JPH08156060A - System for optimizing injection molding condition - Google Patents

Abstract

PURPOSE: To contrive to reduce labour at a molding site and to cut down molding cost through the improvement of running conditions a method wherein an optimization calculating device, which calculates and sets optimum conditions by multiple-regression-analysing data so as to transmit resultant data to a setting panel, is equipped in a controller. CONSTITUTION: This system is equipped with a controller having a setting panel 3, which automatically changes several setting condition variables such as the rotational frequency of a plasticizing screw, injection speed, dwell pressure and the like within a predetermined range during the molding operation of an injection molder 1 to the injection molder 1. Further, the system is equipped with a measuring device 5, which automatically measures molding estimation items of molding under the setting conditions such as resin temperature, cycle time, electric power consumption and the like. In addition, the system is equipped with an optimization calculating device 4, which calculates and sets optimum conditions by multiple-regression-analysing data accumulated by repeating the change of setting condition variables by the predetermined number of cases and then transmits resultant data to the setting panel 3, in the controller 2.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a system for automatically optimizing operating conditions of a plastic injection molding machine during a molding operation.

[0002]

2. Description of the Related Art In a conventional injection molding machine, condition setting is carried out by a trial and error method in which an experienced molding operator changes operating conditions while checking molding quality.
In that case, it often takes a lot of time to solve the defect of the molded product, or the defect cannot be solved in some cases. In addition to the molding quality, it is very difficult to optimize the operating conditions by comprehensively considering the cycle time and the power consumption that greatly affect the molding cost. FIG. 7 shows a condition setting system configuration of an injection molding machine as one conventional example. In the figure, the injection molding machine 101 transmits and receives various data to and from the control device 102 through a line 110. Control device 1
02 is provided with a setting panel 103, and the molding operator 10
4 sets / changes the operating conditions of the molding machine using the same panel. Since the operating conditions of the molding machine are different for each molded product, the molding operator 104 determines the condition for each molded product. The work flow is shown in FIG. In FIG. 8, after performing one shot molding with, the molding result is evaluated with, the operating condition is changed based on the evaluation, and the operations up to this are repeated until the molding result is satisfied. This is performed by the molding operator 104. Therefore, the quality of the condition setting depends on the ability of the molding operator 104, and if the operator is not an experienced operator, the success is rare. In addition, even veteran workers find it difficult to set conditions to solve molding defects, so not only does it take a lot of time, but the operating conditions are optimized by comprehensively evaluating evaluation items such as cycle time and power consumption. It was difficult to realize. Furthermore, the operating conditions were not changed during the production operation of the molded product, and it was completely unthinkable that the molding machine would automatically improve the operating conditions during the production operation.

[0003]

As described above, in the conventional technique, a veteran molding operator is required to set the conditions for injection molding, and a great deal of time is required for setting the conditions. It was difficult to optimize the operating conditions by integrating the evaluation items of. The present invention solves the above-mentioned conventional problems so that the molding machine automatically performs condition setting for specific evaluation items, and further, the molding machine automatically improves operating conditions even during production operation. Accordingly, the present invention intends to provide an injection molding condition optimizing system that significantly reduces the time and effort required for condition setting at the molding site and achieves reduction of molding cost by improving operating conditions.

[0004]

Therefore, according to the present invention, during the molding operation of the injection molding machine, some setting condition variables (plasticizing screw rotation speed, injection speed, A control device that has a setting panel that changes the injection holding pressure, etc.) within a predetermined range, and a measurement that automatically measures the molding evaluation items (resin temperature, cycle time, power consumption, etc.) of molding when molding under the above-mentioned setting conditions. In the control device, the device and the setting condition variable are repeatedly changed for a predetermined number of cases to accumulate data, and the data is subjected to multiple regression analysis to calculate and set optimum conditions and to transmit the data to the setting panel. It is composed of an optimizing calculation device, and is a means for solving the problem. Further, in the plasticizing test of the injection molding machine, the present invention automatically changes some setting condition variables (cylinder temperature, measuring stroke, plasticizing screw rotation speed, etc.) for the injection molding machine within a predetermined range. And a control device having a setting panel, and plasticizing evaluation items (resin temperature, plasticizing time,
(E.g., power consumption) and a measuring device that automatically measures the setting condition variables for a predetermined number of cases to accumulate data, and multiple regression analysis of that data is performed to calculate and set the optimum conditions for plasticization. An optimization calculation device in the control device for transmitting data to the setting panel, which serves as means for solving the problem.

[0005]

Operation: During the molding operation of the injection molding machine 1, the injection molding machine 1
In response to this, the setting panel 3 in the control device 2 automatically sets several setting condition variables (plasticizing screw rotation speed, injection speed,
By changing the injection holding pressure, etc.) within a predetermined range and automatically measuring the molding evaluation items (resin temperature, cycle time, power consumption, etc.) when molding under the above-mentioned setting conditions, the molding operator A molding test can be efficiently performed without operating the. Upon receiving the data from the measuring device 5, the optimization calculation device 4 in the control device 2 repeats the molding test for a predetermined number of cases, accumulates the data, and multiple regression analyzes the data to calculate the optimum conditions. By setting, optimal operating conditions can be obtained without the need for experienced molding operators. In addition, before setting the conditions of each molded product in the injection molding machine 1, in a plasticization test of the injection molding machine 1, several setting condition variables (cylinder temperature, measuring stroke, plasticization Change the screw rotation speed, etc.) within a predetermined range, and automatically measure the plasticization evaluation items (resin temperature, plasticization time, power consumption, etc.) when plasticizing under the conditions, and data for the predetermined number of cases By calculating and setting the optimum conditions for plasticization by multiple regression analysis, it is possible to perform troublesome conditions for plasticization without an experienced worker, and to reduce the number of parameter variables when setting conditions during molding Can be significantly reduced.

[0006]

Embodiments of the present invention will now be described with reference to the drawings. FIGS. 1 to 6 show the embodiments of the present invention. FIG. 1 is a basic configuration diagram of the condition optimization system of this embodiment. In the figure, the injection molding machine 1 and the control device 2 are connected by a line 10, and operating condition data is transmitted and received through the line 10. The control device 2 incorporates an optimization calculation device 4 in addition to the setting panel 3 similar to the conventional one. On the setting panel 3, the molding operator sets the operating conditions of the molding machine, and when the operating condition data is sent from the optimization calculation device 4, the operating conditions on the panel display are changed to the data sent from the device 4. To do.
Further, the injection molding machine 1 is equipped with a measuring device 5 for measuring the evaluation items described later, and the data measured during the molding machine operation is transmitted to the optimization calculation device 4 in the control device 2 through the line 11. In the case where the condition setting test is performed during the molding operation in this system, it is not necessary for the molding operator to change and set the operation conditions on the setting panel 3.

FIG. 2 shows a cross section of the plasticizing device of the injection molding machine 1. In the figure, the cylinder 26 has a screw 25 built-in, and the heaters 20, 21, 22 of each part are provided on the outer periphery.
23 is installed. Nozzle 27 at the tip of cylinder 26
A resin pressure sensor 28 and a resin temperature sensor 29 are incorporated in the. The resin raw material 30 is put into the hopper 24, melted as it is sent from the right side to the left side in the drawing by the rotation of the screw 25, and is stored in the cylinder head at the left end. The stored molten resin becomes a molded product which is injected from the nozzle 27 into a mold (not shown) at the time of molding.

FIG. 3 shows a flow of automatic optimization during the molding operation. Each item in this flow chart is sequentially executed by the computer of this system. In the figure, first, set the condition factors and evaluation items to be tested, automatically create a test plan for the number of required cases with, automatically execute the molding test according to the test measurement, and measure the evaluation items. Multiple regression analysis of test data is performed with and, optimization calculation is performed using multiple regression equation with, and the optimum condition of the calculation result is automatically changed with. The settings shown in FIG. 3 are entered in advance by the molding operator using the setting panel 3. An example of the condition factors is shown in Table 1,
Table 2 shows examples of evaluation items.

[Table 1] List of conditional factors

[Table 2] These are examples, and only a part of them may be adopted or other parts may be added. For each condition factor, the molding operator sets the upper limit value and the lower limit value in order to define the range of variation. Each cylinder temperature of X1 to X4 in Table 1 corresponds to the temperature of each part of the heaters 20 to 23 in FIG. The measuring stroke of X5 is the total length of the screw 25 retracted when plasticized by the screw 25 of FIG. The screw rotational speeds of X6 to X8 are rotational speeds n in three equal intervals of the measuring stroke, and the screw back pressures of X9 to X11 are pressures that add resistance to screw retreat in order to enhance the kneading effect of plasticization. Further, X12 to X19 and X20 to X25 are setting variables for the injection and injection pressure-holding steps. The setting patterns of these variables of injection, injection holding pressure and the plasticization are illustrated in FIG. On the other hand, the maximum resin temperature difference of Y1 and the resin temperature difference between shots of Y2 in Table 2 are obtained by statistically processing the resin temperature T measured by the resin temperature sensor 29 of FIG. The cycle time of Y3 is a required time measured by the injection molding machine every time one shot is molded. Y4
The power consumption is measured as an average power for one shot by attaching a general power meter to the input power source of the injection molding machine. Y
The maximum resin pressure of 5 and the resin pressure difference between shots of Y6 are
The resin pressure p measured by the resin pressure sensor 28 of FIG. 2 is statistically processed. In this way, the evaluation items Y1 to Y6 are measured and processed by the measuring device 5 on the molding machine without human intervention. Further, the smaller the values of Y1 to Y6, the better the evaluation. In the “automatic creation of test plan” in FIG. 3, variable values are randomly set for each condition factor within each domain, and a combination of these is set as one case, and a test plan for the required number of cases is prepared. Here, the required number of cases for the test is usually 10 to 20 and may be added depending on the test results. A more detailed flow of the "automatic test execution" of FIG. 3 is as shown in FIG.
In FIG. 4, the operating conditions of the test plan are automatically changed for each case of the test, the one-shot molding operation and the automatic measurement are performed with, the test data for one case is captured and accumulated, and these processes are repeated for a predetermined number of cases. It should be noted that the above is carried out after the state of each part of the molding machine becomes stable after changing the operating conditions. The system of FIG. 3 and all of FIG. 3 is automatically executed during the molding operation without human intervention, but the method of multiple regression analysis and optimization calculation is applied by the present inventor to Japanese Patent Application No. 4-96.
Similar to No. 613. FIG. 6 shows a conceptual diagram of optimization.
As shown in the figure, the optimization is to maximize the following comprehensive evaluation value G.

[Equation 1] However, L: number of evaluation items W _k : weight of each evaluation item (1 to 5) The more important items are, the larger the weight is. Y _kR : Evaluation rank of each evaluation item (predicted by multiple regression equation) In order to perform optimization by integrating a plurality of evaluation items, the measured value of each evaluation item is replaced with a rank of 10 points. As described above, the molding test and the optimization calculation are automatically performed during the molding operation in this system, and the optimum molding conditions can be automatically changed.

As another embodiment, instead of the molding test of the above embodiment, only a plasticization test is conducted. That is, only the conditional factors X1 to X11 in Table 1 and only Y1 to Y4 in Table 2 are used. In the plasticizing test, the resin stored in front of the screw 25 in FIG. 2 by the plasticizing is discharged from the nozzle 27 into the air at a constant injection speed v. The other procedures are the same as those in the above-mentioned embodiment. In the conventional plasticization test, since the quality evaluation of the plasticized resin is troublesome, the optimum plasticization conditions were hardly searched by changing the set values of various condition factors. In this embodiment, the system automatically evaluates the quality of the resin such as the temperature as described above, so that the optimum conditions for plasticization can be easily calculated. Further, by changing the X5 measuring stroke in Table 1 to various levels and performing a plasticization test, the optimum conditions for plasticizing for an arbitrary measuring stroke can be found before molding. Therefore, if a plasticization test is performed on one resin grade raw material by this system, the optimum plasticization conditions are prepared before molding for various molded products with the same resin grade but different injection resin amounts. be able to.

[0010]

As described in detail above, according to the present invention, the operating conditions of the molding machine are automatically changed and the molding condition is automatically measured and molded without the intervention of the molding operator during the molding operation. Since the test is performed and the test data is subjected to the multiple regression analysis and the optimization calculation is performed to automatically set the optimum condition, the operating condition is automatically improved, and the condition setting time can be significantly reduced. In addition, molding quality, production speed, etc. are improved, and molding cost and power consumption can be reduced. Similarly, for a specific resin, it is possible to automatically perform a plasticization test without the intervention of a molding operator to find the optimum conditions, so the time taken to condition the molded products using the same resin is greatly reduced. Then
In addition, the molding cost can be reduced by better operating conditions than before.

[Brief description of drawings]

FIG. 1 is a basic configuration diagram of a condition optimization system according to an embodiment of the present invention.

2 is a cross-sectional view of a plasticizing device of the injection molding machine of FIG.

FIG. 3 is a flow chart of automatic optimization according to the present embodiment.

FIG. 4 is a detailed flow chart of “automatic execution for test” of FIG.

FIG. 5 is a setting pattern diagram of injection / plasticization condition variables according to the present embodiment.

FIG. 6 is a conceptual diagram of optimization in this embodiment.

FIG. 7 is a block diagram of a conventional condition setting system.

FIG. 8 is a work flow diagram for condition setting in a conventional example.

[Explanation of symbols]

 1 Injection Molding Machine 2 Control Device 3 Setting Panel 4 Optimization Calculation Device 5 Measuring Device 20-23 Cylinder Heater 25 Screw 26 Cylinder 27 Nozzle 28 Resin Pressure Sensor 29 Resin Temperature Sensor

Claims (2)

[Claims] 1. During the molding operation of the injection molding machine, some setting condition variables (plasticizing screw rotation speed, injection speed, injection holding pressure, etc.) are automatically set within a predetermined range for the injection molding machine. A control device having a setting panel to be changed, and molding evaluation items (resin temperature,
Cycle time, power consumption, etc.) and a measuring device that automatically changes the setting condition variables for a specified number of cases to accumulate data, and multiple regression analysis is performed on the data to calculate and set optimal conditions. A condition optimization system for injection molding, comprising an optimization calculation device in the control device for transmitting data to the setting panel. 2. In a plasticizing test of an injection molding machine, some setting condition variables (cylinder temperature, measuring stroke, plasticizing screw rotation speed, etc.) are automatically changed for the injection molding machine within a predetermined range. A control device having a setting panel, a measuring device for automatically measuring plasticization evaluation items (resin temperature, plasticizing time, power consumption, etc.) when plasticized under the setting conditions, and changing the setting condition variables. Data is repeatedly accumulated for a predetermined number of cases, the data is subjected to multiple regression analysis to calculate and set optimum conditions for plasticization, and the data is sent to the setting panel. A condition optimization system for injection molding characterized by the following.