Labeling machine and method for configuring a labeling machine

1 . A labeling machine for the food industry, in particular for the drinks industry, wherein the labeling machine comprises:

a plurality of modules at least for labeling and/or printing on containers;

a control unit for configuring the plurality of modules in accordance with configuration parameters for the plurality of modules, wherein:

the configuration parameters are created by means of an artificial intelligence, AI, module; and

the control unit transmits the configuration parameters created by the AI module to the corresponding modules in order to configure the modules,

wherein the labeling machine is configured to:

transmit information about the operation thereof and the configuration thereof back to the AI module, and

receive optimized configuration parameters from the AI module, the optimized configuration parameters having been optimized by the AI module.

2 . The labeling machine according to claim 1 , wherein the plurality of modules comprises at least one of the following modules:

a servo drive for rotary plates,

a long-stator drive, in particular for transporting containers,

a carousel,

a drive for adjusting a height of a carousel upper part,

a drive for adjusting a height of and/or for radially adjusting labeling and/or printing units,

a drive for adapting label transfer members, print heads and/or sensors.

3 . The labeling machine according to claim 1 , wherein the configuration parameters created by the AI module are created on the basis of data from configured labeling machines.

4 . The labeling machine according to claim 1 , wherein the configuration parameters created by the AI module are created on the basis of operation data, and wherein the operation data comprises at least one of the following: CAD data for a sample container, a scan of a container, information on labels, information on adhesives to be used, information on the material of the containers, information on a container closure.

5 . The labeling machine according to claim 1 , wherein the control unit also automatically makes at least one of the following adjustments:

selecting labeling and/or printing units;

positioning the labeling and/or printing units on a carousel or a long-stator drive;

registering and synchronizing a unit controller with a main machine controller;

configuring a process of applying adhesive according to a label position and/or a label contour;

configuring a direct printing process, wherein parameters for one or more print heads are configured for printing on a container.

6 . A method for configuring a labeling machine in the food industry, in particular for a labeling machine in the drinks industry, wherein the method comprises:

receiving input data relating to at least the labeling and/or printing on a container, wherein the input data comprise parameter datasets for configured labeling machines and operation data;

creating, by means of an artificial intelligence, AI, module, configuration parameters for the labeling machine, wherein the configuration parameters are based on at least the received input data;

applying the configuration parameters in order to configure a plurality of modules of the labeling machine, wherein the plurality of modules are designed at least for labeling and/or printing on containers;

transmitting information about the operation thereof and the configuration thereof back to the AI module, and

receiving optimized configuration parameters from the AI module, the optimized configuration parameters having been optimized by the AI module.

7 . The method according to claim 6 , wherein the creation of configuration data also comprises:

creating a parameterization model on the basis of the received input data; and

training, by means of the AI module 110 , the parameterization model on the basis of the parameter datasets of configured labeling machines, wherein the configuration parameters are derived from the trained parameterization model.

8 . The method according to claim 6 , wherein the plurality of modules comprise at least one of the following modules:

a servo drive for rotary plates,

a long-stator drive, in particular for transporting containers,

a carousel,

a drive for adjusting a height of a carousel upper part,

a drive for adjusting a height of and/or radially adjusting labeling and/or printing units,

a drive for adapting label transfer members, print heads and/or sensors.

9 . The method according to claim 6 , wherein the operation data comprise at least one of the following: CAD data for a sample container, a scan of the container, information on labels, information on adhesives to be used, information on the material of the containers, information on a container closure.

10 . The method according to claim 6 , wherein the configuration parameters are configured to automatically adjust at least one of the following settings:

selecting labeling and/or printing units;

positioning the labeling and/or printing units on a carousel or a long-stator drive;

registering and synchronizing a unit controller with a main machine controller;

configuring a process of applying adhesive according to a label position and/or a label contour;

configuring a direct printing process, wherein parameters for one or more print heads are configured for printing on the container.

11 . The labeling machine according to claim 1 , wherein the AI module comprises a simulation unit and an evaluation unit, wherein the control unit creates the configuration parameters are by means of the AI module based on feedback relating to the evaluation from the evaluation unit to the simulation unit, and wherein the simulation unit simulates the labeling machine in a virtual simulation environment and applies configuration parameters for configuration simulated modules to generate simulated output which is evaluated by the evaluation unit wherein the simulation unit applied configuration parameters for configuring the simulated modules to the simulated labeling machine, and wherein the evaluation unit sends the evaluated data back to the simulation unit for optimization.