Drive waveform creation method, information processing apparatus, and program

A drive waveform creation method of creating a drive waveform to be used for driving a piezoelectric element of a liquid ejection head including the piezoelectric element, the drive waveform creation method comprising:

• via one or more processors, predicting flight of liquid to be ejected by the liquid ejection head in a case of inputting an unknown drive waveform using a machine learning model that is trained through machine learning using data related to an actual flight shape of the liquid in a case where each of a plurality of drive waveforms is applied to the piezoelectric element using the liquid and the liquid ejection head,
• wherein a learning phase of the machine learning model includes a step of compressing each of the plurality of drive waveforms into a latent space in smaller dimensions than dimensions of the drive waveform,
• wherein in the learning phase, the machine learning model is trained to predict an evaluation value based on the actual flight shape in a case of applying the drive waveform using a correspondence relationship between coordinates of each of the plurality of drive waveforms in the latent space and the evaluation value, and
• wherein the machine learning model is a model that outputs an average value and a standard deviation of the evaluation value predicted from the coordinates in the latent space;
• and
• determining a drive waveform suitable for ejecting the liquid based on the prediction of the flight.

A drive waveform creation method of creating a drive waveform to be used for driving a piezoelectric element of a liquid ejection head including the piezoelectric element, the drive waveform creation method comprising:

• via one or more processors, predicting flight of liquid to be ejected by the liquid ejection head in a case of inputting an unknown drive waveform using a machine learning model that is trained through machine learning using data related to an actual flight shape of the liquid in a case where each of a plurality of drive waveforms is applied to the piezoelectric element using the liquid and the liquid ejection head,
• wherein a learning phase of the machine learning model includes a step of compressing each of the plurality of drive waveforms into a latent space in smaller dimensions than dimensions of the drive waveform,
• wherein in the learning phase, the machine learning model is trained to predict an evaluation value based on the actual flight shape in a case of applying the drive waveform using a correspondence relationship between coordinates of each of the plurality of drive waveforms in the latent space and the evaluation value, and
• wherein the one or more processors are configured to, in a case of generating a plurality of the unknown drive waveforms different from the plurality of drive waveforms by randomly extracting a value of a parameter of the drive waveform based on a uniform distribution, clarify a relationship between a distance on the latent space and a variance of the evaluation value in advance through variogram analysis and set a search interval of the drive waveform based on the variogram analysis;
• and
• determining a drive waveform suitable for ejecting the liquid based on the prediction of the flight.