EP4088934A1 - Dumc with variable model - Google Patents

Abstract

Method for configuring inkjet printing machines using a computer, which includes the steps of categorizing individual setting parameters of the inkjet printing machines using a data model created and taught for this purpose by the computer, storing the categorized setting parameters in data sets in a database, creating a new set of setting parameters for configuring the Inkjet printing machine for a specific print job from the stored data records by the computer, recalculating the setting parameters for configuring the inkjet printing machine when certain categorized setting parameters are changed using the learned data model by the computer and adapting the data records in the database to the changed setting parameters by the computer , includes.

Description

The invention deals with a method for the automated configuration of an inkjet printing machine.

The invention lies in the technical field of inkjet printing.

When setting up and configuring inkjet printing machines, the targeted control of the individual print nozzles of the inkjet print heads used for the inkjet printing process to be carried out is a central point. For the highest possible print quality, the individual print nozzles of the inkjet print heads must be controlled in such a way that slight deviations in the ink ejection of the individual nozzles, which are always present due to the production or process, are compensated accordingly. This is done by creating so-called density compensation profiles. Density compensation is also known as DUC or DUMC. Test measurements are used to determine the extent to which the individual print nozzles deviate from one another in terms of their ink output when they are controlled identically. This information can then be used to create the corresponding compensation profile, which regulates the ink ejection for each individual print nozzle in such a way that these deviations are compensated for.

Another important point when configuring the inkjet printing machine is to compensate for defective, i.e. deviating or no longer printing, printing nozzles (also known as missing nozzles - MN or MNC) as far as possible in such a way that even if such defective printing nozzles occur, they affect the resulting print image in terms of print quality as little as possible. In most cases, defective printing nozzles are compensated for by increased ink ejection of the neighboring printing nozzles, with other neighboring printing nozzles usually printing correspondingly less in order to avoid overcompensation.

These two configuration criteria in the form of density compensation and compensation for defective printing nozzles are aimed at a large number of characteristic values. In addition, both types of compensation, but especially the density compensation, must be optimized for each print head composition, each print substrate used and each print speed used.

The disadvantage of the current state of the art is that a test run has to be carried out for each print bar or print head composition, each print substrate used and each print speed. This causes a corresponding effort, especially since the inkjet machine cannot work productively during this time. In addition, if a single head is replaced, or if the ink formulation, the screen, or other essential components of the printing process are changed, all the substrate data sets have to be re-entered.

To solve this problem, a method is known for compensating for position-dependent density fluctuations of print nozzles in an inkjet printing machine using a computer, the computer creating compensation profiles for the position-dependent density fluctuations across all print heads for all printing substrates used, calculating an average profile from these and using this average profile for compensation of the position-dependent density fluctuations in the inkjet printing machine, characterized in that when one or more print heads of the inkjet printing machine are replaced, the computer calculates a new compensation profile for one printing substrate only, the computer derives a new average profile from this and the computer derives the new average profile calculated and applied using the old compensation profiles for the remaining print substrates.

In addition, a method for compensating for position-dependent density fluctuations of printing nozzles in an inkjet printing machine using a computer is known from the prior art, with the computer creating compensation profiles for the position-dependent density fluctuations across all print heads of the inkjet printing machine for all printing substrates used and the compensation profiles for compensation of the position-dependent density fluctuations in the inkjet printing machine applies, which is characterized in that the computer determines the respective printing machine or print head-specific influences and printing substrate-specific influencing factors with a generic reference printing substrate, from which a reference compensation profile is created, which is area coverage and location-dependent and from this creates an overall compensation profile, which is used to compensate for the position-dependent density fluctuations is used.

However, these approaches still have the disadvantage that not all relevant data about the inkjet printing machine to be configured flow into the average or reference compensation profiles used and therefore the compensation profiles created in this way are not precise enough, which means that there is still a significant configuration effort with regard to set-up waste and time exists. In addition, these approaches are not suitable for compensating for defective printing nozzles.

The object of the present invention is therefore to disclose a method for configuring inkjet printing machines which minimizes the duration and effort of the configuration process.

This problem is solved by a method for configuring inkjet printing machines using a computer, which includes the steps of categorizing individual setting parameters of the inkjet printing machines using a data model created and taught for this purpose by the computer, storing the categorized setting parameters in data records in a database, creating a new Set of setting parameters for configuring the inkjet printing machine for a specific print job from the stored data sets by the computer, recalculation of the setting parameters for configuring the inkjet printing machine when certain categorized setting parameters change by means of the taught-in data model by the computer and adjustment of the data sets in the database the changed setting parameters by the computer. What is decisive for the method according to the invention is that the individual setting parameters are assigned to a specific substrate category or machine configuration or the like by means of the taught-in data model. This reduces the effort involved in substrate qualification decisive, e.g. when adding a new substrate. In this case, only the parameters that describe the substrate would be specified; all other parameters, such as pinning setting, DUC characteristic, etc. are calculated by the model, ie the entire substrate qualification process was previously carried out, now some known parameters have to be entered. It is important that the data model used is developed from the currently available data sets in the substrate database of the individual machines. In this way, when a specific setting parameter is changed, for example by exchanging a single print head, the model can automatically calculate the other setting parameters precisely from the model. It is therefore no longer necessary to re-determine all combinations of inkjet print head compositions and print substrate and printing speed or other setting parameters by means of several test runs when replacing a print head or the print substrate. Only the changed parameter has to be made available to the data model, which then automatically calculates the other setting parameters. The changed setting parameter must of course be determined by the user in some form so that the data model can calculate the other parameters. Nevertheless, this procedure reduces the effort involved in configuring the inkjet printing machine enormously compared to the prior art.

Advantageous and therefore preferred developments of the method according to the invention result from the associated subclaims and from the description and the associated drawings.

A preferred development of the method according to the invention is that the categories for the setting parameters include printing substrate-specific, printing machine-specific and printing speed-specific categories. These are the most important categories into which the data model classifies the available setting parameters. Print substrate-specific categories include parameters such as the paper white of the print substrate, the absorption of the ink by the substrate, the dimensions and thickness of the substrate, etc. The control of the individual print nozzles of the inkjet print heads, such as the density compensation profile or the settings to compensate for defective print nozzles, are particularly specific to the printing machine , while Printing speed-specific categories, of course, mainly include the printing speed itself and possibly also certain properties of the substrate transport in the printing press.

A further preferred development of the method according to the invention is that when at least one categorized setting parameter changes, this is quantified by a user, while the computer uses the learned data model to recalculate other setting parameters influenced by the change in the at least one categorized setting parameter. As already mentioned, when changing at least one setting parameter, such as e.g. B. the printing substrate or an inkjet print head used, these specific setting parameters are determined by the user. He can do this either by carrying out test runs himself or, e.g. B. in the case of a modified printhead, take over the density compensation profile from the manufacturer of the printhead and make it available to the data model by entering it accordingly. This can then react to this using this new data on the basis of its taught-in state and calculate correspondingly adapted setting parameters of the other categories for these changed parameters and make them available to the inkjet printing machine for configuration.

A further preferred development of the method according to the invention is that configured data sets of setting parameters of the inkjet printing machine are made available for teaching the data model, as a result of which the relationships and influences between the individual setting parameters are linked by the data model. This means that the data model must of course be taught about the causal relationships between the individual setting parameters in order to be able to assume a corresponding role in the method according to the invention. B. which printing speed and which influence on the control of a defective printing nozzle neighboring printing nozzles with regard to the ink droplet size necessary for compensation. Only then can it be able to adapt the other parameters accordingly when one or a few modified setting parameters are changed.

A further preferred development of the method according to the invention is that the setting parameters include ink limits, pinning and corona settings and characteristics for density compensation and compensation for defective printing nozzles, as well as parameters for the printing speed and the printing substrate transport and the printing substrate properties. These are only the most important and most common setting parameters. However, the method according to the invention is not limited to these parameters, but can record all other relevant parameters for the configuration of the inkjet printing machine or be extended to them.

A further preferred development of the method according to the invention is that the data sets in the database have historical setting parameters both for the inkjet printing machines currently in use and for other inkjet printing machines. In order to teach the data model used as precisely and optimally as possible, it is advisable not only to use the historical setting parameters of the inkjet printing machine currently used, but also to access the setting parameters of other inkjet printing machines that are as similar as possible. Because the more data is available for teaching the data model, the more precisely it can later lead to the calculation of new configuration sets of setting parameters that work in the relevant inkjet printing machine.

• Figur 1: schematisch die am Verfahren beteiligten Komponenten und ihre Abhängigkeiten zueinander
• Figur 2: schematisch den Ablauf des erfindungsgemäßen Verfahrens

The invention as such as well as structurally and/or functionally advantageous developments of the invention are explained in more detail below with reference to the associated drawings using at least one preferred exemplary embodiment. In the drawings, corresponding elements are given the same reference numbers. The drawings show:

• figure 1 : Schematic of the components involved in the process and their interdependencies
• figure 2 : schematic of the sequence of the method according to the invention

A digital data model is developed from the currently available data sets in the substrate database of the individual printing machines, which enables the to assign individual parameters and/or characteristic values, for example, to a substrate category or a machine configuration. figure 1 shows an example of the components involved. [Here template for figure 1 insert!!] The creation and application of the data model are each carried out by a computer. This can be a central workflow computer in the respective print shop, which carries out both processes. Alternatively, however, the data model is at least created and pre-trained by the provider of the workflow system, of which the data model is a component, before it is used in the print shop. In this case, the computer for creating and teaching or further adapting the data model are not identical. However, it is of no fundamental importance for the method according to the invention whether the data model is created or operated on one or more computers. It is important that it receives historical data on the configuration of inkjet printing machines, in particular with regard to DUC and MNC, and is thus taught in and can then create such configurations for the desired inkjet printing machine.

• substratspezifisch
• maschinenspezifisch, z.B. die Druckbalkenkonfiguration
• geschwindigkeitsspezifisch sind
• sowie mögliche Kombinationen der drei Bereiche

To create the data model, the individual data sets are divided into areas with the help of process knowledge

• substrate specific
• machine-specific, e.g. the print bar configuration
• are speed specific
• as well as possible combinations of the three areas

The global goal is that setting factors from different origins can be combined into a new, valid data set. For example, with a given, known substrate and speed, all parameters are taken over from another inkjet printing machine and only the DUC data are recalibrated. The underlying model is designed in such a way that the model is constantly adapted to new data, which means that the quality of the prediction is constantly improving; the more the better.

• Die erste Frage ist, welchen Einfluss die Substratparameter, die vom Bediener eingegeben werden, auf die Inklimits für jede Farbe haben (erster Prozessschritt).
• Die zweite Frage ist welchen Einfluss die Substratparameter und die Inklimits auf die Pinning- und Coronaeinstellungen haben (zweiter Prozessschritt).
• Die dritte Frage ist welchen Einfluss die Substratparameter, Inklimits und Pinning- und Coronaeinstellungen auf die DUC- und MNC-Kennlinien haben (dritter und vierter Prozessschritt, kein direkter Zusammenhang).
• Die vierte Frage ist welchen Einfluss alle vorangegangen Parameter und Prozesse auf die Kalibrierkurven haben.

The question for the model to solve the task, i.e. the creation of a suitable configuration of the inkjet printing machine, taking into account the DUC and defective printing nozzles, can be formulated as follows:

• The first question is what influence the substrate parameters entered by the operator have on the ink limits for each color (first process step).
• The second question is what influence the substrate parameters and the ink limits have on the pinning and corona settings (second process step).
• The third question is what influence the substrate parameters, ink limits and pinning and corona settings have on the DUC and MNC characteristics (third and fourth process step, no direct connection).
• The fourth question is what influence all previous parameters and processes have on the calibration curves.

figure 2 shows the process flow schematically. The result that is supplied from the database for the individual cases is a suggestion that the operator must check on the basis of a suitable test print. If this check is passed, the data record is transferred to the database. If the test is assessed as failed, some or all of the points of the current substrate qualification process must be performed, as the case may be. Depending on the staggering, one or more intermediate examinations can take place. If one of the intermediate tests is passed, the data record is rated as good and filed.

1. a) neues Substrat:
   Hierbei würden nur die Parameter, die das Substrat beschreiben vorgegeben; alle anderen Parameter, wie Pinningeinstellung, DUC-Kennlinie etc. werden vom Modell errechnet
2. b) neue Druckgeschwindigkeit:
   Hierbei würde nur eine neue Druckgeschwindigkeit vorgegeben werden; alle anderen Parameter werden von dem Modell errechnet.
3. c) Druckkopftausch:
   Hierbei würden bis auf die DUC-Kennlinien alle Parameter beibehalten, nur der DUC-Prozess müsste einmal durchgeführt werden. Die DUC-Kennlinien würden dann für alle vorhandenen Substrate aktualisiert werden.

For a better understanding, here are some examples of how the answers to this question are implemented in reality:

1. a) new substrate:
   In this case, only the parameters that describe the substrate would be specified; all other parameters such as pinning setting, DUC characteristic etc. are calculated by the model
2. b) new print speed:
   In this case, only a new print speed would be specified; all other parameters are calculated by the model.
3. c) Printhead replacement:
   In this case, all parameters would be retained except for the DUC characteristics, only the DUC process would have to be carried out once. The DUC characteristics would then be updated for all existing substrates.

However, this list is not complete. Any number of other scenarios are possible, which are only limited by the number of characteristic values of the model to be taken into account.

If the head is exchanged, which is quite common, then only a single DUC profile needs to be recorded on a reference substrate. The other profiles are calculated using the model. Since the data model is adaptive, the prediction quality will steadily increase. As a result, the effort for substrate qualification will steadily decrease.

Reference List

Claims (6)

Method for configuring inkjet printing machines using a computer with the following steps: • Categorization of individual setting parameters of the inkjet printing machines by means of a data model created and taught for this purpose by the computer • Saving the categorized setting parameters in data sets in a database • Creation of a new set of setting parameters for configuring the inkjet printing machine for a specific print job from the stored data sets by the computer • Recalculation of the setting parameters for the configuration of the inkjet printing machine when changing certain categorized setting parameters by means of the taught-in data model by the computer • Adaptation of the data records in the database to the changed setting parameters by the computer Method according to claim 1,
characterized,
that the categories for the setting parameters include substrate-specific, printing machine-specific and printing speed-specific categories. Method according to one of the preceding claims,
characterized,
that when at least one categorized setting parameter changes, this is quantified by a user, while the computer recalculates other setting parameters influenced by the change in the at least one categorized setting parameter using the taught-in data model. Method according to one of the preceding claims,
characterized,
that configured data sets of setting parameters of the inkjet printing machine are made available for teaching the data model, as a result of which the relationships and influences between the individual setting parameters are linked by the data model. Method according to one of the preceding claims,
characterized,
that the setting parameters include ink limits, pinning and corona settings as well as characteristic curves for density compensation and the compensation of defective printing nozzles, as well as parameters for the printing speed and the printing substrate transport as well as the printing substrate properties. Method according to one of the preceding claims,
characterized,
that the data records in the database contain historical setting parameters for both the currently used and other inkjet printing machines.

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