Seiko Epson Corporation

Yuichi Shikagawa

Mamoru Ukita

Tetsuo Tatsuda

Haruhisa Kurane

Shigenori Katayama

Kazunari Tsukada

Kazuaki Sakurada

Kazunori Hiramatsu

A liquid ejecting apparatus includes an ejecting head and a camera unit for acquiring a state of the ejecting head, in which the camera unit includes a camera for imaging an object, a housing in which the camera is housed and which includes an opening on an imaging path that couples the camera and the object, and an opening/closing member, the opening/closing member includes a reference chart serving as a reference for adjustment of the camera and a chart cover, in a first mode, the opening/closing member brings the opening into a closed state, and the chart cover covers the reference chart, in a second mode, the opening/closing member brings the opening into an open state, and the chart cover covers the reference chart, and in a third mode, the opening/closing member brings the opening into the closed state, and the chart cover does not cover the reference chart.

Liquid ejecting apparatus

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 method for determining a working gap includes a first recording step for ejecting ink from a recording head onto a first recording medium to record a test pattern, a first imaging step for capturing the test pattern recorded on the first recording medium in each of a state where a distance between the recording head and the first recording medium is a first distance, and a state where a distance between the recording head and the first recording medium is a second distance, a function calculating step, a second recording step for recording the test pattern on a second recording medium, a second imaging step for capturing the test pattern recorded on the second recording medium, and a working gap determining step for determining, based on the number of pixels of the captured test pattern and a function, a distance between the recording head and the second recording medium.

Method for determining working gap, and recording device

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

An image processing device includes: a sensor that reads a print region and a non-print region of a print medium, an image being printed in the print region, the non-print region being a blank area; and a controller that makes shading correction for an output from the sensor, based on a read result from the sensor. The controller sets a patch image printed in the print region to a black reference, sets the non-print region to a white reference, and makes the shading correction, based on properties of the black reference and the white reference at individual locations.

Image processing device configured to make shading correction, non-transitory computer-readable computer medium storing image processing program for shading correction, and method of generating readout data using shading correction

There is provided a printing apparatus including: an ink jet head that prints on a print medium; a camera that captures an image on the print medium; an LED 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 mounts the ink jet head, the camera, and the LED light source; and a processor that executes calculation based on captured-image-data-of-unprinted-area obtained by capturing an image on the print medium by the camera and captured-image-data-of-test-pattern obtained by capturing an image of a test pattern by the camera, the test pattern being printed on the print medium by the ink jet head, and that acquires corrected captured-image-data representing a captured image of the test pattern, the corrected captured-image-data in which an influence of light irradiation by the LED light source is adjusted.

Growing community of inventors

Azumino, Japan

Yuichi Shikagawa