# US 12,594,759 B2 · Method of creating head model, drive waveform creation method, information processing apparatus, and program > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,594,759 B2 - Document ID: US12594759B2 - Status: Granted - Kind: B2 - Application number: 18/522,281 - Filed: 2023-11-29 - Published: 2024-06-20 - Granted: 2026-04-07 - Earliest claimed: 2022-12-14 - Assignees: FUJIFILM Corporation - Applicants: FUJIFILM Corporation - Inventors: Baku Nishikawa; Yuta Mizouchi; Kimito Katsuyama - Web page: https://patentgrep.com/patent/US12594759B2 ## CPC - B41J 2/04588: {using a specific waveform} - B41J 2/04581: {controlling heads based on piezoelectric elements} - B41J 29/393: Devices for controlling or analysing the entire machine {; Controlling or analysing mechanical parameters involving printing of test patterns} - B41J: TYPEWRITERS; SELECTIVE PRINTING MECHANISMS, i.e. MECHANISMS PRINTING OTHERWISE THAN FROM A FORME; CORRECTION OF TYPOGRAPHICAL ERRORS ## Abstract A method of creating a head model that can accurately simulate behavior of a liquid ejection head, a drive waveform creation method of creating a proper drive waveform using the head model, and an information processing apparatus and a program for executing the methods are provided. Provided is a method of creating a head model that simulates behavior of a liquid ejection head including a piezoelectric element, the head model being configured using a fluid analysis 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 ejecting liquid by applying each of a plurality of drive waveforms to the piezoelectric element using the liquid ejection head and the liquid ejected from the liquid ejection head as the learning data. ## Claims ### Claim 1 A method of creating a head model that simulates behavior of a liquid ejection head including a piezoelectric element, the head model being configured using a fluid analysis model, the method comprising: 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 ejecting liquid by applying each of a plurality of drive waveforms to the piezoelectric element using the liquid ejection head and the liquid ejected from the liquid ejection head as the learning data. ### Claim 2 (depends on 1) The method of creating a head model according to claim 1, wherein the head model is a model in which an equivalent circuit model and the fluid analysis model are connected to each other. ### Claim 3 (depends on 2) The method of creating a head model according to claim 2, wherein the one or more first processors are configured to optimize a parameter related to a circuit constant of the equivalent circuit model and to at least one of a viscosity coefficient, surface tension, or density of the fluid analysis model. ### Claim 4 (depends on 1) The method of creating a head model according to claim 1, wherein the one or more first processors are configured to update a parameter of the head model such that a flight shape predicted by the head model with respect to input of each of the plurality of drive waveforms approximates the actual flight shape. ### Claim 5 (depends on 1) The method of creating a head model according to claim 1, wherein the data related to the actual flight shape is a flight shape image obtained by imaging the liquid ejected from the liquid ejection head. ### Claim 6 (depends on 1) The method of creating a head model according to claim 1, wherein the data related to the actual flight shape is a flight shape image group in time series obtained by imaging the liquid ejected from the liquid ejection head at at least two time points. ### Claim 7 (depends on 6) The method of creating a head model according to claim 6, wherein the one or more first processors are configured to calculate a first evaluation value based on the flight shape at at least two time points as an indicator of the optimization. ### Claim 8 (depends on 1) The method of creating a head model according to claim 1, wherein a parameter of the drive waveform includes at least one of a pulse width, a slope, a pulse height, or a pulse interval. ### Claim 9 (depends on 1) An information processing apparatus that executes the method of creating a head model according to claim 1, the information processing apparatus comprising: the one or more first processors; and one or more first storage devices in which the head model is stored. ### Claim 10 (depends on 1) A non-transitory, computer-readable tangible recording medium which records thereon a program for causing, when read by a computer, the computer to execute the method of creating a head model according to claim 1. ### Claim 11 (depends on 1) A drive waveform creation method using a head model created by executing the method of creating a head model according to claim 1, the drive waveform creation method comprising: via one or more second processors, predicting flight of the liquid using the head model with respect to each of a plurality of new drive waveforms; and executing processing of determining a drive waveform suitable for ejection of the liquid based on a flight prediction result with respect to each of the plurality of new drive waveforms. ### Claim 12 (depends on 11) The drive waveform creation method according to claim 11, wherein the one or more second processors are configured to: calculate a second evaluation value from the flight prediction result; and determine an optimal drive waveform from among the plurality of new drive waveforms based on the second evaluation value. ### Claim 13 (depends on 12) The drive waveform creation method according to claim 12, wherein the second evaluation value includes a flight characteristic characterized by at least one of a droplet amount, a droplet speed, or a length of a thread of at least one of a mother droplet or a satellite droplet. ### Claim 14 (depends on 12) The drive waveform creation method according to claim 12, wherein the one or more second processors are configured to determine a drive waveform that has the second evaluation value satisfying a designated condition and that has a most promising second evaluation value among the plurality of new drive waveforms. ### Claim 15 (depends on 11) The drive waveform creation method according to claim 11, wherein the one or more second processors are configured to perform forward prediction of predicting a flight shape of the liquid from each of the plurality of new drive waveforms using the optimized head model and determine a drive waveform suitable for ejection of the liquid from among the plurality of new drive waveforms based on the flight prediction result of the forward prediction. ### Claim 16 (depends on 11) An information processing apparatus that executes the drive waveform creation method according to claim 11, the information processing apparatus comprising: the one or more second processors; and one or more second storage devices in which the optimized head model is stored. ### Claim 17 (depends on 11) A non-transitory, computer-readable tangible recording medium which records thereon a program for causing, when read by a computer, the computer to execute the drive waveform creation method according to claim 11. ### Claim 18 (depends on 1) The method of creating a head model according to claim 1, wherein the head model predicts a flight shape of the liquid ejected from the liquid ejection head via application of a drive waveform. ### Claim 19 (depends on 1) The method of creating a head model according to claim 1, wherein a computational fluid dynamic technique is used in the fluid analysis model, and a motion of the flight of the liquid after ejection by the liquid ejection head is predicted using the computational fluid dynamic technique. ### Claim 20 (depends on 1) The method of creating a head model according to claim 1, wherein the one or more first processors are configured to optimize a parameter of the head model, including a parameter related to at least one of a viscosity coefficient, surface tension, or density of the fluid analysis model based on the learning data.