Methods for quality assurance in the production of a product, as well as computing equipment and computer program

Abstract

Method for quality assurance in the production of a product that is assembled from several components and / or subassemblies (112, 122, 132, 142, 152) during production, wherein the manufactured sub-products are measured at one or more inline measuring stations (114, 124, 134, 144, 154, 164), wherein the measurement data are collected in a database, characterized in that, after each measurement of a manufactured sub-product, a virtually manufactured product is calculated by a production monitoring computer (205) using the measurement data collected in the database by means of simulation calculation before it is actually produced in production, and an overall view (300) with different views (305 - 360) of the manufactured sub-products is calculated from the measurement data, in which problem areas (380, 390) with relevant manufacturing deviations are highlighted by color marking.

Description

[0001] The proposal concerns the technical field of quality assurance in product manufacturing. Various components / assemblies that are put together during production are measured for inspection purposes. The effects of manufacturing deviations are visualized using a visualization tool.

[0002] In complex manufacturing processes, numerous different measurement systems from various vendors are used, providing measurement data. The companies offering these systems invariably use their own visualization tools. While some of these tools have been in use for a long time, they rarely allow for the integration of other measurement data to create a comprehensive overview of the impact on production.

[0003] From DE 102 36 843 A1, a device and a method for monitoring at least one production plant are known, comprising data acquisition means for recording process and product information during production. This information is stored in a database. An evaluation unit can generate at least one view for a user group, with the user group being classified into at least two categories.

[0004] German patent DE 102 42 811 A1 discloses a quality assurance method in which measuring stations are integrated into the manufacturing process. The actual values ​​of functional dimensions are measured on the assemblies, and these actual values ​​are compared with target values. A statistical evaluation is then performed. If tolerance values ​​are exceeded or fallen below, the production process is interrupted. Increased process reliability is achieved through the additional comparison of deviations with intervention limits. This quality control loop reports negative trends in dimensional deviations and enables planned adjustments to the production equipment before defective parts are produced.

[0005] From DE 199 17 003 A1, a method for measuring components of at least one component group, each exhibiting several characteristics of interest, is known. The measurement data are provided electronically in a database-compatible data structure, and a component- and characteristic-related electronic evaluation is performed.

[0006] US 2010 / 0215246A1 describes a system for monitoring and visualizing the manufactured parts of a production process, whereby the parts are inspected by one or more inspection units.

[0007] DE 10 2007 059 201 A1 relates to a manufacturing process, in particular an assembly process for motor vehicles, whereby the representation of the processes in the assembly process is visualized.

[0008] US patent 2016 / 0124424A1 describes a device for generating three-dimensional (3D) visualizations of products in the production process.

[0009] DE 10 2006 014 634 B4 concerns a human-machine interface for a system for controlling and / or monitoring an industrial process.

[0010] However, existing solutions are isolated systems suitable for a single manufacturer of measuring systems, but unsuitable for complex production environments with numerous different measuring systems. In particular, there is a need for visualizing measurement data and its tolerance deviations for various specialists who monitor the production process during the manufacture of complex products. This need was recognized within the scope of the invention.

[0011] The invention aims to provide such an approach, namely a method for production monitoring that goes beyond the mere visualization of deviations in individual manufactured parts and enables comprehensive quality assurance for complex products. This objective is achieved by a method for quality assurance in the production of a product according to claim 1, a computing device for use in the method according to claim 10, and a computer program according to claim 11.

[0012] The dependent claims include advantageous further developments and improvements of the invention in accordance with the following description of these measures.

[0013] The solution consists of collecting the various measurement data in a central database and using a proprietary visualization tool that processes and displays the measurement data and their deviations in a unique way. This resulting platform is based on the manufacturing company's knowledge and experience regarding diversity, complexity, efficiency, and quality requirements.

[0014] This solution makes it possible to process the generated measurement data in a customer- / user-friendly manner, regardless of the specific measurement system supplier, and to provide the user with the necessary presentation, evaluation, weighting, and networking capabilities. During model changes or new production launches, the necessary process monitoring can be activated independently from any computer location in production with minimal effort. It is also possible to react immediately and independently to changes in the production process or prioritization without having to make any adjustments or modifications to the system supplier's installed measurement technology. The software solution is developed and implemented in addition to the existing measurement system tools. It is a separate application program that works with the data in the database.Therefore, it is not necessary to adapt the proprietary measurement system visualization tools of the individual measurement system providers.

[0015] The proposal concerns a quality assurance procedure for the production of a product assembled from several components / assemblies. The resulting sub-products are measured at one or more inline measuring stations, and the measurement data is collected in a database. The procedure is characterized by the fact that, after each measurement of a manufactured sub-product, a production monitoring computer uses the collected measurement data to calculate a virtually manufactured product through simulation before it is actually produced. An overall view with various views of the sub-products is then generated from the measurement data. In this overall view, problem areas with relevant manufacturing deviations are highlighted by color coding. In one instance, the overall view can also include a clear and concise presentation of the individual sub-products.

[0016] Where the term "component" is used below, "assembly" is also used synonymously. While a strict distinction is always made in product manufacturing between a component as a non-disassemblable part and an assembly composed of several components, this distinction is not important for the purposes of this proposal. The inventive method can be used both in the production of a product assembled from individual components and in the production of a product composed of several assemblies or of assemblies and components. The respective intermediate product generated during manufacturing at an assembly station is referred to as a sub-product.

[0017] The production employee monitoring the production process receives a needs-based visualization of problem areas and their impact on downstream processes. Furthermore, all measurement data is summarized in a single view and can be weighted accordingly. Storing the measurement data in a central database has the advantage that the data can be recalculated with each new measurement. This ensures that the overall view is continuously updated. Problems with components, assemblies, or sub-products and their effects on the final product are thus identified early on, even before the finished product is manufactured.

[0018] It is also advantageous to collect multiple measurement data sets for different model variants of the product in the database, with the overall view focusing on a selected model variant. Furthermore, it is beneficial if the overall view also displays which model variants exist. The user can then select a model variant and access the corresponding overall view. By summarizing all measurement data from all sub-products, the measurement data can be compared. This allows for the identification of inconsistencies before these components / assemblies are installed. The advantage lies in the fact that even if all measurement points are within their own tolerances, trend deviations across stations can be detected and compared / calculated. Early warnings or alarms can then be issued.

[0019] To calculate the problem areas, the measured manufacturing deviations are used. It is advantageous to weight the manufacturing deviations according to their location within the product and their associated customer relevance. This makes it possible to somewhat reduce the frequency of production interruptions if manufacturing deviations relate to areas that are not relevant to the customer.

[0020] It is also advantageous if the individual views are displayed as selectable options within the overall view, and if, after selection, a detailed view for the sub-product is calculated, in which manufacturing deviations are visualized by symbols at the locations of the problem areas. The symbols can be designed so that they can be interpreted intuitively by the user.

[0021] A particularly easy-to-interpret symbol is a directional arrow, whose direction indicates the direction of the component's deviation from the norm, and whose length visualizes the magnitude of the deviation in that direction. The symbol is displayed at the location of the problem area of ​​the component.

[0022] Furthermore, color coding can be used to highlight the extent of the deviation. Red would be chosen for particularly large deviations. Green would be chosen for deviations close to the tolerance range. Corresponding intermediate shades are suitable for other deviations.

[0023] Analyzing successive measurements may reveal a trend indicating that defective parts are imminent. Therefore, analyzing these measurements is advantageous for the process, enabling early detection of trend deviations. To identify these deviations, it is beneficial to correlate manufacturing deviations and their tolerances. When a trend deviation is detected, appropriate color coding is applied in the overall and / or detailed view, and / or a warning message is generated, identifying the specific problem area.

[0024] It is also advantageous if a status bar is displayed for a selected detail view, showing views of the sub-products that interact with the sub-product in the detail view. The views in the status bar can be selected to access the respective detail view. The benefit is that the quality problem can be identified very quickly.

[0025] To fix existing manufacturing deviations where quality criteria are still met, it can be advantageous to calculate a zero model. In this model, the current state, including the existing manufacturing deviations for one or more sub-products, is virtually set to zero, and preferably smaller tolerances are defined for this state. This allows costly adjustment measures to be postponed until a significant deterioration occurs, enabling early detection of such deterioration in the measurement data because smaller tolerances are defined and can be taken into account during the analysis.

[0026] For a computing device designed to perform the steps of the procedure, the corresponding advantages apply. This also applies to a suitably designed computer program.

[0027] Exemplary embodiments of the invention are shown in the drawings and are explained in more detail below with reference to the figures.

[0028] They show: Fig. 1 a schematic view of a production line with the feeding of components and inline measurement of assemblies at several stations of the production process; Fig. 2 the current typical view of the measuring points of an assembly with display of deviations; Fig. 3. A representation of the variety of measurement display options when different model variants are manufactured on a production line; Fig. 4. An overall view of several views showing the various components that are joined together during manufacturing, with highlighting of problem areas; Fig. 5 a flowchart for a program that is executed on a production monitoring computer to calculate an overall view; Fig. 6. A first example of a detailed view of an assembly with symbols displayed to indicate manufacturing deviations in the problem areas; Fig. 7 A second example of a detailed view of an assembly with symbols displayed to indicate manufacturing deviations in problem areas; and Fig. 8 a representation of an assembly with a measuring point that lies outside a tolerance range, whereby the measuring point can be monitored more accurately by temporarily defining a zero model.

[0029] The present description illustrates the principles of the inventive disclosure. It is therefore understood that those skilled in the art will be able to design various arrangements which, although not explicitly described here, embody principles of the inventive disclosure and which are also intended to be protected in their scope.

[0030] A measurement model describes an object that is measured in a measuring device. Measuring devices are capable of measuring various components. If four different components are to be measured in a measuring device, the device generates four different measurement models.

[0031] Inline metrology involves taking and measuring either random samples during product production or measuring all manufactured or assembled components during the production process. This allows for continuous monitoring of production quality. Inline metrology measures specific characteristics of the intermediate products manufactured during production. Typically, these characteristics are crucial for product assembly and are often referred to as priority points in production engineering. The measurement results are then displayed graphically for a user, such as a machine operator, a supervisor coordinating a production team, a production engineer, or even a production manager.

[0032] Fig. Figure 1 shows a typical production line in block form for the mass production of a complex product. The example refers to the body manufacturing of a passenger car. There are various assembly stations 110, 120, 130, 140, and 150, where the product is progressively completed. At each station, one or more components / assemblies are added. The production flow is from left to right. This is explained using the example of body manufacturing. In station 110, a base frame, the so-called substructure, is welded to a floor panel 112. The floor panel 112 originates from component manufacturing, which is not shown in detail. However, the floor panel 112 is fed to assembly station 110 via a component feeder. At the other assembly stations 120, 130, 140, 150, further components / assemblies 122, 132, 142, 152 are assembled, typically a body upper part, various front and rear parts and side parts.After each assembly station 120, 130, 140, 150, the resulting sub-product is measured in an inline measuring cell 114, 124 to 154. The measured values ​​are transmitted via network connections to a central server 200 and collected in a database installed on the server. A production monitoring computer 205 communicates with server 200 and has access to the database. Additional production monitoring computers can be installed, for example, at all assembly stations 110 to 150. Such production monitoring computers can also be installed at other locations, such as the offices of the production engineers, the production manager, etc. All production monitoring computers 205 access database 200. The production monitoring computers 205 can retrieve the measurement data from the database via network connections.The measurement data is typically stored in a uniform format in the database and can be enriched with various metadata. This can include: component type, serial number, time, date, etc.

[0033] The measurement data is acquired separately in a program that is processed by a measuring station computer. The data is then transferred in a structured format to server 200, where the database is stored. The measurement data could be formatted and stored in the manner described in document DE 199 17 003 A1.

[0034] In assembly station 120, the inner side panel 122 is mounted onto the base 110. This side panel originates from the corresponding component manufacturing process, which is also not shown in detail. Since it is a crucial component that must fit precisely with the base, this component is also measured inline in a dedicated measuring cell 115 before being fed to the subsequent assembly station 120. During the ongoing production process, the dimensions of this specific component are compared with the dimensions of the base sub-product assembled in assembly station 110. If, for example, opposing deviations are detected in both components / assemblies, this would pose a problem for the subsequent assembly steps. By transmitting the data from all inline measuring cells to server 200, the production monitoring computer 205 can calculate a simulation of the virtually manufactured product before it is actually produced.Problems become apparent in the simulation calculations through the corresponding visualization, allowing for swift corrective action before defective parts are produced. The same process occurs during assembly at station 130, where an outer side panel is mounted. This outer side panel is measured in measuring cell 125, and the measurement is immediately checked using simulation calculations. Dashed arrows indicate the comparison of measured values ​​between inline measuring cells 115 and 114, and 125 and 124.

[0035] Fig. Figure 2 shows a typical example of how the measurement data is currently displayed. The user sees an overview of all measurement points 301, 302, 303, and 304 at a measuring station. This view represents a combined sub-product that has just been measured in a selected measuring cell. This view is updated with each subsequent measurement. If a measurement of the same or a different sub-product occurs in this displayed measuring cell, the new sub-product is shown accordingly.

[0036] The problem becomes clear in typical mass production with short production times. Furthermore, different vehicle model variants A, B, and C are mixed and assembled on a single production line. A typical production sequence is as follows: AAABACCBAACA B_⇒

[0037] The underlining indicates that a body shell for vehicle model B is currently being measured in measuring cell 124. The cycle time for this inline measurement is 60 seconds. Should a problem be detected on this body shell, the machine operator has exactly 60 seconds to locate the problem. The measurement results for the next vehicle are displayed no later than 60 seconds later. In the example shown, the next vehicle is model A.

[0038] In passenger car production, there are many different measurement models that must be considered due to the wide variety of variants. For example, 32 different measurement models must be considered for body production. A description of the different measurement models can be found in Fig. Three images are visible. Behind each measurement model are up to 60 different views of measurement points, providing detailed information about the vehicle. Finding the right view to pinpoint the manufacturing problem within this short timeframe is extremely challenging. In extreme cases, it's possible that the vehicle has already been produced 1,500 times before the complete defect pattern is identified. This can result in an enormous amount of rework, which increases production costs.

[0039] Fig. Figure 4 shows an example of the proposed form of measurement data display. Instead of a view of all measurement points, an overall view (300) is generated with different views for the sub-products manufactured on the production line. Views for larger components that were also measured can also be displayed.

[0040] The in Fig. The four views shown relate to one view 355 of the underbody, four different views 305, 340, 350, 360 of the superstructure, which is assembled from several components, three different views 320, 325, 335 of the front section of the body, two different views 310, 330 of the floor section, and two views of the rear section 345, 315. In view 305 of the superstructure, two different problem areas 380, 390 are highlighted in color. The red color indicates that this is a serious problem where manufacturing tolerances are exceeded. Countermeasures should be taken because the production of defective parts is imminent. Also shown is a symbol in the form of a directional arrow 396, indicating the direction of the deviation for problem area 390. In addition, a second directional arrow could be displayed for the deformation in the other problem area 380. On the right side of the Fig. The various model variants are listed outside of the overall view (300). The selection elements for the different model variants are labeled with the reference numbers 10, 20, 30, and 40. The overall view (300) always refers to the currently selected model variant.

[0041] To generate this overall view, a comprehensive software solution is required. Fig. Figure 5 shows a flowchart for a program that calculates the overall view. The program is executed in the production monitoring computer 205 and starts in program step 180. Measurement data analysis takes place in program step 182.

[0042] During measurement data analysis, the measurement data for the selected model variant is retrieved from the database. The measurement data is then compared with its associated tolerances. If measurement data falls outside the tolerance range, a problem zone is defined. Then, in program step 184, an overall view is calculated, in which the problem zone is highlighted with color coding in the relevant views. Fig. Figure 4 shows an example of view 305 highlighting two problem areas, 380 and 390. A production worker can quickly get an overview of the problem areas using the overall view. To obtain more detailed information about the problem areas, they will select view 305 within the overall view. In query 186, the program checks whether a detailed view has been selected. If not, the program returns to the beginning. Otherwise, in program step 188, a detailed view is calculated, displaying more precise information about the problem areas at their location. Two detailed views are included in the Fig. 6 and Fig. 7 shown. Fig. Figure 6 shows the same rear view as view 305 in Fig. 4. Directional arrows 392 and 394 are positioned where problem areas 380 and 390 have been identified. The length of the arrows provides qualitative information about the size of the deviation. The exact dimensions of the measured deviations are also indicated. The arrow direction shows the direction in which the manufacturing process deviates from the standard. Contrary to the illustration shown, directional arrows 392 and 394 should preferably be displayed in three dimensions. The 3D representation of the directional arrows is important to clearly demonstrate to production staff the type of deviation. A 2D representation is often insufficient for this purpose because it lacks depth information. For example, a length offset in the rear view can be... Fig. 6. Without the 3D representation, the image would be poorly displayed. Alternatively, the entire view could be rotated in three dimensions. However, this would require that a corresponding 3D model, such as one provided by a CAD program, be embedded during implementation. A production monitoring computer would then need to be able to calculate this model. This would require a powerful computer, which is not preferred for production monitoring due to cost considerations. Furthermore, operation would be more complex, which is not necessarily desirable in production monitoring.

[0043] In the status bar 400 of the Fig. Six other views of the sub-product are shown. These views can be quickly selected by clicking on them.

[0044] In Fig. Figure 6 shows a measurement point diagram in the detailed view. This gives production staff a quick overview of the development of the critical measurement points where the deviation was detected. The diagram doesn't simply show the user the value of the last measurement, but provides information about all individual measurements of the last X components. Trend patterns, such as those described in [reference to diagram], can easily be identified using such diagrams. Fig. The 6 measurement points in the middle part of the diagram can be identified. Component variations can also be easily detected in such measurement point trends. The batch size of the measurements shown in the diagram is freely configurable. This allows the user to act as needed, draw their own conclusions, and take appropriate action.

[0045] In Fig. Figure 7 shows a detailed view of the underbody, where directional arrows 382 and 384 also highlight the bent wheel arches. The arrow direction again indicates the direction of deformation. The direction of the deviation can also be roughly determined from the sign of the dimension measurement. In body construction, it is common practice to specify dimensional deviations relative to an axis. In the example shown, this is the Y-axis, which is the axis that runs parallel to the road surface and is perpendicular to the axis along which the vehicle would travel. If the deviation is given as negative, it lies to the left of the vehicle relative to the direction of travel. For positive deviations, it lies to the right. The X-axis corresponds to the direction of travel. The Z-axis corresponds to the vehicle's vertical axis. In this case, the deviations on both sides are quite different, so the lengths of directional arrows 382 and 384 are also different.Directional arrows 382 and 384 are also designed with different colors.

[0046] The red color is used for deviations greater than 1.5 mm. The green color is reserved for deviations less than 0.5 mm. Also in Fig. The corresponding status bar 400 is visible in image 7. In the actual implementation of the software for the visualization tool, the tolerance specifications and the use of colors to visualize measurement deviations are programmed as freely configurable software elements. The colors and deviations mentioned are therefore only examples for a specific assembly. For other assemblies / components, these values ​​may be larger or smaller.

[0047] In Fig. Figure 8 illustrates a so-called zero model. The detailed view shown is the rear view 305. The measurement point profile in the diagrams on the right of the image refers to problem area 390. In the upper part, it can be seen that the measurement points remain relatively stable above the tolerance range. In this example, the measurement points are consistent throughout the process from one sub-product to the next. This measurement point, which is outside the tolerance range, could result from a defective component. Corrective measures for the defective component are planned but, for various reasons, cannot be implemented until a significantly later date. By adjusting other assemblies in this area, the error pattern can be counteracted in the later process, so that no defect arises for the customer / end user. Function, appearance, and feel continue to meet the quality standards and can be guaranteed.

[0048] With the old visualization system, this point is simply displayed as a red measurement point (outside the tolerance). However, this obscures the process reliability / stability, as the point is outside the tolerance. Theoretically, this point can fluctuate between +2.5 and -2.5. The initial message for the system user would always be the same... the point would still be red. However, by narrowing down this point, reducing the tolerance, and considering the mean value of the measurement point as a new (temporary) baseline, any further deviations / fluctuations of the measurement point can be immediately detected, and targeted corrective actions can be taken. The visualization with measurement point trends therefore provides additional information and helps production staff to take appropriate measures. In the lower part of the Fig.Figure 8 shows the zero model. The tolerance was set so that it is closer to the actual measured values, but at the level of the current measured values, which are therefore temporarily considered acceptable.

[0049] All examples mentioned herein, as well as conditional formulations, are to be understood without limitation to such specifically cited examples. For instance, it is recognized by those skilled in the art that a depicted flowchart, state transition diagram, pseudocode, and the like represent different ways of representing processes that are essentially stored in computer-readable media and can thus be executed by a computer or processor. The object mentioned in the patent claims may expressly also be a person.

[0050] It should be understood that the proposed method and associated apparatus can be implemented in various forms of hardware, software, firmware, specialized processors, or a combination thereof. Specialized processors can include application-specific integrated circuits (ASICs), reduced instruction set computers (RISCs), and / or field-programmable gate arrays (FPGAs). 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 a program storage device. Typically, this is a machine based on a computer platform that includes hardware such as one or more central processing units (CPUs), random access memory (RAM), and one or more input / output (I / O) interfaces.A computer platform typically also has an operating system installed. The various processes and functions described here can be part of the application program or a component that is executed by the operating system. Reference symbol list 10 - 40 different model variants 110 assembly station 112 Component A 114 Inline measuring cell 115 Inline measuring cell 120 assembly stations 122 Component B 124 Inline measuring cell 125 Inline measuring cell 130 assembly station 132 Component C 134 Inline measuring cell 140 assembly stations 142 Component D 144 Inline measuring cell 150 assembly stations 152 Component E 154 Inline measuring cell 180-189 different program steps 200 servers 205 production monitoring computers 300 Overall view 301 - 304 different measuring points 305 - 360 different sub-product views 382 Manufacturing deviation symbol 384 Manufacturing deviation symbol 392 Manufacturing deviation symbol 394 Manufacturing deviation symbol 396 Manufacturing deviation symbol 400 Status bar

Claims

[1] Method for quality assurance in the production of a product which is assembled from several components and / or subassemblies (112, 122, 132, 142, 152) during production, wherein the manufactured sub-products are measured at one or more inline measuring stations (114, 124, 134, 144, 154, 164), wherein the measurement data are collected in a database, characterized by , that after each measurement of a manufactured sub-product by a production monitoring computer (205) a virtually manufactured product is calculated by simulation calculation using the measurement data collected in the database before it is actually produced in production, and an overall view (300) with different views (305 - 360) of the manufactured sub-products is calculated from the measurement data, in which problem areas (380, 390) with relevant manufacturing deviations are highlighted by color marking. [2] Method according to claim 1, wherein several measurement data sets (10, 20, 30, 40) for different model variants of the product are collected in the database and the overall view (300) relates to a selected model variant. [3] Method according to one of the preceding claims, wherein for the calculation of the problem zones (380, 390) the manufacturing deviations are weighted according to their location in the product and their associated customer relevance. [4] Method according to one of the preceding claims, wherein after selecting a sub-product in the overall view (300) a detailed view for the sub-product is calculated in which the manufacturing deviations are visualized by a symbol (382, 384, 392, 394, 396). [5] Method according to claim 4, wherein the symbol (382, 384, 392, 394, 396) is a directional arrow, the direction of which indicates the direction of the deviation of the sub-product from the norm and the length of which visualizes the magnitude of the deviation from the norm in that direction. [6] Method according to claim 5, wherein color marking highlights whether the deviation indicated by the direction arrow is within the tolerance range or not. [7] Method according to one of the preceding claims, wherein, for the purpose of detecting trend deviations, the manufacturing deviations and their tolerances are related to each other, wherein, upon detection of trend deviations, a corresponding color marking is applied in the overall view (305) and / or detail view and / or a warning message is calculated in which the corresponding problem area is identified. [8] Method according to any one of claims 4 to 7, wherein a status bar (400) is displayed in the detail view, in which the components / assemblies (112, 122, 132, 142, 152) or sub-products are shown that function with the sub-product of the detail view. [9] Method according to one of the preceding claims, wherein a zero model is calculated in which the actual state with the existing manufacturing deviations for a sub-product is virtually set to zero and wherein smaller tolerances are preferably specified for this state. [10] Computing device for carrying out steps of the method according to one of the preceding claims, comprising a computing unit connected to a server (200) which maintains a database, characterized by, that the computing device (205) is set up to retrieve the measurement data from the database and, after each measurement of a manufactured sub-product, to calculate a virtually manufactured product using the measurement data collected in the database by means of simulation calculation before it is actually produced in production and to calculate an overall view (300) of the sub-products that are produced during manufacturing from the measurement data, wherein the calculation of the overall view is carried out in such a way that problem areas (380, 390) in which relevant manufacturing deviations have been found are highlighted by color marking. [11] Computer program, characterized by , that the computer program is designed, when executed in a computing device (205), to perform at least the steps of carrying out the simulation calculation and the calculation of the overall view (300) in the method for quality assurance in the production of a product according to claim 1. [12] Computer program according to claim 11, characterized by that the computer program is designed to perform one or more of the steps according to one of claims 2 to 9 when executed in the computing device (205).

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