Hybrid model and method for determining manufacturing properties of an injection-molded part

The present patent application claims the right of priority under 35 U.S.C. §119 (a)-(d) of German Patent Application No. 101 20 476.0, filed Apr. 25, 2001.

The invention relates to a method of determining properties relating to the manufacture of an injection-molded article. The method includes the use of a hybrid model, which includes at least one neural network and at least one rigorous model.

U.S. Pat. No. 5,914,884 discloses a method for evaluating the properties of a plastic material in a plastic injection-molding method. An empirical model is used to describe the relationship between process variables and a multiplicity of quality characteristics. The empirical model is produced with reference to empirical data determined by means of a plastic injection-molding process. One of the disadvantages of this method is the practical difficulties and the necessary expenditure involved in the generation of the empirical model.

The invention is therefore based on the object of providing an improved method, with a neural network, for determining a manufacturing property of an injection-molded part.

In accordance with the present invention, there is provided a method of determining properties relating to the manufacture of an injection-molded part comprising:

(a) providing a hybrid model comprising,

(i) a first input ( 1 ) for inputting parameters, (ii) at least one neural network (NN 1 , . . . NN k ) for inputting at least one of said parameters into the neural network, and having a first output for outputting a forecast value, and (iii) at least one rigorous model (R 1 , R 2 , R 3 ; R 4 , R 5 , . . . ) having a second input for inputting at least one of said parameters and said forecast value, and a second output for outputting a calculated value relating to the properties,

(b) selecting said parameters; (c) inputting said parameters into said hybrid model; and (d) obtaining at least one of said forecast value and said calculated value relating to at least one of said properties.

The features that characterize the present invention are pointed out with particularity in the claims, which are annexed to and form a part of this disclosure. These and other features of the invention, its operating advantages and the specific objects obtained by its use will be more fully understood from the following detailed description and the accompanying drawings in which preferred embodiments of the invention are illustrated and described.

Other than in the examples, or where otherwise indicated, all numbers or expressions, such a those expressing structural dimensions, etc., used in the specification and claims are to be under stood as modified in all instances by the term “about.”

FIG. 1 is a representative schematic illustration of an embodiment of the hybrid model used in the method of the present invention;

FIG. 2 is a representative schematic illustration of an application of the hybrid model of FIG. 1 ;

FIG. 3 is a representative flow chart of the steps for generating a database for training the neural network of the hybrid model used in the method of the present invention; and

FIG. 4 is a representative flow chart of the steps involved in training of the neural networks of the hybrid model used in the method of the present invention.

In FIGS. 1-4 , like reference numerals designate the same components and structural features.

The invention permits various properties relating to the manufacture of an injection-molded part to be forecast (or predicted), specifically both with respect to the injection-molding method and with respect to the properties of the resulting injection-molded part.

In particular, the forecasting of process times and processing properties during injection-molding of plastic molded parts is of great practical significance because the manufacturing costs of injection-molded parts are decisively influenced by the productivity of the injection-molding fabrication. The characteristic parameter here is the process time of the injection-molding machine for manufacturing a molded part.

To calculate the process time requires not only the material-specific properties to be characterized by the raw material manufacturer but also the customer-specific application (geometry, mold) to be taken into account. The corresponding process times thus result from the interaction of material properties, the process parameters of the injection-molding machine and the construction of the injection mold.

These complex relationships can be mapped using neural networks. The neural networks are supplemented by rigorous computational models which are connected to the neural network. For example, in such a rigorous computational model it is possible to calculate the plastification capacity taking into account the screw geometry of the injection-molding machine used.

The comprehensive modelling of the injection-molding process permits, over and beyond the forecasting of a process time, a large amount of information to be obtained on important quality features of the injection-molded part. By taking into account the process control, it is possible, for example, to forecast the dimensional accuracy of the molded parts, the modules of elasticity, the fracture stresses and elongations as a function of the direction of flow and wall thickness, the resistance to demolding under heat and further material properties.

A further advantage of the invention is that these forecasts relating to the injection-molding process and/or the expected properties of the molded parts can be carried out by the user without special knowledge. It is a particular advantage here that the invention can be used for planning and designing new injection-molding applications. The hybrid neural network according to the invention makes it possible to test the feasibility of a new project, even in an extremely early planning phase, by means of a computer simulation which is easy to carry out. This is in particular of great significance for the reduction of what is referred to as the “time to market” phase.

Furthermore, the invention for computationally configuring parts permits the design engineer to make available suitable material characteristic values by forecasting using the hybrid model according to the invention. Owing to a complex material characteristic of plastics, such material characteristic values may be indispensable for a reliable configuration of parts. The invention makes it possible to determine such material characteristic values without carrying out test series or the like.

In addition, the invention also makes possible a forecast with respect to the machine and process parameters of the plastic injection-molding machine which are necessary for a desired molded part. It is thus possible to test in advance whether it is at all possible to manufacture a specific part on an existing injection-molding machine.

An embodiment of the invention will be explained in more detail below with reference to the drawings.

The hybrid neural network in FIG. 1 has an input 1 for inputting parameters P 1 , P 2 , P 3 . . . P i . These parameters can be material parameters or recipe (or formulation) parameters, for example for specifying composite recipes made up of commercial products, and/or process and machine parameters relating to the injection-molding process or the injection-molding machine, and/or design parameters and molded-part parameters, for example relating to the wall thickness of the molded part and of the flow length.

In the embodiment under consideration here, the input 1 is used to input, inter alia, the following parameters:

In addition, the hybrid model has a neural network module 2 . The neural network module 2 has an input 3 for inputting parameters PN 1 , PN 2 , PN 3 . . . PN j . The input 3 is connected to the input 1 so that some of the parameters P 1 to P i of the input 1 are also entered into the input 3 of the neural network module 2 .

By virtue of a corresponding connection between the input 3 and the input 1 , the input parameter PN 1 of the neural network module 2 is therefore identical to the parameter P 3 of the input 1 of the hybrid model. The parameters PN 2 and P 4 , and PN 3 and P 5 , are also identical in the example shown.

The parameters PN 1 to PN j therefore form a subset of the parameters P 1 to P i . The further corresponding connections between the input 1 and the input 3 are not shown in detail in FIG. 1 for the sake of clarity.

The input 3 of the neural network module 2 is connected to a mapping module 4 . The mapping module 4 is itself connected to the neural networks NN 1 , NN 2 , NN 3 , NN 4 , . . . NN k . Each of the neural networks NN 1 to NN k has a corresponding output AN 1 to AN k .

Each of the neural networks NN 1 to NN k is trained for forecasting a specific property which is output via the corresponding output if the necessary input parameters are applied to the input of the respective neural network. The number of necessary input parameters may vary here.

In order to apply the input parameters necessary for a specific neural network to the input of this network, the mapping module 4 carries out corresponding transformations of the parameter vector of input 3 which is composed of the parameters PN 1 to PN j . If, for example, the neural network NN 1 requires only the input parameters PN 1 , PN 2 and PN 3 , but not the further parameters PN 4 to PN j , the mapping module 4 ensures that only the parameters PN 1 to PN 3 are present at the input of the neural network NN 1 . The same applies to the further neural networks. The mapping can be carried out by multiplying the parameter vector of the input 3 in each case by a specific matrix adapted to the respective neural network.

The neural network module 2 also has a training module 5 which is active in a training mode of the neural network module 2 . The training module 5 is respectively connected to the mapping module 4 and to the neural networks NN 1 to NN k as well as to their corresponding outputs. The training of the individual neural networks can be carried out sequentially here, that is to say the individual neural networks are trained in a known fashion independently of one another by applying different input parameters under the supervision of the training module 5 .

What is referred to as the back-propagation method or the “NN-Tool 2000” program, for example, which is commercially available from Professor Frank Bärmann, Fachhochschule Gelsenkirchen, Fachbereich physikalische Technik [Technical University of Gelsenkirchen, Department of Physical Technology] is suitable for this purpose. The corresponding training method is also described in the publication “Neural Network”, volume 5, pages 139 to 144, 1992, “On a class of efficient learning algorithms for neural networks”, Frank Bärmann, Friedrich Biegler-König.

The hybrid neural network also has a number of rigorous models R 1 , R 2 , R 3 , R 4 , . . . . The rigorous models are model elements which can be represented by means of mathematical formulae. Those parts of the model for which mathematical formulae cannot be specified are dealt with by means of the neural network module 2 . The rigorous models can be connected to one another and to the neural network module.

In the embodiment shown, the rigorous model R 1 is connected only to the input 1 , but not to other rigorous models or to an output of the neural network module 2 . The output of the rigorous model R 1 is connected to the output A 1 of the output 6 .

The rigorous model R 2 is, on the other hand, connected to the parameter P 1 of the input 1 and to the output AN 1 of the neural network module 2 . The output of the rigorous model R 2 is connected to the output A 2 .

The rigorous model R 3 is connected to the parameters P 3 , P 4 and P 5 of the input 1 and to the outputs AN 2 and AN 3 of the neural network module 2 . The output of the rigorous model R 3 is connected both to the output A 3 and to the input of the rigorous model R 4 . The rigorous model R 4 has a further input which is connected to the parameter P 3 . The output of the rigorous model R 4 is connected to the output A 4 .

In the embodiment discussed herein, the parameters PN 1 to PN j of the input 3 can be independently selected from recipe, thickness, stock temperature, mold temperature, shear rate and holding pressure.

The output variables which are present at the outputs AN 1 to AN k can correspondingly be the density, sealing period, sealing temperature, demolding temperature, standard filling pressure, zero viscosity, reciprocal transitional shear rate, Carreau increase and mechanical properties.

The mass of the molded part, the mold-filling time, the effective thermal conductivity, the cooling time, the filling pressure, the metering time, the process time and further mechanical properties, for example, can then be determined computationally using the rigorous models.

The following values are then correspondingly available at the output 6 , for example:

FIG. 2 shows an application case of the hybrid neural network in FIG. 1 . The following parameters P 1 to P 5 are shown by way of example at the input 1 :

vft=volume of molded part ve=injection speed s=thickness t m =mass, temperature t w =mold temperature

The further input parameters of the input 1 are not illustrated in FIG. 2 for the sake of simplicity.

The parameters vft and ve are input into the rigorous model 7 in order to calculate the mold-filling time. For this purpose, the following formula:

zff = ( vft 0.85 ) · 4 ( ve · s 2 · π )

is stored in the rigorous model 7 . The mold-filling time zff calculated with the rigorous model 7 is output via the output A 1 .

The following formula:

mft=vft·rho

is stored in the rigorous model 8 in order to calculate the mass mft of the molded part.

Correspondingly, the inputs of the rigorous model 8 are combined with the parameter vft of the input 1 or with the output of the neural network NN 1 . The output of the neural network NN 1 supplies the density rho. The rigorous model 8 then calculates the mass mft of the molded part from the input parameter vft or the forecast value rho, and the mass mft of the molded part is then output via the output A 2 .

The rigorous model 9 is used to calculate the effective thermal conductivity aeff. In order to calculate the effective thermal conductivity, the following formula:

aeff = s 2 π 2 · zs · log ⁡ ( 4 π · t m - t w t s - t w )

is stored in the rigorous model 9 .

The input of the rigorous model 9 is correspondingly connected to the corresponding parameters s, t m and t w of the input 1 . In addition, the input of the rigorous model 9 is also connected to the outputs of the neural networks NN 2 and NN 3 which supply the sealing period zs and the sealing temperature t s as forecast values. The effective thermal conductivity aeff which is determined in this way is output via the output A 3 of the rigorous model 9 .

In addition, the effective thermal conductivity aeff is also fed from the output of the rigorous model 9 to an input of the rigorous model 10 . The rigorous model 10 is used to calculate the cooling time zk. The following formula:

zk = s 2 π 2 · aeff · log ⁡ ( 4 π · t m - t w t e - t w )

is stored in the rigorous model 10 in order to calculate the cooling time.

The rigorous model 10 correspondingly has a further input which is connected to the parameter s of the input 1 , and further inputs which are not shown in FIG. 2 for the sake of clarity and which are connected to the input parameters t m and t w and to the forecast value t e of the output of the neural network NN 4 . The cooling time zk which is determined on the basis of this input parameter and/or on the basis of the forecast value and the effective thermal conductivity is output by the rigorous model 10 via the output A 4 .

The hybrid neural model can contain further rigorous models, for example for calculating the filling pressure for the customer's application, the calculation of the metering time, the processing time and the transformation of specific mechanical properties. These further rigorous model elements are not illustrated in FIG. 2 for the sake of clarity. Corresponding calculation specifications for implementing such further rigorous model elements can be found, for example, in the publications Anwendungstechnische Information [Technical Application Information] 1104, “optimierte Werkzeugtemperierung [Optimized Mold Temperature Control]”, Olaf Zöllner, Bayer AG, Geschäftsbereich Kunststoffe [Plastics Division].

FIG. 3 shows the sequence for the generation of a database, for training the neural networks of the hybrid model. Firstly, in step 30 , a series of tests is carried out during which respective plastic molded parts are manufactured while varying, for example, recipes and injection molds (thickness) and varying processing parameters and machine parameters of the plastic injection-molding machine, said molded parts then being subjected to analysis. In step 32 , a database is generated from the data determined in step 30 . This database includes the input parameters for each data record, that is to say the respective recipe parameters and mold parameters as well as the processing parameters and machine parameters and the corresponding resulting properties of the manufactured plastic molded part, in particular its mechanical properties as well as the characteristics of the manufacturing process, that is to say the processing properties, thermal properties, rheological properties and the processing time.

FIG. 4 shows the procedure for training the individual neural networks of the hybrid model. In step 41 , the serial variable m is firstly initialized with the value 1.

In step 42 , the first neural network NN 1 is trained. To do this, the database is accessed in order to call the input parameters necessary for training the respective neural network NN 1 . The output of the neural network NN 1 —the density in the example in FIG. 2 —is compared with the value determined in the experiment. Given a difference between the forecast value and the actual value, the weightings of the neurons of the neural network NN 1 are correspondingly adapted, as is known per se. After the training of the neural network NN 1 has been terminated in step 42 , the serial variable m is incremented in step 43 , and step 42 is repeated until all the neural networks NN m have been trained.

Although the invention has been described in detail in the foregoing for the purpose of illustration, it is to be understood that such detail is solely for that purpose and that variations can be made therein by those skilled in the art without departing from the spirit and scope of the invention except as it may be limited by the claims.