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注塑機機筒內注塑壓力損失研究

二月 18, 2023

前言

注塑工藝參數設置係一個經驗強、理論薄弱嘅領域, 而且好難建立 [1] 準確嘅數學模型, 而數據分析技術係一種由歷史數據中尋找知識嘅方法, 不需要複雜嘅理論結構, 所以喺呢個領域應用越嚟越廣泛. 例如, 张玲丽等. [2] 採用多元回歸分析建立了注射壓力與模具溫度、成型幾何尺寸關係嘅回歸模型谢培平等 [3] 透過研究不同零件嘅型腔壓力曲線，探討產品殘餘應力之間嘅關係; 沈春英等 [4] 優化注塑工藝參數，降低成型製品體積收縮率, 和 [5] 提出咗一種透過網絡模型嘅注射過程監控方法, 從而實現故障監控和質量預測.

喺以前嘅生產實踐中, 工程師只能獲得注塑機機筒中嘅注射壓力數據, 所以機筒內嘅注射壓力往往相當于模腔內嘅注射壓力, 而料筒中嘅注射壓力損失被忽略. 隨著注塑技術嘅發展, 越嚟越多嘅從業者意識到，影響注塑產品質量嘅關鍵指標係模腔注射壓力，而唔係注塑機嘅注射壓力反饋, 並開始重視注塑機注塑壓力損失的研究, 但好少有系統嘅研究數據同結論被報道。.

根據塑料注射成型領域嘅技術發展趨勢, 結合行業現狀, 而家喺注射壓力損失研究中, 通過一系列測試獲得數據, 並利用數據分析技術探索注射壓力損失模式, 提高注射壓力損失預測精度.

研究設備和模具

注塑測試係喺 1 200 kN electric injection molding machine, 採用全電機驅動和PLC, 變頻與伺服控制技術, 並可實現高精度控制. 控制注塑機以確保穩定性

性能可以保證測試結果嘅可靠性和穩定性. 用于研究嘅模具係普通流道嘅兩板模具，一對澆口处裝有壓力傳感器緊. 腔體尺寸為 301 毫米 57 毫米 2.5 毫米. 成型產品厚度均勻，結構簡單, 可實現低成本高效率嘅注塑測試工藝.

便於數據採集和分析, ComoDataCen被利用了 (用于存儲R注射過程數據嘅中央存儲系統), 集中收集和處理模具澆口处嘅注射壓力和傳感器壓力, 注射測試模型如圖所示 1.

實驗方法

由於研究試驗旨在探索影響注射過程中壓力嘅因素, 壓力

注射速度, 熔體溫度, 和材料類型為 3 對象

研究變量, 具有單點參考粘度指數 (六) 51.6~

327.2 (單點參考粘度指數在規定溫度和剪切速率 1 000 / s) 使用Cross-WLFT粘度模型計算嘅粘度可以喺一定程度上反映材料嘅流動性, 在不同的熔體溫度條件下以螺杆速度 20 ~ 180 毫米/秒, 並喺各種注射測試下收集注射壓力和注射傳感器壓力. 研究測試材料清單見表 1.

測試數據收集

通過一系列注塑測試, 得到不同工藝條件下各種材料注塑機嘅注射壓力同模具傳感器壓力曲線, 同注射壓力曲線V / P切換

時間壓力值PF 1 同芯片傳感器壓力曲線V / P掣

PF嘅壓差值 2 獲得PF並表徵灌裝階段注塑機機筒中嘅注射壓力損失. 如圖所示 2, 填充階段注射機筒內嘅注射壓力損失ΔPF = 1 649-946=703巴.

用于計算料筒級嘅注射壓力損失, 壓力損失後, 壓力值PP2 Δ P之差為機筒內壓力值中的壓力損失，機筒內壓力損失為機筒內壓力損失與注射壓力損失Δ PP=999-732=267 bar.

試驗數據分析

測試數據排序規則

透過以上方法, 整理各組測試數據，構建測試數據表. 總共 132 進行了一組注射測試： 6 材料. 由於空間有限, 桌子 2 僅列出一些測試數據.

試驗數據嘅線性回歸分析

根據測試數據, ΣPF同PF 1, ΣPF同PF V, Δ PF, 分別, 同熔體溫度T, 如圖所示 3 到圖 5.

散啲圖係最有效的圖形方法之一 [6] 確定是否存在連接, 模式, 或介乎兩者之間嘅趨勢 2 數值變量. 因此, 圖中散射分布嘅比較 3 到圖 5 表明Δ PF與PF之間存在很強的相關性 1 (繪製啲嘅圖案由左下角向右上角傾斜

奥布克, 意味住PF 1 值隨PF值增加, 暗示正相關 [5]), 而注射速度V同熔体溫度T幾乎唔相關.

自然科學中嘅皮尔逊相關系數被廣泛用于衡量兩個變量之間嘅相關程度, 值介乎1同 1. 皮爾

劣相關系數通常用字母R表示, 其值為負表示變量之間嘅負相關, and being regular indicates a positive correlation, and the greater the absolute value of R, the higher the correlation of the 2 sets of data. 因此, the Pearson correlation coefficient between the Δ PF and PF 1, V, T, 和 3 data sets,

Fang, used to measure the degree of correlation between Δ PF and the 3 variables. R2=0.981, Δ PF and V number for 1 data sets for Δ PF and PF 1

According to R2=0.282 in the set and R2=0.534 in the T data set, the highest correlation was determined between Δ PF and PF 1, and then the number was found by minimizing the sum of squares of the error

Pressure loss coefficient of the segment. Based on the regression analysis method, for a large number of statistics

Data mathematical processing, determine the correlation between dependent variables and some independent variables, establish good correlation regression equation (function expression) [8], constructed the injection machine barrel injection pressure loss prediction mode, can optimize the injection pressure setting in the trial process, to assist technicians quickly find more reasonable injection pressure and pressure. Due to the limited test conditions, the injection pressure loss coefficient of the barrel injection between different injection molding machines is not studied and analyzed, and it is not clear between the pressure loss coefficient of the injection barrel and the equipment parameters of the injection molding machine. With the deepening of the research, more and more are in the barrel

根據數學優化技術嘅最佳函數匹配 [(7]

最小二乘法),

與注射壓力損失相關嘅影響因素將被挖掘出嚟以獲得完美

擬合ΔPF同PF嘅最佳匹配函數 1 數據集以獲得關於PF嘅ΔPF 1 表達:

ΔPF =0.410 1×PF1 （1）

公式 (1) 可用作灌裝階段注塑機筒內注射壓力損失的預測模型. 實際生產中, the injection pressure value in the barrel of the injection machine is easily obtained, so this model has wide applicability.

同樣地, according to the study test data, draw the scatter plot of the pressure holding stage, 如圖所示 6, and fit the best matching function of the pressure loss Δ PP in the barrel and the pressure injection pressure PP1 data set in the pressure holding stage:

ΔPP =0.258 9×PP1

The above studies show that the loss of injection pressure is not correlated with the injection speed and melt temperature, but is strongly associated with the injection pressure in the barrel, and can be determined by constructing (fx) =kx functionShooting pressure loss, x is the barrel injection pressure of the injection molding machine, and k is the pressureloss efficiency. Furthermore, equations (1) 和 (2) reveal the basic phenomenon of different injection pressure loss coefficient in the barrel during different injection stages (filling and pressure retention).

tag

For a long time, engineers have no research on the injection pressure loss in the barrel of the injection molding machine. 麻麻, the flow simulation analysis generally only considers the pressure transfer in the mold, so that the estimation of the injection pressure deviates. A new pattern of injection pressure loss is revealed: Δ P =k P, Δ P =k P; where k k is the filling and pressure preservation stepPressure loss coefficient of the segment. Based on the regression analysis method, for a large number of statistics

Data mathematical processing, determine the correlation between dependent variables and some independent variables, establish good correlation of regression equation (function expression) [8], the construction of barrel injection pressure loss prediction mode, can optimize the injection pressure setting in the trial process, to assist technicians quickly find more reasonable injection pressure and pressure. Due to the limited test conditions, the injection pressure loss coefficient of the barrel injection between different injection molding machines is not studied and analyzed, and it is not clear between the pressure loss coefficient of the injection barrel and the equipment parameters of the injection molding machine. With the deepening of the research, more and more are in the barrelThe influencing factors related to the injection pressure loss will be mined for perfectionAnd improve the prediction model of injection pressure loss in the barrel of injection molding machine, and promote the improvement of the pressure solver of flow simulation analysis software, and promote the automation and intelligent development of injection process optimization method.