Abb Patent Gmbh

Abb Research Ltd.

Abb Ag

Dietmar Eickmeyer

Gunter Boerner

Gunter B&ouml;rner

Horst D&ouml;rlich

Gunter BÖRNER

Marc Heye

A method for restarting a robot after a premature interruption of a processing program, which controls it, at an interruption point. The robot has an atomizer apparatus for automatic coating of workpieces, and the processing program is used to preset a nominal movement of the atomizer apparatus with respect to a workpiece, which is to be coated and can be moved by means of a feed device, and to preset the associated nominal atomizer parameters. After the interruption of the processing program, the atomizer apparatus is first of all moved to a restarting point, which is located on the nominal movement path and is located ahead of the interruption point. After this, the atomizer apparatus is then moved on the nominal movement path, corresponding to a predeterminable velocity function, to a transfer point which is located on the nominal movement path and is located behind the interruption point. The atomizer apparatus is switched on again between the restarting point and the transfer point. The nominal atomizer parameters which are provided by the processing program are matched and are applied at least once, between the restarting point and the transfer point, to the respective current velocity of the atomizer apparatus, in relation to the nominal velocity preset, and all of the nominal movement parameters and nominal atomizer parameters in the processing program are used when the transfer point is reached.

Method for restarting a robot

A method for determining spraying parameters for controlling a paint-spraying apparatus using a spraying agent is disclosed. A known spray pattern is provided which has been determined by means of known spraying parameters for the use of a first spraying agent. A provisional spray pattern is calculated using the known spraying parameters and the characteristics of a second spraying agent. The known spraying parameters are altered in order to acquire changed spraying parameters which yield a further spray pattern. The changed spraying parameters are altered to the point where the further spray pattern is similar to the known spray pattern within a similarity criterion. The changed spraying parameters corresponding to the further spray pattern are intended as spraying parameters for the second spraying agent and are provided to the paint-spraying apparatus whenever the second spraying agent is used. The spraying parameters comprise a plurality of air currents which influence the spraying behavior of the paint-spraying apparatus.

Method for determining spraying parameters for controlling a paint-spraying apparatus using a spraying agent

A method for determining of spraying parameters for controlling a painting appliance which sprays and is moved over an area to be painted, in particular a robot with a painting application. A known spraying map is produced, using known spraying parameters and paint amount, for a predetermined movement speed of the painting appliance, and a paint amount is matched to a new movement speed in comparison to the predetermined movement speed. Furthermore, new spraying parameters are calculated for the adapted paint amount, while maintaining a spraying map which is similar to the known spraying map.

Method for determination of spraying parameters for controlling a painting appliance which uses spraying means

A method for determining a layer thickness distribution in a paint layer produced during paint spraying after inputting specific spraying parameters into an electrostatically based paint spraying device. A data processing device sets up and uses a phenomenological mathematical model of a quasi-stationary three-dimensional spray pattern. Specific parameters, such as an angle of rotation of electrodes and a rate of movement of the spraying device are input into the phenomenological model as fixed input parameters. In addition, real physical input parameters such as paint volume, directing air data and a voltage value, whose influence on the spraying result is not accurately known, are fed to an artificial neural network. The neural network having been previously trained using real input data such as a configuration of the spraying device, a paint type, operating parameters, and measured values of the layer thickness distribution. The neural network carries out a conversion of the input parameters into model input parameters which are fed to the phenomenological model. Spray patterns formed by the phenomenological model are integrated in a further functional unit as a function of movement data of the spraying device which are contained in the input parameters to form the overall paint layer which is output.

Method and device for determining the layer thickness distribution in a paint layer

An insulation piece for a pipeline conveys electrically conductive liquids from a point that is at a ground potential to a point connected to a high voltage. The liquid is conveyed discontinuously by portion-by-portion transport by the carriage flow of the liquid being interrupted. The insulation piece is periodically free of liquid, corresponding to the interruptions in the carriage flow. The insulation piece is a hollow body, and its unobstructed width is equal to or greater than that of the pipeline. Connecting pieces for connecting to the pipeline are provided on the end faces of the insulation piece, which are closed in places by terminating elements, and which connecting pieces are of the same diameter as the adjacent pipeline. The connecting pieces pass through the terminating elements, rest against them to form a seal, and each have a toroidal-form attachment on the outside at their inner end.

Insulation piece for a pipeline

A method for determining spraying parameters that are suitable as input values for a paint spraying unit that can electrostatically charge a liquid paint. In this case, at least one artificial neural network is used to determine the spraying parameters, an output of such a neural network being available for each spraying parameter. A suitable number of real measured values are fed to the one neural network or a plurality of neural networks as input values, initially in a learning phase. The measured values further contain associated real spraying parameters in addition to a paint thickness distribution in the form of discrete values. Input values are fed to the one neural network or a plurality of neural networks in the application phase. The input values being the result of an analysis of the paint thickness distribution of a targeted, that is to say prescribed, spraying result.

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Friedberg, Germany

Dietmar Eickmeyer