Method for operating a printing material processing machine by applying a varnish consumption prediction

Abstract

A method for operating a printing material processing machine by using a computer includes acquiring print job parameters from print jobs for the printing material processing machine and machine parameters by using the computer, evaluating the acquired parameters to determine the machine state by using the computer, and requesting and providing fluid consumable materials for optimizing the operation of the machine on the basis of the determined machine state by using the computer. Maintenance measures carried out on the machine are optimized on the basis of the determined machine state, by using the computer.

Claims

1. A method for operating a printing material processing machine, the method comprising the following steps: using a computer to acquire print job parameters from print jobs for the printing material processing machine and from machine parameters; selecting the print job parameters and the machine parameters as an area coverage of the print job, or a corresponding printing time, or a job length, or a temperature in the printing material processing machine or an engraved roller type being used; using the computer to evaluate the acquired parameters to determine a machine state; using the computer to request fluid consumable materials for optimizing operation of the machine based on the determined machine state to carry out a consumption prediction of the fluid consumable materials by using a regression model; providing varnish for a varnishing unit, ink for a printing unit or dampening solution for a dampening unit of the printing material processing machine, as the fluid consumable materials; and using the computer to carry out maintenance measures on the machine being optimized based on the determined machine state.

2. The method according to claim 1, which further comprises causing the computer to use a linear regression model or a self-learning model for the regression model for the consumption prediction of the fluid consumable materials.

3. The method according to claim 2, which further comprises using a support vector machine for the linear regression model or self-learning model.

4. The method according to claim 1, which further comprises selecting the optimized maintenance measures as a performance of washing cycles and varnish or ink changes in the machine, in order to avoid drying out and accumulation of varnish or ink residues.

5. The method according to claim 1, which further comprises carrying out an examination for possible leakage and pump monitoring in the determination of the machine state by the computer.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

(1) FIG. 1 is a diagram showing an example of a result of a consumption prediction in the form of a time to change a container, depending on temperature;

(2) FIG. 2 is a diagram showing an example of a result of a consumption prediction in the form of a consumption in liters, depending on temperature; and

(3) FIG. 3 is a block diagram showing a structure of a logistics system between a printing press and a storage area or stockpile.

DETAILED DESCRIPTION OF THE INVENTION

(4) The invention provides that, firstly, data from the prepress stage relating to the respective print job parameters and, secondly, data from the stock in storage about the removal of the fluid consumable materials, primarily varnish but also ink or dampening solution, is processed, with the aim of producing an accurate consumption model.

(5) Referring now to the figures of the drawings in detail and first, particularly, to FIG. 3 thereof, there is seen a block diagram of the structure of a logistics system between a printing press 10 and a storage area or stockpile 7. A computer 6, which has access to the data of all of the components involved, acquires all of the important consumption data of the printing press(es) 10 being used, and the storage data. In the case of varnish as a fluid consumable material, varnish is used in a varnishing unit 9 of the printing press 10. Once the supply is exhausted, a further varnish container 8 from the storage area or storeroom 7 must be organized in order to appropriately fill up the storage area of the varnishing unit 9 again. The varnish consumption of the printing press 10 depends on various print job parameters, which also means the machine parameters of the printing press 10 to be used.

(6) Important print job parameters are, for example, the subject occupancy in the form of the percentage area coverage, the format, the printing substrate and in this case, in particular, the surface condition, the setting behavior and the absorption behavior, the varnish/ink/dampening solution type with respect to manufacturer, type, name, batch, the pressure, the varnishing plate in the case of varnish, or, in the case of ink, the printing plate with respect to manufacturer, type, name, batch, the engraved roller being used, for example with reference to the cell size or the temperature in the varnishing/ink/dampening unit, and the printing speed.

(7) Necessary data from the stock in the storage area or warehouse relate primarily to the removal time and quantity for new varnish containers 8 or ink or dampening solution containers and the corresponding varnish, ink or dampening solution grade, which also in this case include data relating to the manufacturer, type, name, batch, for a correct assignment.

(8) Starting from the assumption that a new container is always removed when the old container 8 is empty, the overall varnish consumption can thus be determined over a relatively long time period. Possible quantities of residues or losses are thus included, but this is intended, since the actual varnish consumption is to be determined.

(9) The modeling is most illustrative if the sum of the varnish consumed, for example over a year, is divided by the sum of the area printed in this year. This is the average varnish consumption per unit area printed. This will include all of the quantities of varnish: the varnish on the paper, varnish residues in the machine 10, varnish residues in the container 8, quantities of varnish washed away, etc. The same also applies to ink and dampening solution but, for simplicity, mention will be made only of varnish below.

(10) This simple conceptual model becomes more detailed and improved step-by-step by applying big-data solutions, so that at the end a computer-assisted model is created which, depending on the aforementioned parameters, depicts the varnish consumption as accurately as possible for an individual printing press 10.

(11) In detail, a mathematical regression model which couples the relationships between the print job parameters and the real varnish consumption is preferably adapted by a computer 6. The input variables are the print job parameters, which can be of a continuous nature (subject occupancy, job length, temperature) or categorical (engraved roller type). The output variable is the storage removal of the varnish, preferably in liters (L). Alternatively, in a further preferred construction variant, if sufficient data is present, a machine learning algorithm, in particular in the form of a support vector machine, can also be used by the computer 6.

(12) A basic precondition for the creation and application of the consumption model is the access to the data of the stock in the storage area and the print job parameters over a long time period and for a large machine group, so that a suitably large data base (big data) is available.

(13) By using such a model, the computer 6 can firstly optimize the stock in the storage area. Furthermore, the varnish consumption prediction is important in order to determine leakages, to be able to carry out pump monitoring and to improve pump utilization and construction for future development.

(14) In addition, use can be made of the knowledge in order to optimize washing cycles and varnish changes in the printing press 10, and to avoid the drying out and accumulation of varnish residues in the varnishing unit 9.

(15) The method according to the invention will be explained in more detail below in its preferred structural variant, by using a fictitious example with appropriate data.

(16) With respect to the data and preconditions from which the consumption model was created, the following assumptions are made for the example: 1. Three different engraved roller types with different varnish consumption quantities, which have a different temperature dependence, are available. 2. An unknown quantity of leakage is assumed. 3. 100,000 print jobs with job lengths of 100 to 10,000 with a known date/time of day for the processing are available. 4. For the processing of these print jobs, 6000 removals of varnish from the storage area with a known date/time of day are forecast by the model created.

(17) The data is managed by the computer in a database, wherein the data is preferably organized as follows:

(18) Print Job Data/Machine Data:

(19) TABLE-US-00001 Date Time Length Coverage Temperature RW Aug. 3, 2019 11:33:43 5082 0.83734 30.534013 V1 Aug. 3, 2019 11:48:07 5411 0.153003 36.07285 V1 Aug. 3, 2019 12:02:31 9182 0.557279 33.62523 V1 Aug. 3, 2019 12:16:55 3269 0.405539 30.160181 V3 Aug. 3, 2019 12:31:19 8841 0.862206 22.781552 V1 Aug. 3, 2019 12:45:43 7104 0.496435 25.569903 V2 Aug. 3, 2019 13:00:07 5634 0.441237 31.200093 V2 Aug. 3, 2019 13:14:31 5983 0.669666 33.003459 V2 Aug. 3, 2019 13:28:55 814 0.848586 29.058173 V2 Aug. 3, 2019 13:43:19 9006 0.548472 33.48739 V1

(20) Storage Data Regarding Varnish:

(21) TABLE-US-00002 Date Time Aug. 3, 2019 11:33:43 Aug. 3, 2019 13:28:55 Aug. 3, 2019 16:43:19 Aug. 3, 2019 19:11:10 Aug. 3, 2019 22:43:59

(22) The varnish data is prepared by the computer 6 in such a way that a realistic consumption can be determined from the data. Every time the volume of varnish in the tank falls below a value of 25 I, a “refill” is triggered. The input value for the regression or machine learning algorithm is the time between two varnish refills.

(23) For this case, the consumption prediction model, both in the form of a classic linear regression and in the form of a self-learning algorithm, such as a support vector machine or an artificial neural network, is easily capable of concluding the real varnish consumption from that data. The three lines illustrated in FIG. 1 are consumption curves 1, 2, 3 for the three different engraved roller types. The lines themselves indicate the real consumption values 4 without quantities of leakage and losses. The data points in the consumption curves 1, 2, 3 are the modeled consumption values 5 supplied by the model, i.e. the varnish refill times. Both the real consumption values 4 and the modeled consumption values 5 in the present example depend on the temperature in the varnishing unit. In this case, according to the values used, the temperature ranges lie in the range from 20 to 40° C. Given other data in other embodiments, corresponding other dependencies of course exist.

(24) In FIG. 2, the same fact is illustrated once more in the form of the varnish consumption in liters, depending on the temperature.

(25) The following is a summary list of reference numerals and the corresponding structure used in the above description of the invention: 1 Time/temperature consumption curve for first engraved roller 2 Time/temperature consumption curve for second engraved roller 3 Time/temperature consumption curve for third engraved roller 4 Real consumption values 5 Modeled consumption values 6 Computer 7 Storage area 8 Varnishing unit container 9 Varnishing unit of a printing press 10 Printing press