Seiko Epson Corporation

Kazunari Tsukada

Tetsuo Tatsuda

Shigenori Katayama

Haruhisa Kurane

Mamoru Ukita

Yuichi Shikagawa

Takayoshi Kagata

Naoki Kayahara

Masashi Kamibayashi

Toshinobu Yamazaki

Yohei Shibata

Masakazu Tokunaga

A failure time estimation device includes: a memory configured to store a machine-learned model obtained by performing machine learning using teaching data associating printer information including at least one of operation history of a printer, state information indicating a current state, and a print result image indicating a print result with failure time of the printer; and a controller configured to obtain the printer information and estimate the failure time of the printer using the obtained printer information and the machine-learned model.

Failure time estimation device, machine learning device, and failure time estimation method

A printer includes: a memory configured to store a machine-learned model obtained by machine learning using teaching data associating at least one of reflectance of a print medium, transmittance of the print medium, and image data obtained by capturing an image of a surface of the print medium with a type of the print medium; and a print controller configured to determine a type of a print medium using at least one of reflectance of the print medium, transmittance of the print medium, and image data obtained by capturing an image of a surface of the print medium and the machine-learned model.

Printer, machine learning device, and machine learning method

A printer including a motor for transporting an object to be transported, the printer includes: a memory storing a machine-learned model configured to output a control parameter of the motor that brings a transport position of the object to be transported close to a reference based on one or more state variables including at least one of a speed of the object to be transported, an acceleration of the object to be transported, a movement amount of the object to be transported, a start position of movement of the object to be transported, an ambient environment of the printer, a value of a current flowing through the motor, a type of a print medium onto which printing is to be performed by the printer, and an accumulated movement amount of the object to be transported; and a controller configured to control the motor to perform printing by using the control parameter obtained based on the machine-learned model.

Printer, learning device, and learning method

Provided is technology enabling detecting cockling in a print medium without printing a cockling detection pattern on the print medium. An image processing device has: a light source configured to illuminate a print medium; a sensor configured to scan the print medium; and a controller configured to control the light source and sensor. The controller determines if there is cockling in an evaluation area of the print medium based on change in the detection values of the sensor in the evaluation area.

Image processing device, cockling detection method, machine learning system

There is provided a printing apparatus including: a transport mechanism; an ink jet head; a camera; a light source that irradiates a capturing area of the camera with light at a predetermined angle with respect to a print surface of the print medium; a carriage that includes the ink jet head, the camera, and the light source; and a controller (processor) that acquires reflectance characteristic data of irradiation light from the light source based on a captured image obtained by capturing an image of a printed test pattern by the camera, determines a specific initial-and-final-image-data range or a specific acquisition range in which the reflectance is within a specific reflectance range based on the acquired reflectance characteristic data, and acquires captured image data corresponding to the specific initial-and-final-image-data range or the specific acquisition range.

Printing apparatus and control method of printing apparatus

A printer performs a method for correcting density irregularity that includes: acquiring imaging data by imaging, using an imaging device mounted in a printer, a correcting pattern that is used to correct density irregularity, which contains a strip region (first pattern) with first density and a strip region (second pattern) with second density different from the first density; and correcting the density irregularity based on the imaging data.

Method for correcting density irregularity, printing apparatus, and imaging module

A liquid ejecting apparatus includes a forming section which forms an image on a medium, a transport section which transports the medium in a first direction, and a heating section which heats a transport path on which the medium is transported, in which the forming section includes a first forming section which forms a pre-treatment liquid layer that includes a thermosetting resin on the medium, a liquid ejecting head which forms an image layer on the medium by ejecting liquid, and a second forming section which forms a post-treatment liquid layer that includes a thermosetting resin and is different from the pre-treatment liquid layer on the medium, and B&#x3c;A and B&#x3c;C in a case where content of the thermosetting resin in the pre-treatment liquid is A, content of the thermosetting resin in the liquid is B, and content of the thermosetting resin in the post-treatment liquid is C.

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Matsumoto, Japan

Kazunari Tsukada