Method for creating head model, method for creating drive waveform, information processor, and program

Abstract

To provide a method for creating a head model which can accurately simulate the behavior of a liquid discharge head, a method for creating a drive waveform which creates an appropriate drive form using the head model, and an information processor and a program for performing the methods.SOLUTION: A method for creating a head model simulates the behavior of a liquid discharge head having a piezoelectric element, wherein the head model is configured using a fluid analytic model, the method including, via one or more first processors, optimizing the head model based on learning data using data related to an actual flight shape in a case of ischaring liquid by applying each of a plurality of drive waveforms to the piezoelectric element using the liquid discharge head and the liquid discharged from the liquid discharge head as the learning data.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a head model creating method, a drive waveform creating method, an information processing device and a program, and in particular to an information processing technology for creating a head model that simulates the behavior of a piezoelectric liquid ejection head and a drive waveform creating technology that utilizes the head model. [Background technology]

[0002] In inkjet printing, when the ink used is different, even a slight change in the physical properties of the ink changes the flight shape of the ink ejected from the inkjet head, so obtaining good flight characteristics has been a major challenge. Flight characteristics can include, for example, landing position accuracy, the presence or absence of satellite droplets, droplet speed, droplet volume, and stability. Inkjet heads that eject ink by driving a piezoelectric element have a degree of freedom in the drive waveform, so developers often optimize the drive waveform for each ink used.

[0003] However, in order to optimize the drive waveform so as to simultaneously achieve multiple favorable flight characteristics, the developer is required to have specialized knowledge and experience, and optimization involving trial and error requires a significant amount of time.

[0004] In response to the above problem, attempts have been made to shorten the time required to optimize the drive waveform. Patent Document 1 discloses a method for creating a drive waveform that optimizes (minimizes) an evaluation function related to the ink flight characteristics by using an equation of motion (equivalent circuit) of an inkjet head. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 9-174835 Summary of the Invention [Problem to be solved by the invention]

[0006] The method described in Patent Document 1 is based on the premise that the circuit constants of the equation of motion are determined in advance. However, since the circuit constants of the equation of motion differ for each ink used, it is necessary to determine the optimal circuit constants that can adequately simulate the behavior of each ink. Since it takes a lot of time to derive the optimal circuit constants, it ultimately takes a lot of time to create the optimal drive waveform for each ink.

[0007] In addition, when searching for an optimal drive waveform, a method has generally been used in the past in which a group of candidate drive waveforms is prepared in advance based on the developer's past knowledge, etc., and the flight characteristics of each drive waveform in the group are evaluated in sequence to determine the optimal drive waveform. However, with this method, the range of the search is limited to the group of candidate drive waveforms prepared in advance, and it is not possible to search for completely unknown drive waveforms.

[0008] The above-mentioned problem is not limited to inkjet devices for printing purposes, but is a common problem to devices that use liquid ejection heads that eject various functional liquids.

[0009] The present disclosure has been made in consideration of these circumstances, and aims to provide a method for creating a head model that can accurately simulate the behavior of a liquid ejection head, as well as a drive waveform creation method that creates an appropriate drive waveform using that head model, and an information processing device and program for implementing these methods. [Means for solving the problem]

[0010] A method for creating a head model according to a first aspect of the present disclosure is a method for creating a head model that simulates the behavior of a liquid ejection head equipped with a piezoelectric element, the head model being constructed using a fluid analysis model, and including using a liquid ejection head and liquid to be ejected from the liquid ejection head, data relating to the actual flight shape when liquid is ejected by applying each of a plurality of drive waveforms to the piezoelectric element as learning data, and optimizing the head model based on the learning data by one or more first processors.

[0011] According to the first aspect, data on the actual flight shape for a combination of the liquid to be discharged and the liquid discharge head is used as learning data, and the head model is optimized by one or more first processors. This makes it possible to construct a head model that can accurately simulate the discharge behavior for a combination of the liquid to be discharged and the liquid discharge head.

[0012] "Optimization" means approaching an optimal state, and is not limited to reaching a truly optimal state.

[0013] A head model creating method according to a second aspect may be the head model creating method according to the first aspect, wherein the head model is a model obtained by coupling an equivalent circuit model and a fluid analysis model.

[0014] According to the second aspect, the calculation cost can be reduced compared to the case where the head model is constructed using only the fluid analysis model.

[0015] A head model creation method according to a third aspect may be configured in such a way that, in the head model creation method according to the second aspect, one or more first processors optimize parameters related to at least one of the circuit constants of the equivalent circuit model, the viscosity coefficient, the surface tension, and the density of the fluid analysis model.

[0016] A head model creation method according to a fourth aspect may be configured in such a way that, in the head model creation method according to any one of the first to third aspects, one or more first processors update parameters of the head model so that the flight shape predicted by the head model for each input of a plurality of drive waveforms approaches the actual flight shape.

[0017] A head model creation method according to a fifth aspect is a head model creation method according to any one of the first to fourth aspects, in which the data regarding the actual flight shape may be a flight shape image obtained by photographing liquid ejected from the liquid ejection head.

[0018] A head model creation method according to a sixth aspect is a head model creation method according to any one of the first to fourth aspects, wherein the data regarding the actual flight shape may be a group of time-series flight shape images obtained by photographing liquid ejected from the liquid ejection head at at least two times.

[0019] A head model creation method according to a seventh aspect may be configured in such a way that, in the head model creation method according to the sixth aspect, one or more first processors calculate a first evaluation value based on the flight shape at at least two times as an optimization index.

[0020] A head model creation method according to an eighth aspect may be configured such that, in the head model creation method according to any one of the first to seventh aspects, the parameters of the drive waveform include at least one of a pulse width, a slope, a pulse height, and a pulse interval.

[0021] A drive waveform creation method according to a ninth aspect is a drive waveform creation method that uses a head model created by implementing the head model creation method described in any one of the first to eighth aspects, in which one or more second processors perform liquid flight prediction using the head model for each of a plurality of new drive waveforms, and execute a process of determining a drive waveform suitable for ejecting liquid based on the flight prediction results for each of the plurality of new drive waveforms.

[0022] A drive waveform creation method according to a tenth aspect may be configured in the drive waveform creation method according to the ninth aspect, wherein one or more second processors calculate a second evaluation value from the flight prediction result and determine an optimal drive waveform from among a plurality of new drive waveforms based on the second evaluation value.

[0023] A drive waveform creation method according to an eleventh aspect may be configured such that, in the drive waveform creation method according to the tenth aspect, the second evaluation value includes flight characteristics characterized by at least one of the droplet volume, droplet speed, and thread length of at least one of the main droplet and the satellite droplet.

[0024] A drive waveform creation method according to a twelfth aspect may be configured in such a way that, in the drive waveform creation method according to the tenth or eleventh aspect, one or more second processors determine, from among a plurality of new drive waveforms, a drive waveform whose second evaluation value satisfies a specified condition and whose second evaluation value is the most promising.

[0025] A drive waveform creation method according to a 13th aspect may be configured in such a way that, in the drive waveform creation method according to any one of the 9th to 12th aspects, one or more second processors use an optimized head model to perform forward prediction to predict the flight shape of liquid from each of a plurality of new drive waveforms, and determine a drive waveform suitable for ejecting liquid from among the plurality of new drive waveforms based on the flight prediction result of the forward prediction.

[0026] An information processing device according to a 14th aspect is an information processing device that executes a head model creation method according to any one of the 1st to 8th aspects, and includes one or more first processors and one or more first storage devices in which the head model is stored.

[0027] A program according to a fifteenth aspect causes a computer to execute the head model creating method according to any one of the first to eighth aspects.

[0028] An information processing device according to a 16th aspect is an information processing device that executes a drive waveform creation method according to any one of the 9th to 13th aspects, and includes one or more second processors and one or more second storage devices in which an optimized head model is stored.

[0029] A program according to a seventeenth aspect causes a computer to execute the drive waveform generating method according to any one of the ninth to thirteenth aspects. Effect of the Invention

[0030] According to the present disclosure, it is possible to create a head model that can accurately simulate the ejection behavior for a combination of a liquid to be used and a liquid ejection head. Furthermore, according to the present disclosure, it is possible to efficiently create a drive waveform that can obtain desired flight characteristics using the created head model. [Brief description of the drawings]

[0031] [Figure 1] FIG. 1 is a flowchart showing a processing procedure of a head model creating method and a drive waveform creating method according to the embodiment. [Diagram 2] FIG. 2 is a waveform diagram showing an example of a driving waveform. [Diagram 3] FIG. 3 shows an example of an image of the flying shape of ink ejected from an inkjet head. [Figure 4] FIG. 4 is a schematic diagram of a head model according to the embodiment. [Diagram 5] FIG. 5 is a diagram showing an example of a predicted flight profile obtained by a simulation using a head model optimized according to this embodiment. [Figure 6] FIG. 6 shows a scatter plot comparing the drop velocity predicted using the head model optimized according to the present invention with the actual drop velocity. [Figure 7] FIG. 7 is a block diagram showing an example of a hardware configuration of an information processing device that executes at least a part of the processing of the head model creating method and the drive waveform creating method according to the embodiment. [Figure 8] FIG. 8 is an explanatory diagram that shows, in outline, an example of the configuration of an inkjet device used in a discharge experiment for creating a learning data set used in optimizing a head model. [Figure 9]FIG. 9 is a block diagram showing an outline of the functional configuration of an information processing device that executes the process of optimizing the head model. [Figure 10] FIG. 10 is a block diagram showing an outline of the functional configuration of an information processing device that executes a process for determining a promising drive waveform using a head model optimized according to this embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0032] Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

[0033] Overview of the embodiment In this embodiment, an example of a method and device for creating a head model that simulates the behavior of an inkjet head equipped with a piezoelectric element, and a method and device for searching for a drive waveform that uses the head model to obtain desired flight characteristics will be described.

[0034] Fig. 1 is a flowchart showing a processing procedure of a head model creating method and a drive waveform creating method according to an embodiment. Each step S1 to S3 shown in Fig. 1 is executed by one or more processors. Here, an example is described in which a first processor executes a process of optimizing a head model (step S1), and then a second processor different from the first processor executes a process of searching for an optimal drive waveform using the optimized head model (steps S2 and S3). However, instead of the second processor, the first processor may execute steps S2 and S3. Also, the second processor may execute step S2, and a third processor different from the second processor may execute step S3.

[0035] As shown in FIG. 1, in step S1, using a combination of the ink and inkjet head to be used, the first processor uses as learning data the actual flight shape when ink is ejected by applying each of a number of drive waveforms to a piezoelectric element, and optimizes the head model using a fluid analysis model.

[0036] Then, in step S2, the second processor uses this optimized head model to perform flight prediction of a new drive waveform group. That is, the second processor provides each of the multiple new drive waveforms as an input to the optimized head model, and simulates the ink ejection operation for various drive waveforms.

[0037] Then, in step S3, the second processor determines the most promising drive waveform based on the flight prediction result (simulation result) obtained from the processing of step S2. Each of steps S1 to S3 will be described in more detail below.

[0038] [Step S1: Optimization of head model] In order to execute the process of step S1, it is preferable to carry out an ejection experiment or the like using a combination of the ink and the inkjet head to be used, collect data on the ink flight shape when each of a plurality of driving waveforms is applied to the piezoelectric element, and prepare the data in advance as a data set for learning. The first processor learns the actual flight shape from this data set and optimizes the parameters of the head model.

[0039] [Example of driving waveform] FIG. 2 is a waveform diagram showing an example of a drive waveform. The horizontal axis represents time, and the vertical axis represents potential. The drive waveform 20 shown in FIG. 2 includes a preliminary vibration pulse 22, an ejection pulse 24, and a reverberation suppression pulse 26, and the pulse width, slope, pulse height, and pulse interval of each pulse are parameters of the drive waveform. In the example of the drive waveform 20 shown in FIG. 2, there are 12 parameters, namely, times t1 to t9 that define the pulse width, slope, and pulse interval of the pulse, and potential differences E1 to E3 that define the pulse height.

[0040] A plurality of drive waveforms with different combinations of these parameter values ​​are applied to a piezoelectric element of an inkjet head filled with the ink to be used, and the flight shape of the ejected ink is used as learning data.

[0041] The parameters of the drive waveform are not limited to the types (12 types) shown in Fig. 2. For example, the potential of the drive waveform may be changed in a curved manner with time, and the shape of the curve may be included in the parameters. The types of drive waveforms used for learning may be, for example, 100 types.

[0042] [Example of flight shape] Figure 3 shows an example of an image of the flight shape of ink ejected from an inkjet head. Figure 3 shows the flight shape at each time as understood from a time-series image group obtained by continuously photographing ink ejected from an inkjet head by application of a drive waveform at regular time intervals. Figure 3 shows an example of images taken at 1 microsecond intervals. Note that 1 microsecond is an example of a regular time interval.

[0043] It is desirable to set the shooting area to an area sufficient to obtain flight characteristics from the nozzle, which is the ink ejection port. To grasp the flight state in the chronological direction (time axis direction), images are taken at regular time intervals, with the number of steps (number of images taken) sufficient to show the ink droplets outside the screen. Therefore, for one drive waveform, images for the number of time series are obtained. Figure 3 shows an example in which the area of ​​interest is cropped from images for the number of time series and then arranged in chronological order.

[0044] Considering subsequent image processing, it is preferable that the color contrast between the ink area and the background area is as clear as possible. Also, it is preferable that the area that separates the ink droplets from the background area has a sharp resolution.

[0045] As shown in FIG. 3, ink begins to be ejected from the nozzle of the inkjet head, forming a liquid column, which then separates from the nozzle and transforms into a droplet as it flies.

[0046] By acquiring a time-series image group corresponding to each of a plurality of drive waveforms, a flight shape corresponding to each drive waveform is obtained, and these flight shapes are used as learning data.

[0047] [Head model overview] 4 is a schematic diagram of a head model 40 according to an embodiment. The head model 40 is configured by coupling an equivalent circuit model 42 and a fluid analysis model 44. It is possible to configure the head model using only the fluid analysis model by applying the fluid analysis model to the equivalent circuit model 42, but configuring the head model using only the fluid analysis model results in a very high calculation cost. Therefore, in this embodiment, the head model 40 is configured by coupling the equivalent circuit model 42 and the fluid analysis model 44. In other words, the ink behavior inside the head flow path is simulated by the equivalent circuit model 42, and the ink behavior after ejection is simulated by the fluid analysis model 44, and the equivalent circuit model 42 and the fluid analysis model 44 are coupled in the nozzle portion.

[0048] In the equivalent circuit model 42 shown in Fig. 4, the symbols of the circuit parameters have the following meanings: ma, ra, and ca respectively indicate the inertance, resistance, and compliance of the diaphragm, ms and rs respectively indicate the inertance and resistance of the supply path, ci respectively indicates the compliance of the pressure chamber, and mn and rn respectively indicate the inertance and resistance of the nozzle part. Note that the equivalent circuit model 42 in Fig. 4 is just an example, and the form of the equivalent circuit model differs depending on the inkjet head used.

[0049] A discharge simulation method is known that uses a computational fluid dynamics (CFD) method for a fluid analysis model and couples it with an equivalent circuit model. The CFD method divides the three-dimensional space into which the ink is discharged into small spaces (mesh or grid), and simultaneously solves the equation of conservation of mass and the equation of conservation of momentum (equation of motion) in each small space, thereby simulating the movement of the ink in each small space, and as a result, it is possible to simulate the discharge and flight movement of the ink as a free surface fluid.

[0050] Because the CFD method is computationally expensive, in the case of a head model constructed using a three-dimensional fluid analysis model, the process of optimizing the model (step S1) and predicting flight using the optimized head model (step S2) takes a significant amount of time.

[0051] Therefore, in this embodiment, it is preferable to perform a CFD method on the fluid analysis model 44 in two-dimensional axisymmetrical instead of three-dimensional manner, thereby simulating the ejection and flying motion of ink at high speed.

[0052] [Learning method] When optimizing the head model 40 using the learning data, the first processor optimizes the parameters of the head model 40 so that the flight shape obtained by a simulation using the head model 40 with the drive waveform included in the learning data as input (the predicted flight shape predicted using the head model 40) approaches the actual flight shape.

[0053] Here, the parameters of the head model 40 include the circuit constants of the equivalent circuit model 42, and the viscosity coefficient, surface tension, and density of the ink in the fluid analysis model 44. Among these parameters, those that are known in advance by measurement or calculation may be treated as fixed and excluded from the parameters to be optimized. Furthermore, the parameters of the head model 40 may be optimized including unknown parameters other than these.

[0054] The first processor can optimize the parameters of the head model 40 using an evaluation value based on the difference between the actual flight shape understood from the learning data and the flight shape obtained as a result of simulation using the head model 40. Specifically, the first processor compares the actual flight shape with the simulated flight shape at each time of the time series flight shape included in the learning data, and calculates the sum of the areas where ink does not overlap. Then, the sum calculated at all times is integrated to obtain the first evaluation value. In this way, by incorporating the difference between the actual flight shape and the simulated flight shape at multiple times into the first evaluation value, it is possible to effectively use the captured image of the ink ejection state as learning data.

[0055] The first processor optimizes the parameters of the head model 40 so as to reduce the first evaluation value thus obtained. There are various optimization methods, such as the steepest descent method, but since there are many local optimum solutions for the parameters of the head model 40, it is preferable to optimize the parameters by a method for finding a global optimum solution. Research into global optimization methods has been conducted for a long time, and many methods have been proposed. As a global optimization method, for example, optimization is performed using a genetic algorithm.

[0056] FIG. 5 shows an example of a flying shape simulated by applying the same driving waveform as that during the ejection operation in FIG. 2 as an input to the head model 40 optimized according to the present embodiment.

[0057] As can be seen by comparing the simulation results (predicted flight shape) shown in FIG. 5 with the actual flight shape shown in FIG. 3, the head model 40 optimized according to this embodiment can accurately simulate the behavior of an actual inkjet head.

[0058] [Step S2: Creating multiple new drive waveform groups and evaluating the flight shape] In step S2, the second processor inputs each waveform of the new drive waveform group into the optimized head model 40, simulates the ink flight shape for each drive waveform, and obtains flight characteristics such as the droplet volume and droplet speed of the main droplet and satellite droplet, position, and string length from the simulation results. The "string" can also be called a "liquid column." The new drive waveform group can be set by "setting multiple levels for each parameter of the drive waveform and setting all combinations of them" or "setting the level of each parameter of the drive waveform randomly." As mentioned above, the parameters of the drive waveform are the pulse width, slope, pulse height, and pulse interval of each pulse, for example, the 12 parameters shown in the example of FIG. 2.

[0059] The inventors of the present application applied approximately 100 levels of drive waveforms to an inkjet head, optimized head model 40 using the actual flight shapes of the ejected ink as learning data, and used the optimized head model 40 to determine flight characteristics (flight predictions) of approximately 400 levels of a new group of drive waveforms. An example of the results is shown in FIG. 6.

[0060] Fig. 6 shows a scatter diagram comparing the droplet velocity predicted using the head model 40 optimized according to this embodiment with the actual droplet velocity. The droplet velocity here is the droplet velocity of the combined main droplet and satellite droplet. Fig. 6 shows that the droplet velocity can be predicted with high accuracy by the head model 40 optimized in step S1. In other words, it was confirmed that the head model 40 optimized according to this embodiment can predict with such accuracy that the correlation between the droplet velocity predicted by the model and the actual droplet velocity is 0.95 or more.

[0061] [Step S3: Determining promising driving waveforms] In step S3, the second processor determines a drive waveform that satisfies a quality of a certain level or more and has the most promising flight characteristics from the flight prediction results of each of the new drive waveform groups. The criterion of the "certain level" that determines the allowable range of the target flight characteristics may be a judgment criterion that is specified in advance, or may be specified from a user interface or the like as necessary. For example, the second processor determines a drive waveform with the largest droplet amount or the fastest droplet speed from a drive waveform group that does not generate satellite droplets and has a droplet amount and droplet speed that are each at or above a certain level. The certain level specified for the target flight characteristics is an example of a "specified condition". The value of the flight characteristics (characteristic value) used as an index when determining the optimal drive waveform is an example of a second evaluation value in this disclosure.

[0062] It is not necessary to record all flight prediction results of the new drive waveform group in step S2, and it is possible to record prediction results only for drive waveforms whose flight characteristics meet a certain level of quality or higher. This reduces the memory capacity required for recording and makes it possible to efficiently determine the drive waveform in step S3.

[0063] Example of Hardware Configuration of Information Processing Device The processes from step S1 to step S3 can be performed by a computer system including one or more computers.

[0064] FIG. 7 is a block diagram showing an example of a hardware configuration of an information processing device 100 that executes at least a part of the processing of the head model creating method and the drive waveform creating method according to the embodiment.

[0065] The information processing device 100 includes a processor 102, a computer-readable medium 104 which is a non-transient tangible entity, a communication interface 106, an input / output interface 108, and a bus 110. The processor 102 is connected to the computer-readable medium 104, the communication interface 106, and the input / output interface 108 via the bus 110. The form of the information processing device 100 is not particularly limited, and may be a server, a personal computer, a workstation, a tablet terminal, or the like.

[0066] The processor 102 can be at least one of a first processor and a second processor. The processor 102 includes a CPU (Central Processing Unit). The processor 102 may include a GPU (Graphics Processing Unit). The computer-readable medium 104 includes a memory 112 which is a main storage device and a storage 114 which is an auxiliary storage device. The computer-readable medium 104 may be, for example, a semiconductor memory, a hard disk drive (HDD) device, a solid state drive (SSD) device, or a combination of a plurality of these. The computer-readable medium 104 is an example of a "first storage device" and a "second storage device" in this disclosure.

[0067] A plurality of programs and data for performing various processes are stored in the computer-readable medium 104. The term "program" includes the concept of a program module. The processor 102 executes the instructions of the programs stored in the computer-readable medium 104 to function as various processing units.

[0068] The information processing device 100 can be connected to an electric communication line (not shown) via the communication interface 106. The electric communication line may be a wide area communication line, a private network communication line, or a combination of these.

[0069] The information processing device 100 may include an input device 152 and a display device 154. The input device 152 is configured, for example, by a keyboard, a mouse, a multi-touch panel, or other pointing device, or a voice input device, or an appropriate combination of these. The display device 154 is configured, for example, by a liquid crystal display, an organic electro-luminescence (OEL) display, or a projector, or an appropriate combination of these. The input device 152 and the display device 154 are connected to the processor 102 via the input / output interface 108.

[0070] 《How to collect learning data》 FIG. 8 is an explanatory diagram that shows a schematic configuration example of an inkjet device 200 used in a discharge experiment for creating a learning data set used for optimizing a head model.

[0071] The inkjet device 200 includes an inkjet head 202 , a drive circuit 250 , an information processing device 300 , and a camera 320 .

[0072] 8 shows a cross-sectional view of the three-dimensional structure of one ejector 210 in the inkjet head 202, but the inkjet head 202 includes a plurality of ejectors 210. The inkjet head 202 is an example of a "liquid ejection head" in this disclosure. The inkjet device 200 may be an experimental device or an inkjet printing device used for printing.

[0073] The ejector 210 of the inkjet head 202 includes a nozzle 212, a pressure chamber 214, and a piezoelectric element 216. The nozzle 212 communicates with the pressure chamber 214 via a nozzle flow path 218. The pressure chamber 214 communicates with a supply-side common flow path 224 via an individual supply path 220.

[0074] The vibration plate 226 that constitutes the top surface of the pressure chamber 214 includes a conductive layer (not shown) that functions as a common electrode corresponding to the lower electrode of the piezoelectric element 216. The pressure chamber 214, the walls of the other flow path parts, and the vibration plate 226 can be made of silicon.

[0075] The material of the diaphragm 226 is not limited to silicon, and it may be made of a non-conductive material such as resin, etc. The diaphragm 226 itself may be made of a metal material such as stainless steel, and may serve as a diaphragm that also serves as a common electrode.

[0076] A piezoelectric unimorph actuator is formed by a structure in which a piezoelectric element 216 is laminated on a vibration plate 226. The piezoelectric element 216 is connected to a drive circuit 250 and is driven by a drive voltage supplied from the drive circuit 250. A drive voltage is applied to an individual electrode 228, which is the upper electrode of the piezoelectric element 216, to deform the piezoelectric body 230, which deflects the vibration plate 226 and changes the volume of the pressure chamber 214. A pressure change accompanying the change in volume of the pressure chamber 214 acts on the ink, causing the ink to be ejected from the nozzle 212.

[0077] When the piezoelectric element 216 returns to its original state after ejecting the ink, new ink is filled into the pressure chamber 214 from the supply-side common flow path 224 through the individual supply path 220. The inkjet head 202 may be provided with an ink recovery path (not shown) for recovering ink not used for ejection.

[0078] The shape of the pressure chamber 214 in a plan view is not particularly limited, and may be a rectangle, another polygon, a circle, an ellipse, etc. A cover plate 232 is provided above the individual electrode 228. The cover plate 232 is a member that ensures a movable space 234 for the piezoelectric element 216 and seals the periphery of the piezoelectric element 216.

[0079] A supply-side ink chamber (not shown) and a recovery-side ink chamber (not shown) are formed above the cover plate 232. The supply-side ink chamber is connected to the supply-side common flow path 224 via a communication path (not shown). The recovery-side ink chamber is connected to the recovery-side common flow path (not shown) via a communication path (not shown).

[0080] The information processing device 300, which controls the ejection operation of the inkjet head 202, includes a control unit 302, a waveform generating unit 304, an image processing unit 306, and a data storage unit 308. The information processing device 300 may include a driving circuit 250. The hardware configuration of the information processing device 300 may be similar to that of Fig. 7. The processing functions of each unit of the information processing device 300 can be realized by the processor 102 executing program instructions.

[0081] The information processing device 300 is connected to a camera 320. The camera 320 is placed at a position where it can capture an image of the flying state of the ink ejected from the nozzle 212. The control unit 302 controls the entire system including the inkjet head 202 and the camera 320. The waveform generating unit 304 can generate various types of driving waveforms DWj according to instructions from the control unit 302. For example, the waveform generating unit 304 can generate multiple driving waveforms DWj with different combinations of the values ​​of the 12 parameters described in FIG. 2. The subscript j represents an index for identifying multiple driving waveforms. For example, when generating 100 types of driving waveforms DWj, j is an integer from 1 to 100.

[0082] The driving circuit 250 supplies the driving voltage of the driving waveform DWj generated by the waveform generating unit 304 to the piezoelectric element 216. In this manner, the piezoelectric element 216 is driven, causing ink to be ejected from the nozzle 212. The camera 320 captures images of the flight state of the ink ejected from the nozzle 212 at regular time intervals. The control unit 302 controls the timing of image capture by the camera 320 in synchronization with the drive of the piezoelectric element 216. A group of time-series images captured by the camera 320 is sent to the image processing unit 306.

[0083] The image processor 306 performs necessary processing such as extraction of a region of interest and cropping on the acquired images to generate a time-series flight shape image group FSj(t) showing the flight shape of the ink. The subscript t indicates the time in the time series.

[0084] The control unit 302 associates (links) the driving waveforms DWj with the flight shape image groups FSj(t) and stores the driving waveforms DWj and the flight shape image groups FSj(t) in the data storage unit 308. In this way, a data set including a plurality of driving waveforms DWj and a plurality of corresponding flight shape image groups FSj(t) is created. A part or the whole of this data set is used as a learning data set. Note that such a data set is created for each combination of the ink used and the inkjet head 202.

[0085] How to create a head model Fig. 9 is a block diagram showing a schematic functional configuration of an information processing device 400 that executes the process of optimizing the head model 40. The hardware configuration of the information processing device 400 may be similar to the configuration described in Fig. 7. The processing functions of each unit of the information processing device 400 are realized by the processor 102 executing program instructions.

[0086] The information processing device 400 includes a learning data storage device 402, a data acquisition unit 404, a head model 40, and a model parameter update unit 406. The learning data storage device 402 stores a learning data set TDS including a plurality of data sets in which the drive waveforms TDWj and the corresponding flight shapes TFSj are linked. The drive waveforms TDWj and the corresponding flight shapes TFSj may be the drive waveforms DWj and the time-series flight shape image group FSj(t) collected by the method described in FIG.

[0087] The data acquisition unit 404 acquires the learning data from the learning data storage device 402. The driving waveforms TDWj acquired via the data acquisition unit 404 are input to the head model 40.

[0088] The head model 40 is actually a program, and causes a computer to realize a function of simulating the behavior of the inkjet head 202. The head model 40 receives the input of the driving waveform TDWj, simulates the ink ejection operation caused by the application of the driving waveform TDWj, and outputs a predicted flight shape PFSj as a simulation result. The predicted flight shape PFSj may also be referred to as a flight prediction result.

[0089] The model parameter update unit 406 performs the process of comparing the predicted flight shape PFSj with the correct (actual) flight shape TFSj to calculate an evaluation value indicating the difference between the two, the process of calculating an update amount of the parameters of the head model 40 based on the evaluation value, and the process of updating the parameters of the head model 40 according to the calculated update amount. The parameters of the head model 40 are called model parameters.

[0090] By updating the model parameters multiple times using multiple learning data, the model parameters of the head model 40 are optimized, and a head model 40 that can predict the flight shape with high accuracy is created. A corresponding head model 40 is created for each combination of the ink to be used and the inkjet head 202.

[0091] <<Method of searching for driving waveform using optimized head model 40>> Fig. 10 is a block diagram showing a schematic functional configuration of an information processing device 500 that executes a process for determining a promising drive waveform using the head model 40 optimized according to this embodiment. The hardware configuration of the information processing device 500 may be similar to the configuration described in Fig. 7. The processing functions of each unit of the information processing device 500 can be realized by the processor 102 executing program instructions.

[0092] The information processing device 500 includes a control unit 502, a waveform generating unit 504, a head model 40, a flight characteristics calculating unit 506, a drive waveform determining unit 508, and a storage unit 510. The control unit 502 controls the processing of each unit. The control unit 502 issues an instruction to the waveform generating unit 504 to generate a new drive waveform.

[0093] The waveform generating unit 504 generates a plurality of drive waveforms CDWk of various waveforms in accordance with instructions from the control unit 502. The subscript k is an index that identifies the drive waveform. For example, if 400 types of drive waveforms are generated, k can be an integer from 1 to 400.

[0094] The head model 40 is a model optimized using the information processing device 400 described in FIG. 9. The head model 40 receives the drive waveform CDWk, simulates the ink ejection operation, and outputs a predicted flight shape SRk as a simulation result. That is, the head model 40 is used to perform a forward flight prediction from the drive waveform CDWk. The flight characteristic calculation unit 506 calculates flight characteristics from the predicted flight shape SRk output by the head model 40. The flight characteristics calculated from the predicted flight shape SRk are called "predicted flight characteristics PFCk." Each of the predicted flight shape SRk and the predicted flight characteristics PFCk is an example of a flight prediction result predicted using the head model 40.

[0095] The control unit 502 associates the drive waveform CDWk, the predicted flight shape SRk, and the predicted flight characteristic PFCk, and stores these data in the storage unit 510. In this manner, a set of data including a plurality of drive waveforms CDWk (k=1, 2, . . .) and a plurality of corresponding predicted flight shapes SRk and predicted flight characteristics PFCk is stored in the storage unit 510.

[0096] The drive waveform determination unit 508 determines a promising drive waveform based on the predicted flight characteristics PFCk. The drive waveform determination unit 508 determines, as the optimal drive waveform, a drive waveform that satisfies a predetermined allowable range of the flight characteristics PFCk and achieves the best flight characteristics. For example, the drive waveform determination unit 508 determines an optimal drive waveform based on an evaluation value of flight characteristics characterized by at least one of the droplet speed, droplet volume, and thread length of the main droplet and satellite droplet.

[0097] If the predicted flight characteristics PFCk do not satisfy the conditions of the allowable flight characteristics, the drive waveform may be excluded from the candidates, and the data may not be stored in the storage unit 510.

[0098] Using a head model 40 corresponding to the combination of the ink to be used and the inkjet head 202, a drive waveform suitable for ejecting the ink is created.

[0099] About the programs that run computers A program that causes a computer to realize some or all of the processing functions of each of information processing devices 300, 400, and 500 can be recorded on a computer-readable medium, which is a non-transitory information storage medium such as an optical disk, a magnetic disk, a semiconductor memory, or other tangible object, and the program can be provided through this information storage medium.

[0100] In addition, instead of providing the program by storing it on such a tangible, non-transitory computer-readable medium, it is also possible to provide the program signal as a download service using a telecommunications line such as the Internet.

[0101] Furthermore, some or all of the processing functions of each of the above-mentioned devices may be realized by cloud computing, and may also be provided as SaaS (Software as a Service).

[0102] <Hardware configuration of each processing unit> The hardware structure of the processing units that execute various processes, such as the control unit 302, waveform generation unit 304, and image processing unit 306 in the information processing device 300, the data acquisition unit 404 and model parameter update unit 406 in the information processing device 400, and the control unit 502, waveform generation unit 504, flight characteristic calculation unit 506, and drive waveform determination unit 508 in the information processing device 500, is, for example, various processors as shown below.

[0103] Various types of processors include CPUs, which are general-purpose processors that execute programs and function as various processing units, GPUs, programmable logic devices (PLDs), such as FPGAs (Field Programmable Gate Arrays), which are processors whose circuit configuration can be changed after manufacture, and dedicated electrical circuits, such as ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically to execute specific processes.

[0104] A processing unit may be composed of one of these various processors, or may be composed of two or more processors of the same type or different types. For example, a processing unit may be composed of multiple FPGAs, or a combination of a CPU and an FPGA, or a combination of a CPU and a GPU. Also, multiple processing units may be composed of one processor. As an example of multiple processing units being composed of one processor, first, as represented by a computer such as a client or a server, there is a form in which one processor is composed of a combination of one or more CPUs and software, and this processor functions as multiple processing units. Second, as represented by a system on chip (SoC), there is a form in which a processor that realizes the functions of the entire system including multiple processing units is used in one IC (Integrated Circuit) chip. In this way, the various processing units are composed of one or more of the above various processors as a hardware structure.

[0105] Furthermore, the hardware structure of these various processors is, more specifically, an electric circuit that combines circuit elements such as semiconductor elements.

[0106] Advantages of the embodiment According to the above-described embodiment, the following effects can be obtained.

[0107] [1] The process of optimizing the model parameters of head model 40 is automated using the actual flight shape as learning data for the combination of ink and inkjet head used, making it possible to create a head model 40 that can accurately simulate the ejection behavior.

[0108] [2] Even an engineer who does not have expertise in creating head models can create a high-performance head model 40.

[0109] [3] By automating the process of searching for a drive waveform using the optimized head model 40, even an engineer who does not have expertise in creating drive waveforms can create a drive waveform that can achieve a certain level of flight characteristics.

[0110] [4] The user can select the drive waveform that best suits their needs. For example, if the quality of a solid print is important, the user can set the conditions that must be met for the flight characteristics according to the purpose, such as prioritizing the drop volume and allowing satellite drops, and then create a drive waveform that matches those conditions.

[0111] [5] It can be used to evaluate inks. Conventionally, it has been difficult to judge the quality of inks based on the quality of the optimization of the drive waveform, but the method of this embodiment makes it possible to compare inks based on indicators such as the number of candidate waveforms that can produce favorable flight characteristics.

[0112] [6] It is possible to find high-quality drive waveforms that are difficult to create using conventional drive waveform creation methods by engineers. In other words, it is possible to search for completely unknown drive waveforms that would be difficult to imagine as candidates by hand.

[0113] [7] This method allows engineers to create promising drive waveforms more efficiently and in a shorter time than traditional methods for creating drive waveforms.

[0114] Variation 1 In the above embodiment, an example was described in which a group of time-series images taken at a fixed time interval is used as data on the actual flight shape, but instead of this group of time-series images, it is also possible to use, for example, a single image taken of the flight state of ink a predetermined time after the application of a drive waveform. In this case, it is desirable to set the predetermined time so that flight characteristics such as the ink droplet speed, droplet volume, thread length, and the presence or absence of satellite droplets can be identified from the position of the ink in a single image taken at that timing.

[0115] As described in the embodiment, by using a group of two or more time-series images taken at different times, it is possible to grasp flight characteristics such as droplet speed more accurately, and a head model 40 with high predictive accuracy can be created.

[0116] Variation 2 The data on the actual flight shape used as learning data is not limited to photographed images, but may be information such as feature values, characteristic values, or numerical values ​​indicating some physical quantity obtained from two-dimensional image information, such as the position of the tip of the towed thread (liquid column) and the length of the towed thread.

[0117] For example, in the above-described embodiment, an example was described in which the model parameters of the head model 40 were updated while evaluating the image overlap between the actual flight shape observed in a discharge experiment and the flight shape predicted by the head model 40. However, instead of a method of evaluating the image overlap, it is also possible to optimize the model parameters by evaluating the difference between the actual and predicted values ​​using parameter values ​​(characteristic values) related to one or more flight characteristics, such as droplet volume or droplet speed.

[0118] Since time-series image information contains the most information, and using a time-series group of flight shape images results in the least amount of information loss, it is expected that optimizing head model 40 by evaluating image overlap using a time-series group of flight shape images will result in higher prediction accuracy for the final head model 40.

[0119] On the other hand, as a simpler method, the head model 40 may be optimized using parameter values ​​such as appropriate amounts obtained from a captured image.

[0120] Variation 3 The head model 40 optimized according to this embodiment can be used not only for searching for drive waveforms, but also for, for example, evaluating ink and assisting in the design of inkjet heads.

[0121] <Device application examples> In the above embodiment, an example of an inkjet device used for inkjet printing has been described, but the scope of application of the present invention is not limited to this example. Regardless of the type and purpose of the liquid used, the technology disclosed herein can be applied to devices that eject liquid using a piezoelectric liquid ejection head. For example, the technology can be widely applied to liquid ejection devices that draw various shapes and patterns using liquid functional materials (collectively referred to as "liquids"), such as wiring drawing devices that draw wiring patterns for electronic circuits, manufacturing devices for various devices, resist printing devices that use resin liquid as a functional liquid for ejection, color filter manufacturing devices, and microstructure forming devices that form microstructures using materials for material deposition.

[0122] "others" The present disclosure is not limited to the above-described embodiment, and various modifications are possible without departing from the spirit and scope of the technical idea of ​​the present disclosure. [Explanation of symbols]

[0123] 20 Driving Waveform 22 Preliminary vibration pulse 24 Discharge Pulse 26 Reverberation suppression pulse 40 Head Model 42 Equivalent circuit model 44 Fluid Analysis Model 100 Information processing device 102 processors 104 Computer-readable medium 106 Communication Interface 108 Input / Output Interface 110 Bus 112 Memory 114 Storage 152 Input Device 154 Display device 200 Inkjet device 202 Inkjet head 210 Ejector 212 Nozzle 214 Pressure Chamber 216 Piezoelectric element 218 Nozzle flow path 220 Individual supply route 224 Supply side common flow path 226 Diaphragm 228 individual electrodes 230 Piezoelectric 232 Cover Plate 234 Movable space 250 Drive circuit 300 Information processing device 302 Control section 304 Waveform generator 306 Image Processing Unit 308 Data Storage Department 320 Camera 400 Information processing device 402 Learning Data Storage Device 404 Data Acquisition Department 406 Model Parameter Update Unit 500 Information processing equipment 502 Control section 504 Waveform generator 506 Flight Characteristics Calculation Unit 508 Drive Waveform Determination Unit 510 Storage section DWj drive waveform CDWk drive waveform E1,E2,E3 potential difference FSj Flying Shape Images FSj(t) Flight shape image set PFCk predicted flight characteristics PFSj predicted flight shape SRk predicted flight shape t1~t9 hours TDS training dataset TDWj drive waveform TFSj flight shape ca Diaphragm Compliance ra Resistance of the diaphragm ma Inertance of the diaphragm ms Supply line inertance mr Supply line resistance ci Pressure Chamber Compliance mm Nozzle inertance mr Nozzle resistance Steps for creating an S1 head model S2~S3 Steps for creating driving waveforms

Claims

1. A method for creating a head model that simulates the behavior of a liquid ejection head equipped with a piezoelectric element, comprising the steps of: The head model is constructed using a fluid analysis model, data relating to an actual flight shape when a plurality of drive waveforms are applied to the piezoelectric element using the liquid ejection head and the liquid to be ejected from the liquid ejection head, is used as learning data, and one or more first processors optimize the head model based on the learning data. How to create a head model.

2. The head model is a model obtained by coupling an equivalent circuit model and the fluid analysis model. A method for creating a head model according to claim 1.

3. the one or more first processors; optimizing a circuit constant of the equivalent circuit model and a parameter related to at least one of a viscosity coefficient, a surface tension, and a density of the fluid analysis model; A method for creating a head model according to claim 2.

4. the one or more first processors; updating parameters of the head model so that a flight shape predicted by the head model for each of the inputs of the plurality of drive waveforms approaches the actual flight shape; A method for creating a head model according to claim 1.

5. the data on the actual flight shape is a flight shape image obtained by photographing the liquid discharged from the liquid discharge head; A method for creating a head model according to claim 1.

6. the data on the actual flight shape is a time-series flight shape image group obtained by photographing the liquid discharged from the liquid discharge head at at least two points in time; A method for creating a head model according to claim 1.

7. the one or more first processors; calculating a first evaluation value based on the flight trajectory at least at two times as an index of optimization; A method for creating a head model according to claim 6.

8. The parameters of the drive waveform include at least one of a pulse width, a slope, a pulse height, and a pulse interval. A method for creating a head model according to claim 1.

9. A driving waveform generating method using a head model generated by carrying out the head model generating method according to any one of claims 1 to 8, comprising the steps of: one or more second processors, performing a flight prediction of the liquid using the head model for each of a plurality of new driving waveforms; executing a process of determining a drive waveform suitable for ejecting the liquid based on flight prediction results for each of the plurality of new drive waveforms; How to create a driving waveform.

10. the one or more second processors; Calculating a second evaluation value from the flight prediction result; determining an optimal drive waveform from among the plurality of new drive waveforms based on the second evaluation value; 10. The driving waveform generating method according to claim 9.

11. the second evaluation value includes flight characteristics characterized by at least one of a droplet volume, a droplet velocity, and a string length of at least one of a main droplet and a satellite droplet; The driving waveform generating method according to claim 10.

12. the one or more second processors; determining a drive waveform that satisfies a specified condition and has the most promising second evaluation value among the plurality of new drive waveforms; The driving waveform generating method according to claim 10.

13. the one or more second processors; performing a forward prediction for predicting a flight shape of the liquid from each of the plurality of new drive waveforms using the optimized head model, and determining a drive waveform suitable for ejecting the liquid from among the plurality of new drive waveforms based on the flight prediction result of the forward prediction; 10. The driving waveform generating method according to claim 9.

14. An information processing device that executes the head model creation method according to any one of claims 1 to 8, the one or more first processors; One or more first storage devices in which the head model is stored; An information processing device comprising:

15. A program for causing a computer to execute the head model creating method according to any one of claims 1 to 8.

16. An information processing device for executing the driving waveform generating method according to claim 9, the one or more second processors; and one or more second storage devices in which the optimized head model is stored; An information processing device comprising:

17. A program for causing a computer to execute the drive waveform generating method according to claim 9.

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