example, the processor 108 determines whether the trajectory of the curve of the histogram HG2 is consistent with the reference histogram HG R_2 When the consistency of the trajectory of the curve is smaller than a preset threshold, the processor 108 determines that it is "yes". As another example, when the standard deviation of the histogram HG2 is larger than a preset threshold, the processor 108 determines that it is "yes". When the processor 108 determines that it is "yes" in step S53, it sends a Fig. 20 In step S20, if the result is "No", Fig. 20 Transfer to step S33 in.

[0166] In step S33, the processor 108 executes the high level 1 cleaning determination scheme. Fig.23 Step S33 will be described below. In step S61, the processor 108 functions as the image data generating unit 28 to generate image data ID 3_1 (3rd image data), the image data ID 3_1The image data ID represents the image area 54a' of the height level 1 in the image data ID1 captured in step S11. 1_1 The image data ID2 of the image area 54a' at the height level 1 in the image data ID2 captured in the most recent step S15 is the same as the image data ID2 of the image area 54a' at the height level 1 in the image data ID2 captured in the most recent step S15. 2_1 The degree of brightness change between.

[0167] Specifically, the processor 108 uses the mathematical formula (1), the mathematical formula (2), or the mathematical formula (3) in the same manner as in step S4 above to obtain the image data ID 3_1 The brightness BR3 of each pixel is generated, thereby generating a pixel having the same brightness as the image data ID 1_1 and ID 2_1 Image data IDs with the same number of pixels 3_1 Image data ID 3_1 The brightness of each pixel BR3 corresponds to the image data ID 1_1 The pixel brightness BR1 and the image data ID 1_1 The image data ID corresponding to the pixel 2_1 The value of the degree of change between the brightness of the pixels BR2.

[0168] In step S62, processor 108 obtains the image data ID generated in step S61. 3_1 The histogram HG1 represents the image data ID 3_1 The brightness of each pixel BR3 and the image data ID 3_1 In step S63, the processor 108 functions as the determination unit 146 to determine whether the area 54a at height level 1 needs to be cleaned based on the histogram HG1.

[0169] As an example, the processor 108 performs the same operation as in step S6 above, and generates a value in the range [α 1_1 , α 2_1 ] The number of pixels N X_1 Relative to the total number of pixels N T_1 The ratio R 1_1 =N X_1 / N T_1 is the predetermined threshold R th1_1 When the brightness BR3 is within the range [α 1_1 , α 2_1 ]Number of pixels outside Y_1 Relative to the total number of pixels N T_1 The ratio R 2_1 =N Y_1 / NT_1 is the predetermined threshold R th2_1 If the above, it is judged as "yes".

[0170] As another example, the processor 108 determines whether the trajectory of the curve of the histogram HG1 is consistent with the reference histogram HG R_1 When the consistency of the trajectory of the curve is smaller than a preset threshold, the processor 108 determines that it is "yes". As another example, when the standard deviation of the histogram HG1 is larger than a preset threshold, the processor 108 determines that it is "yes". When the processor 108 determines that it is "yes" in step S63, it sends a Fig. 20 If the result of the determination is "No", the Fig. 20 Transfer to step S24 in.

[0171] Thus, in this embodiment, the processor 108 obtains the histograms HG3, HG2, and HG1 for the image areas 60a', 58a', and 54a' set in step S16, and determines whether cleaning is required for each area 60a, 58a, and 54a based on the histograms HG3, HG2, and HG1. According to this structure, it is possible to determine whether cleaning is required for each area 60a, 58a, and 54a with high accuracy by using a statistical method.

[0172] In addition, the cleaning system 100 may include a plurality of cleaning nozzles and a plurality of attachment and detachment devices. Fig.24 This method is shown. Fig.24 The cleaning system 100' shown differs from the above-mentioned cleaning system 100 in that it has a plurality of cleaning nozzles 16A and 16B and a plurality of loading and unloading devices 106A and 106B. The fluid supply device 18 supplies fluid to the cleaning nozzle 16A via the fluid supply pipe 26A and supplies fluid to the cleaning nozzle 16B via the fluid supply pipe 26B.

[0173] The loading and unloading devices 106A and 106B are respectively arranged on the baffle 54 in the robot coordinate system C R The side walls 54b are opposed to each other in the x-axis direction. The cleaning nozzle 16A is detachably mounted on the mounting and dismounting device 106A, while the cleaning nozzle 16B is detachably mounted on the mounting and dismounting device 106B.

[0174] The processor 108 divides the working area 62 into robot coordinate systems C R After the robot 104 holds the cleaning nozzle 16A and uses the cleaning nozzle 16A to clean the area 62A (or before cleaning), the robot 104 holds the cleaning nozzle 16B and uses the cleaning nozzle 16B to clean the working area 62.

[0175] For example, the processor 108 executes the following operations for the region 62A and the region 62B, respectively. Fig.16 or Fig. 20 The process shown in FIG. 1 is used to clean the regions 62A and 62B respectively. Fig.16 or Fig. 20 In the illustrated process, in steps S11 and S15 , processor 108 uses imaging device 14 to image region 62A.

[0176] On the other hand, when executing Fig.16 or Fig. 20 In the flow shown, in steps S11 and S15, processor 108 uses imaging device 14 to image area 62B. According to the present embodiment, cleaning operations can be reliably performed on areas 62A and 62B of work area 62 using different cleaning nozzles 16A and 16B.

[0177] In addition, in the above-mentioned cleaning system 100 or 100', the case where the shooting device 14 is a three-dimensional visual sensor capable of measuring the distance to the subject image is described, but it is not limited to this. The cleaning system 100 or 100' also has a height measuring device for measuring the height h of the working area 62, and the shooting device 14 is a camera capable of capturing image data.

[0178] In addition, in the above embodiment, the case where the working area 62 includes the areas 54a, 58a, and 60a of three levels of height is described, but it is not limited to this, and it can be understood that the working area 62 can include areas of any number of height levels. In addition, in the above embodiment, as the elements constituting the areas 54a, 58a, and 60a of different height levels, the bottom wall 54a, the telescopic cover 58, and the processing table 60 are exemplified, but the working area 62 can also have any elements different from the bottom wall 54a, the telescopic cover 58, and the processing table 60.

[0179] In addition, the cleaning system 10, 100, or 100' may also have a plurality of camera devices 14A and 14B. For example, the camera device 14A may capture a partial area of ​​the working area 62 (e.g., the area 62A) and the camera device 14B may capture other areas of the working area 62 (e.g., the area 62B).

[0180] In addition, a shooting auxiliary light source (not shown) may be provided, and when the working area 62 is photographed by the photographing device 14, 14A, 14B in the above-mentioned steps S1, S3, S11 or S15, the shooting auxiliary light source is used to increase the light irradiating the working area 62. The shooting auxiliary light source is a fluorescent lamp or an LED, etc., and may be integrally assembled to the photographing device 14, 14A, 14B, or may be provided separately from the photographing device 14, 14A, 14B.

[0181] In addition, in the above embodiment, the processor 20 performs the simulated machining process in steps S1 and S11. However, the processor 20 may not perform the simulated machining process in steps S1 or S11 and allow the camera 14 to capture the work area 62 when the machining fluid is not used in the above steps S2 or S14.

[0182] In addition, in the simulated processing performed in the above step S1 or S11, the tool 64 is not in contact when the simulated processing is performed, and a virtual workpiece of any shape can be used. In addition, in step S1 or S11, after the simulated processing is performed, the working area 62 is photographed after the virtual workpiece is removed, and then, in the above step S3 or S15, the working area 62 can be photographed after the workpiece processed in step S2 or S14 is removed from the fixture.

[0183] In addition, the cleaning system 100 performs Fig.16 In the process shown in the figure, the image data ID1 representing the state before processing is not necessarily photographed by the photographing device 14 in step S11, and for example, the image data can also be made into computer graphics by the operator. In addition, the robot 104 can also be any type of robot such as a horizontal multi-joint robot and a parallel link robot. The present disclosure is described above through the embodiments, but the above embodiments do not limit the scope of the invention related to the requested patent protection.

Claims

1. A cleaning system for cleaning the working area of ​​a machine tool, wherein the machine tool processes a workpiece according to a processing program, characterized in that: The cleaning system has: a cleaning nozzle which is loaded and unloaded by a loading and unloading device provided on the machine tool, and which sprays a fluid; a robot capable of holding the cleaning nozzle; and processor, The processor executes the machining program before performing the machining, thereby performing a simulated machining process, wherein the simulated machining process causes the machine tool to simulate the actions for the machining, and The processor determines whether the working area needs to be cleaned based on the image data of the working area taken after the simulated processing process is completed and before the processing, and after the processing, and When the processor determines that cleaning is required, it activates the robot to perform a dismantling action and a cleaning action. The dismantling action is to cause the robot to grasp the cleaning nozzle installed on the loading and unloading device and remove it from the loading and unloading device. The cleaning action is to use the robot to move the cleaning nozzle to the working area and spray fluid from the cleaning nozzle to clean the working area.

2. The cleaning system according to claim 1, characterized in that: The loading and unloading device is arranged inside the machine tool. The robot is provided outside the machine tool so as to be able to move forward and backward into the machine tool through an opening provided in the machine tool.

3. The cleaning system according to claim 1 or 2, characterized in that: The cleaning system also has: A photographing device photographs the first image data after the simulated processing process is completed and before the processing, and photographs the second image data after the processing.

4. A cleaning system for cleaning the working area of ​​a machine tool, characterized in that: The cleaning system has: a cleaning nozzle, which is loaded and unloaded by a loading and unloading device provided on the machine tool, and the cleaning nozzle sprays a fluid; a robot capable of holding the cleaning nozzle; A photographing device for photographing the working area; a determination unit, which determines whether the working area needs to be cleaned according to the image data of the working area photographed by the photographing device; as well as a cleaning execution unit that moves the robot to execute a removal action and a cleaning action, wherein the removal action is an action of causing the robot to hold the cleaning nozzle installed on the loading and unloading device and remove it from the loading and unloading device, and the cleaning action is an action of using the robot to move the cleaning nozzle to the working area and spray fluid from the cleaning nozzle to clean the working area. The working area includes a plurality of areas at different heights. The determination unit determines whether cleaning is required for each of the areas based on the image data captured by the camera. The cleaning system further includes a cleaning target region setting unit that automatically sets the one region and the region whose height is lower than the one region as cleaning target regions when the determination unit determines that one region needs to be cleaned.

5. The cleaning system according to claim 4, characterized in that: The cleaning execution unit performs the cleaning operation on each of the plurality of areas set as the cleaning target areas by the cleaning target area setting unit in descending order of the height.

6. A cleaning system for cleaning the working area of ​​a machine tool, characterized in that: The cleaning system has: a cleaning nozzle, which is loaded and unloaded by a loading and unloading device provided on the machine tool, and the cleaning nozzle sprays a fluid; a robot capable of holding the cleaning nozzle; A photographing device for photographing the working area; a determination unit, which determines whether the working area needs to be cleaned according to the image data of the working area photographed by the photographing device; as well as a cleaning execution unit that moves the robot to execute a removal action and a cleaning action, wherein the removal action is an action of causing the robot to hold the cleaning nozzle installed on the loading and unloading device and remove it from the loading and unloading device, and the cleaning action is an action of using the robot to move the cleaning nozzle to the working area and spray fluid from the cleaning nozzle to clean the working area. The working area includes a plurality of areas at different heights. The cleaning system further comprises: an image area setting unit which sets a plurality of image areas according to the height in the image data captured by the imaging device. The determination unit determines whether cleaning is necessary for each of the image areas based on the image data of each of the image areas set by the image area setting unit.

7. The cleaning system according to claim 6, characterized in that: The photographing device is configured to photograph the image data and measure the height. The image region setting unit sets the plurality of image regions in the image data based on the information on the height measured by the imaging device.

8. A method for cleaning a working area of ​​a machine tool, wherein the machine tool performs processing on a workpiece according to a processing program, characterized in that: Before performing the machining, the machining program is executed to perform a simulated machining process, wherein the simulated machining process causes the machine tool to simulate the actions for the machining. Determining whether the working area needs to be cleaned according to image data of the working area taken after the simulated processing process is completed and before the processing, and after the processing; When it is determined that cleaning is necessary, the robot is activated to perform a removal action and a cleaning action. The removal action is to cause the robot to grasp a cleaning nozzle installed on a loading and unloading device provided on the machine tool and remove it from the loading and unloading device. The cleaning action is to use the robot to move the cleaning nozzle toward the working area and spray fluid from the cleaning nozzle to clean the working area.

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