Printhead maintenance for recommending printhead replacement

Patent metadata

Field	Value
Publication number	US-12459264-B2
Application number	US-202318116932-A
Country	US
Kind code	B2
Filing date	Mar 3, 2023
Priority date	Mar 3, 2023
Publication date	Nov 4, 2025
Grant date	Nov 4, 2025

Abstract

Systems and methods of recommending replacement of printheads. In an embodiment, a system trains a first neural network to generate anomaly scores for printheads using an unsupervised learning algorithm based on first training samples of conforming printhead data from a pool of conforming printheads. The system generates a training dataset for a recurrent second neural network by identifying training printhead data for a pool of training printheads, inputting second training samples of the training printhead data into the first neural network to generate training anomaly scores for the training printheads over a plurality of time units, and formatting third training samples for the training printheads. The system trains the recurrent second neural network to generate scaled anomaly scores for printheads using a supervised learning algorithm based on the second training dataset.

First claim

What is claimed is:

1 . A printhead maintenance supervisor, comprising:
at least one processor and memory; the at least one processor is configured to cause the printhead maintenance supervisor at least to:
train a first neural network to generate anomaly scores for printheads using an unsupervised learning algorithm based on first training samples of conforming printhead data from a pool of conforming printheads;
generate a training dataset for a recurrent second neural network by:
identifying training printhead data for a pool of training printheads;
inputting second training samples of the training printhead data into the first neural network to generate training anomaly scores for the training printheads over a first plurality of time units; and
formatting third training samples for the training printheads, wherein each of the third training samples comprises a first time-series of training data objects over a number of consecutive time units for a training printhead, and a label for the training printhead, and wherein each of the training data objects includes a training anomaly score generated by the first neural network for the training printhead, and at least a subset of the training printhead data for the training printhead; and

train the recurrent second neural network to generate scaled anomaly scores for printheads using a supervised learning algorithm based on the training dataset.

2 . The printhead maintenance supervisor of claim 1 , wherein:
the label represents a printhead condition of a corresponding training printhead.

3 . The printhead maintenance supervisor of claim 1 , wherein:
the subset of the training printhead data in the third training samples comprises a nozzle failure value indicating a number of failed nozzles.

4 . The printhead maintenance supervisor of claim 1 , wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
identify deployed printhead data for a plurality of deployed printheads; operate the first neural network to generate deployment anomaly scores for the deployed printheads; operate the recurrent second neural network to scale the deployment anomaly scores generated by the first neural network to produce the scaled anomaly scores for the deployed printheads; and provide a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads.

5 . The printhead maintenance supervisor of claim 4 , wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
store the replacement recommendation in a data log corresponding with a deployed printhead.

6 . The printhead maintenance supervisor of claim 4 , wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
transmit a message indicating the replacement recommendation via a network interface.

7 . The printhead maintenance supervisor of claim 4 , wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
display the replacement recommendation via a user interface.

8 . The printhead maintenance supervisor of claim 4 , wherein the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
perform a comparison of one or more of the scaled anomaly scores for each of the deployed printheads to a threshold value; determine whether the one or more of the deployed printheads are experiencing a failure condition based on the comparison of the scaled anomaly scores; and provide the replacement recommendation for the one or more of the deployed printheads when the failure condition is determined.

9 . The printhead maintenance supervisor of claim 4 , wherein to operate the first neural network, the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
input first input samples of the deployed printhead data into the first neural network to generate the deployment anomaly scores for the deployed printheads over a second plurality of time units.

10 . The printhead maintenance supervisor of claim 9 , wherein to operate the recurrent second neural network, the at least one processor is configured to further cause the printhead maintenance supervisor at least to:
format second input samples for the deployed printheads, wherein each of the second input samples comprises a second time-series of deployment data objects over the number of consecutive time units for a deployed printhead, and wherein each of the deployment data objects includes a deployment anomaly score generated by the first neural network for the deployed printhead, and at least a subset of the deployed printhead data for the deployed printhead; and input the second input samples for the deployed printheads into the recurrent second neural network to output the scaled anomaly scores for the deployed printheads.

11 . The printhead maintenance supervisor of claim 1 , wherein:
the first neural network comprises an autoencoder; and the recurrent second neural network comprises a Long Short-Term Memory (LSTM) neural network.

12 . A cloud computing platform comprising the printhead maintenance supervisor of claim 1 .

13 . A method of recommending replacement of printheads, the method comprising:
training a first neural network to generate anomaly scores for printheads using an unsupervised learning algorithm based on first training samples of conforming printhead data from a pool of conforming printheads; generating a training dataset for a recurrent second neural network by:
identifying training printhead data for a pool of training printheads;
inputting second training samples of the training printhead data into the first neural network to generate training anomaly scores for the training printheads over a first plurality of time units; and
formatting third training samples for the training printheads, wherein each of the third training samples comprises a first time-series of training data objects over a number of consecutive time units for a training printhead, and a label for the training printhead, and wherein each of the training data objects includes a training anomaly score generated by the first neural network for the training printhead, and at least a subset of the training printhead data for the training printhead; and
training the recurrent second neural network to generate scaled anomaly scores for printheads using a supervised learning algorithm based on the training dataset.

14 . The method of claim 13 , wherein:
the label represents a printhead condition of a corresponding training printhead.

15 . The method of claim 13 , wherein:
the subset of the training printhead data in the third training samples comprises a nozzle failure value indicating a number of failed nozzles.

16 . The method of claim 13 , further comprising:
identifying deployed printhead data for a plurality of deployed printheads; operating the first neural network to generate deployment anomaly scores for the deployed printheads; operating the recurrent second neural network to scale the deployment anomaly scores generated by the first neural network to produce the scaled anomaly scores for the deployed printheads; and providing a replacement recommendation for one or more of the deployed printheads based on the scaled anomaly scores for the deployed printheads.

17 . The method of claim 16 ,

Assignees

• Hajiahmadifoomani Farnaz
• Zhang Ziling
• Johnson Scott R
• Manchester William Edward
• Ricoh Co Ltd

Inventors

• Hajiahmadifoomani Farnaz
• Zhang Ziling
• Johnson Scott R
• Manchester William Edward

Classifications

• B41J2/0451

  for detecting failure, e.g. clogging, malfunctioning actuator · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title

• G06N3/045

  Combinations of networks · CPC title