Dumc with variable model

Abstract

A method for configuring inkjet printing presses using a computer, comprising the steps of categorizing individual setting parameters of the inkjet printing presses using a data model created and trained for this purpose, storing the categorized setting parameters in data records of a database, creating a new set of setting parameters for configuring the inkjet printing press for a specific print job from the stored data records by the computer, recalculating the setting parameters for configuring the inkjet printing press when certain categorized setting parameters are changed using the trained data model by the computer, and adapting the data records in the database to the changed setting parameters by the computer.

Description

[0001] The invention relates to a method for the automated configuration of an inkjet printing machine.

[0002] The invention lies in the technical field of inkjet printing. US2002 / 080375 discloses a method for configuring inkjet printing machines using a computer.

[0003] When setting up and configuring inkjet printing machines, the targeted control of the individual print nozzles of the inkjet print heads used for the respective inkjet printing process is a key consideration. To achieve the highest possible print quality, the individual print nozzles of the inkjet print heads must be controlled in such a way that minor deviations in the ink output of the individual nozzles, which are always present due to manufacturing or process factors, are compensated accordingly. This is achieved by creating so-called density compensation profiles. Density compensation is also known as DUC or DUMC. Test measurements are used to determine how much the individual print nozzles differ in their ink output when controlled identically.This information can then be used to create the appropriate compensation profile, which regulates the ink output for each individual print nozzle to compensate for these deviations. Another important aspect when configuring the inkjet printing machine is to compensate for defective print nozzles (i.e., nozzles that are printing incorrectly or no longer print at all) (also known as missing nozzles - MN or MNC) in such a way that, even if such defective print nozzles do occur, their impact on the resulting print image in terms of print quality is as little as possible. In most cases, defective print nozzles are compensated for by increased ink output from the neighboring print nozzles, with other neighboring print nozzles usually printing at a correspondingly reduced level to avoid overcompensation.

[0004] These two configuration criteria, density compensation and defective nozzle compensation, target a variety of parameters. Furthermore, both compensation types, especially density compensation, must be optimized for each printhead composition, each printing substrate used, and each printing speed used.

[0005] The disadvantage of current technology is that a test run must be performed for each printhead composition, each printing substrate used, and each printing speed. This creates a significant amount of effort, especially since the inkjet press cannot operate productively during this time. In addition, replacing a single head, changing the ink formulation, the screen, or other essential components of the printing process requires all substrate data sets to be re-entered.

[0006] To solve this problem, a method for compensating position-dependent density fluctuations of print nozzles in an inkjet printing machine by means of a computer is known, wherein the computer creates compensation profiles for the position-dependent density fluctuations across all print heads for all print substrates used, calculates an average profile from these and applies this average profile to compensate for 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 only one print substrate, the computer derives a new average profile from this and the computer calculates and applies the new average profile using the old compensation profiles for the remaining print substrates.

[0007] In addition, a method for compensating position-dependent density fluctuations of print nozzles in an inkjet printing machine by means of a computer is known from the prior art, wherein the computer creates compensation profiles for the position-dependent density fluctuations across all print heads of the inkjet printing machine for all print substrates used and applies the compensation profiles to compensate for the position-dependent density fluctuations in the inkjet printing machine, which is characterized in that the computer determines printing machine- or print head-specific influences and print substrate-specific influencing factors using a generic reference printing substrate, from which a reference compensation profile is created which is area coverage and location-dependent and from which an overall compensation profile is created which is used to compensate for the position-dependent density fluctuations.

[0008] However, these approaches continue to have the disadvantage that the average or reference compensation profiles used do not incorporate all relevant data about the inkjet printing press being configured. Therefore, the resulting compensation profiles are not sufficiently accurate, resulting in significant configuration effort in terms of setup waste and time. Furthermore, these approaches are not suitable for compensating for defective print nozzles.

[0009] 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.

[0010] This object is achieved by a method for configuring inkjet printing machines using a computer according to claim 1.

[0011] A key aspect of the method according to the invention is that the individual setting parameters are assigned to a specific substrate category or machine configuration or similar using the learned data model. This significantly reduces the effort required for substrate qualification, 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 settings, DUC characteristics, etc. are calculated by the model, i.e. whereas previously the entire substrate qualification process was carried out, now a few known parameters are entered. It is important that the data model used is developed from the data sets currently available in the substrate database of the individual machines. This means that if a specific setting parameter is changed, e.g. by replacing an individual print head, the model can automatically calculate the other setting parameters precisely from the model.This eliminates the need to redetermine all combinations of inkjet printhead compositions, print substrate, print speed, or other setting parameters through multiple test runs when replacing a printhead or print substrate. Simply provide the changed parameter to the data model, which then automatically calculates the other setting parameters. Of course, the changed setting parameter must be determined by the user in some way so that the data model can calculate the other parameters. Nevertheless, this approach significantly reduces the effort required to configure the inkjet printing machine compared to the previous state of the art.

[0012] Advantageous and therefore preferred developments of the method according to the invention according to claim 1 emerge from the associated subclaims as well as from the description and the associated drawings.

[0013] If at least one categorized setting parameter is changed, it is quantified by the user, while the computer uses the learned data model to recalculate other setting parameters influenced by the change in at least one categorized setting parameter. As already mentioned, if at least one setting parameter is changed, such as the printing substrate or an inkjet printhead used, these specific setting parameters must be determined by the user. This can be done either through specially conducted test runs or, for example, in the case of a modified printhead, by adopting the density compensation profile from the printhead manufacturer and making it available to the data model by entering it accordingly.The latter can then react to this new data based on its learned state and calculate adjusted setting parameters of the other categories for these changed parameters and make them available to the inkjet printing machine for configuration.

[0014] To teach the data model, configured data sets of setting parameters of the inkjet printing machine are made available to it according to claim 1, whereby the relationships and influences between the individual setting parameters are linked by the data model. This means that, in order to be able to play a corresponding role in the method according to the invention, the data model must of course be taught via the causal relationships between the individual setting parameters, i.e. it must be set up in such a way that it practically knows, for example, which printing speed has what influence on the control of a defective printing nozzle and neighboring printing nozzles with regard to the ink droplet size required for compensation. Only then can it be able to adapt the other parameters accordingly if one or more of the changed setting parameters are changed.

[0015] A further preferred development of the method according to the invention is that the setting parameters include ink limits, pinning and corona settings, as well as characteristic curves for density compensation and compensation for defective print nozzles, as well as parameters relating to the printing speed and the printing substrate transport, as well as the printing substrate properties. These are only the most important and frequently used setting parameters. However, the method according to the invention is not limited to these parameters, but can capture or be expanded to include all other relevant parameters for configuring the inkjet printing machine.

[0016] A further preferred development of the method according to the invention is that the database data records contain historical setting parameters of both the currently used and other inkjet printing machines. In order to train the data model used as accurately and optimally as possible, it is recommended not only to use the historical setting parameters of the currently used inkjet printing machine, but also to access the setting parameters of other inkjet printing machines that are as similar as possible. This is because the more data available for training the data model, the more accurately it can subsequently lead to the calculations of new configuration sets of setting parameters for the inkjet printing machine in question.

[0017] 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 accompanying drawings using at least one preferred embodiment. In the drawings, corresponding elements are provided with the same reference numerals. The drawings show: Figure 1: schematically shows the components involved in the process and their interdependencies Figure 2: schematically shows the sequence of the process according to the invention

[0018] A digital data model is developed from the currently available data sets in the substrate database of the individual printing machines, which makes it possible to assign the individual parameters and / or characteristic values, for example, to a substrate category or a machine configuration. Figure 1 shows examples of the components involved. [Here is a template for Figure 1use!!] 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 printing company, which carries out both processes. Alternatively, however, the data model is at least created and pre-taught before use in the printing company by the provider of the workflow system, of which the data model is a component. In this case, the computers for creating and teaching or further adapting the data model are not identical. However, for the method according to the invention, it is not fundamentally important whether the data model is created or operated on one or more computers. What is important is 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.

[0019] To create the data model, the individual data sets are divided into areas using process knowledge, which substrate-specific machine-specific, e.g. the pressure beam configuration is speed-specific as well as possible combinations of the three areas

[0020] The overall goal is to combine adjustment factors from different sources to create a new, valid data set. For example, given a known substrate and speed, all parameters are taken from another inkjet printing press, and only the DUC data are remeasured. The underlying model is designed to continuously adapt to new data, which continuously improves the quality of the prediction; the more data, the better.

[0021] 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 DUC and defective print 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 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 steps, no direct relationship). The fourth question is what influence all previous parameters and processes have on the calibration curves.

[0022] Figure 2shows the process flow schematically. The result provided from the database for each case is a suggestion that the operator must check based on a suitable test print. If this check is passed, the data set is transferred to the database. If the check is assessed as failed, some or all points of the current substrate qualification process must be performed, depending on the case. Depending on the grading, one or more intermediate checks may be performed. If one of the intermediate checks is passed, the data set is assessed as good and filed.

[0023] For a better understanding, here are some examples of how the answers to this question are implemented in reality: a) New substrate: Only the parameters describing the substrate would be specified; all other parameters, such as pinning settings, DUC characteristics, etc., would be calculated by the model. b) New print speed: Only a new print speed would be specified; all other parameters would be calculated by the model. c) Printhead replacement: In this case, all parameters except the DUC characteristics would be retained; only the DUC process would have to be performed once. The DUC characteristics would then be updated for all existing substrates.

[0024] However, this list is not exhaustive. Any number of additional scenarios are possible, limited only by the number of model parameters to be considered.

[0025] When replacing the head, which happens quite frequently, only a single DUC profile needs to be recorded on a reference substrate. The other profiles are calculated using the model. Because the data model is adaptive, the prediction quality will steadily increase. This will steadily reduce the effort required for substrate qualification. List of reference symbols

[0026] 1Prepare and operate WF computer 2Prepare and operate RIP 3Prepare printing machine DM 4Create rasterized print image 5Prepare print jobs 6aStep: Input substrate parameters 6bStep: Input new print speed 6cStep: Confirm head change 7Step: Calculate data set 8Step: Test print 9Step: Check if test print is OK 10aStep: Requalification 10bStep: Requalification of speed-dependent processes 10cStep: Requalification 11Step: End

Claims

1. Method for configuring inkjet printing presses by means of a computer and a data model when the printing substrate, the print head composition or the printing speed is changed with the following steps: • Categorization of individual setting parameters of the inkjet printing presses by means of a data model created and taught in for this purpose by the computer, • Saving the categorized setting parameters in data records in a database, • Creation (6a, 6b, 6c) by the computer from the stored data records of a new set of setting parameters for configuring the inkjet printing press for a particular print job when the printing substrate, print head composition or printing speed is changed, • Recalculation (7) by the computer, using the taught-in data model, of the setting parameters for configuring the inkjet printing press when the printing substrate, the print head composition or the printing speed is changed, • Performing (8) a test print and checking (9) that the test print is OK, and • Adaptation (10a, 10b, 10c) by the computer of the data records in the database to the changed setting parameters, characterized in that the setting parameters relate to the printing substrate, the print head composition or the printing speed, and the categories for the setting parameters comprise print substrate-specific, printing press-specific and printing speed-specific categories, when at least one categorized setting parameter is changed, this is quantified by a user by executing test runs or by undertaking a density compensation profile, while the computer recalculates other setting parameters influenced by the change in the at least one categorized setting parameter by means of the taught-in data model, and configured data records of setting parameters of the inkjet printing press are made available to the data model for teaching the data model, whereby the correlations and influences between the individual setting parameters are linked by the data model.

2. Method according to claim 1, characterized in that the setting parameters comprise ink limits, pinning and corona setting as well as characteristic curves for density compensation and compensation of defective printing nozzles, as well as parameters relating to the printing speed and the printing substrate transport as well as the printing substrate properties.

3. Method according to one of the preceding claims, characterized in that the data records of the database comprise historical setting parameters of both the inkjet printing presses currently in use and other inkjet printing presses.

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