Injection molding adaptive compensation method based on melt viscosity fluctuation

Abstract

An injection molding adaptive compensation method based on melt viscosity fluctuation comprising: initializing equipment; in a pre-calculation stage, introducing melt into a mold cavity at a constant rate, collecting pre-calculation parameters in each sampling period T, and obtaining a first injection work in the pre-calculation stage by using a first calculation formula; in a self-adaptation stage, introducing the melt into the mold cavity at a constant rate, collecting adaptive parameters in each sampling period T, and obtaining a second injection work in the self-adaptation stage by using a second calculation formula; calling the PVT characteristics of current processing raw materials to construct a PVT relation function, and obtaining an optimized V/P switching point by using a PVT weight control model; and according to the injection work at the pre-calculation stage and the injection work at the present stage, obtaining an optimized holding pressure according to an injection work adjustment model.

Claims

1. An injection molding adaptive compensation method based on melt viscosity fluctuation, characterized by comprising: S1, initializing equipment, and setting an initial V/P switching point, an initial holding pressure and an initial barrel temperature; S2, entering a pre-calculation stage, introducing melt into a mold cavity at a constant rate, and collecting pre-calculation parameters in each sampling period T, including injection pressure, screw displacement and melt temperature; S3, according to the pre-calculation parameters, obtaining a first injection work in the pre-calculation stage by using a first calculation formula; S4, entering a self-adaptation stage, introducing the melt into the mold cavity at a constant rate, and collecting adaptive parameters in each sampling period T, including injection pressure, screw displacement and melt temperature; S5, according to the adaptive parameters, obtaining a second injection work in the self-adaptation stage by using a second calculation formula; S6, calling the PVT characteristics of current processing raw materials to construct a PVT relation function, and obtaining an optimized V/P switching point by using a PVT weight control model; and S7, according to the injection work at the pre-calculation stage and the injection work at the present stage, obtaining an optimized holding pressure according to an injection work adjustment model; in S3, the first calculation formula is: $\begin{array}{cc}{W}_0=.Math.K*{\int }_{\mathrm{Xstart}}^{\mathrm{Xswitch}}\mathrm{pdx}.Math.=.Math.K*.Math.\frac{p_i+p_{i-1}}{2}\left(x_i-x_{i-1}\right).Math.;& \left(1\right)\end{array}$ where W.sub.0 is the first injection work; a pressure value at a starting point of the pre-calculation stage is set to be P.sub.start, and a screw displacement value is set to be X.sub.start; a pressure at an end point of the pre-calculation stage is set to be P.sub.switch, and a screw displacement value is set to be X.sub.switch; and p.sub.i and x.sub.i respectively represent the injection pressure and screw displacement in an i.sup.th sampling period T, and K is a material correction coefficient related to the material itself; in S5, the second calculation formula is: $\begin{array}{cc}{W}_t=.Math.K*{\int }_{\mathrm{Xstart}}^{X_{\mathrm{switch}}^{*}}\mathrm{pdx}.Math.=.Math.K*.Math.\frac{p_j+p_{j-1}}{2}\left(x_j-x_{j-1}\right).Math.;& \left(2\right)\end{array}$ where W.sub.t is the second injection work; a pressure value at a starting point of the self-adaptation stage is set to be P.sub.start, a screw displacement value is set to be X.sub.start, and the displacement value is consistent with X.sub.start in the pre-calculation stage; a pressure value at an end point of the self-adaptation stage is set to be P*.sub.switch, and a screw displacement value is set to be X*.sub.switch; and p.sub.j and x.sub.j respectively represent the injection pressure and screw displacement in a j.sup.th sampling period T, and K is a material correction coefficient related to the material itself; in S6, the PVT weight control model is: $\begin{array}{cc}{x}_t=\frac{x_0*V\left(T_t,P_t\right)}{V\left(T_0,P_0\right)};& \left(3\right)\end{array}$ where x.sub.t is an optimized V/P switching point position at the current stage, and x.sub.0 is an initial V/P switching point position; V(T,P) is a PVT relation function of currently processed materials, T.sub.0 and T.sub.t are melt temperature in the pre-calculation stage and the self-adaptation stage respectively, and P.sub.0 and P.sub.t are the pressure values of characteristic points on injection pressure curves of the pre-calculation stage and the self-adaptation stage respectively; and the characteristic point is a point on a collected injection pressure curve which is separated from the V/P switching point by a safe distance X.sub.s; $\begin{array}{cc}V\left(T,P\right)=\left[b_1+b_2\left(T-b_5\right)\right]\left\{1-C\ln \left[1+\frac{P}{b_3\exp \left[-b_4\left(T-b_5\right)\right]}\right]\right\};& \left(5\right)\end{array}$ where V(T,P) is the specific volume under temperature T and pressure P, C is a universal constant, and b.sub.1, b.sub.2, b.sub.3, b.sub.4, and b.sub.5 respectively represent the state constants of polymer materials in the molten state; in S7, the injection work adjustment model is: $\begin{array}{cc}{P}_{KL}=P_{K0}*K_0*\frac{W_t}{W_0};& \left(4\right)\end{array}$ where P.sub.KL is an optimized holding pressure value of each mold, P.sub.K0 is an initial holding pressure, and K.sub.0 is a correction coefficient related to a material product.

2. The injection molding adaptive compensation method based on melt viscosity fluctuation according to claim 1, characterized in that before S1, S0 is also included: establishing a PVT characteristic library according to raw material types.

3. The injection molding adaptive compensation method based on melt viscosity fluctuation according to claim 1, characterized in that in S2, pre-calculated parameters of a preset number of sampling periods T are collected.

4. The injection molding adaptive compensation method based on melt viscosity fluctuation according to claim 1, characterized in that the sampling periods T of the pre-calculation stage and the self-adaptation stage are consistent.

5. The injection molding adaptive compensation method based on melt viscosity fluctuation according to claim 1, characterized in that after S3, S31 is also included: entering a pressure holding stage, and introducing residual melt into the mold cavity under the initial holding pressure.

6. The injection molding adaptive compensation method based on melt viscosity fluctuation according to claim 1, characterized in that after S7, the method further comprises: S71, entering a pressure holding stage, and introducing residual melt into the mold cavity under the optimized holding pressure; and S8: returning to S4 until the injection molding task is completed.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 is a flow chart of an injection molding adaptive compensation method based on melt viscosity fluctuation; and

(2) FIG. 2 is a comparison chart of weight fluctuation before and after adaptive adjustment.

DETAILED DESCRIPTION OF THE INVENTION

(3) The following are specific embodiments of the invention and a further description of the technical scheme of the invention with reference to the drawings, but the invention is not limited to these embodiments.

Embodiment 1

(4) In order to solve the above problems, ensure that the melt viscosity change caused by the change in the water content of raw materials, the change in the batch of raw materials and the change in the proportion of recycled materials contained in raw materials (that is, the change in the PVT characteristics of polymer materials) is taken into account during injection molding of an injection molding machine, ensure the stability of quality fluctuation of molded products, and improve the repetition accuracy and yield of molded products, as shown in FIG. 1, the invention provides an injection molding adaptive compensation method based on melt viscosity fluctuation, which comprises the following steps: S1, initializing equipment, and setting an initial V/P switching point, an initial holding pressure and an initial barrel temperature; S2, entering a pre-calculation stage, introducing melt into a mold cavity at a constant rate, and collecting pre-calculation parameters in each sampling period T, including injection pressure, screw displacement and melt temperature; S3, according to the pre-calculation parameters, obtaining a first injection work in the pre-calculation stage by using a first calculation formula; S4, entering a self-adaptation stage, introducing the melt into the mold cavity at a constant rate, and collecting adaptive parameters in each sampling period T, including injection pressure, screw displacement and melt temperature; S5, according to the adaptive parameters, obtaining a second injection work in the self-adaptation stage by using a second calculation formula; S6, calling the PVT characteristics of current processing raw materials to construct a PVT relation function, and obtaining an optimized V/P switching point by using a PVT weight control model; and S7, according to the injection work at the pre-calculation stage and the injection work at the present stage, obtaining an optimized holding pressure according to an injection work adjustment model.

(5) Before S1, S0 is also included: establishing a PVT characteristic library according to raw material types.

(6) Further, in S2, pre-calculated parameters of a preset number of sampling periods T are collected, and the sampling periods T of the pre-calculation stage and the self-adaptation stage are consistent.

(7) In the invention, S2-S3 are the data pre-calculation stage in the invention, in which collected pre-calculation parameters are sorted out, and an integral value of pressure versus displacement in this stage is obtained by the following first calculation formula, and is defined as injection work W.sub.0 to characterize the melt viscosity in this stage:

(8) $\begin{array}{cc}{W}_0=.Math.K*{\int }_{\mathrm{Xstart}}^{\mathrm{Xswitch}}\mathrm{pdx}.Math.=.Math.K*.Math.\frac{p_i+p_{i-1}}{2}\left(x_i-x_{i-1}\right).Math.;& \left(1\right)\end{array}$
where W.sub.0 is the first injection work; a pressure value at a starting point of the pre-calculation stage is set to be P.sub.start, and a screw displacement value is set to be X.sub.start; a pressure at an end point of the pre-calculation stage is set to be P.sub.switch, and a screw displacement value is set to be X.sub.switch; and K is a material correction coefficient related to the material itself. Starting from position P.sub.start, the injection pressure and screw displacement of the i (i=1˜n).sup.th period are sampled every other fixed period T, and sampling points are recorded as p.sub.i and x.sub.i respectively.

(9) Further, S3 also comprises:

(10) S31, entering a pressure holding stage, and introducing the residual melt into the mold cavity under the initial holding pressure.

(11) Steps S4-S7 are the self-adaption stage in the invention, in which collected self-adaption parameters are sorted out, and an integral value of pressure versus displacement in this stage is obtained by the following second calculation formula, and is defined as injection work W.sub.t to characterize the melt viscosity in this stage:

(12) $\begin{array}{cc}{W}_t=.Math.K*{\int }_{\mathrm{Xstart}}^{X_{\mathrm{switch}}^{*}}\mathrm{pdx}.Math.=.Math.K*.Math.\frac{p_j+p_{j-1}}{2}\left(x_j-x_{j-1}\right).Math.;& \left(2\right)\end{array}$
where W.sub.t is the second injection work; a pressure value at a starting point of the self-adaptation stage is set to be P.sub.start, a screw displacement value is set to be X.sub.start, and the displacement value is consistent with X.sub.start in the pre-calculation stage; a pressure value at an end point of the self-adaptation stage is set to be P*.sub.switch, and a screw displacement value is set to be X*.sub.switch; and K is a material correction coefficient related to the material itself. Starting from position P.sub.start, the injection pressure and screw displacement of the j (j=1˜n).sup.th period are sampled every other fixed period T, and sampling points are recorded as p.sub.j and x.sub.j respectively.

(13) By comparing the first injection work and the second injection work, and based on the following injection work adjustment model, the optimized holding pressure value can be obtained through calculation:

(14) $\begin{array}{cc}{P}_{KL}=P_{K0}*K_0*\frac{W_t}{W_0};& \left(4\right)\end{array}$
where P.sub.KL is an optimized holding pressure value of each mold, P.sub.K0 is an initial holding pressure, and K.sub.0 is a correction coefficient related to a material product. Because pressure, specific volume and temperature are three very important parameters in the plastic molding process, which have great influence on the properties of materials in all aspects and also play a decisive role in the quality of final injection molded products, PVT characteristics are added to the control elements of injection molding in the invention. According to a two-domain Tait equation of the melt, the specific volume of polymer materials in a molten state can be expressed as:

(15) $\begin{array}{cc}V\left(T,P\right)=\left[b_1+b_2\left(T-b_5\right)\right]\left\{1-C\ln \left[1+\frac{P}{b_3\exp \left[-b_4\left(T-b_5\right)\right]}\right]\right\};& \left(5\right)\end{array}$
where V(T,P) is the specific volume under temperature T and pressure P, C is a universal constant, and b.sub.1, b.sub.2, b.sub.3, b.sub.4 and b.sub.5 respectively represent the state constants of polymer materials in the molten state.

(16) Therefore, in order to solve the injection molding quality deviation caused by the melt viscosity change which results from the change in PVT characteristics, the invention provides a PVT weight control model based on the following to recalculate and obtain the optimized V/P switching point:

(17) $\begin{array}{cc}{x}_t=\frac{x_0*V\left(T_f,P_f\right)}{V\left(T_0{P}_0\right)};& \left(3\right)\end{array}$
where x.sub.t is an optimized V/P switching point position at the current stage, and x.sub.0 is an initial V/P switching point position; V(T,P) is a PVT relation function of currently processed materials, T.sub.0 and T.sub.t are melt temperature in the pre-calculation stage and the self-adaptation stage respectively, and P.sub.0 and P.sub.t are the pressure values of characteristic points on injection pressure curves of the pre-calculation stage and the self-adaptation stage respectively; and the characteristic point is a point on a collected injection pressure curve which is separated from the V/P switching point by a safe distance X.sub.s (ensure that the characteristic point is as close as possible to the V/P switching point, but will not affect the output and execution of the self-optimized V/P switching point).

(18) The optimized V/P switching point is obtained through the PVT weight control model, compared with the traditional idea, the method combines the PVT characteristic relationship and melt index measurement mechanism of polymer materials to adjust the V/P switching point and holding pressure in the injection molding process; and compared with an existing V/P switching point and holding pressure control method, the method of the invention can respond to the fluctuation of melt viscosity in the injection molding process, and make adjustment and optimization responding to the fluctuation, so as to improve the repetition accuracy and yield of molded products.

(19) After obtaining the optimized V/P switching point and holding pressure, in the self-adaption stage, the screw pushes the injection melt to the optimized V/P switching point, and then stops pushing, and the pressure holding stage starts at the same time, namely S71: introducing the residual melt into the mold cavity under the optimized holding pressure.

(20) After completing the above steps, the injection molding task of this stage is completed. At this point, S4 is executed again to start the injection molding task of the next stage. Steps S4-S71 are repeated to continuously compare the injection molding work in the current stage with the first injection molding work, and obtain a new V/P switching point according to new PVT characteristics until all injection molding tasks are completed.

(21) By continuously comparing the injection work and PVT characteristics between the current stage and the pre-calculation stage, and introducing the injection work adjustment model and PVT weight control model to obtain the optimized holding pressure and V/P switching point, the intelligent degree of an injection molding machine is greatly improved, the self-learning process can be completed within only one production stage, and the V/P switching point and holding pressure can be adjusted adaptively from the second stage, and the adjustment can be completed automatically in the whole process without manual intervention.

(22) Meanwhile, the injection molding based on melt viscosity fluctuation is adaptively compensated by the method, and there is no need to install a sensor on a mold, which reduces the requirements for the mold itself and improves the adaptability and universality to a certain extent.

Embodiment 2

(23) In order to better explain the invention, so that the technical points can be reflected more clearly, the dynamic adjustment of the invention is explained with a specific embodiment.

(24) In this embodiment, polystyrene with different viscosities was used as processing raw materials, and standard warpage pieces were used as experimental products. Collection and preparation of parameters were also conducted, to ensure that the injection pressure and screw displacement of an injection molding machine can be acquired in real time, and the sampling period T was set to be 10 ms. Meanwhile, some preset process parameters were set as shown in Table 1.

(25) TABLE-US-00001 TABLE 1 V/P Injection switching Holding Holding Barrel Mold speed point pressure time temperature temperature 40 mm/s 13 mm 37 mpa 15 s 210° C. 40° C.

(26) Before entering the self-adaption stage, the pre-calculation stage was conducted for one cycle, the pre-calculation parameters in the processing cycle were collected (in order to ensure the accuracy of injection work calculation, in the present embodiment, the processing cycle included 360 sampling cycles T), and a series of sampling points were obtained. The integral value of injection pressure versus screw displacement was calculated, and the corresponding injection work W.sub.0 was obtained. Specific sampling points are shown in Table 2.

(27) TABLE-US-00002 TABLE 2 Injection pressure Screw displacement Sampling point p.sub.i/mpa x.sub.i/mm 1 3.9633 80.999 2 4.0238 80.999 3 5.0222 80.9728 . . . . . . . . . 356 41.5397 15.0219 357 42.0238 14.3800 358 42.9920 13.7288 359 42.3566 13.1328

(28) Then the first injection work W.sub.0 of the current cycle (i.e., the pre-calculation stage) was calculated based on the first calculation formula proposed by the invention, and the melt viscosity of the current cycle was characterized by this value:

(29) 0$W_0=.Math.K*{\int }_{\mathrm{Xstart}}^{\mathrm{Xswitch}}\mathrm{pdx}.Math.=.Math.K*.Math.\frac{p_i+p_{i-1}}{2}\left(x_i-x_{i-1}\right).Math.$
where X.sub.start is 80.9999 mm, P.sub.start is 3.9633 mpa, X.sub.switch is 13.1328 mm and P.sub.switch is 42.3566 mpa. The injection work W.sub.0 of the current cycle calculated by this formula was 141181.84 pa.Math.mm.

(30) After the screw reached the preset V/P switching point (i.e., upon entering the 359.sup.th sampling cycle), the screw stopped pushing the melt, the injection molding machine entered the pressure holding stage, and the residual melt was introduced into the mold cavity under the preset holding pressure to supplement the shrinkage of the product. The pre-calculation stage ended.

(31) Then the injection molding machine was started to enter the self-adaption stage, the polystyrene raw material with reduced viscosity was added to continue processing, process curves including injection pressure and screw displacement were acquired in real time, and sampling operation was conducted with the sampling period of 10 ms on the injection pressure and screw curves. The corresponding real-time information obtained is shown in Table 3.

(32) TABLE-US-00003 TABLE 3 Injection pressure Screw displacement Sampling point p.sub.i/mpa x.sub.i/mm 1 4.4474 81.0001 2 4.9315 80.9906 3 8.0780 80.7286 . . . . . . . . . 356 36.8503 15.1506 357 37.3343 14.5090 358 38.0907 13.8604

(33) Similarly, the second injection work W.sub.t was calculated by the second calculation formula of the invention,

(34) and the injection work W.sub.t of the current cycle (i.e., the self-adaption stage) can be expressed as:

(35) $W_t=.Math.K*{\int }_{\mathrm{Xstart}}^{X_{\mathrm{switch}}^{*}}\mathrm{pdx}.Math.=.Math.K*.Math.\frac{p_j+p_{j-1}}{2}\left(x_j-x_{j-1}\right).Math.$
where X.sub.start is 81.0001 mm, P.sub.start is 4.4474 mpa, X*.sub.switch is 13.8604 mm and P*.sub.switch is 38.0907 mpa. The injection work of the current cycle calculated by this formula was 133950.4 pa.Math.mm.

(36) After calculating the injection work in the pre-calculation stage and the current stage, a key value of a PVT characteristic function of this kind of polystyrene in the PVT characteristic library was called, as shown in Table 4.

(37) TABLE-US-00004 TABLE 4 b.sub.1(m.sup.3/kg) 9.88 × 10.sup.−4 $b_2\left(\frac{m^3}{\mathrm{kg}}.Math.°C.\right)$ 6.10 × 10.sup.−7 b.sub.3(Pa) 1.15 × 10.sup.8 b.sub.4(° C..sup.−1) 3.66 × 10.sup.−3 b.sub.5(° C.) 112.0 C 0.0894

(38) That is, the PVT characteristic function of the polystyrene is:

(39) $V\left[T,P\right]=\left[9.88\times 10^{-4}+6.10\times 10^{-7}\left(T-112.0\right)\right]\times \left\{1-0.0894\ln \left[1+\frac{P}{1.15\times 10^8\exp \left[-3.66\times 10^{-3}\left(T-112.0\right)\right]}\right]\right\}$

(40) It can be seen from the above tables 2 and 3 that after entering the self-adaption stage, the injection pressure was reduced from 42.3566 mpa to 38.0907 mpa, and the barrel temperature was constant at 210° C., so according to the PVT key parameter table as shown in table 4, it can be calculated that the specific volume of the melt changed from 10.05×10.sup.−4 m.sup.3/kg to 10.12×10.sup.−4 m.sup.3/kg. Because the barrel temperature did not change, but the viscosity of the processed melt decreased, based on the PVT weight control model:

(41) $x_t=\frac{x_0*V\left(T_t,P_t\right)}{V\left(T_0,P_0\right)}$
where x.sub.0 is 13 mm, P.sub.0 is 42.0238 mpa, P.sub.t is 36.8503, so the optimized x.sub.t is 13.1 mm.

(42) At the same time, by using the obtained change rate of injection work (i.e., viscosity) and the injection work adjustment model, the optimized holding pressure value in the current stage was output:

(43) $P_{KL}=P_{K0}*K_0*\frac{W_t}{W_0}$

(44) By substituting P.sub.K0, W.sub.0 and W.sub.t, the optimized holding pressure P.sub.KL is 35.10 mpa. Processing was continued step by step, the injection work and PVT characteristics of the current processing stage and the pre-calculation stage were calculated and compared in turn, formed experimental pieces were weighed, and the final results were plotted, so as to obtain a weight fluctuation comparison diagram before and after adaptive adjustment as shown in FIG. 2. From FIG. 2, it can be clearly seen that the fluctuation of product weight is obviously optimized after the self-optimization function is started.

(45) To sum up, compared with the traditional idea, the injection molding adaptive compensation method based on melt viscosity fluctuation in the invention combines the PVT characteristic relationship and melt index measurement mechanism of polymer materials to adjust the V/P switching point and holding pressure in the injection molding process; and compared with an existing V/P switching point and holding pressure control method, the method of the invention can respond to the fluctuation of melt viscosity in the injection molding process, and make adjustment and optimization responding to the fluctuation, so as to improve the repetition accuracy and yield of molded products.

(46) By means of the pre-calculation stage and the self-adaption stage, the intelligent degree of an injection molding machine is greatly improved, the self-learning process can be completed within only one production stage, and the V/P switching point and holding pressure can be adjusted adaptively from the second stage, and the adjustment can be completed automatically in the whole process without manual intervention. Besides, there is no need to install a sensor on a mold, which reduces the requirements for the mold itself and improves the adaptability and universality of the method to a certain extent.

(47) The specific embodiments described herein are only illustrative of the spirit of the invention. Those skilled in the art to which the invention belongs can make various modifications or supplements to the specific embodiments described or replace them in a similar way, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.