CN113492589A

CN113492589A - Compensation characteristic curve (DUMC) with variable model

Info

Publication number: CN113492589A
Application number: CN202110294200.2A
Authority: CN
Inventors: N·R·诺瑞克; S·内布; A·亨; R·施滕策尔; A·坎珀; N·马丁
Original assignee: Heidelberger Druckmaschinen AG
Current assignee: Heidelberger Druckmaschinen AG
Priority date: 2020-03-20
Filing date: 2021-03-19
Publication date: 2021-10-12
Anticipated expiration: 2041-03-19
Also published as: EP4088934A1; CN113492589B

Abstract

The invention relates to a compensation characteristic curve (DUMC) with a variable model, namely to a method for configuring an inkjet printer by means of a computer, comprising the following steps: the computer classifies the individual setting parameters of the inkjet printer by means of a data model created and trained for this purpose; storing the classified tuning parameters in a dataset set of a database; creating, by the computer, a new set of setup parameters for configuring the inkjet printer for the determined print job based on the stored set of data sets; in the event of a change in the determined classified setting parameters, the computer recalculates the set of setting parameters for configuring the inkjet printer by means of the trained data model; the computer adapts the set of data sets in the database to the changed tuning parameters.

Description

Compensation characteristic curve (DUMC) with variable model

Technical Field

The invention relates to a method for automatically configuring an inkjet printer.

The invention belongs to the technical field of ink jet printing.

Background

In the setting (Einstellung) and setting (konconfiguration) of inkjet printers, the individual printing nozzles of the inkjet printing heads used are controlled in a targeted manner, which is an important central point for the inkjet printing processes to be carried out in each case. In order to increase the printing quality as much as possible, the individual printing nozzles of the inkjet printing head must be controlled in such a way that small deviations in the ink ejection of the individual nozzles, which are always present as a function of production or as a function of the process, are compensated accordingly. This is achieved in such a way that: i.e. to create a so-called density compensation profile (dichtekompensationsprrofile). Such density compensation is also known as DUC or DUMC. In this case, the following are determined by means of test measurements: in the case where the individual printing nozzles are operated in unison, the individual printing nozzles will differ from each other in their degree of ink ejection. With the aid of this information, it is then possible to create a corresponding compensation characteristic curve, which adjusts the ink ejection for each individual printing nozzle in such a way that these deviations are compensated.

A further important point in configuring an inkjet printer is that defective (i.e. printed with deviations or no longer printed at all) printing nozzles (also known as missing nozzles — MN or MNC) are compensated as far as possible in such a way that, even in the event of such defective printing nozzles, they have as little influence as possible on the resulting printed image with respect to the printing quality. In many cases, the compensation of defective printing nozzles is achieved by an increased ink jet of adjacent printing nozzles, wherein in many cases further adjacent printing nozzles are printed in a correspondingly reduced amount in order to avoid overcompensation.

Here, the two configuration criteria in the form of density compensation and defective printing nozzle compensation are for a large number of characteristic values. Furthermore, both compensation methods (in particular, however, the density compensation) have to be optimized for each print head component (druckkopfuzusammensetzng), for each print substrate used and for each printing speed used.

A disadvantage in the prior art today is that tests have to be performed for each print bar or print head component, each substrate used and each printing speed. This entails a corresponding expenditure, mainly because the inkjet printer cannot work effectively in this time. Furthermore, all substrate data sets must be entered anew when a single print head is replaced, or when an ink formulation is changed, when a Raster is changed, or when other important components of the printing process are changed.

In order to solve this problem, a method is known for compensating, by a computer, position-dependent density fluctuations of the printing nozzles in an inkjet printer, wherein the computer creates a compensation characteristic curve for the position-dependent density fluctuations with respect to all print heads for all used printing substrates, calculates an average characteristic curve based on the compensation characteristic curve, and using the average characteristic to compensate for position-dependent density fluctuations in the ink jet printer, the method is characterized in that, in the case of a change of one or more print heads of the inkjet printer, a new compensation characteristic curve is calculated by the computer for only one printing substrate, the computer derives a new average characteristic curve on the basis of the new compensation characteristic curve, and the computer calculates and uses the new average characteristic curve for the remaining substrate taking into account the old compensation characteristic curve.

Furthermore, a method is known from the prior art for compensating position-dependent density fluctuations of the printing nozzles in an inkjet printer by a computer, wherein the computer generates a compensation characteristic curve for the position-dependent density fluctuations for all printing heads of the inkjet printer for all used printing substrates and uses said compensation characteristic curve to compensate the position-dependent density fluctuations in the inkjet printer, which method is characterized in that the computer uses a general reference printing substrate to separately determine: on the basis of this generic reference printing substrate, a reference compensation characteristic is created which is dependent on the area coverage and on the position, and thus an overall compensation characteristic is created which is used to compensate for the position-dependent density fluctuations.

However, these solutions of the prior art also have the drawback that: not all the relevant data relating to the ink jet printer to be configured are taken into account in the average characteristic curve used (or the reference compensation characteristic curve), and the compensation characteristic curve thus created is not sufficiently accurate, for which there is, in addition, a considerable configuration outlay in terms of setup waste and setup time (einrichtemakular-zeit). Furthermore, these solutions are not suitable for compensating defective printing nozzles.

Disclosure of Invention

The object of the present invention is therefore to provide a method for configuring an inkjet printer, which minimizes the duration and effort of the configuration process.

This object is achieved by a method for configuring an inkjet printer by means of a computer, comprising the following steps: classifying, by the computer, the individual tuning parameters (Einstellparameter) of the inkjet printer by means of the data model created and trained for this purpose; storing the classified tuning parameters in a dataset set of a database; creating, by the computer, a new set of setup parameters (Satz) for configuring the inkjet printer for the determined print job based on the stored set of data sets; in the event of a change in the determined classified setting parameters, recalculating, by the computer, the set of setting parameters for configuring the inkjet printer by means of the trained data model; adapting, by a computer, the set of data sets in the database to the changed tuning parameters. In this case, it is decisive for the method according to the invention that the individual setting parameters are assigned to a specific substrate class or machine configuration or the like by means of the trained data model. This reduces the cost of substrate identification to some extent (for example in the case of the addition of a new substrate). Only the parameters describing the substrate need to be specified here; all other parameters (e.g. nail setting, DUC characteristic curve, etc.) are calculated from the model, i.e. the entire identification process of the substrate has been carried out until now, and known parameters are now input. In this case, it is important to develop the data model used on the basis of the currently existing dataset in the substrate database of the respective machine. Then, when a change in a determined setting parameter occurs (for example, due to a replacement of a single print head), the model can thus automatically aim to accurately calculate further setting parameters from the model. It is therefore no longer necessary, when changing the printing heads or the printing substrate, to retrieve all combinations of ink jet printing head components and printing substrate and printing speeds or other setting parameters by means of a plurality of tests. Only the parameters that have changed have to be provided to the data model for use, whereupon the data model automatically calculates the other tuning parameters. Naturally, the setting parameters that have changed must be somehow ascertained by the user so that the data model can calculate the other parameters. This process greatly reduces the cost of configuring the ink jet printer compared to the prior art.

Advantageous and therefore preferred developments of the method according to the invention emerge from the dependent claims and from the description and the associated drawings.

In this case, a preferred development of the method according to the invention provides that the categories for setting the parameters include: a category specific to the substrate being printed, a category specific to the printer, and a category specific to the printing speed. These categories are the most important categories into which the data model can put the current tuning parameters. The classes specific to the printing substrate include the following parameters: such as the paper white of the substrate being printed, the absorption of ink through the substrate, the size and thickness of the substrate, and the like. In particular printer-specific, this means in particular the actuation of the individual printing nozzles of an inkjet printing head (for example density compensation characteristic curves or settings for compensating defective printing nozzles), whereas the class specific to the printing speed naturally comprises primarily the printing speed itself and possibly also certain characteristics of the substrate transport in the printer.

In this case, a further preferred development of the method according to the invention provides that, in the event of a change in at least one of the classified manipulated parameters, the classified manipulated parameters are quantified by the user (quantifizer), and the computer recalculates the further manipulated parameters affected by the change in the at least one classified manipulated parameter by means of the trained data model. As already mentioned, these defined setting parameters must be ascertained by the user when at least one setting parameter is changed, for example in the case of a change in the substrate or in the inkjet print head used. The user can either carry out this determination by means of specially performed experiments or (for example in the case of a change of the print head) use the density compensation characteristic from the manufacturer of the print head and provide it to the data model for use by means of inputs accordingly. The data model can then react to this by means of the new data on the basis of the trained state of the data model, and correspondingly adapted setting parameters of the other classes can be calculated for the changed parameters and can be used for configuring the inkjet printer.

In this case, a further preferred development of the method according to the invention provides for the data model to be used for training (Einlernen) of the data model with a set of configured data sets of setup parameters of the inkjet printer, whereby the interrelations and influences between the individual setup parameters are linked by the data model. In other words, in order to be able to assume a corresponding role in the method according to the invention, the data model must naturally be trained by way of the functional relationships between the individual setting parameters, i.e. the data model must be set such that it actually knows: for example, which printing speed, which influence on the manipulation of those printing nozzles adjacent to the defective printing nozzle is necessary with respect to the ink drop size necessary for compensation. Only in this way, when one or some of the changed setting parameters are changed, the other parameters can be adapted accordingly.

In this case, a further preferred development of the method according to the invention provides that the setting of the parameters comprises: ink level limits (Inklimits), nail and corona setups (Pinning-und Coronaesenstellung), and characteristic curves for density compensation and defective printing nozzle compensation (Kenninien), and parameters relating to printing speed and substrate transport and substrate properties. These are the only most important and most common tuning parameters. The method according to the invention is not, however, limited to these parameters, but can detect all other relevant parameters (or extend to these other relevant parameters) for configuring an inkjet printer.

In this case, a further preferred development of the method according to the invention provides that the data record of the database has historical setup parameters of not only the currently used inkjet printer but also other inkjet printers. In order to train the data model used as accurately and optimally as possible, the method according to the invention proposes using not only the historical setup parameters of the currently used inkjet printer, but also the setup parameters of other inkjet printers of as similar a construction as possible. Since there is more data available for training the data model, it can be more accurately guided in the subsequent calculations regarding the set of new configurations of the setting parameters, and the ink jet printer concerned can also work more accurately.

Drawings

The invention itself and the structurally and/or functionally advantageous embodiments of the invention are explained in more detail below with reference to the attached drawings according to at least one preferred exemplary embodiment. In the drawings, elements corresponding to each other are provided with the same reference numerals, respectively. The drawing shows that:

FIG. 1: schematically showing the components participating in the method and their dependence on each other;

fig. 2a, 2b, 2 c: the progress of the method according to the invention is schematically shown.

Detailed Description

In this case, a digital data model 2 is developed on the basis of the currently existing data set in the substrate database of the individual printing press 3, which digital data model 2 enables individual parameters and/or characteristic values to be assigned to, for example, the substrate class 5 or the

machine configuration

4, 4 a. Fig. 1 shows the components involved here by way of example. The workflow system runs on one or more computers 1, by means of which one or more computers 1 process print jobs to be processed. The creation and use of the data model 2 is performed separately by the computer 1. This may be a central workflow computer 1 in the respective printing plant, which central workflow computer 1 performs both processes. However, instead, the data model 2 is at least created and pre-trained by the provider of the workflow system of which it is a component, before being used in the printing plant. In this case, the computers used for the creation and training (or further adaptation) of the data model 2 are not identical. However, it is not important to the method according to the invention whether the data model 2 is created (or run) on one or more computers. It is important that the data model 2 obtains historical data for the

configuration

4, 4a of the ink jet printer 3 (particularly with respect to DUC and MNC) and is thereby trained and then able to create such a configuration for the desired ink jet printer 3.

To create the data model 2, the individual data set sets 4, 4a are divided into such ranges by means of processing knowledge:

a substrate-specific tuning factor 5;

machine-specific tuning factors, such as printbar configuration 8;

a speed-specific adjustment factor 7; and

possible combinations of the three ranges mentioned above.

The global goal is that the different source adjustment factors 5, 7, 8 can be combined into a new set of

valid data sets

4, 4 a. For example, in the case where the substrate 5 and the speed 7 are known in a predefined manner, all parameters are taken over from the other inkjet printer 3 and only the DUC data 9 are re-measured. The basic model is designed such that model 2 is continuously adapted to the new data, whereby the quality of the prediction is continuously better; the more the better.

The question of the model 2 for solving the task (i.e. creating a

suitable configuration

4, 4a of the inkjet printer 3 in view of the DUC 9 and defective printing nozzles) may be expressed exemplarily as:

the first question is what kind of influence the substrate parameter 5 input by the operator has on the ink amount limit for each color (first process step).

The second question is what kind of influence the substrate parameters 5 and ink volume limits have on the pinning and corona setting (second process step).

The third question is which influence the substrate parameters 5, ink limit and nail and corona setting have on the DUC and MNC characteristics (third and fourth process steps, no direct correlation)

The fourth question is which influence all previous parameters and processes have on the calibration curve.

Fig. 2 shows a schematic representation of the method sequence according to the invention. The results provided by the database for each case are such proposals: the verification is performed by the operator on the basis of a suitable test print (probedraw). If this check passes, the

dataset

4, 4a is imported into the database. If the test is evaluated as failed, then (as the case may be) some or all of the points of the current substrate identification process need to be performed. Depending on how the ranking is performed here, one or more intermediate checks may be performed. If one of these intermediate tests passes, the

dataset

4, 4a is evaluated as good and saved.

For a better understanding, examples of how such a question may actually be answered are listed here:

a) new substrate 5:

here, only the parameter 5 describing the substrate is given in advance; all other parameters (such as nail set settings, DUC characteristic curves, etc.) are calculated by model 2;

b) new printing speed 7:

here, only the new printing speed 7 is specified; all other parameters are calculated by model 2.

c) Print head replacement 8:

here, all parameters are retained except for the DUC characteristic curve, only the DUC procedure 9 has to be performed once. The DUC profile is then updated for all substrates present.

This enumeration is not complete, of course. Any number of other scenarios are possible, limited only by the number of eigenvalues of model 2 to be considered.

In the case of a print head change 8, which occurs absolutely frequently, it is only necessary to detect a unique DUC characteristic curve from the reference substrate. Other characteristic curves are calculated according to model 2. Because the data model 2 is adaptive, the prediction quality continues to improve. Thus, the costs for substrate identification will be continuously reduced.

List of reference numerals

1 workflow computer

2 data model

3 printing machine

4, 4a created configuration data set

5 parameters of the substrate

6a, 6b, 6c test print

7 printing speed

8 print head exchange

9 Density Compensation (DUC treatment)

Claims

1. Method for configuring an inkjet printer (3) by means of a computer (1), having the following steps:

classifying, by means of a computer (1), individual setting parameters (5, 7, 8) of the inkjet printer (3) by means of a data model (2) created and trained for this purpose;

storing the sorted tuning parameters (5, 7, 8) in a dataset of a database;

creating, by the computer (1), a new set of setup parameters (4, 4a) for configuring the inkjet printer (3) for the determined print job based on the stored set of data sets;

in the event of a change in the determined classified setup parameters (5, 7, 8), the computer (1) recalculates the set of setup parameters (4, 4a) for configuring the inkjet printer (3) using the trained data model (2);

adapting, by the computer (1), the set of data sets (4, 4a) in the database to the changed tuning parameters (5, 7, 8).

2. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,

it is characterized in that the preparation method is characterized in that,

the categories for setting the parameters (5, 7, 8) include: a category (5) specific to the substrate to be printed, a category (8) specific to the printing press, and a category (7) specific to the printing speed.

3. The method according to any one of the preceding claims,

it is characterized in that the preparation method is characterized in that,

in the event of a change in at least one of the classified tuning parameters (5, 7, 8), said at least one classified tuning parameter is quantified by the user, while the computer (1) recalculates the other tuning parameters affected by the change in the at least one classified tuning parameter by means of the trained data model (2).

4. The method according to any one of the preceding claims,

it is characterized in that the preparation method is characterized in that,

for training the data model (2), a set of configured data sets (4, 4a) of the setting parameters (5, 7, 8) of the inkjet printer (3) is provided for use, whereby the interrelations and influences between the individual setting parameters (5, 7, 8) are linked by the data model (2).

5. The method according to any one of the preceding claims,

it is characterized in that the preparation method is characterized in that,

the setting parameters comprise:

ink volume limitation, pinning and corona setting, and

-characteristic curves for density compensation (9) and defective printing nozzle compensation, and

-parameters relating to the printing speed (7) and the substrate transport and the substrate properties (5).

6. The method according to any one of the preceding claims,

it is characterized in that the preparation method is characterized in that,

the data set (4, 4a) of the database has historical adjustment parameters not only with respect to the currently used inkjet printer but also with respect to other inkjet printers (3).