WO2022025890A1 - Configuration d'un élément de dispositif d'impression - Google Patents

Configuration d'un élément de dispositif d'impression Download PDF

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Publication number
WO2022025890A1
WO2022025890A1 PCT/US2020/044169 US2020044169W WO2022025890A1 WO 2022025890 A1 WO2022025890 A1 WO 2022025890A1 US 2020044169 W US2020044169 W US 2020044169W WO 2022025890 A1 WO2022025890 A1 WO 2022025890A1
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WO
WIPO (PCT)
Prior art keywords
ink
printing
machine learning
temperature
learning system
Prior art date
Application number
PCT/US2020/044169
Other languages
English (en)
Inventor
Maurizio BORDONE
Javier CASTRO SORIANO
Pablo Carmelo MENA RODRIGUEZ
Original Assignee
Hewlett-Packard Development Company, L.P.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hewlett-Packard Development Company, L.P. filed Critical Hewlett-Packard Development Company, L.P.
Priority to PCT/US2020/044169 priority Critical patent/WO2022025890A1/fr
Publication of WO2022025890A1 publication Critical patent/WO2022025890A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B41—PRINTING; LINING MACHINES; TYPEWRITERS; STAMPS
    • B41J—TYPEWRITERS; SELECTIVE PRINTING MECHANISMS, i.e. MECHANISMS PRINTING OTHERWISE THAN FROM A FORME; CORRECTION OF TYPOGRAPHICAL ERRORS
    • B41J2/00—Typewriters or selective printing mechanisms characterised by the printing or marking process for which they are designed
    • B41J2/005—Typewriters or selective printing mechanisms characterised by the printing or marking process for which they are designed characterised by bringing liquid or particles selectively into contact with a printing material
    • B41J2/01—Ink jet
    • B41J2/17—Ink jet characterised by ink handling
    • B41J2/195—Ink jet characterised by ink handling for monitoring ink quality

Definitions

  • Certain inks are sensitive to thermal fluctuations. Temperature variations in the ink itself can result in the physical appearance of a printed output changing over the course of a single printing operation and between printing operations. Such temperature variations may be caused by changes in temperature of a printing apparatus performing the printing operation and changes in environmental conditions around the printing apparatus.
  • Figure 1 is a plot showing the evolution of the variation in lightness in color for several printing operations
  • Figure 2 is a plot showing the effect of trickle warming temperature on the color variation in a printed output
  • Figure 3 is a schematic block diagram of a printing system according to an example
  • Figure 4 is a memory according to an example
  • Figure 5 is a flowchart illustrating a method of configuring components of a printer
  • Figure 8 is a memory according to a second example
  • Figure 7 is an artificial neural network according to an example.
  • Inkjet printers print dots by ejecting very small drops of ink onto a print substrate and include a movable carriage that supports one or more print cartridges each having a printhead with a nozzle member having ink ejecting nozzles.
  • thermal injection printing TIJ
  • TIJ thermal injection printing
  • Small drops of ink are ejected from the nozzles through orifices by rapidly heating a small volume of ink located in the vaporization chambers with small electric heaters, such as small thin film resistors.
  • Other examples of inkjet printing wherein the present disclosure may be applicable include piezoelectric injection printing (P!J),
  • temperature controls the uniformity of the drop size of the ejected ink.
  • the heat from the resistors causing the vaporization in the chamber also causes the size of the drop of ink formed in the chamber to vary. If the temperature is too low the ink droplets formed will be smaller and have a lower drop-weight, affecting image quality. As the temperature rises, the drop-weight of the ink droplet will rise. The variation in drop weight varies with the type of ink being used. These variations in drop-weight will cause visible variations in color in the printed output.
  • This variation in color can be significant in a printing operation, herein also referred to as a printing process, that involves printing many pages and/or is performed over a long period of time, in the case of a many page printing operation, the internal temperature of a printer arranged to perform the printing operation tends to heat up due to the internal processes of the printer. This can warm the ink as the printing operation proceeds, leading to differences in the perceived color between the first printed output, with a later, subsequent printed output, when depositing the same ink.
  • Color consistency refers to the average amount of variation in color among a batch of supposedly identical samples.
  • One way of determining color consistency involves mapping measured color data, corresponding to the two supposedly identical samples, to points in a defined color space and computing the Euclidean distance between the points.
  • An example of a color space is the CIELAB color space which assigns a 3-tuple ( L*,a*,b * ) to a color.
  • the range of values of a* and b * may be chosen according to a specific implementation. Possible examples include [-100,100] or [-128,127],
  • Equation 1 Equation 1
  • AL*, Aa* and Ab* are the respective differences in L* a* and b* values between the two colors in CIELAB color space.
  • FIG. 1 shows the evolution of 4L * for a number of different printing operations.
  • the different printing operations involve different ink pre-warming temperatures, different process lengths, different ambient air temperatures and different printing apparatus, for example.
  • 4L * is the difference between the value of L* after x meters of printed output has been produced and the value of L * immediately after 0 meters of printed output has been produced, i.e. at the beginning of printing.
  • the thermal stabilization temperature of the ink is different owing to one or more of: intrinsic properties of the printing operation and / or printing apparatus, such as the particulars of the print mode, the length of the printing operation and ink and substrate settings; and extrinsic properties of the printing operation and / or printing apparatus, such as ink temperatures, external temperature and external humidity.
  • Trajectories A-K in Figure 1 show an increase in AL* as the printing operation proceeds. This is because the temperature of the ink increases during the printing operation due to changes in the extrinsic and intrinsic properties of the printing operation. Trajectory L shows an approximately constant AL*, because the ink temperature changes little over the duration of the printing operation. Trajectory M shows a decrease in AL* during the printing operation. This is because the ink temperature decreases over the duration of the printing operation.
  • gathered data shows that the variation in color of a printed output can be carefully controlled by varying certain component settings of a printing apparatus to adjust the temperature of ink.
  • An example is an output to a warming device used to raise the temperature of a printhead.
  • a printhead assembly may include a device to control the electrical current to the firing resistors so that their temperature is below the threshold for ejecting an ink drop.
  • This device could be a power field effect transistor (FET).
  • FET power field effect transistor
  • the device provides an ability to control the warming of the printhead assembly to the desired temperature before or during a printing operation. This process is called trickle warming because the printhead assembly allows a trickle of energy to flow through separate FETs to firing resistors.
  • Figure 2 shows the relationship between a change in color variation in a printed output and trickle warming temperature. As the trickle warming temperature is increased from 45°C to 65°C, ⁇ E is observed to approximately linearly Increase. This shows that a desired ⁇ E can be achieved by selecting a trickle warming temperature based on a regression analysis of the data plotted in Figure 2, for example.
  • the color consistency of a printed output may also be adjusted by controlling an amount of ink to purge from a print head prior to a printing process. Ink may be purged, or dumped, using an actuator to eject a certain amount of ink. The actuator may be an air pump. Ink purging causes motion of the ink through the various conduits of the printing apparatus.
  • ink within the printing apparatus can reach a desired temperature in a controlled way. If the thermal stabilization temperature of the ink can be determined for a printing operation, then the ink may be pre-warmed to said thermal stabilization temperature prior to commencement of the printing operation.
  • Certain examples described herein relate to using a machine learning system to determine the thermal stabilization temperature of ink for a printing operation. Settings for at least one component of a printer arranged to perform the printing operation are then determined based on the determined thermal stabilization temperature of the ink.
  • FIG. 3 shows a printing system 300 according to an example.
  • the printing system 300 comprises: a printing device 310 to apply ink to a print substrate to produce a printed output 340 in a printing operation, a memory 320 to store computer-readable instructions 400, and a processor 330 to execute the computer-readable instructions 400.
  • the printing device 310 comprises a component arranged to control a temperature of at least a first ink to be used in the printing operation.
  • the component may be an element for adjusting a temperature of a print head and/or the at least a first ink.
  • the element may affect at least one of a trickle warming temperature, an ink purge (or dump) content and amount, and a pre-warming temperature of printheads.
  • Figure 4 shows functional blocks performed by the processor 330, when executing the instructions 400. These blocks include receiving 405, by a machine learning system, data comprising a value indicative of an extrinsic property and/or a value indicative of an intrinsic property of the printing operation to be performed by the printing device 310, which utilizes at least the first ink.
  • a printing operation involves performing a set of actions that create a spatial and temporal distribution of ink onto the print substrate in order to produce a desired printed output 340.
  • Extrinsic properties of the printing operation are properties not related to the particulars of the printing operation.
  • Examples of extrinsic properties include the temperature of air outside the printing system 300, also referred to as the ambient temperature, the humidity of the air outside the printing system 300, also referred to as the ambient humidity, the air pressure outside the printing system 300, a time of day, a calendar date, a geographical location and current ink temperature.
  • Intrinsic properties of the printing operation are properties related to the particulars of the printing operation. Examples of intrinsic properties include the type of printmode used for the printing operation, the length of the printing operation, also referred to as the job length, print head temperature, type of print substrate used in the printing operation.
  • the printmode may include details of the types of ink to be utilized in the printing process, for example whether to print using black ink or whether to print in color.
  • Another example of a printmode is a tilling application mode, wherein several printed outputs are to be placed together, or tiled, to produce a larger image.
  • the printing system 300 may comprise at least one sensor arranged to measure the value indicative of the extrinsic property.
  • sensors include temperature sensors, humidity sensors and pressure sensors.
  • the printing system 300 may also comprise at least one sensor arranged to measure the value indicative of the intrinsic property.
  • the printing system 300 may comprise a temperature sensor arranged to monitor the temperature of the at least the first ink.
  • the type of substrate will also affect the quality of the printed output 340, For example, a paper substrate is more absorbent than a pane of glass, so that ink droplets ejected onto a paper substrate will disperse farther than those ejected onto glass, where surface tension causes the ink to minimise its surface area. This will affect the perception of color in the printed output 340,
  • the media setting may indicate what type of ink is to be utilized in the printing operation. Different types of inks have different thermal properties and so can be expected to have different thermal stabilization temperatures for a fixed set of environmental conditions. Examples of the types of ink available include latex, aqueous, dry sublimation, solvent and UV cured inks,
  • the printing system 300 is communicatively connected to a machine learning system.
  • the printing system 300 may comprise a machine learning system stored in the memory 320.
  • the printing system 300 may be coupled to a communication network, wherein a machine on the communication network comprises a machine learning system.
  • the machine learning system may be any of any type. This may be, for example, one or more of: a support vector machine, polynomial regression, an artificial neural network, decision tree, a Bayesian network or any other techniques suitable for use in a supervised learning environment.
  • the machine learning system may be implemented in a high-level programming language for example, Java, C++, Python, Scala, or any other suitable language.
  • the machine learning system may be implemented via a defined architecture, e.g. in computer program code that uses functions implemented in a machine learning library, e.g, a library of computer program code.
  • the machine learning system is arranged to learn from inputted data to create a model or classifier for making predictions on new data.
  • the machine learning procedure is presented with example inputs and ground-truth valued outputs.
  • the example inputs may be referred to as training data or labelled data and the desired outputs may be the labels of the labelled data.
  • the machine learning procedure then learns from the mapping of the inputs to the outputs to generate a model.
  • the machine learning system is in turn used to predict an output given new data.
  • the machine learning system takes as its input, a set of values that are deterministic and indicative of at least one extrinsic and/or intrinsic property, in one example, the machine learning system takes an intrinsic property values as input. This may be the case if the machine learning system is trained in a temperature and humidity-controlled environment, wherein every possible printing operation is performed in substantially the same environmental conditions,
  • the machine learning system additionally or alternatively takes an extrinsic property values as input. This may be the case if the machine learning system is trained for a single printing operation under different environmental conditions.
  • the received data may comprise values indicative of any number of extrinsic and intrinsic properties
  • the functional blocks performed by the processor 330, when executing the instructions 400, further include determining 410, by the machine learning system, a thermal stabilization temperature of the first ink for the received data. That is, the machine learning system is trained to output the thermal stabilization temperature of ink for the particular printing process, taking at least one value indicative of an intrinsic property of the printing process and/or indicative of an extrinsic property of the printing process as input data.
  • the functional blocks performed by the processor 330, when executing the instructions 400 also include determining 415 settings for the component of the printing device 310 to perform the printing operation based on the determined thermal stabilization temperature of the first ink.
  • the color variation between printed outputs can be controlled by adjusting certain component settings within a printing device 310 to vary the temperature of ink, prior to deposition of the ink onto a print substrate.
  • adjusting certain component settings within a printing device 310 to vary the temperature of ink, prior to deposition of the ink onto a print substrate.
  • Such data may be stored e.g. in a look up table, wherein the desired ink temperature is mapped to settings of the component of the system that will cause the at least first ink to reach said desired ink temperature.
  • the look up table may be stored in the memory 320.
  • the printing system 300 may be coupled to a communication network and the determined settings for the at least one printing component may be received from an external source.
  • the determined thermal stabilization temperature of the ink may be transmitted to a remote server and the settings for the component of the printing device 310 may be received from said remote server.
  • the functional blocks performed by the processor 330, when executing the instructions 400 further include configuring 420 the component of the printing device 310 with the determined settings.
  • the settings are applied to the component of the printing device 310 so that the at least first ink is caused to reach the determined thermal stabilization temperature, prior to printing.
  • Pre-warming ink to the thermal stabilization temperature prior to printing results in fewer thermal fluctuations in ink temperature during the printing process.
  • Setting the ink temperature too high for a printing process may result in a lower image quality due to unnecessarily high ink temperatures and higher energy consumption.
  • Setting the ink temperature too low for a printing process will result in the temperature of the ink rising during the printing operation, causing the color in the printed output to shift, therefore decreasing color consistency performance.
  • the approach to thermal stabilization of the ink is expedited by pre-warming the ink, resulting in less waste material at the beginning of the printing operation.
  • the printing device 310 is arranged to apply a plurality of inks to a substrate to produce the printed output 340.
  • the thermal stabilization temperature for each of the plurality of inks is determined.
  • the machine learning system may be arranged to determine the thermal stabilization temperature of each of the plurality of inks consecutively by receiving respective data comprising a value indicative of an extrinsic property and/or a value indicative of an intrinsic property of the printing operation for each of the plurality of inks separately.
  • the machine learning system may be modified to receive a plurality of ink temperatures and media settings, corresponding to respective properties of the plurality of inks, to determine the thermal stabilization temperature of each of the plurality of inks simultaneously.
  • the modified machine learning system is trained for different possible combinations of inks.
  • FIG. 5 is a flowchart illustrating an example method 500 of configuring components of a printer, which may be performed by a printing apparatus, such as the printing system 300,
  • data comprising a value indicative of an extrinsic property and/or a value indicative of an intrinsic property of a printing operation to be performed by a printer is received by a machine learning system such as machine learning system 320, wherein the printing operation utilizes at least a first ink.
  • a thermal stabilization temperature of the first ink for the received data is determined by the machine learning system.
  • the data is inputted into the machine learning system to provide an output value corresponding to the thermal stabilization temperature of the at least first ink.
  • settings for components of the printer to perform the printing operation are determined based on the determined thermal stabilization temperature of the first ink. The settings may be determined such that, when applied to the components of the printer, cause the at least first ink to reach said thermal stabilization temperature. Examples of how the settings are determined has been discussed above with reference to the printing system 300.
  • Figure 6 is a flowchart illustrating an example computer-implemented method 600 of training a machine learning system for determining a thermal stabilization temperature of a first ink to be utilized by a printer in a printing process.
  • the computer-implemented method 600 involves receiving, by a machine learning system such as machine learning system 320, a data set comprising values indicative of an extrinsic property and/or values indicative of an intrinsic property of the printing process to be performed by the printer, the printing process utilizing at least the first ink.
  • a machine learning system such as machine learning system 320
  • the machine learning system may be stored in memory in the printer.
  • the machine learning system may be stored in memory external from the printer, for example in an external computing device coupled to the printer.
  • the machine learning system may be one or more of: a support vector machine, polynomial regression, an artificial neural network, a decision tree, a Bayesian network or any other techniques suitable for use in a supervised learning environment.
  • the training of the machine learning system begins by the machine learning system receiving a data set.
  • the data set may be referred to as a training data set.
  • the data set comprises values indicative of at least one extrinsic property and/or values indicative of at least one intrinsic property of the printing process. Examples of extrinsic and intrinsic properties have been described above with reference to the printing system 300,
  • the machine learning system is an artificial neural network 700 as shown in Figure 7.
  • the artificial neural network 700 comprises a set of input nodes 705.
  • Each node of the set of input nodes 705 corresponds to a value indicative of an extrinsic or intrinsic property of a printing operation.
  • the intrinsic properties include a printmode, a job length (printing process length) and media settings.
  • the extrinsic properties include ink temperatures, external temperature, external humidity and air pressure.
  • the artificial neural network 700 comprises N hidden layers. Hidden layer 1 710 and hidden layer N 715 are shown in the figure for brevity, although N could take any integer value more than or equal to 1.
  • the output layer 720 of the artificial neural network comprises a single node. The value of said single node provides, or can be used to determine, an estimate for the thermal stabilization temperature of ink, for the set of values provided in the input nodes 705, and based on a current configuration of the nodes in the hidden layers 710, 715,
  • the computer-implemented method 600 involves estimating, by the machine learning system, a value of the thermal stabilization temperature of the at least first ink for the data set by using an initial set of configuration parameters of the machine learning system.
  • the initial set of configuration parameters includes system specific values and/or logical connections within the machine learning system. Values in the data set are provided as inputs of the machine learning system.
  • the output of the machine learning system is, or can be used to determine, an estimate of the thermal stabilization temperature of the first ink according to whichever configuration parameters of the machine learning system are current, which is to say, those that are expressly linked to a current output of the machine learning system.
  • the configuration parameters comprise the connections and weights associated with each node in the hidden layers 710, 715.
  • the data set (values indicative of an extrinsic property and/or values indicative of an intrinsic property of the printing process to be performed by the printer) is processed by the artificial neural network 700 to produce the single output in the output layer 720.
  • the value of the single node in the output layer 720 corresponds to an estimate of the thermal stabilization temperature of ink for the given data set.
  • the computer-implemented method 600 involves obtaining a ground-truth value of the thermal stabilization temperature of the first ink for the data set.
  • An actual thermal stabilization temperature of the ink for the printing processes used to generate the data set is determined. As has been described with reference to Figure 1 , the temperature of ink in a printing process approximately reaches a steady value, the thermal stabilization temperature, after a certain amount of printing has been performed. In some examples, this thermal stabilization temperature is reached after around 20 meters of printing has taken place. After this, the difference in color between a first printed output and subsequent printed outputs roughly stabilizes because the temperature of the ink utilized in the printing process has stabilized.
  • the ground-truth value of the thermal stabilization temperature may be determined by measuring the temperature of the ink after a certain amount of printing has been performed using a temperature sensor. In one example, this could involve monitoring the ink temperature during a printing process. At some point, the ink temperature stabilizes, and this stabilized temperature can be stored as the ground-truth value of the thermal stabilization temperature of the ink for the particular printing process.
  • the ink temperature may be measured at a fixed time after a printing operation has commenced or after a certain number of printing operations have been completed. This time may be determined so that at least 20 meters of printing has occurred, at which point the ink temperature may be expected to have stabilized.
  • an optical spectrometer is arranged to measure color in the printed output.
  • the measured color may be stored as data and processed to determine when the color consistency reached a fixed value.
  • the temperature of the ink at, or after, the point at which the color consistency reached a steady value may be determined and stored as the ground-truth value.
  • the ground-truth value may be stored in a memory.
  • the memory may be the same memory that stores the machine learning system, or may be external from the memory that stores the machine learning system.
  • the computer-implemented method 600 involves updating the configuration parameters of the machine learning system based on a comparison of the estimated value of the thermal stabilization temperature of the first ink and the ground-truth value of the thermal stabilization temperature of the first ink.
  • the machine learning system is trained based on the difference between the estimated and ground-truth values of the thermal stabilization temperature of the ink.
  • this may involve determining the cost function associated with the estimated and ground- truth values of the thermal stabilization temperature of the ink. Methods such as backpropagation can be performed to adjust the weights between nodes in the hidden layers 710, 715, based on the gradient of the cost function. The weights may then be updated using gradient descent methods.
  • the computer-implemented method 600 involves storing at least the updated configuration parameters of the machine learning system.
  • the updated configuration parameters may replace the initial configuration parameters stored in memory.
  • the computer-implemented method 600 can be repeatedly executed until the difference between the estimated and ground-truth values converges to a predetermined difference. Each time it is executed, updated configuration parameters are stored, either in addition to, or as a replacement of, previously generated configuration parameters. For a sufficiently wide array of values of input data, the machine learning system can increase the solution space of thermal stabilization temperatures of ink for a given set of inputs.
  • the machine learning system of printing system 300 shown in Figure 3 may be trained according to the computer-implemented method 600 shown in Figure 6.
  • the printing system 300 may further comprise an optical spectrometer.
  • the machine learning system can continue to be trained using the computer-implemented method 600 shown in Figure 6 because the ground-truth value of the thermal stabilization temperature of the first ink can be determined using the optical spectrometer, as described above.

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  • Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Ink Jet (AREA)

Abstract

Selon certains exemples, la présente invention concerne la configuration de réglages pour un élément d'une imprimante. Dans certains cas, des données comprenant une valeur indiquant une propriété extrinsèque et/ou une valeur indiquant une propriété intrinsèque d'une opération d'impression à exécuter par une imprimante sont reçues par un système d'apprentissage automatique. Le système d'apprentissage automatique détermine une température de stabilisation thermique d'une première encre pour les données reçues. Des réglages d'éléments de l'imprimante permettant d'effectuer l'opération d'impression sont déterminés sur la base de la température de stabilisation thermique déterminée de la première encre et les éléments de l'imprimante sont configurés au moyen des réglages déterminés.
PCT/US2020/044169 2020-07-30 2020-07-30 Configuration d'un élément de dispositif d'impression WO2022025890A1 (fr)

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PCT/US2020/044169 WO2022025890A1 (fr) 2020-07-30 2020-07-30 Configuration d'un élément de dispositif d'impression

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PCT/US2020/044169 WO2022025890A1 (fr) 2020-07-30 2020-07-30 Configuration d'un élément de dispositif d'impression

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030063275A1 (en) * 2001-09-10 2003-04-03 Xerox Corporation Diagnostics for color printer on-line spectrophotometer control system
US20060109329A1 (en) * 2004-11-25 2006-05-25 Oce-Technologies B.V. Printer with a paper treatment system
US20070081018A1 (en) * 2005-10-11 2007-04-12 Silverbrook Research Pty Ltd Method of purging using purging ink and printing using printing ink from an inkjet printhead
US20140111595A1 (en) * 2012-10-19 2014-04-24 Zink Imaging, Inc. Thermal printer with dual time-constant heat sink
WO2017194124A1 (fr) * 2016-05-12 2017-11-16 Hewlett-Packard Development Company, L P Correction de température par application d'un agent d'impression

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030063275A1 (en) * 2001-09-10 2003-04-03 Xerox Corporation Diagnostics for color printer on-line spectrophotometer control system
US20060109329A1 (en) * 2004-11-25 2006-05-25 Oce-Technologies B.V. Printer with a paper treatment system
US20070081018A1 (en) * 2005-10-11 2007-04-12 Silverbrook Research Pty Ltd Method of purging using purging ink and printing using printing ink from an inkjet printhead
US20140111595A1 (en) * 2012-10-19 2014-04-24 Zink Imaging, Inc. Thermal printer with dual time-constant heat sink
WO2017194124A1 (fr) * 2016-05-12 2017-11-16 Hewlett-Packard Development Company, L P Correction de température par application d'un agent d'impression

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