METHOD AND SYSTEM FOR IMPROVING THE PRODUCTION OF A CASED DEVICE

Abstract

A method for improving the production of a cased device (1), wherein a casing component (2) of the cased device (1) is produced, which production of the casing component (2) comprises injecting a polymer mixture (4) for creating a solid foam interior into a cavity, which cavity is formed by the casing component (2), which injecting is done by an injection apparatus (3) and which injecting is based on injection control data (5) input to the injection apparatus (3), wherein during the injection of the polymer mixture (4) into the cavity forming data (6) for describing the injection process is recorded, wherein after injecting the polymer mixture (4) into the cavity at least one image of the casing component (2) is recorded and wherein based on the forming data (6), the at least one image and the injection control data (5) a prediction model (8) for generating new injection control data (10) for inputting to the injection apparatus (3) is updated.

Claims

1.-14. (canceled)

15. A method for improving the production of a cased device, comprising producing a casing component of the cased device, wherein the casing component comprises a shell, which production of the casing component comprises injecting a polymer mixture for creating a solid foam interior into a cavity, which cavity is formed by the shell of the casing component, which injecting is done by an injection apparatus and which injecting is based on injection control data input to the injection apparatus, wherein, during the injection of the polymer mixture into the cavity forming data for describing the injection process is recorded, that after injecting the polymer mixture into the cavity at least one image of the casing component is recorded, that the at least one image comprises a thermographic image for determining a distribution of the solid foam interior in the cavity, and that based on the forming data, the at least one image and the injection control data a prediction model for generating new injection control data for inputting to the injection apparatus is updated.

16. The method according to claim 15, wherein the at least one image comprises a visual inspection for detecting defects of the casing component.

17. The method according to claim 16, wherein, the visual inspection comprises a stripe light scan of the casing component.

18. The method according to claim 15, wherein a component charge of the polymer mixture is produced in a precursor production process based on recipe data at a precursor production facility and that during the precursor production process of the component charge precursor production data is recorded.

19. The method according to claim 18, wherein the precursor production data comprises precursor production settings which are input to a precursor production apparatus for controlling the precursor production process of the component charge.

20. The method according to claim 18, wherein the component charge of the polymer mixture is stored in precursor storage process, that during the precursor storage process of the component charge precursor storage data is recorded.

21. The method according to claim 18, wherein charge properties data is measured from the component charge and that the prediction model is updated based on the charge properties data.

22. The method according to claim 15, wherein the prediction model comprises a correlation between correlation variables, which correlation variables comprise the injection control data, the forming data and the at least one image.

23. The method according to claim 22 wherein the injection control data for the injection apparatus injecting the polymer mixture is generated by the prediction model based on the precursor storage data or the precursor production data or the charge properties data of a component charge of the injected polymer mixture.

24. The method according to claim 22, wherein the prediction model generates updated recipe data or updated precursor production settings based on the correlation variables.

25. The method according to claim 22, wherein updating the prediction model comprises a numerical analysis of the correlation variables.

26. The method according to claim 15, wherein the injection control data comprises material output data, core temperature, mold temperature, material temperature, dosage pressure, hold time and/or pump output.

27. The method according to claim 15, wherein the forming data comprises ambient data, in particular temperature, air pressure and/or humidity, preferably, that the forming data comprises foam leakage data.

28. The method according to claim 15, wherein the device is a home appliance.

Description

[0041] Further advantageous and preferred features are discussed in the following description with respect to the FIGURES. In the following it is shown in

[0042] FIG. 1 a schematic view of a system for carrying out an embodiment of the method according to the invention.

[0043] The system shown in FIG. 1 concerns the production process of a cased device 1, which cased device 1 in the present case is a home appliance and in particular a refrigerator. As part of the production of the cased device 1, a casing component 2 of the cased device 1 is produced, which casing component 2 in the present case is a refrigerator door with a shell. In an injection apparatus 3, a polymer mixture 4 is injected into a cavity formed by the shell of the refrigerator door. This injection occurs as part of a foam injection molding process. Thus, a solid form interior within the cavity formed by the shell is formed and the casing component 2 thereby produced. The operation of the injection apparatus 3 is controlled by injection control data 5. During the foam injection molding process, forming data 6 is recorded to provide measurements for describing the injection process.

[0044] After the injection process, an image which is a thermographic image 7 is recorded by an infrared camera. Based on the recorded heat distribution, a distribution of the solid foam interior within the cavity of the casing component 2 can be determined. The forming data 6, the thermographic image 7 and the injection control data 5 is provided to an analysis system 9 to update a prediction model 8 implemented as a software application and residing in the analysis system 9, which prediction model 8 configured to generate new injection control data 10 for inputting to the injection apparatus 3. Such new injection control data 10 may in itself be incomplete and for example be modification information with respect to the injection control data 5, which thereby serves as a baseline.

[0045] In addition to the thermographic image 7 and after the casing component 2 has been assembled to become the cased device 1, a further image which is here a visual inspection 11 is recorded which involves performing a stripe light scan for detecting deviating surface contours as defects on the cased device 1.

[0046] A precursor production facility 13 produces a component charge 12, which here is a polyol charge, of the polymer mixture 4. This production is based on the one hand on recipe data 14 and on precursor production settings 15 which are input to a precursor production apparatus 16 of the precursor production facility 13. During the precursor production process, precursor production measurement data 18 is recorded, which forms precursor production data 17 together with the precursor production settings 15.

[0047] Prior to the injection process, the component charge 12 is stored for some time. Precursor storage data 19 describing the precursor storage process, including storage time and ambient variables such as temperature, air pressure and humidity is recorded. Likewise, tests are conducted on the component charge 12 to measure and record charge properties data 20.

[0048] The visual inspection 11, the recipe data 14, the precursor storage data 19, the charge properties data 20 and the precursor production data 17 are also provided to the analysis system 6 for updating the prediction model 8 based on the visual inspection 11, the recipe data 14, the precursor storage data 19, the charge properties data 20 and the precursor production data 17.

[0049] In particular, by means of numerical analysis the prediction model 8 is updated based on the continuing supply of the different data provided to the analysis system 6. In this way, correlations, dependencies and functional relationships between these correlation values may be identified and the prediction model 8 updated accordingly.

[0050] Then, the prediction model 8 may provide new injection control data 10 which particularly suitable for e.g. avoiding defects under particular circumstances. For example, it may be determined by the prediction model 8 with the benefit of the information provided that for a component charge 12 stored under particular circumstances as shown by the precursor storage data 19, new injection control data 10 should be generated to adjust the foam injection molding process so as to avoid an irregular foam distribution. On the other hand, a component charge 12 with slightly different recipe data 14 may require different new injection control data 10 to avoid the same defect. Processing large amounts of the correlation values may help come to such insights e.g. by numerical analysis, which insights are then reflected in the prediction model 8. Beside the new injection control data 10, the prediction model 8 also provides updated recipe data 21 and updated precursor production settings 22, which are both provided to the precursor production facility 13 and in particular the precursor production apparatus 16 in order to improve the precursor production process for a subsequent component charge 12.