TW202205161A

TW202205161A - Quality prediction method for injection modeling

Info

Publication number: TW202205161A
Application number: TW109124971A
Authority: TW
Inventors: 黃明賢; 粘世智
Original assignee: 國立高雄科技大學
Priority date: 2020-07-23
Filing date: 2020-07-23
Publication date: 2022-02-01
Also published as: CN113971336A

Abstract

The present invention relates to a quality prediction method for injection modeling, which includes the following steps: establishing a trial curve for an injection molded product which meets the quality requirement; performing a disturbance experiment according to the trial curve to obtain a plurality of characteristic indexes of a forming curve which is relevant to process parameters; measuring injection molded products formed in the disturbance experiment to obtain their respective quality values, and performing a correlation analysis operation to obtain a plurality of correlations between the quality values and the characteristic indexes of the forming curve; selecting the characteristic indexes which have higher correlations with the quality values as quality characteristic indexes, for quality monitoring. Thereby, the quality values can be monitored simultaneously based on their respective quality characteristic indexes.

Description

射出成形的品質預測方法Quality prediction method for injection molding

本發明係有關於一種射出成形的品質預測方法，特別是指監測與各種品質數值高度關聯的成形曲線之特徵指標，由射出過程之成形曲線之特徵指標運算預測射出成形產品的多個品質數值，藉以即時預測射出成形產品品質是否合乎驗收要求。The present invention relates to a quality prediction method for injection molding, in particular to monitoring the characteristic index of the forming curve that is highly correlated with various quality values, and predicting multiple quality values of the injection molded product by calculating the characteristic index of the forming curve in the injection process, In order to immediately predict whether the quality of injection molded products meets the acceptance requirements.

在這波工業4.0的智能化浪潮裡，各工業先進國家的機械製造產業也相繼投入此一大趨勢的競逐中，國際各大射出機大廠莫不積極發展自我之智能化系統。In this wave of intelligentization of Industry 4.0, the machinery manufacturing industries of various industrially advanced countries have also invested in the competition of this major trend. All major international injection machine manufacturers are actively developing their own intelligent systems.

智慧化試模技術是本案發明人近幾年來亟力投入發展之技術，智慧化試模建立在科學試模上，科學試模應用模內感測技術獲取模穴內熔膠之狀態，將傳統射出的機械控制進步到熔膠狀態控制，將傳統射出之「機械觀點」進化為「塑料觀點」，而智慧化試模更進一步的建立各試模階段之試模法則、建立特徵指標與數據運算之數學模型，對射出成形過程之機械及模內感測數據進行成形曲線樣態分析與特徵擷取。試模時將傳統的參數調整進化為成形曲線之樣態調整，最後結合品質量測及學習歷程產出強健參數，將只適合單一試模射出機之傳統機械參數試模表單進化為適用於任一射出機之成形曲線(函數)。The intelligent mold test technology is a technology that the inventor of this case has devoted to development in recent years. The intelligent mold test is based on the scientific mold test. The mechanical control of injection has been advanced to the control of the melted state, and the traditional "mechanical point of view" of injection has evolved into a "plastic point of view", and the intelligent mold test further establishes mold test rules for each test stage, and establishes feature indicators and data operations. The mathematical model of the injection molding process is used to analyze the shape of the forming curve and extract the characteristics of the mechanical and in-mold sensing data of the injection molding process. During the mold test, the traditional parameter adjustment is evolved into the form adjustment of the forming curve. Finally, the product quality test and the learning process are combined to produce robust parameters, and the traditional mechanical parameter mold test form, which is only suitable for a single mold injection machine, has been evolved to be suitable for any one. A forming curve (function) of an injection machine.

但是目前量產之品質監控技術大都以單一品質或總體品質指標(重量)之監控行為(壓力峰值、壓力時間積分、大柱最大延伸量)為主，然而絕大部分之塑件皆有一個以上之品質要求，由於各品質的曲線監控特徵並不一定相同，無法以單一曲線特徵來解析塑件之各項成形品質。However, the current mass production quality monitoring technology mostly focuses on monitoring behaviors of single quality or overall quality index (weight) (pressure peak, pressure time integral, maximum extension of large column), but most plastic parts have more than one Because the curve monitoring characteristics of each quality are not necessarily the same, it is impossible to analyze the forming quality of plastic parts with a single curve characteristic.

基於前案對於射出成形產品品質監控的不便，本發明提出一種射出成形的品質預測方法，包括有：Based on the inconvenience of the previous case for quality monitoring of injection molding products, the present invention proposes a quality prediction method for injection molding, including:

對一射出成形產品，獲得合乎品質要求之一試模曲線；根據上述試模曲線，利用擾動實驗獲得與製程參數有關的成形曲線之複數特徵指標；將上述擾動實驗獲得的射出成形產品進行複數品質數值量測，接著與成形曲線之複數特徵指標的值進行關聯性分析運算，以獲得複數品質數值與成形曲線之特徵指標的關聯度；選擇與各品質數值關聯度高的成形曲線之特徵指標作為品質特徵指標進行監測，藉以根據上述各品質特徵指標同時監測各品質數值。For an injection molded product, obtain a mold test curve that meets the quality requirements; according to the above mold test curve, use the disturbance experiment to obtain the complex characteristic index of the forming curve related to the process parameters; The injection molded product obtained by the above disturbance experiment is subjected to complex quality evaluation. Numerical measurement, and then perform correlation analysis operation with the value of the complex characteristic index of the forming curve to obtain the correlation degree between the complex quality value and the characteristic index of the forming curve; select the characteristic index of the forming curve with a high degree of correlation with each quality value as the The quality characteristic index is monitored, so as to simultaneously monitor each quality value according to the above-mentioned quality characteristic index.

進一步，上述成形曲線特徵指標包括壓力峰值指標、殘留壓力指標、殘留壓力差指標、能量指標、速率指標及黏度指標。Further, the above-mentioned characteristic indexes of the forming curve include a pressure peak index, a residual pressure index, a residual pressure difference index, an energy index, a velocity index and a viscosity index.

更進一步，上述壓力峰值指標包括系統壓力峰值T1、豎澆道壓力峰值T2、近澆口0秒至1.2秒的壓力峰值T3、近澆口1.2秒至5秒的壓力峰值T4、流動末端壓力峰值T5及中間位置壓力峰值T6；上述殘留壓力指標包括近澆口殘留壓力R1、中間位置殘留壓力R2、流動末端殘留壓力R3；上述殘留壓力差指標包括近端至末端殘留壓力差RP1、近端至中端殘留壓力差RP2、中端至末端殘留壓力差RP3及最大壓力差RP4；上述能量指標包括全時間壓力積分、射出峰值時間積分、壓力峰值時間積分及射出峰值時間乘積，其中全時間壓力積分包括系統全時間壓力積分E1、豎澆道全時間壓力積分E2、近澆口全時間壓力積分E3、流動末端全時間壓力積分E4、中間位置全時間壓力積分E5、0秒至5秒螺桿位移壓力積分E6、豎澆道螺桿位移壓力積分E7及近澆口螺桿位移壓力積分E8，射出峰值時間積分包括近澆***出峰值時間積分E9，壓力峰值時間積分包括系統壓力峰值時間積分E10、豎澆道壓力峰值時間積分E11、近澆口壓力峰值時間積分E12、流動末端壓力峰值時間積分E13及中間位置壓力峰值時間積分E14，射出峰值時間乘積包括T1乘上第一時間t1(E15)、T2乘上第二時間t2(E16)、T3乘上第三時間t3(E17)、T4乘上第四時間t4(E18)、T5乘上第五時間t5(E19)及T6乘上第六時間t6(E20)；上述速率指標包括豎澆道速率S1、近澆口速率S2、流動末端速率S3及中間位置速率S4；上述黏度指標包括豎澆道至近澆口壓力差的黏度V1、豎澆道至中間位置壓力差的黏度V2及豎澆道至末端壓力差的黏度V3。Further, the above-mentioned pressure peak indicators include system pressure peak T1, vertical runner pressure peak T2, pressure peak T3 near the gate for 0 seconds to 1.2 seconds, pressure peak T4 near the gate for 1.2 seconds to 5 seconds, and peak pressure at the end of the flow. T5 and peak pressure T6 at the middle position; the above residual pressure indicators include the residual pressure R1 near the gate, the residual pressure R2 at the middle position, and the residual pressure R3 at the end of the flow; the above residual pressure difference indicators include the residual pressure difference from the proximal end to the end RP1, the proximal end to the end Residual pressure difference RP2 at the middle end, residual pressure difference RP3 from the middle end to the end, and maximum pressure difference RP4; the above energy indicators include the full-time pressure integral, the injection peak time integral, the pressure peak time integral and the injection peak time product, of which the full-time pressure integral Including system full-time pressure integral E1, vertical runner full-time pressure integral E2, near gate full-time pressure integral E3, flow end full-time pressure integral E4, middle position full-time pressure integral E5, screw displacement pressure from 0 seconds to 5 seconds Integral E6, vertical runner screw displacement pressure integral E7 and near gate screw displacement pressure integral E8, injection peak time integral includes near gate injection peak time integral E9, pressure peak time integral includes system pressure peak time integral E10, vertical runner The pressure peak time integral E11, the near gate pressure peak time integral E12, the flow end pressure peak time integral E13 and the middle position pressure peak time integral E14, the injection peak time product includes T1 multiplied by the first time t1 (E15), T2 multiplied by The second time t2 (E16), T3 times the third time t3 (E17), T4 times the fourth time t4 (E18), T5 times the fifth time t5 (E19) and T6 times the sixth time t6 (E20 ); Above-mentioned speed index comprises vertical runner speed S1, near gate speed S2, flow end speed S3 and intermediate position speed S4; Above-mentioned viscosity index comprises vertical runner to near gate pressure difference viscosity V1, vertical runner to intermediate position The viscosity V2 of the pressure difference and the viscosity V3 of the pressure difference from the sprue to the end.

進一步，上述關聯性分析可包括皮爾森相關係數分析、斯皮爾曼等級相關係數檢定或非線性回歸二次曲線模型R檢驗之一或組合。Further, the above-mentioned correlation analysis may include one or a combination of Pearson correlation coefficient analysis, Spearman rank correlation coefficient test or nonlinear regression quadratic curve model R test.

進一步，根據其中一品質數值量測結果與該品質特徵指標的XY散布圖取得該品質特徵指標與該品質數值間的關係式，每模次的該品質數值可經由該模次品質特徵指標的值經由關係式反推得知，達到經由品質特徵指標預測當模次射出成形產品的品質。Further, the relationship between the quality characteristic index and the quality value is obtained according to the XY scatter diagram of a quality value measurement result and the quality characteristic index, and the quality value of each model can be obtained through the value of the model quality characteristic index. It is known through the reverse inference of the relational expression that the quality of the injection-molded product can be predicted through the quality characteristic index.

進一步，根據該XY散布圖依據品質數值要求區間取得該品質特徵指標的一合格範圍，根據該品質特徵指標是否落入該合格範圍區間，判斷該次射出成形產品的該監測品質是否合格。Further, a qualified range of the quality characteristic index is obtained according to the quality numerical requirement interval according to the XY scatter diagram, and whether the monitoring quality of the injection molded product is qualified or not is determined according to whether the quality characteristic index falls within the qualified range interval.

根據上述技術特徵可達成以下功效：According to the above technical features, the following effects can be achieved:

1.本發明透過取得與各種品質數值高度關聯的成形曲線特徵指標，將只要監測各個品質相對應的成形曲線特徵指標即可即時預測射出成形產品的各個品質數值。1. The present invention can instantly predict the various quality values of injection-molded products by obtaining the characteristic indexes of the forming curves that are highly correlated with various quality values.

2.本發明根據其中一品質的XY散布圖取得該品質特徵指標的一合格範圍(可允許的品質範圍)，即可根據該品質特徵指標是否落入該合格範圍區間，即時判斷該模次射出成形產品之品質是否合格。2. The present invention obtains a qualified range (allowable quality range) of the quality characteristic index according to the XY scatter diagram of one of the qualities, and can immediately judge the mode injection according to whether the quality characteristic index falls within the qualified range interval. Whether the quality of the formed product is qualified.

3.本發明可同時針對射出成形產品的多個品質經由相對應的品質特徵指標進行品質預測。3. The present invention can simultaneously perform quality prediction for multiple qualities of injection-molded products through corresponding quality characteristic indicators.

4.本發明可作為射出成形量產過程的射出成形產品品質變異監測之用，即時反應製程變異。4. The present invention can be used for monitoring the quality variation of injection molding products in the mass production process of injection molding, so as to reflect the process variation in real time.

綜合上述技術特徵，本發明射出成形的品質預測方法的主要功效將可於下述實施例清楚呈現。In view of the above technical features, the main effects of the injection molding quality prediction method of the present invention will be clearly presented in the following embodiments.

參閱第一圖及第二圖所示，本實施例包括有：Referring to the first and second figures, this embodiment includes:

取一射出成形產品1，通常為塑件，本實施例以長/寬皆為76 mm之小托盤為例。其中，該射出成形產品1合格的品質指標以左側寬度W1、中間寬度W2、右側寬度W3以及整體翹曲量Warpage為例。Take an injection-molded product 1, which is usually a plastic part. In this embodiment, a small tray with a length/width of 76 mm is taken as an example. Among them, the quality indicators that the injection molded product 1 is qualified are taken as examples of the left width W1 , the middle width W2 , the right width W3 and the overall warpage amount Warpage.

配合參閱第三圖至第五圖所示，首先獲得該射出成形產品1合乎品質要求之一試模曲線。再根據上述試模曲線，利用擾動實驗獲得與製程參數有關的成形曲線之複數特徵指標。Referring to Figures 3 to 5, first obtain a mold test curve that meets the quality requirements of the injection molded product 1 . Then, according to the above-mentioned mold test curve, the complex characteristic index of the forming curve related to the process parameters is obtained by using the disturbance experiment.

上述特徵指標包括壓力峰值指標、殘留壓力指標、殘留壓力差指標、能量指標、速率指標及黏度指標。其中，上述壓力峰值指標包括系統壓力峰值T1、豎澆道壓力峰值T2、近澆口0秒至1.2秒的壓力峰值T3、近澆口1.2秒至5秒的壓力峰值T4、流動末端壓力峰值T5及中間位置壓力峰值T6；上述殘留壓力指標包括近澆口殘留壓力R1、中間位置殘留壓力R2、流動末端殘留壓力R3；上述殘留壓力差指標包括近端至末端殘留壓力差RP1、近端至中端殘留壓力差RP2、中端至末端殘留壓力差RP3及最大壓力差RP4；上述能量指標包括全時間壓力積分、射出峰值時間積分、壓力峰值時間積分及射出峰值時間乘積，其中全時間壓力積分包括系統全時間壓力積分E1、豎澆道全時間壓力積分E2、近澆口全時間壓力積分E3、流動末端全時間壓力積分E4、中間位置全時間壓力積分E5、0秒至5秒螺桿位移壓力積分E6、豎澆道螺桿位移壓力積分E7、及近澆口螺桿位移壓力積分E8，射出峰值時間積分包括近澆***出峰值時間積分E9，壓力峰值時間積分包括系統壓力峰值時間積分E10、豎澆道壓力峰值時間積分E11、近澆口壓力峰值時間積分E12、流動末端壓力峰值時間積分E13及中間位置壓力峰值時間積分E14，射出峰值時間乘積包括T1乘上第一時間t1(E15)、T2乘上第二時間t2(E16)、T3乘上第三時間t3(E17)、T4乘上第四時間t4(E18)、T5乘上第五時間t5(E19)及T6乘上第六時間t6(E20)；上述速率指標包括豎澆道速率S1、近澆口速率S2、流動末端速率S3及中間位置速率S4；上述黏度指標包括豎澆道至近澆口壓力差的黏度V1、豎澆道至中間位置壓力差的黏度V2及豎澆道至末端壓力差的黏度V3。The above characteristic indexes include pressure peak index, residual pressure index, residual pressure difference index, energy index, velocity index and viscosity index. Among them, the above-mentioned pressure peak indicators include system pressure peak value T1, vertical runner pressure peak value T2, pressure peak value T3 near the gate for 0 seconds to 1.2 seconds, pressure peak value T4 near the gate for 1.2 seconds to 5 seconds, and flow end pressure peak T5 and the peak pressure T6 at the middle position; the above residual pressure indicators include the residual pressure R1 near the gate, the residual pressure R2 at the middle position, and the residual pressure R3 at the end of the flow; the above residual pressure difference indicators include the residual pressure difference from the proximal end to the end RP1, the proximal end to the middle End residual pressure difference RP2, middle end to end residual pressure difference RP3 and maximum pressure difference RP4; the above energy indicators include full-time pressure integral, injection peak time integral, pressure peak time integral and injection peak time product, of which the full-time pressure integral includes System full-time pressure integration E1, vertical runner full-time pressure integration E2, near gate full-time pressure integration E3, flow end full-time pressure integration E4, middle position full-time pressure integration E5, screw displacement pressure integration from 0 seconds to 5 seconds E6, vertical sprue screw displacement pressure integral E7, and near gate screw displacement pressure integral E8, injection peak time integral includes near gate injection peak time integral E9, pressure peak time integral includes system pressure peak time integral E10, vertical runner The pressure peak time integral E11, the near gate pressure peak time integral E12, the flow end pressure peak time integral E13 and the middle position pressure peak time integral E14, the injection peak time product includes T1 multiplied by the first time t1 (E15), T2 multiplied by The second time t2 (E16), T3 times the third time t3 (E17), T4 times the fourth time t4 (E18), T5 times the fifth time t5 (E19) and T6 times the sixth time t6 (E20 ); Above-mentioned speed index comprises vertical runner speed S1, near gate speed S2, flow end speed S3 and intermediate position speed S4; Above-mentioned viscosity index comprises vertical runner to near gate pressure difference viscosity V1, vertical runner to intermediate position The viscosity V2 of the pressure difference and the viscosity V3 of the pressure difference from the sprue to the end.

參閱下表一，將上述擾動實驗獲得的射出成形產品進行複數品質數值量測，並與上述成形曲線之特徵指標的值利用關聯性分析運算，以獲得各品質數值與成形曲線之特徵指標的關聯度，上述關聯性分析包括皮爾森相關係數分析、斯皮爾曼等級相關係數檢定或非線性回歸二次曲線模型R檢驗之一或組合。Referring to Table 1 below, the injection-molded products obtained from the above disturbance experiments are subjected to complex quality numerical measurement, and correlation analysis is performed with the values of the characteristic indexes of the forming curve to obtain the correlation between each quality value and the characteristic indexes of the forming curve. The above correlation analysis includes one or a combination of Pearson correlation coefficient analysis, Spearman rank correlation coefficient test or nonlinear regression quadratic curve model R test.

皮爾森Pearson相關係數分析：Pearson correlation coefficient analysis:

皮爾森公式(式1)用於度量兩變數X(品質指標)和Y(特徵指標)之間的線性相依程度，其值介於±1之間，相關係數＜ 0.3為低相關，＞ 0.7為高相關。

(1)Pearson's formula (Equation 1) is used to measure the degree of linear dependence between two variables X (quality index) and Y (characteristic index). high correlation.

(1)

斯皮爾曼Spearman等級相關係數檢定：Spearman's rank correlation coefficient test:

Spearman將變數化為排序(X_i , Y_i 化為排序之x_i , y_i )，計算ρ值(式2)比較兩變量的等級相關。本試驗之n為24，在α = 0.01(99%信心水準)下Spearman’s等級相關係數臨界值為0.537(查表)，ρ值大於0.537為相關。Spearman transforms the variables into ordering (X _i , Y _i into ordering _xi , y _i ), and calculates the ρ value (Equation 2) to compare the rank correlation of the two variables. The n of this test is 24, and the critical value of Spearman's rank correlation coefficient is 0.537 (check table) under α = 0.01 (99% confidence level), and the ρ value greater than 0.537 is correlated.

其中，d_i =x_i - y_i (2)

where d _i = _xi - y _i (2)

非線性回歸二次曲線模型R檢驗：Nonlinear regression quadratic model R test:

非線性回歸預測法是指自變數與因變數不是線性關係，而是某種非線性關係時的回歸預測法，本試驗諸多特徵指標與品質指標間呈二次曲線關係(式3)，故本試驗以全指標、全品質二次曲線模型R檢驗(式4)以提高預測精度，R值之相關程度與pearson同。The nonlinear regression prediction method refers to the regression prediction method when the independent variable and the dependent variable are not linear, but some kind of nonlinear relationship. The relationship between many characteristic indicators and quality indicators in this experiment is a quadratic curve (Equation 3). The test uses the full index and full quality quadratic curve model R test (formula 4) to improve the prediction accuracy, and the correlation degree of the R value is the same as that of pearson.

(3)

(4) 表一：

品質特徵指標 T1 T2 T3 T4 T5 T6 R1 R2 R3 R P 1 R P 2 R P 3 R P 4 SC1 S1 S2 S3 S4 V1 V2 V3 W1 pearson(r) 0.31 0.91 0.92 0.94 0.93 0.93 0.91 0.94 0.92 -0.92 -0.91 0.83 0.91 -0.89 0.10 -0.37 0.69 0.27 0.18 0.05 0.70 Spearman (ρ) 0.30 0.72 0.86 0.89 0.88 0.89 0.83 0.91 0.87 -0.89 -0.89 0.80 0.89 -0.81 0.00 -0.47 0.68 0.11 0.18 0.15 0.68 二次曲線(R) 0.33 0.92 0.92 0.95 0.94 0.94 0.92 0.94 0.93 0.92 0.91 0.84 0.91 0.89 0.14 0.38 0.77 0.27 0.24 0.47 0.76 W2 pearson(r) 0.34 0.91 0.93 0.94 0.94 0.94 0.91 0.94 0.92 -0.92 -0.90 0.81 0.90 -0.90 0.07 -0.40 0.68 0.26 0.17 0.05 0.70 Spearman (ρ) 0.31 0.72 0.87 0.90 0.88 0.90 0.85 0.91 0.86 -0.87 -0.86 0.79 0.88 -0.82 0.07 -0.52 0.68 0.12 0.12 0.15 0.69 二次曲線(R) 0.36 0.92 0.93 0.95 0.95 0.95 0.92 0.94 0.93 0.92 0.91 0.82 0.90 0.91 0.10 0.42 0.77 0.27 0.21 0.49 0.76 W3 pearson(r) 0.33 0.88 0.91 0.94 0.93 0.94 0.89 0.93 0.91 -0.93 -0.91 0.82 0.91 -0.90 0.07 -0.42 0.71 0.29 0.15 -0.02 0.70 Spearman (ρ) 0.34 0.70 0.85 0.87 0.87 0.88 0.82 0.92 0.85 -0.89 -0.88 0.77 0.88 -0.82 0.02 -0.47 0.69 0.15 0.13 0.08 0.65 二次曲線(R) 0.33 0.88 0.91 0.94 0.93 0.94 0.89 0.93 0.92 0.93 0.92 0.83 0.91 0.90 0.14 0.43 0.78 0.30 0.21 0.44 0.72 W arpage pearson(r) 0.08 0.82 0.86 0.86 0.86 0.86 0.87 0.86 0.88 -0.78 -0.86 0.89 0.86 -0.84 0.08 -0.62 0.70 0.24 0.19 0.29 0.68 Spearman (ρ) 0.11 0.78 0.84 0.83 0.83 0.81 0.80 0.79 0.78 -0.72 -0.70 0.71 0.70 -0.81 0.10 -0.55 0.67 0.15 0.06 0.40 0.66 二次曲線(R) 0.12 0.90 0.90 0.88 0.88 0.88 0.91 0.91 0.91 0.82 0.88 0.89 0.89 0.84 0.20 0.67 0.72 0.27 0.26 0.47 0.69 品質指標特徵指標 E1 E2 E3 E4 E5 E6 E7 E8 E9 E 10 E 11 E 12 E 13 E 14 E 15 E 16 E 17 E 18 E 19 E 20 W1 pearson(r) 0.84 0.94 0.92 0.94 0.93 0.19 0.19 -0.34 0.55 0.80 0.60 0.87 0.82 0.85 0.47 0.88 0.92 0.93 0.93 0.93 Spearman(ρ) 0.82 0.92 0.87 0.91 0.89 0.29 0.27 -0.29 0.45 0.82 0.44 0.84 0.80 0.81 0.38 0.82 0.89 0.89 0.88 0.90 二次曲線(R) 0.84 0.95 0.93 0.94 0.94 0.19 0.23 0.35 0.62 0.81 0.64 0.87 0.82 0.85 0.47 0.89 0.93 0.93 0.93 0.93 W2 pearson(r) 0.82 0.93 0.92 0.94 0.93 0.17 0.14 -0.37 0.60 0.82 0.63 0.89 0.85 0.87 0.42 0.87 0.93 0.91 0.92 0.92 Spearman(ρ) 0.82 0.91 0.88 0.90 0.88 0.27 0.23 -0.32 0.48 0.81 0.47 0.85 0.80 0.82 0.34 0.79 0.90 0.88 0.89 0.90 二次曲線(R) 0.82 0.94 0.93 0.94 0.93 0.17 0.18 0.38 0.66 0.84 0.67 0.89 0.85 0.87 0.42 0.87 0.95 0.91 0.92 0.92 W3 pearson(r) 0.83 0.93 0.91 0.93 0.92 0.23 0.19 -0.32 0.54 0.80 0.58 0.87 0.82 0.85 0.41 0.87 0.93 0.92 0.94 0.93 Spearman(ρ) 0.82 0.89 0.86 0.91 0.87 0.34 0.28 -0.27 0.42 0.80 0.42 0.82 0.77 0.79 0.35 0.81 0.88 0.88 0.89 0.90 二次曲線(R) 0.83 0.93 0.91 0.93 0.92 0.23 0.23 0.37 0.57 0.81 0.59 0.87 0.82 0.85 0.43 0.87 0.93 0.92 0.94 0.93 W arpage pearson(r) 0.67 0.86 0.89 0.87 0.89 -0.02 0.11 -0.44 0.59 0.65 0.64 0.84 0.81 0.83 0.19 0.84 0.82 0.81 0.83 0.81 Spearman(ρ) 0.64 0.80 0.83 0.81 0.82 0.04 0.15 -0.46 0.54 0.64 0.57 0.83 0.80 0.80 0.05 0.65 0.83 0.78 0.77 0.75 二次曲線(R) 0.67 0.88 0.91 0.91 0.91 0.10 0.19 0.46 0.59 0.66 0.64 0.87 0.83 0.85 0.23 0.86 0.82 0.84 0.87 0.83

(4) Table 1:

quality Feature index T1 T2 T3 T4 T5 T6 R1 R2 R3 R P 1 R P 2 R P 3 R P 4 SC1 S1 S2 S3 S4 V1 V2 V3 W1 pearson(r) 0.31 0.91 0.92 0.94 0.93 0.93 0.91 0.94 0.92 -0.92 -0.91 0.83 0.91 -0.89 0.10 -0.37 0.69 0.27 0.18 0.05 0.70 Spearman (ρ) 0.30 0.72 0.86 0.89 0.88 0.89 0.83 0.91 0.87 -0.89 -0.89 0.80 0.89 -0.81 0.00 -0.47 0.68 0.11 0.18 0.15 0.68 Quadratic (R) 0.33 0.92 0.92 0.95 0.94 0.94 0.92 0.94 0.93 0.92 0.91 0.84 0.91 0.89 0.14 0.38 0.77 0.27 0.24 0.47 0.76 W2 pearson(r) 0.34 0.91 0.93 0.94 0.94 0.94 0.91 0.94 0.92 -0.92 -0.90 0.81 0.90 -0.90 0.07 -0.40 0.68 0.26 0.17 0.05 0.70 Spearman (ρ) 0.31 0.72 0.87 0.90 0.88 0.90 0.85 0.91 0.86 -0.87 -0.86 0.79 0.88 -0.82 0.07 -0.52 0.68 0.12 0.12 0.15 0.69 Quadratic (R) 0.36 0.92 0.93 0.95 0.95 0.95 0.92 0.94 0.93 0.92 0.91 0.82 0.90 0.91 0.10 0.42 0.77 0.27 0.21 0.49 0.76 W3 pearson(r) 0.33 0.88 0.91 0.94 0.93 0.94 0.89 0.93 0.91 -0.93 -0.91 0.82 0.91 -0.90 0.07 -0.42 0.71 0.29 0.15 -0.02 0.70 Spearman (ρ) 0.34 0.70 0.85 0.87 0.87 0.88 0.82 0.92 0.85 -0.89 -0.88 0.77 0.88 -0.82 0.02 -0.47 0.69 0.15 0.13 0.08 0.65 Quadratic (R) 0.33 0.88 0.91 0.94 0.93 0.94 0.89 0.93 0.92 0.93 0.92 0.83 0.91 0.90 0.14 0.43 0.78 0.30 0.21 0.44 0.72 Warpage _ pearson(r) 0.08 0.82 0.86 0.86 0.86 0.86 0.87 0.86 0.88 -0.78 -0.86 0.89 0.86 -0.84 0.08 -0.62 0.70 0.24 0.19 0.29 0.68 Spearman (ρ) 0.11 0.78 0.84 0.83 0.83 0.81 0.80 0.79 0.78 -0.72 -0.70 0.71 0.70 -0.81 0.10 -0.55 0.67 0.15 0.06 0.40 0.66 Quadratic (R) 0.12 0.90 0.90 0.88 0.88 0.88 0.91 0.91 0.91 0.82 0.88 0.89 0.89 0.84 0.20 0.67 0.72 0.27 0.26 0.47 0.69 Quality index Feature index E1 E2 E3 E4 E5 E6 E7 E8 E9 E 10 E 11 E 12 E 13 E 14 E 15 E 16 E 17 E 18 E 19 E 20 W1 pearson(r) 0.84 0.94 0.92 0.94 0.93 0.19 0.19 -0.34 0.55 0.80 0.60 0.87 0.82 0.85 0.47 0.88 0.92 0.93 0.93 0.93 Spearman(ρ) 0.82 0.92 0.87 0.91 0.89 0.29 0.27 -0.29 0.45 0.82 0.44 0.84 0.80 0.81 0.38 0.82 0.89 0.89 0.88 0.90 Quadratic (R) 0.84 0.95 0.93 0.94 0.94 0.19 0.23 0.35 0.62 0.81 0.64 0.87 0.82 0.85 0.47 0.89 0.93 0.93 0.93 0.93 W2 pearson(r) 0.82 0.93 0.92 0.94 0.93 0.17 0.14 -0.37 0.60 0.82 0.63 0.89 0.85 0.87 0.42 0.87 0.93 0.91 0.92 0.92 Spearman(ρ) 0.82 0.91 0.88 0.90 0.88 0.27 0.23 -0.32 0.48 0.81 0.47 0.85 0.80 0.82 0.34 0.79 0.90 0.88 0.89 0.90 Quadratic (R) 0.82 0.94 0.93 0.94 0.93 0.17 0.18 0.38 0.66 0.84 0.67 0.89 0.85 0.87 0.42 0.87 0.95 0.91 0.92 0.92 W3 pearson(r) 0.83 0.93 0.91 0.93 0.92 0.23 0.19 -0.32 0.54 0.80 0.58 0.87 0.82 0.85 0.41 0.87 0.93 0.92 0.94 0.93 Spearman(ρ) 0.82 0.89 0.86 0.91 0.87 0.34 0.28 -0.27 0.42 0.80 0.42 0.82 0.77 0.79 0.35 0.81 0.88 0.88 0.89 0.90 Quadratic (R) 0.83 0.93 0.91 0.93 0.92 0.23 0.23 0.37 0.57 0.81 0.59 0.87 0.82 0.85 0.43 0.87 0.93 0.92 0.94 0.93 Warpage _ pearson(r) 0.67 0.86 0.89 0.87 0.89 -0.02 0.11 -0.44 0.59 0.65 0.64 0.84 0.81 0.83 0.19 0.84 0.82 0.81 0.83 0.81 Spearman(ρ) 0.64 0.80 0.83 0.81 0.82 0.04 0.15 -0.46 0.54 0.64 0.57 0.83 0.80 0.80 0.05 0.65 0.83 0.78 0.77 0.75 Quadratic (R) 0.67 0.88 0.91 0.91 0.91 0.10 0.19 0.46 0.59 0.66 0.64 0.87 0.83 0.85 0.23 0.86 0.82 0.84 0.87 0.83

參閱第六圖所示，之後選擇與各品質數值關聯度高的特徵指標作為一品質特徵指標進行監測，藉以根據上述關聯度同時監測各品質數值。例如根據上述關聯性分析結果，顯示近澆口1.2秒至5秒的壓力峰值T4對品質為強相關，因此以該近澆口1.2秒至5秒的壓力峰值T4為品質特徵指標。根據其中一品質數值(例如上述射出成形產品1的左側寬度W1)量測結果與該品質特徵指標T4的XY散布圖取得該品質特徵指標與該品質數值間的關係式。每模次的該品質數值可經由該模次品質特徵指標的值經由關係式反推得知，達到經由品質特徵指標預測當模次射出成形產品的品質，另外，可根據該XY散布圖依據品質數值要求區間取得該品質特徵指標的一合格範圍，根據該品質特徵指標是否落入該合格範圍區間，判斷該次射出成形產品的該監測品質是否合格。Referring to Fig. 6, a feature index with a high degree of correlation with each quality value is selected as a quality feature index for monitoring, so as to simultaneously monitor each quality value according to the above-mentioned correlation degree. For example, according to the above correlation analysis results, it is shown that the pressure peak T4 near the gate for 1.2 seconds to 5 seconds is strongly correlated with the quality, so the pressure peak T4 near the gate for 1.2 seconds to 5 seconds is used as the quality characteristic index. The relationship between the quality characteristic index and the quality numerical value is obtained according to the measurement result of one of the quality numerical values (eg, the left side width W1 of the injection molded product 1 ) and the XY scatter diagram of the quality characteristic index T4 . The quality value of each mold can be obtained through the inverse relationship of the value of the quality characteristic index of the mold, so that the quality of the injection molded product can be predicted through the quality characteristic index. In addition, the quality of the injection molded product can be predicted according to the XY scatter diagram. The numerical requirement interval obtains a qualified range of the quality characteristic index, and according to whether the quality characteristic index falls within the qualified range interval, it is judged whether the monitoring quality of the injection molded product is qualified.

進一步採用複迴歸F值檢定，該近澆口1.2秒至5秒的壓力峰值T4對該射出成形產品1的品質確實呈現高度相關。Further using the complex regression F value test, the pressure peak value T4 from 1.2 seconds to 5 seconds near the gate is indeed highly correlated with the quality of the injection molded product 1 .

複迴歸F值檢定：Complex regression F value test:

式5、式6為本試驗之複迴歸方程式(假設常數為零)及複判定係數R² 。實驗F值用以檢定所有自變數X1(壓力峰值)，X2(峰值時間)和依變數Y(翹曲品質)的關係是否達到顯著水準，此F值須大於顯著值(查表)。而複判定係數R² 代表迴歸方程式可以解釋(說明)依變數Y變異量的比例。

(5)

(6)Equation 5 and Equation 6 are the complex regression equation (assuming that the constant is zero) and the complex determination coefficient R ² of the experiment. The experimental F value is used to check whether the relationship between all independent variables X1 (pressure peak), X2 (peak time) and dependent variable Y (warping quality) has reached a significant level. The F value must be greater than the significant value (check the table). The complex coefficient of determination R ² represents the proportion of the variance of the dependent variable Y that can be explained (explained) by the regression equation.

(5)

(6)

綜合上述實施例之說明，當可充分瞭解本發明之操作、使用及本發明產生之功效，惟以上所述實施例僅係為本發明之較佳實施例，當不能以此限定本發明實施之範圍，即依本發明申請專利範圍及發明說明內容所作簡單的等效變化與修飾，皆屬本發明涵蓋之範圍內。Based on the descriptions of the above embodiments, one can fully understand the operation, use and effects of the present invention, but the above-mentioned embodiments are only preferred embodiments of the present invention, which should not limit the implementation of the present invention. Scope, that is, simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the contents of the description of the invention, all fall within the scope of the present invention.

1:射出成形產品1: Injection molding products

[第一圖]係本發明方法的流程圖。[Figure 1] is a flow chart of the method of the present invention.

[第二圖]係本發明方法試驗時，射出成形產品為托盤的示意圖。[Second Figure] is a schematic diagram of the injection molding product being a tray during the test of the method of the present invention.

[第三圖]係本發明方法試驗時，不同成形曲線的特徵指標示意圖之一。[Figure 3] is one of the schematic diagrams of the characteristic indexes of different forming curves during the test of the method of the present invention.

[第四圖]係本發明方法試驗時，不同成形曲線的特徵指標示意圖之二。[Figure 4] is the second schematic diagram of the characteristic indicators of different forming curves during the test of the method of the present invention.

[第五圖]係本發明方法試驗時，不同成形曲線的特徵指標示意圖之三。[Figure 5] is the third schematic diagram of the characteristic indicators of different forming curves during the test of the method of the present invention.

[第六圖]係本發明方法試驗時，左側寬度品質(W1)與品質特徵指標(T4)的XY散佈圖，示意成形產品的品質 W1與品質特徵指標T4的關係。[Figure 6] is an XY scatter diagram of the left width quality (W1) and the quality characteristic index (T4) during the test of the method of the present invention, showing the relationship between the quality W1 of the molded product and the quality characteristic index T4.

Claims

一種射出成形的品質預測方法，包括有：對一射出成形產品，獲得合乎品質要求之一試模曲線；根據上述試模曲線，利用擾動實驗獲得與製程參數有關的成形曲線之複數特徵指標；將上述擾動實驗獲得的射出成形產品進行複數品質數值量測，接著與成形曲線之複數特徵指標的值進行關聯性分析運算，以獲得複數品質數值與成形曲線之特徵指標的關聯度；選擇與各品質數值關聯度高的成形曲線之特徵指標作為品質特徵指標進行監測，藉以根據上述各品質特徵指標同時監測各品質數值。A quality prediction method for injection molding, including: For an injection molded product, obtain a mold test curve that meets the quality requirements; According to the above-mentioned mold test curve, the complex characteristic index of the forming curve related to the process parameters is obtained by using the disturbance experiment; The injection molded product obtained by the above-mentioned disturbance experiment is subjected to complex quality numerical measurement, and then a correlation analysis operation is performed with the value of the complex characteristic index of the forming curve to obtain the degree of correlation between the complex quality value and the characteristic index of the forming curve; The characteristic index of the forming curve with high correlation with each quality value is selected as the quality characteristic index for monitoring, so as to simultaneously monitor each quality value according to the above-mentioned quality characteristic index.

如請求項1之射出成形的品質預測方法，其中，上述特徵指標包括壓力峰值指標、殘留壓力指標、殘留壓力差指標、能量指標、速率指標及黏度指標。The quality prediction method for injection molding as claimed in claim 1, wherein the characteristic indexes include a pressure peak index, a residual pressure index, a residual pressure difference index, an energy index, a rate index and a viscosity index.

如請求項2之射出成形的品質預測方法，其中，上述壓力峰值指標包括系統壓力峰值T1、豎澆道壓力峰值T2、近澆口0秒至1.2秒的壓力峰值T3、近澆口1.2秒至5秒的壓力峰值T4、流動末端壓力峰值T5及中間位置壓力峰值T6；上述殘留壓力指標包括近澆口殘留壓力R1、中間位置殘留壓力R2、流動末端殘留壓力R3；上述殘留壓力差指標包括近端至末端殘留壓力差RP1、近端至中端殘留壓力差RP2、中端至末端殘留壓力差RP3及最大壓力差RP4；上述能量指標包括全時間壓力積分、射出峰值時間積分、壓力峰值時間積分及射出峰值時間乘積，其中全時間壓力積分包括系統全時間壓力積分E1、豎澆道全時間壓力積分E2、近澆口全時間壓力積分E3、流動末端全時間壓力積分E4、中間位置全時間壓力積分E5、0秒至5秒螺桿位移壓力積分E6、豎澆道螺桿位移壓力積分E7及近澆口螺桿位移壓力積分E8，射出峰值時間積分包括近澆***出峰值時間積分E9，壓力峰值時間積分包括系統壓力峰值時間積分E10、豎澆道壓力峰值時間積分E11、近澆口壓力峰值時間積分E12、流動末端壓力峰值時間積分E13及中間位置壓力峰值時間積分E14，射出峰值時間乘積包括T1乘上第一時間t1(E15)、T2乘上第二時間t2(E16)、T3乘上第三時間t3(E17)、T4乘上第四時間t4(E18)、T5乘上第五時間t5(E19)及T6乘上第六時間t6(E20)；上述速率指標包括豎澆道速率S1、近澆口速率S2、流動末端速率S3及中間位置速率S4；上述黏度指標包括豎澆道至近澆口壓力差的黏度V1、豎澆道至中間位置壓力差的黏度V2及豎澆道至末端壓力差的黏度V3。The quality prediction method for injection molding according to claim 2, wherein the pressure peak index includes a system pressure peak value T1, a sprue pressure peak value T2, a pressure peak value T3 from 0 seconds to 1.2 seconds near the gate, and a pressure peak value of 1.2 seconds to 1.2 seconds near the gate. 5-second pressure peak T4, flow end pressure peak T5, and intermediate pressure peak T6; the above residual pressure indicators include near gate residual pressure R1, intermediate residual pressure R2, and flow end residual pressure R3; the above residual pressure difference indicators include End-to-end residual pressure difference RP1, near-end to middle-end residual pressure difference RP2, middle-end to end-end residual pressure difference RP3 and maximum pressure difference RP4; the above energy indicators include full-time pressure integration, injection peak time integration, and pressure peak time integration and the injection peak time product, of which the full-time pressure integral includes the system full-time pressure integral E1, the vertical runner full-time pressure integral E2, the near-gate full-time pressure integral E3, the flow end full-time pressure integral E4, and the middle position full-time pressure integral Integral E5, 0 seconds to 5 seconds screw displacement pressure integration E6, vertical runner screw displacement pressure integration E7 and near gate screw displacement pressure integration E8, injection peak time integration includes near gate injection peak time integration E9, pressure peak time integration Including the system pressure peak time integration E10, the vertical runner pressure peak time integration E11, the near gate pressure peak time integration E12, the flow end pressure peak time integration E13 and the middle position pressure peak time integration E14, the injection peak time product includes multiplying T1 by The first time t1 (E15), T2 times the second time t2 (E16), T3 times the third time t3 (E17), T4 times the fourth time t4 (E18), T5 times the fifth time t5 (E19) ) and T6 are multiplied by the sixth time t6 (E20); the above-mentioned speed index includes the vertical runner speed S1, the near gate speed S2, the flow end speed S3 and the intermediate position speed S4; the above-mentioned viscosity index includes the vertical runner to the near gate pressure Poor viscosity V1, viscosity V2 of the pressure difference from the vertical runner to the middle position, and viscosity V3 of the pressure difference from the vertical runner to the end.

如請求項3之射出成形的品質預測方法，其中，上述關聯性分析包括皮爾森相關係數分析、斯皮爾曼等級相關係數檢定或非線性回歸二次曲線模型R檢驗之一或組合。The quality prediction method for injection molding according to claim 3, wherein the correlation analysis includes one or a combination of Pearson correlation coefficient analysis, Spearman rank correlation coefficient test or nonlinear regression quadratic curve model R test.

如請求項4之射出成形的品質預測方法，進一步，根據其中一品質數值量測結果與該品質特徵指標的XY散布圖取得該品質特徵指標與該品質數值間的關係式，每模次的該品質數值可經由該模次品質特徵指標的值經由關係式反推得知，達到經由品質特徵指標預測當模次射出成形產品的品質。According to the quality prediction method for injection molding of claim 4, further, the relationship between the quality characteristic index and the quality value is obtained according to the measurement result of a quality value and the XY scatter diagram of the quality characteristic index. The quality value can be obtained through the inversion of the relational expression through the value of the quality characteristic index of the mold, so as to predict the quality of the injection-molded product at the current mold through the quality characteristic index.

如請求項5之射出成形的品質預測方法，進一步，根據該XY散布圖依據品質數值要求區間取得該品質特徵指標的一合格範圍，根據該品質特徵指標是否落入該合格範圍區間，判斷該次射出成形產品的該監測品質是否合格。According to the quality prediction method for injection molding of claim 5, further, according to the XY scatter diagram, a qualified range of the quality characteristic index is obtained according to the quality numerical requirement interval, and the judgment is made according to whether the quality characteristic index falls within the qualified range interval. Whether the monitoring quality of the injection molded product is qualified.