MOULDING-PARAMETERS PROCESSING METHOD FOR AN INJECTION PRESS

Abstract

A method is described for processing moulding parameters (P.sub.i+1) for an injection moulding machine (10) obtained by CAE.

The CAE simulation generates simulation results (A.sub.i), first machine parameters (P.sub.i) are generated by electronically processing the simulation results (A.sub.i), second machine parameters (P.sub.i+1) are obtained, different from the first ones, from the execution of another moulding process for the same object; and in an electronic database (M) accessible by a user the first and second machine parameters are saved associating them in a common collection.

Claims

1. Method for processing moulding parameters (P.sub.i+1) for an injection moulding machine (10) obtained by CAE comprising the steps of (i) simulating through CAE a moulding process needed to mould an object, wherein the CAE simulation generates simulation results (A.sub.i), (ii) generating first machine parameters (P.sub.i) by electronically processing the simulation results (A.sub.i) to make them compatible with the data protocol of a control unit (20) of the machine, so that the machine can perform an actual moulding process according to the first machine parameters; (iii) obtaining second machine parameters (P.sub.i+1), different from the first ones, from the execution of another moulding process for the same object; (iv) saving in an electronic database (M) accessible by a user the first and second machine parameters associating them in a common collection.

2. Method according to claim 1, wherein in said common collection generic machine parameters and/or process simulated data are saved too.

3. Method according to claim 1, wherein in said collection real process data are saved, that is values of physical quantities relative to the moulding process detected by sensors on board the machine during an actual injection moulding process.

4. Method according to claim1, wherein the real process data obtained during the moulding with the first and/or second machine parameters are saved in said common collection.

5. Method according to claim 1, wherein step (ii) is performed by a software.

6. Method according to claim 1, wherein step (ii) takes place with the further step of generating from the simulation results obtained by the simulation software CAE a file readable by a software installed in the control unit, wherein said file undergoes a data conversion process to adapt the data protocol of the CAE simulation software to the data protocol of the software installed in the control unit, the conversion process being performed by a software.

7. Method according to claim 1, wherein said other moulding process is performed N times with machine parameters different from the first machine parameters, where N>=2, in each N-th iteration the used second machine parameters being machine parameters generated by the following steps: processing with a software the machine parameters of the (N-1)-th iteration and the machine parameters of the (N-2)-th iteration to generate new machine parameters, and using said new machine parameters as machine parameters in the N-th iteration of step (iii).

8. Method according to claim 1, with the further steps of (v) processing the data contained in the common collection with a software, (vi) modifying the machine parameters calculated with a subsequent CAE simulation as a function of the processing produced by said software in step (v), to obtain optimized machine parameters.

9. Method according to claim 8, wherein optimized machine parameters are obtained by internally modifying the CAE software.

10. Method according to claim 8, wherein optimized parameters are obtained by modifying the data that the CAE software generates.

11. Method according to claim 8, wherein a conversion software, which performs said conversion between the simulation results and the machine parameters to be loaded in the control unit, is modified to optimize the machine parameters.

12. Method according to claim 8, wherein step (v) is carried out through a software.

13. (canceled)

Description

[0105] The advantages of the invention will be even clearer from the following description of a preferred method, in which reference is made to the attached drawing in which

[0106] FIG. 1 shows a schematic view of a press;

[0107] FIG. 2 shows a block scheme.

[0108] In the figures: equal numerical references indicate equal elements, and arrows symbolize a transfer of data and/or data itself (where indicated).

[0109] The method is applied to an injection molding machine 10. The machine 10 generally comprises e.g. an injection device 14, arranged on a base 12 and provided with a mold 16 with one or more hot runners 17.

[0110] A control unit 20, equipped with intelligence, drives various members of the molding machine during the molding steps, and comprises e.g. a display 22 and an operating panel 24 (e.g. a keyboard or touch screen). The control unit 20 is e.g. connectedin a known wayto actuators (not shown) to move parts of the mold and injectors, and/or to sensors for detecting the state of the actuators and the mold.

[0111] The control unit 20 also comprises a computer or microprocessor 26, connected to the panel 24.

[0112] The control unit 20 can perform data exchange, that is bidirectional data communications, with the outside of the machine 10.

[0113] Note that the control unit 20 could also refer to

[0114] a control unit designed to control the actuators for moving parts of the mold and/or

[0115] an control unit X designed to control the injectors and/or the hot runner 17; and/or

[0116] a control unit Y located at a remote place that sends commands, e.g. via wireless means, to the machine 10.

[0117] FIG. 2 shows in a block diagram preferred steps of the method.

[0118] 50 indicates a software or CAE environment in which an operator can model an injection molding process and perform a simulation to virtually study the outcome, i.e. the moulded product.

[0119] In the following, the subscript i, i>=1, generally indicates the i-th iteration, if there are multiple iterations.

[0120] A first step of the method involves modeling a process of injection molding and running a simulation with the software 50, in order to generate simulation results A.sub.1.

[0121] Preferably, the simulation results at the first iteration A.sub.1 comprise generic machine parameters H.sub.1 (as defined above) and/or simulated process data S.sub.1, as defined above.

[0122] A second step of the method involves extracting from the CAE environment 50 the simulation results A.sub.1 and inserting them into a memory or electronic database M, accessible by a user, such as a server.

[0123] Then, dedicated machine parameters P.sub.1 (arrow F0) are generated from the simulation results A.sub.1.

[0124] This step of the method may be performed by a software, indicated with the block 90, which fetches data from memory M and transfers them as input to the control unit 20 as machine parameters to be used. In the block 90 a format conversion may take place, to adapt the parameters to the control unit 20 if the protocol is different. For this purpose, preferably the block 90 generates a data file containing machine parameters P.sub.1, for facilitating the circulation (e.g. via email) and/or the storage.

[0125] The software of block 90 may reside in the memory M, in the control unit 20 or elsewhere remotely.

[0126] A third step of the method comprises molding in the machine 10 with the parameters P.sub.1 coming from block 90. It is expected that this molding is not optimal, so, as a next step, by varying, e.g. manually, the machine parameters entered earlier new machine parameters are obtained and with them another molding is performed in the machine 10. This step, which can be repeated several times until reaching a moulded product of better quality or desired features, generates at least another set of machine parameters P.sub.2, or various sets P.sub.2, P.sub.3, P.sub.4, etc., it depends on the number of repetitions.

[0127] A further step of the method involves exporting (arrow F1) from the control unit 20 the data used and/or generated during one or the last iteration. This data may be the machine parameters P.sub.2 (or P.sub.i+1) used during one or the last iteration and/or the actual process data B.sub.i related to one or the last iteration (see definition above).

[0128] The machine parameters P.sub.2 (or P.sub.i+1) are exported preferably via a data file that contains them, to facilitate their circulation (e.g. via email) and/or their memorization. In the example, the parameters P.sub.2 are exported to the memory M.

[0129] Another step of the method involves exporting (arrow F1) from the machine 10, together with the data P.sub.i, the actual process data B.sub.i and importing them e.g. in the memory M.

[0130] Then a software in a block 80 processes the data present in the memory M, e.g. H.sub.1, P.sub.2 ( . . . P.sub.i+1) and/or S.sub.1, B.sub.2 ( . . . B.sub.i+1). The software of the block 80 resides preferably in the memory M, or it can reside in the control unit 20 or elsewhere remotely.

[0131] The data transfer to the input of software 80 (arrow F3) may, for example, take place by migrating a file containing the data and generated respectively by the control unit 20 and/or by the software 50. Almost certainly a data conversion is necessary to adapt the data protocol between the software 80 and the control unit 20 and the software 50. Preferably the conversion is performed by the software 80, thereby avoiding to modify pre-existing systems.

[0132] The 80 software processes the input data F3 and generates a data output F4.

[0133] In this case too a data conversion may be necessary for adapting the data protocol between software 80 and e.g. the software 50. Preferably the conversion is performed by the software 80, thereby avoiding to modify the software 50.

[0134] The data output F4 is the product e.g. of intelligent algorithms that gradually learn from the differences between the data H.sub.1, P.sub.2, P.sub.i and/or S.sub.1, B.sub.2, B.sub.i how to generate the data F4 so that a subsequent software simulation 50 generates parameters for the control unit 20 capable of leading to a more accurate moulded product. In particular, the data output F4 is generated as a function of the differences between the data generated during an iteration by the simulator 50 to the molding in the machine. For this purpose, preferably the software 80 is associated with the memory or database M, in which there are saved

[0135] the data P.sub.i as the control unit 20 uses them, and/or

[0136] the results A.sub.i of the processing performed by the software 50, and/or

[0137] the actual process data B.sub.i detected by the sensors of the machine 10, and/or

[0138] the results F.sub.i of the i processing operations performed by the software 80 itself.

[0139] Preferably all the aforesaid data are saved in the database M, to improve the corrective and optimizing capacity of the software 80.

[0140] In the database M, a historical archive is thus created which contains data related to subsequent developments and improvements for the object's molding process.

[0141] As it can be seen, the system of FIG. 2 altogether can allow the software 50 to generate more realistic simulations thanks to the contribution of the output F4 processed by the software 80. In particular, the software 80 includes algorithms and/or mathematical functions that have as argument e.g. the data H.sub.1, P.sub.2, P.sub.i and/or H.sub.1, B.sub.2, B.sub.i and/or F.sub.i, and give as output optimized data or correction data.

[0142] In particular, the output F4 of the software 80 can be used to

[0143] pass to the software 50 optimized parameters (arrow F4s) on which then the software 50 calculates a new simulation giving optimized results A.sub.i+1; and/or

[0144] generate correction parameters applied in cascade manner by an optional software module 70 to the simulation results A.sub.i+1 (arrow F4q), so as to optimizemodifying themthe parameters A.sub.i+1 without altering the operation of software 50;

[0145] generate parameters P.sub.i+1 to send them directly to the control unit 20 (arrow F4p) to perform a molding with the machine 10; and/or

[0146] enlarge the database M to make up-to-date data F.sub.i (arrow F4m) available; and/or

[0147] pass data to the conversion software 90 (arrow F4c) to generate an optimized output F0 with new parameters P.sub.i+1. Data in the F4c stream may for example modify mathematical functions and/or conversion coefficients used within the software 90 to generate the output F0 from the data in the database M.

[0148] The software 80 may be implemented in many ways. E.g. it can work to integrate the functions of the software 90, or the software 80 may coincide with or be part of the software 90. Or, or even, the software 80 may be an integrated module or part of/in the software 50 or a separate software, distinct from the software 50, from which it receives and to which it sends data. As separate software, distinct from the software 50, the software 80 may be e.g. an integrated module or part of/in the software of the control unit 20 or a software that runs on a device or computer other than the one running the CAE software and other than the control unit 20.

[0149] In particular, the software 80 may apply a mathematical function or algorithms to the data F3 to obtain data F.sub.i, especially by exploiting the knowledge or the history of the data accumulated in the memory M.

[0150] Note that the database or memory M, intended as a permanent storage of historical data, could also be absent. A case is e.g. the one in which the system of FIG. 2 functions with multiple iterations on parameters A.sub.i generated in real time to optimize on the fly the parameters PI but does not save the results.

[0151] Note that each data stream F4s, F4q, F4m, F4c or F4p creates a feedback ring that originates in the data A.sub.i and/or the data F0 (or P.sub.2 and B.sub.2) and, through the software 80, arrives to new optimized data A.sub.i+1 and/or data P.sub.i+1 (or P.sub.i+1 and B.sub.i+1).