CN112236798A

CN112236798A - Method for quality assurance in the case of producing products, and computing device and computer program

Info

Publication number: CN112236798A
Application number: CN201980037400.0A
Authority: CN
Inventors: K·莫根施特恩; E·克拉尔
Original assignee: Volkswagen AG
Current assignee: Volkswagen AG
Priority date: 2018-06-05
Filing date: 2019-05-17
Publication date: 2021-01-15
Also published as: DE102018208782A1; WO2019233735A1

Abstract

The proposal relates to a method for quality assurance in the case of a production product which is composed of a plurality of components (112,122,132,142,152). In this case, the partial products formed in the case of production are measured at one or more measuring locations (114,124,134,144,154), wherein the measurement data are collected in a database. The method is distinguished by the fact that an overall view (300) with views of the partial product is calculated from the measurement data, in which problem areas (380,390) with significant manufacturing deviations at the partial product are highlighted by color markings. The proposal also relates to a computing device (205) and to a correspondingly designed computer program.

Description

Method for quality assurance in the case of producing products, and computing device and computer program

Technical Field

The present proposal relates to the technical field of quality assurance in the context of manufacturing products. Here, the different components/parts assembled in the case of manufacturing are measured for control. The influence of the manufacturing variations is made visible by means of a visualization tool.

Background

In the case of complex manufacturing, many different measurement systems of different suppliers providing measurement data are used. The companies that provide the measurement system always work with their own visualization tools. These visualization tools have been used in part since long, and have hardly allowed the combination of other measurement data for forming an overall view on the effects in production.

DE 10236843 a1 discloses an apparatus and a method for monitoring at least one manufacturing facility, which includes data acquisition means in order to acquire process information and product information during the manufacturing process. This information is stored in a database. At least one view for a user group for which a classification into at least two categories is carried out can be generated by the evaluation unit.

A method for quality assurance is known from DE 10242811 a1, in which a measuring station is integrated into the production process. The actual value of the functional dimension (functionionma β en) is measured at the component and the measured actual value is compared with the theoretical value. Which enables a static evaluation. In case of a higher or lower tolerance value, the production process is interrupted. Increased process reliability is made possible by an additional comparison of the deviation with the action limit. Via these quality control loops, the development of negative trends in dimensional deviations is reported and planned corrections of the manufacturing facility are made possible before producing waste pieces.

DE 19917003 a1 discloses a method for measuring components of at least one component group, which accordingly have a plurality of interesting features.

The measurement data is provided in electronic form in a data structure of a database, and it enables electronic assessment regarding components and features.

These known solutions however involve independent applications that can be used for the manufacturer of the measuring system and are not suitable for complex production with a large number of different measuring systems. In particular, there is a need in the field of visualization of measurement data and tolerance deviations thereof for different experts monitoring production processes in the case of the manufacture of complex products. This is recognized in the case of the present invention.

Disclosure of Invention

The invention is based on the object of finding such a way. This object is achieved by a method for quality assurance in the case of producing a product according to claim 1, a computing device for use in the case of the method according to claim 10 and a computer program according to claim 11.

The dependent claims contain advantageous refinements and improvements of the invention in accordance with the following description of these measures.

The solution consists in the collection of the different measurement data in a central database and in the use of an own visualization tool which edits and displays the measurement data and its deviations in a special form. The resulting platform is based on the knowledge and experience of the manufacturing enterprise in terms of diversity, complexity, efficiency and quality requirements.

This solution makes it possible for the customer/user to appropriately process the generated measurement data and to make it available to the user in the necessary graphical representation, evaluation, weighting and data link, independently of the respective measurement system provider. The necessary process monitoring can be automatically actively opened at very little effort from each computer location of production in the case of a model change or a new start of production. It is also possible to react to changes in the production process or in priority automatically immediately without matching/changes at the installed measurement technology acted on by the system supplier. Software solutions have been developed and implemented in addition to existing measurement system tools. Which is a separate application that works with data in the database. Therefore, matching the own measurement system visualization tools of the respective measurement system providers is not necessary.

The proposal relates to a method for quality assurance in the case of a production product which is composed of a plurality of components in the case of production, wherein the formed sub-products (Teilprodukte) are measured at one or more series of measuring positions, wherein the measurement data are collected in a database. This method is distinguished by the fact that an overall view with different views of the sub-product is calculated from the measurement data, wherein problem areas with significant (sometimes called relevant) manufacturing deviations in the overall view have been highlighted by color marking. In a certain case, the overall view may relate to the illustration of the individual sub-products in a visually clear form.

The following references to components are synonymous with one another. In the case of the production of products, it is always strictly necessary to distinguish whether a component means an assembly that is no longer decomposable or a component consisting of a plurality of components, but this does not play a significant role for the proposal. The method according to the invention can be used not only in the case of producing a product consisting of a single component but also in the case of producing a product consisting of a plurality of parts or of parts and components. The intermediate products formed in the assembly station in the case of production are correspondingly referred to as sub-products.

The staff monitoring the production process in production thus obtains a satisfactory visualization of the problem points with the impact on the downstream area. Furthermore, all measurement data is aggregated on one view and may be weighted accordingly. The storage of the measurement data in the central database has the advantage that the measurement data can be billed to each other with each new measurement. Thus, the overall view is updated over and over again. Thus, problems with components/parts/sub-products and their effects on the product are identified early, even before the finished product is formed.

It is furthermore advantageous when a plurality of measurement data sets for different model variants (modelvarients) of a product are collected in the database and the overall view is for a selected model variant. It is also advantageous when it is likewise shown in the overall view which model variant is present. The user may then select a model variation and invoke the corresponding overall view. By aggregating all measurement data of all sub-products, the measurement data can be compared with each other. Thus, an exception (Gegenl ä ufigkeit, sometimes referred to as a reversal) may be identified before these components/parts are installed. The advantage is that even when all measurement points are within their own tolerance of each other, trend deviations (trends) can be identified and compared/calculated across stations. Warnings or alarms may be issued early.

The measured manufacturing variations are used in order to calculate the problem area. For this purpose, it is advantageous if the manufacturing deviations are weighted according to the position in the product and the customer dependency associated therewith. It is thus possible to reduce the frequency of interruptions somewhat in the manufacture, when the manufacturing deviations relate to ranges that are not critical for the customer.

It is also advantageous if the individual views are optionally shown in the overall view and if after selection a detailed view for the partial product is calculated in which the production deviations are visualized symbolically at the location of the problem area. These symbols can be designed such that they can be intuitively interpreted by the user.

A particularly well-defined symbol is a directional arrow, the direction of which indicates the direction of deviation of the component from the standard and the length of which visualizes the magnitude of the deviation from the standard in this direction. The symbol is displayed at the location of the problem area of the sub-product.

In addition, how strong the deviation is can be highlighted by the color mark. The color red is chosen for particularly strong deviations. The color green is selected in case of a deviation close to the tolerance range. The corresponding halftone is provided for the other deviations.

It is possible to identify trends in the production of pieces of waste which soon occur by analyzing successive measured values. It is therefore advantageous for the method if successive measured values are evaluated in order to identify a trend deviation early. In this case, it is advantageous if, for the purpose of identifying trend deviations, the manufacturing deviations and their tolerances are correlated with one another, wherein, in the case of identifying trend deviations, corresponding color markings are implemented in the overall view and/or the detailed view and/or warning information is calculated in which the corresponding problem areas are identified.

It is also advantageous when a status bar (statusleist) is displayed with respect to the selected detailed view, in which a view of the sub-product is shown that functions with the sub-product of the detailed view. The view in the status bar may be selected for reaching to the corresponding detailed view. The advantage is that quality problems can be found very quickly.

In order to determine the existing manufacturing deviations in which the quality criteria are still followed, it may furthermore be advantageous when a zero model (Nullmodell) is calculated in which the actual state is virtually set to zero with the existing manufacturing deviations for one or more sub-products, and in which preferably smaller tolerances are determined for this state. In this way, complex control measures can be delayed first until a greater deterioration occurs, wherein this deterioration can be determined early in the measurement data, since smaller tolerances are determined, which are taken into account in the evaluation.

The corresponding advantages apply to a computing device designed accordingly for carrying out the steps of the method. This applies in the same way to correspondingly designed computer programs.

Drawings

Embodiments of the invention are shown in the drawings and are described further below with the aid of the drawings.

Wherein:

FIG. 1 shows a schematic view of a production line with supply of components and series of measurements of parts in multiple stations of the production process;

FIG. 2 shows a current exemplary view of a measurement point of a component with a deviation display;

fig. 3 shows a diagram of the multiplicity of measured value diagrams, when in the production case different model variants are produced on one production line;

FIG. 4 shows a general view with problem areas thereon highlighting multiple views of different components assembled in manufacture;

FIG. 5 shows a flow chart of a program executed on the production monitoring computer for calculating an overall view;

FIG. 6 shows a first example of a detailed view of an inserted component with symbols for displaying manufacturing variations in problem areas;

FIG. 7 shows a second example of a detailed view of an inserted component with a symbol for displaying manufacturing variations in a problem area; and is

Fig. 8 shows a representation of a component with measurement points outside the tolerance range, wherein the measurement points can be monitored more precisely by a provisional definition of a zero model.

Detailed Description

This specification illustrates the principles of the disclosure in accordance with the present invention. It is therefore obvious that, as will be apparent to the person skilled in the art, different arrangements are conceivable which, although not described in detail here, nevertheless embody the principles of the disclosure according to the invention and are likewise to be protected in their scope.

The measurement model describes an object, which is measured in a measurement device. The measuring device can measure different components. If different components are to be measured in the measuring device 4, the measuring device 4 generates different measuring models.

The so-called string-measuring technique consists in that, in the case of a production product, only a sample is taken and then measured or even all produced components or already assembled components are measured during the continuous production. Thus, the quality of production can be continuously monitored. In the case of the method of the series measurement technique, certain features of the intermediate product produced in production are then measured in series. Typically, such features important to the assembly of the product are measured. These features are often also referred to as priority points in production technology. These measurements are graphically shown to the user. The user may be a machine operator, a lead shift coordinating a production team, or a production engineer up to a production leader.

Fig. 1 shows in block diagram form a typical pipeline in the case of mass production of more complex products. This example relates to body manufacturing for cars. There are different assembly stations 110,120,130,140,150 in which products are increasingly being finished. One or more components/parts are replenished at each station. The production flow is from left to right. This is illustrated in the example of body manufacturing. In the station 110, a bottom carrier (so-called bottom structure) is welded with the bottom part 112. The bottom part 112 is manufactured from a component not further shown. The base part 112 is then conveyed to the assembly station 110 via the component supply. Additional components 122,132,142,152, typically body components, various front and rear components, and side components, are assembled at additional assembly stations 120,130,140, 150. The measurement of the formed sub-products in the series of measuring units 114,124 to 154 takes place after each assembly station 120,130,140, 150. The measured values are transmitted to the central server 200 via a corresponding network connection and collected in a database installed on the server. Connected to the server 200 is a production monitoring computer 205, which accesses the database. Additional production monitoring computers may be installed at all of the assembly stations 110 to 150, for example. Such a production monitoring computer may additionally be installed at another location, for example in the office of a production engineer, production leader or the like. All production monitoring computers 205 access the database 200. The production monitoring computer 205 can call the measurement data in the database there via a corresponding network connection. The measurement data is typically stored in a database in a uniform format and may be provided with different metadata. Such data may be: component type, serial number, time, date, etc.

The measurement data acquisition is implemented solely in a program executed by the measurement location computer. Here, the data is passed in structured form to the server 200 on which the database is stored. The measurement data may be formatted and stored as described in document DE 19917003 a 1.

In the assembly station 120, the side member interior 122 is assembled at the base structure 110. The side parts are produced from corresponding components which are likewise not further shown. Since this is an important component which must be precisely fitted to the base structure, this component is likewise measured in series in its own measuring unit 115 before it is fed to the next assembly station 120. During the running production process, exactly the dimensions of the component are compared with the dimensions of the base structure sub-product assembled in the assembly station 110. This is a problem for the next assembly step when, for example, opposite deviations are determined at both components/parts. By passing the data for all of the measured cell strings to the server 200, a simulation of the virtually manufactured product can be calculated by the product monitoring computer 205 before it is actually formed in production. The problem is then already represented in the simulation calculation by a corresponding visualization and measures can be taken quickly before it leads to the production of a waste part. The same procedure is also carried out in the case of assembly in the assembly station 130. Where the side member exterior is fitted. The side part exterior is measured in the measuring unit 125 and then, in turn, an immediate control by analog calculation is effected. The comparison of the measured values between the series of measuring

units

115 and 114 or 125 and 124 is illustrated by dashed arrows.

Fig. 2 shows a typical example of how measurement data is currently displayed. For the user, the overall view shows all the measurement points 301,302,303,304 of the measurement workstation. This view represents the assembled sub-product that was just measured in the selected measurement unit. The view is updated with each subsequent measurement. If measurements of the same or different sub-products are produced in the displayed measuring unit, the new sub-product is shown accordingly.

This problem becomes apparent in the case of typical mass production with short production times. In addition, different vehicle model variants a, B, C are installed in a mixed manner in the production line. A typical production process is as follows:

it is underlined that the body for the vehicle in the model variant B is currently measured in the measuring unit 124. The beat for this string of measurements is 60 seconds. If a problem should be identified at the body, the machine operator has just a 60 second beat in order to locate the problem. The measurement results for the following vehicle are shown at the latest after 60 seconds. In the illustrated case, a vehicle of type a follows as the following vehicle.

In the case of car production, there are many different measurement models which can be considered on the basis of the diversity of the variants. Thus, for example, different measurement models can be considered for the vehicle body production 32. A schematic representation of the different measurement models can be seen in fig. 3. After each measurement model, up to 60 further different views of the measurement point with a detailed description of the vehicle are inserted. Finding the right view in a shorter beat in order to locate manufacturing problems is very problematic. In the limit case, it is possible that the vehicle has been produced 1500 times until a complete defect map is identified. This can cause significant post-processing costs, which increase production costs.

Fig. 4 shows an example of a proposed form of measurement data display. Instead of all measuring point views, an overall view 300 with different views for the sub-products being manufactured on the production line in the production case is generated. Additionally, views for larger components that are also being measured may be displayed.

The view shown in fig. 4 relates to a view 355 of the bottom structure, four different views 305,340,350,360 of the superstructure composed of a plurality of components, three different views 320,325,335 of the front part of the vehicle body, two different views 310,330 of the bottom part and two views 345,315 of the rear part. In the case of the view 305 of the upper structure, two different problem areas 380,390 are displayed by color highlighting. Red means that it is the most serious problem in which manufacturing tolerances are exceeded. Countermeasures should be taken because the production of waste pieces is approaching. A symbol in the form of a directional arrow 396 is also visible, which illustrates the direction of the deviation for the problem area 390. A second directional arrow may also be displayed for deformation in the other problem area 380. On the right side of fig. 4, different model variants are listed outside the general view 300. The selection elements for the different model variants are designated by the

reference numerals

10,20,30, 40. The overall view 300 is always for the currently selected model variant.

In order to generate this overall view, a comprehensive software solution is necessary. Fig. 5 shows a flow chart of a procedure for calculating an overall view therefrom. The process is executed in the production monitoring computer 205 and begins in process step 180. The measurement data is analyzed in a process step 182.

In the case of measurement data analysis, the measurement data for the selected model variant is called up by the database. These measurement data are compared with the tolerances attached thereto. The definition of the problem area is achieved in the case of measurement data falling out of the tolerance range. Then, in a program step 184, a total view is calculated in which the problem area is highlighted in a color mark in the appropriate view. FIG. 4 shows an example of a highlighted view 305 with two

problem areas

380, 390. Staff in production get a quick overview of the problem area with the general view. To obtain further information on the problem area, it selects view 305 in the overall view. The program queries whether a detailed view is selected, query 186. When not, the program jumps back to the beginning of the program. In other cases, a detailed view is calculated in the program step 188 in which more precise information about the problem area is displayed at the location of the problem area. Two detailed views are shown in fig. 6 and 7. The same rear view as view 305 in fig. 4 can be seen in fig. 6. At the location where the problem area 380,390 is identified, the directional arrow 392,394 is located. The length of the arrow gives information qualitatively about the magnitude of the deviation. The exact size of the measured deviations is likewise specified in this way. Here, the arrow direction illustrates the direction in which the manufacturing deviates from the standard deviation. The directional arrows 392,394 should differ from the illustrated form shown, preferably in three-dimensional form. The 3D illustration of the directional arrows is important in order to make it heuristically visible to the production staff which form of deviation is. Often, this 2D representation is not sufficient, since then depth information is missing. For example, the length offset in the case of the rear view according to fig. 6 can only be shown poorly in the case of no 3D illustration. Alternatively, the entire view may be rotated in three dimensions. For this purpose, it is necessary, however, that the respective 3D model (as it is provided by the CAD program) be stored in the case of execution. However, the production monitoring computer must then calculate the model. This is precisely a prerequisite for an efficient computer, which becomes less preferred for production monitoring for cost reasons. Likewise, the operation is then more complex, which is not necessarily desirable in the case of production monitoring.

Other views of the sub-product are shown in the status bar 400 of fig. 6. By clicking, these views can be selected quickly.

The measuring point diagram is visible in fig. 6 in a detailed view. This measuring point map gives employees in production a quick overview about the development of critical measuring points in which deviations are identified. The chart shows the user not only simply the last measured value, but also all the individual measurements of the last X components. Trend trends can be easily determined at such a graph, as identified in fig. 6 at the measurement points in the middle portion of the graph. Component fluctuations can likewise be easily detected at such a measurement point course. The batch of measurements shown in the diagram can be freely configured. Thus, the user can act on demand and just make his inference and take action.

In fig. 7, a detailed view of the bottom structure is shown, in which the wheel house curvature is likewise highlighted by

directional arrows

382, 384. The direction of the arrow again illustrates the direction of deformation. The direction of the deviation can likewise be roughly determined at the sign of the size. It is common in the manufacture of vehicle bodies to account for dimensional deviations from the shaft. In the example shown, this is the Y axis, which is an axis extending parallel to the movement track and perpendicular to the axis in which the vehicle moves. When the deviation is specified negatively, it then extends to the left of the vehicle relative to the direction of travel. In the case of a positive deviation, correspondingly to the right. The X-axis coincides with the direction of travel. The Z-axis coincides with the vertical axis of the vehicle. In this case, the deviations are considerably different on both sides, so that the lengths of the directional arrows 382,384 are likewise different. The

directional arrows

382 and 384 are likewise designed differently in color. Red is used for deviations larger than 1.5 mm. Green is predetermined for deviations below 0.5 mm. A corresponding status bar 400 is likewise visible in fig. 7. In the case of a specific implementation of the software for the visualization tool, the tolerance specification and the use of the same color for visualizing the measured value deviations are programmed as freely configurable software elements. The mentioned colors and deviations can therefore only be seen as examples for certain components. In the case of other components/members, these values may likewise be greater or smaller.

The so-called zero model is illustrated in fig. 8. The detailed view displayed is a back view 305. The curve of the measurement points in the right graph in the figure is for the problem area 390. In the upper part, it can be seen that the measuring point is relatively stable above the tolerance range. In the case of this example, the measuring point process runs steadily from sub-product to sub-product. The measurement point that is outside the tolerance can be produced by a defective component. The measures for correcting defective components are planned and can be implemented for different reasons, however, at a significantly later point in time. By matching additional components in this area, the error map can be reacted to in a later process, so that no defects are formed for the customer/end user. The function, the vision and the touch are furthermore in accordance with quality standards and can be ensured.

With old visualization systems, this point is only shown as a red measurement point (outside the tolerance). However, an overview of its process reliability/process stability is lost, since this point is outside the tolerance. In theory, this point could also fluctuate back and forth between +2.5 and-2.5. The initial statement for the system user is always the same. If, however, the point is limited, for which purpose the tolerances are reduced and the mean value of the measurement points is regarded as a new (temporary) zero model, the updated deviations/fluctuations of the measurement points can be immediately recognized and they can be appropriately intervened with regulating measures. The graphical form with the curve of the measuring points thus provides additional information and assists the staff in production in order to take appropriate measures. The zero pattern is visible in the lower part of fig. 8. Here, the tolerance is set such that it surrounds the actual measured value more closely, but at the height of the current measured value it is therefore temporarily regarded as normal.

All examples and conditional language recited herein are not to be construed as limiting to such specifically recited examples. It is therefore to be recognized by those skilled in the art, for example, that the illustrated flow charts, state transition diagrams, pseudocode, and the like illustrate various alternative embodiments for illustrating the processes which may be substantially stored in a computer readable medium and so implemented by a computer or processor. The objects mentioned in the patent claims can also be explicitly humans.

It is to be understood that the proposed method and accompanying apparatus may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. The special processor may include an Application Specific Integrated Circuit (ASIC), a Reduced Instruction Set Computer (RISC), and/or a Field Programmable Gate Array (FPGA). Preferably, the proposed method and apparatus are implemented as a combination of hardware and software. The software is preferably installed as an application program on the program storage device. Generally, a machine based computer platform having hardware, such as one or more Central Processing Units (CPUs), a Random Access Memory (RAM), and one or more input/output (I/O) interfaces. On the computer platform, an operating system is usually additionally installed. The different processes and functions described herein may be either part of the application program or part implemented via the operating system.

The disclosure is not limited to the embodiments described herein. There is room for different matches and modifications that the professional considers based on his expertise and in the light of this disclosure.

List of reference numerals

10 -

40 different model variants

110 assembly station

112 Member A

114 strung measurement units

115 tandem measuring cell

120 assembly station

122 member B

124 tandem measuring cell

125 series measuring unit

130 assembly station

132 Member C

134 bunching measurement unit

140 assembly station

142 Member D

144 tandem measuring cell

150 assembly station

152 component E

154 tandem measuring cell

180-

189 different procedural steps

200 server

205 production monitoring computer

300 general view

301-

304 different measuring points

305-

360 different child product views

382 manufacture deviation symbol

384 manufacture deviation symbols

392 manufacture deviation symbol

394 manufacture offset notation

396 manufacturing deviation symbol

400 status bar

Claims

1. Method for quality assurance in the case of production of a product, which in the case of production is composed of a plurality of components and/or parts (112,122,132,142,152), wherein the manufactured sub-products are measured at one or more series of measuring positions (114,124,134,144,154,164), wherein the measuring data are collected in a database, characterized in that an overall view (300) with different views (305 and 360) of the manufactured sub-products is calculated from the measuring data, in which problem areas (380,390) with important manufacturing deviations are highlighted by means of color markings.

2. The method according to claim 1, characterized in that a plurality of measurement data sets (10,20,30,40) for different model variants of the product are collected in the database and the overall view (300) is for one selected model variant.

3. Method according to one of the preceding claims, characterized in that for calculating the problem area (380,390) the manufacturing deviations are weighted according to the position in the product and the customer relevance associated therewith.

4. The method according to any of the preceding claims, characterized in that a detailed view for one sub-product in the overall view (300) is calculated after selecting the sub-product, in which the manufacturing deviations are visualized by a symbol (382,384,392,394,396).

5. The method of claim 4, wherein the symbol (382,384,392,394,396) is a directional arrow, the direction of which describes the direction of deviation of the sub-product from the standard and the length of which visualizes the magnitude of the deviation from the standard in that direction.

6. A method as claimed in claim 5, characterized in that it is highlighted by means of a colored marking whether the deviation indicated by the directional arrow is or is not within the tolerance range.

7. Method according to one of the preceding claims, characterized in that the manufacturing deviations and their tolerances are placed in relation to one another in order to identify a trend deviation, wherein in the case of an identification of a trend deviation a corresponding color marking is implemented in the general view (305) and/or the detailed view and/or a warning message is calculated in which the corresponding problem area is identified.

8. The method according to any one of claims 4 to 7, characterized in that a status bar (400) is displayed in relation to the detailed view, in which the member/component (112,122,132,142,152) or sub-product is shown, which functions with the sub-product of the detailed view.

9. Method according to any of the preceding claims, wherein a zero model is calculated, in which the actual state is virtually set to zero with the manufacturing deviations existing for the sub-product, and wherein preferably a smaller tolerance is determined for this state.

10. Computing device for carrying out the steps of the method according to one of the preceding claims with a computing unit coupled to a server (200) maintaining a database, characterized in that the computing device (205) is provided for retrieving the measurement data from the database and for calculating from the measurement data an overall view (300) of the sub-products formed in the case of manufacturing, wherein the calculation of the overall view is carried out in such a way that problem areas (380,390) in which significant manufacturing deviations are determined are highlighted by a color marking.

11. Computer program, characterized in that it is designed for, in the case of execution in a computing device (205), performing at least the steps of the calculation of the overall view (300) in the case of the method for quality assurance in the case of producing a product according to claim 1.

12. A computer program according to claim 11, characterized in that the computer program is designed for performing one or more of the steps according to any of claims 1 to 9 in the context of execution in the computing device (205).