JP2006095590A - Method for optimizing casting condition for aluminum die-cast product - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for optimizing casting conditions for aluminum die-cast product, in which the optimum casting conditions are accurately obtained with reduced man-hour, cost and time. <P>SOLUTION: In the method for optimizing the casting conditions for aluminum die-cast product using a fluid CAE (Computer Aided Engineering) imitated to a casting facility, the fluid CAE is constituted so that in the case of inputting the casting conditions constituted from the plurality of designing factors, the remained gas quantity in the cast product cast on the casting condition is calculated and further, a process for obtaining the remained gas quantity by inputting the casting conditions in the fluid CAE, a process for planning a predicting formula for predicting the remained gas quantity by using each designing factor as a parameter, based on the remained gas quantity obtained on each casting condition, and a process for obtaining the casting condition on which the remained gas quantity is minimized, are included. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a method for optimizing the conditions for casting an aluminum die cast product, and more particularly to an aluminum die cast casting condition optimization method for optimizing the casting conditions by predicting the residual gas amount of the cast product by flow CAE. .

In order to find the optimum casting conditions, the casting conditions have been narrowed down to several based on the experience of the casting engineer, and trial production is repeated within these casting conditions. We were looking for casting conditions. On the other hand, Patent Document 1 discloses a technique for deriving excellent casting conditions with a small number of trial productions by repeating trial production and inspection while changing the casting conditions (levels) according to the orthogonal arrangement table based on the experimental design method. Is disclosed.
JP 2002-263830 A

  In any of the above-described conventional techniques, the casting conditions have to be evaluated based on the trial production results, and thus many trial productions are unavoidable. Therefore, a great amount of man-hours, costs and time had to be spent before finding the optimum casting conditions. Furthermore, even if a large number of prototypes were obtained by changing the casting conditions, these could not be quantitatively evaluated, so it was difficult to extract truly optimum casting conditions.

  An object of the present invention is to solve the above-described problems of the prior art and to provide a method for optimizing aluminum die-casting conditions in which optimum casting conditions can be accurately obtained with less man-hours, costs and time.

  In order to achieve the above-described object, the present invention provides a method for optimizing the casting conditions of an aluminum die cast product using a flow CAE that simulates a casting facility. When conditions are input, it is configured to calculate a residual gas amount of a cast product cast under the casting conditions, and further includes the following procedure.

  (1) A procedure for obtaining the residual gas amount by inputting casting conditions into the flow CAE and a prediction formula for predicting the residual gas amount using the design factors as parameters based on the residual gas amount obtained for each casting condition. And a procedure for determining a casting condition that minimizes the amount of residual gas in accordance with the prediction formula.

  (2) The procedure for obtaining the residual gas amount is a procedure for registering predetermined experimental conditions in each of m design factors of the first group among n design factors constituting casting conditions, and (n−m ) For each design factor of the second group, a procedure for generating an orthogonal arrangement table based on an experimental design, a procedure for registering the level of each design factor in the orthogonal arrangement table, and a casting condition, the first group Setting the predetermined experimental conditions for each of the design factors, setting levels according to the orthogonal arrangement table for the design factors of the second group, and inputting the values into the flow CAE to obtain the residual gas amount. In addition, in the procedure of formulating the prediction formula, the residual gas amount is predicted using each design factor of the second group as a parameter, using the residual gas amount obtained for each casting condition.

  (3) The second group of design factors includes the high speed range speed of the injection plunger, the speed switching point of the injection plunger, and the mold temperature.

  (4) The design factors of the second group are the high speed range speed of the injection plunger, the speed switching point of the injection plunger, and the mold temperature.

  (5) The prediction formula is designed by combining one or more of a response surface method, a genetic algorithm, and a neural network method.

  (6) A plurality of design factors are represented by the mold temperature, and the method includes a step of optimizing each of the plurality of design factors based on the mold temperature under the optimized casting conditions.

According to the present invention, the following effects are achieved.
(a) Since casting conditions are evaluated using flow CAE, casting conditions can be optimized without trial production. Therefore, optimum casting conditions can be obtained with less man-hours, cost and time.
(b) Since the casting conditions are evaluated based on the residual gas amount of the cast product, the casting conditions can be quantitatively evaluated. Therefore, it becomes possible to accurately evaluate various casting conditions without depending on the experience of engineers.
(c) A large number of design factors that constitute casting conditions are classified into Group 1 and Group 2. The design factors of Group 1 that are expected to have little impact on castings are fixed, and the effects on castings are fixed. Since only the second group of design factors that are predicted to be large is generated by applying the flow CAE by generating the orthogonal array table based on the experimental design, the number of executions of the flow CAE can be reduced.
(d) Optimizing casting conditions by representing a plurality of design factors in terms of mold temperature, and calculating back the plurality of design factors from the mold temperature in the optimized casting conditions. The number of factors can be reduced.

  Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings. FIG. 1 is a diagram showing a configuration of a main part of a casting machine to which an aluminum die cast casting condition optimization method of the present invention is applied. A fixed mold 1 and a longitudinal direction with respect to the fixed mold 1 The mold cavity 3 is formed between the molds 1 and 2 which are clamped and opened by moving forward and backward. Note that a fixed plate for fixing the fixed mold 1 and a movable plate for moving the movable mold 2 back and forth in the front-rear direction are not shown.

  An injection sleeve 4 communicating with the mold cavity 3 is fitted into the fixed mold 1, and a pouring port 5 is formed on the upper side of the rear end portion of the injection sleeve 4. An injection plunger 6 is fitted to the injection sleeve 4 so as to be able to advance and retreat. The injection plunger 6 is driven back and forth by an injection cylinder (not shown). The movable mold 2 is formed with a cooling water channel 11 for controlling the mold temperature, and the movable mold 2 is controlled to a desired temperature by increasing or decreasing the amount of cooling water supplied to the water channel 11. At the time of mold opening, the release agent supplied from the release agent supply device 7 is applied to the inner surfaces of both molds 1 and 2 by the spray 8.

  When molten metal is supplied into the injection sleeve 4 from the hand furnace 9 through the pouring port 10, the injection plunger 6 first moves forward at a low speed [Vlow] to avoid entrainment of the metal melt air, and the mold cavity. 3 is filled with molten metal. When the molten metal tip reaches a predetermined speed switching point [K], the driving speed of the emission plunger 6 is switched to the high speed side and moves forward at a high speed (Vfast), and the molten metal is rapidly filled in the mold cavity 3. . When the mold cavity 3 is filled with the molten metal, the casting pressure by the plunger is raised to the target pressure [P] to pressurize the molten metal. Thereafter, the mold is opened after the molten metal is solidified and cooled, and the solidified casting is taken out from the mold cavity 3.

  In such a die-casting method, it is known that when a gas such as air or water vapor remaining in the mold cavity 3 is caught inside the product, casting defects such as nests and blowholes are generated. In the present embodiment, it is newly found that the afterimage gas amount [Mgas] of the cast product can be an index for evaluating the casting conditions, and the casting conditions in which the afterimage gas amount [Mgas] shows the minimum value are optimal casting conditions. The point is that it is considered to be.

  FIG. 2 is a flowchart showing an optimization procedure for aluminum die-casting casting conditions according to the present invention. In this embodiment, flow (or casting) CAE (Computer Aided Engineering) simulating the casting machine is used. The residual gas amount of the cast product is calculated while changing the casting condition, and the casting condition in which the residual gas amount shows the minimum value is the optimum value.

Fluid CAE is software that enables analysis of molten metal flow and solidification, prediction of casting defects, examination of casting bills, etc. on a computer based on part information designed by CAD. When a plurality of constituent parameters are input, a residual gas amount [Mgas] of a cast product cast under the casting conditions is obtained. In this embodiment, nine design factors are set as casting conditions.
1. Hand furnace temperature [Tf]
2. Casting pressure [P]
3. Casting pressure rise speed [ΔP]
4). Low speed range of injection plunger [Vlow]
5. High speed range of injection plunger [Vfast]
6). Low speed / high speed switching point [K]
7). Movable cooling water volume [W]
8). Release agent application time [t1]
9. Cycle time [t2]

  However, as shown in FIG. 3, in this embodiment, the cooling water amount [W], the release agent application time [t1], and the cycle time [t2] are collectively represented by the mold temperature [Tc]. There are seven design factors input to.

  Further, in the present embodiment, the seven design factors are classified into a group (first group) and a large group (second group) that have a small influence on the residual gas amount of the cast product. The furnace temperature [Tf], casting pressure [P], casting pressure rising speed (ΔP), and injection plunger low speed region velocity [Vlow] belong to the second group, the injection plunger high velocity region velocity [Vfast] ], The speed switching point [K] of the injection plunger and the mold temperature [Tc] belong.

  In step S1, the design factors of the hand furnace temperature [Tf], casting pressure [P], casting pressure rising speed (ΔP), and injection plunger low speed [Vlow] included in the first design factor group are Experimental conditions are registered in a fixed manner based on past empirical rules and constraints. In step S2, an example is shown in FIG. 4 for each of the design factors of the high speed range speed [Vfast] of the injection plunger, the speed switching point [K] of the injection plunger, and the mold temperature (Tc) included in the second design factor group. As shown, an L9 orthogonal array table based on the experimental design (a table assigned so that all combinations of each level appear the same number of times) is generated, and in step S3, each level (experimental condition: Vfast (1) to (3), K (1) to (3), Tc (1) to (3)) are registered. In this arrangement table, the standard values of each design factor are registered as the second level (2), and the values obtained by increasing or decreasing the median by the predetermined value are the first level (1) and the third level (3), respectively. Registered as

  In step S4, a casting condition composed of a combination of a plurality of design factors is input to the flow CAE simulated in the casting machine, and a residual gas amount is calculated for each casting condition. At this time, the experimental conditions registered in step S1 are fixedly set for the four design factors included in the first design factor group. The three design factors included in the second design factor group are dynamically set according to the L9 orthogonal array table, and the residual gas amounts [Mgas (1) to Mgas ( 9)] is required.

In step S5, a response surface methodology (RSM: Response Surface methodology) is used to predict the residual gas amount at a point not actually sampled from the set of residual gas amounts obtained by discrete sampling in step S4. ) Is used to obtain the prediction formula (1) for predicting the residual gas amount [Mgas] using the three design factors (Vfast, K, Tc) included in the second design factor group as parameters.
(Equation 1) Mgas = f (Vfast, K, Tc) (1)

  That is, in the present embodiment, a plurality of sampling points are set for the design factor to be analyzed, and the residual gas amount at these sampling points is calculated using the flow CAE. From these calculation results, a predictive formula for predicting the residual gas amount at the sampling point that is not calculated by the flow CAE is obtained using an approximate formula typified by the response surface methodology.

  FIG. 5 is a diagram schematically showing an example of a response curved surface. The prediction result of the gas amount [Mgas] using the speed switching point [K] and the high speed range velocity [Vfast] of the injection plunger as parameters is the above prediction. It is expressed by a curved surface including the predicted value obtained by Expression (1). In the present embodiment, such a response curved surface is generated for each mold temperature [Tc].

  In step S6, using the prediction formula, a combination of three design factors in which the residual gas amount [Mgas] exhibits a minimum value is obtained while changing the three design factors included in the second design factor group. In the present embodiment, the experimental conditions registered in the design factors of the first design factor group in step S1 and the design factors of the second design factor group when the residual gas amount [Mgas] shows the minimum value. The combination with the level (experimental conditions) is the optimized casting condition.

  In step S7, based on the mold temperature [Tc] under the optimized casting conditions, the amount of cooling water [W], the release agent application time [t1], and the cycle time [t2] of the movable mold are calculated back. This is the optimum condition for the three design factors. This back calculation is obtained by performing multiple regression analysis on the experimental results for the relationship between the mold temperature [Tc], the movable cooling water amount [W], the release agent application time [t1], and the cycle time [t2]. The prediction formula can be used.

  As a result of actual casting according to the optimized casting conditions as described above, when the desired conditions cannot be satisfied, the above-described processes are performed while varying the experimental conditions of the design factors of the first design factor group. Is repeated.

  In the above-described embodiment, the response surface method is used to predict the residual gas amount at other sampling points from a plurality of sampling points obtained according to the L9 orthogonal array table. However, the present invention is not limited to this, and one or a plurality of other optimization methods such as a genetic algorithm and a neural network method may be combined.

It is the figure which showed the structure of the principal part of the casting machine to which the optimization method of the aluminum die-casting conditions of this invention is applied. It is the flowchart which showed the optimization procedure of the aluminum die-casting casting condition which concerns on this invention. It is the figure which showed the classification method of the design factor. It is the figure which showed an example of the L9 orthogonal sequence table. It is the figure which showed typically an example of the response curved surface which estimates gas amount [Mgas] by using the speed switching point [K] and the high speed area speed [Vfast] of an injection plunger as parameters.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Fixed mold, 2 ... Movable mold, 3 ... Mold cavity, 4 ... Injection sleeve, 5 ... Pouring inlet, 6 ... Injection plunger, 7 ... Release agent supply apparatus, 8 ... Spray, 9 ... Hand furnace, 10 ... Pouring port, 11 ... Cooling channel

Claims (6)

In the method of optimizing the casting conditions of aluminum die-cast products using fluid CAE imitating casting equipment,
The flow CAE is configured to calculate a residual gas amount of a cast product cast under the casting conditions when a casting condition composed of a plurality of design factors is input,
A procedure for obtaining a residual gas amount by inputting casting conditions to the flow CAE;
Based on the amount of residual gas obtained for each casting condition, a procedure for formulating a prediction formula for predicting the amount of residual gas using each design factor as a parameter;
A method for optimizing the casting conditions of an aluminum die-cast product, comprising the step of obtaining a casting condition that minimizes the amount of residual gas in accordance with the prediction formula. The procedure for obtaining the residual gas amount is as follows:
A procedure for registering predetermined experimental conditions in each of the m first group of design factors among the n design factors constituting the casting conditions;
For each (n−m) second group of design factors, a procedure for generating an orthogonal array table based on an experimental design method;
Registering the level of each design factor in the orthogonal array table;
As casting conditions, the predetermined experimental conditions are set for the design factors of the first group, the levels are set for the design factors of the second group according to the orthogonal arrangement table, and these are input to the flow CAE. A procedure for determining the amount of residual gas,
2. The residual gas amount is predicted by using the residual gas amount obtained for each casting condition as a parameter for each design factor of the second group in the procedure of formulating the prediction formula. Method for optimizing casting conditions for aluminum die-cast products.   2. The casting of an aluminum die cast product according to claim 1, wherein the design factors include a high speed range velocity [Vfast] of the injection plunger, a speed switching point [K] of the injection plunger, and a mold temperature [Tc]. Condition optimization method.   3. The aluminum die according to claim 2, wherein the design factors of the second group are a high-speed region speed [Vfast] of the injection plunger, a speed switching point [K] of the injection plunger, and a mold temperature [Tc]. Casting conditions optimization method for cast products.   The casting condition of the aluminum die-cast product according to any one of claims 1 to 4, wherein the prediction formula is formulated by combining one or more of a response surface method, a genetic algorithm, and a neural network method. Optimization method.   A plurality of design factors are represented by the mold temperature, and the method includes a step of optimizing each of the plurality of design factors based on a mold temperature in the optimized casting condition. 5. A method for optimizing casting conditions of an aluminum die-cast product according to any one of 5 above.

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