, 15 1 , 2021
J. Korea Society of Die & Mold Engineering, Vol.15 No.1, 2021 ISSN 2092-9692
. , , , 1). 1, : E-mail: [email protected] .
(Artificial Neural Network ANN) , , 2-10). , . , 11). r r 1 1 1,
A study on the comparison of the predicting performance of quality of
injection molded product according to the structure of artificial neural network
Dong-Cheol Yang
1Jun-Han Lee
1Jong-Sun Kim
Shape Manufacturing R D Department, Korea Institute of Industrial Technology1, (Received March 19, 2021 / Revised March 27, 2021 / Accepted March 31, 2021)
Abstract: The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.
r
12). Multiple Input Single Output ( MISO) Multiple Input Multiple Output ( MIMO)
. ,
(pressure at the end of fill) 2
MISO1, MISO2 x,y,z
MIMO1 , , x,y,z MIMO2 . 4 (MISO1-2, MIMO1-2) . Fig. 1 , , , . , , , , , , , 8 , , , 5 . , 8 5 . 13). .
(orthogonal sampling method) 14).
.
. ,
15)
(random sampling method)
. 27 23 50 , Table 1 . Process condition Level Unit 1 2 3 Melt. temp. 200 220 240 °C Mold temp. 40 50 60 °C Injec. speed 30 90 150 mm/s
Pack. pres. 300 500 700 bar
Pack. time 1 5 9 sec
Cool. time 45 55 65 sec
Back pres. 12 16 20 bar
(LEGO) . Fig. 2 . (FANUC,
Japan) 250 Ton (α-250iA)
. (Polypropylene)
HOPELEN J-150(Lotte chemical, Korea) . Table 2
. ɑ
Item Value Unit
Clamping force 250 ton
Screw diameter 32 mm
Max. injection speed 1200 mm/s
Max. injection pressure 3800 bar
Injection acceleration 5.02 G
0.05 mm
(Starlite 200, RAM OPTICAL
INSTURUMENT) Fig. 3 x-y-z . x . y z , x-y-z 200.0 mm, 40.0 m m, 20.0 mm . . Fig. 4 . 2.1. 50 , 5 . Table 3 .
No. Melt. temp. [°C] Mold temp. [°C] Injec. speed [mm/s] Pack. pres. [bar] Pack. time [sec] Cool. time [sec] Screw speed [rpm] Back. pres. [bar] Mass
[g] x_axis[mm] y_axis[mm] z_axis[mm] V/P pres. [bar] remark 1 200 40 30 300 1 45 50 12 56.79 199.742 39.826 19.730 1452 Ortho. 2 200 40 30 300 5 55 88 16 58.65 199.791 39.913 19.738 1453 Ortho. 3 200 40 30 300 9 65 150 20 59.46 199.816 39.902 19.791 1424 Ortho. 4 200 50 90 500 1 45 50 16 56.39 199.945 39.870 19.848 1737 Ortho. 5 200 50 90 500 5 55 88 20 58.99 199.964 40.133 19.972 1720 Ortho. 6 200 50 90 500 9 65 150 12 60.14 199.985 40.214 20.042 1703 Ortho. 7 200 60 150 700 1 45 50 20 57.15 199.804 39.990 19.878 1873 Ortho. 8 200 60 150 700 5 55 88 12 59.73 200.024 40.250 20.082 1877 Ortho. 9 200 60 150 700 9 65 150 16 61.01 200.174 40.351 20.067 1884 Ortho. 10 220 40 90 700 1 55 150 12 57.50 200.210 40.043 19.918 1412 Ortho. 11 220 40 90 700 5 65 50 16 60.27 200.321 40.202 20.058 1358 Ortho. 12 220 40 90 700 9 45 88 20 61.02 200.130 40.329 20.103 1414 Ortho. 13 220 50 150 300 1 55 150 16 55.04 200.182 39.825 19.831 1645 Ortho. 14 220 50 150 300 5 65 50 20 57.75 200.155 39.992 19.830 1637 Ortho. 15 220 50 150 300 9 45 88 12 59.06 199.900 40.075 19.927 1640 Ortho. 16 220 60 30 500 1 55 150 20 56.91 199.567 39.913 20.079 967 Ortho. 17 220 60 30 500 5 65 50 12 59.39 199.610 40.108 19.985 952 Ortho. 18 220 60 30 500 9 45 88 16 60.10 199.371 40.206 20.073 967 Ortho. 19 240 40 150 500 1 65 88 12 56.15 200.212 39.935 19.956 1312 Ortho. 20 240 40 150 500 5 45 150 16 58.73 199.971 40.183 20.138 1334 Ortho. 21 240 40 150 500 9 55 50 20 60.37 199.998 40.291 20.100 1284 Ortho. 22 240 50 30 700 1 65 88 16 58.01 199.811 40.073 19.974 830 Ortho. 23 240 50 30 700 5 45 150 20 60.41 199.646 40.256 20.073 839 Ortho. 24 240 50 30 700 9 55 50 12 61.33 199.842 40.336 20.076 829 Ortho. 25 240 60 90 300 1 65 88 20 55.02 199.945 39.740 19.929 1067 Ortho. 26 240 60 90 300 5 45 150 12 57.32 199.701 39.958 19.882 1086 Ortho. 27 240 60 90 300 9 55 50 16 58.82 199.735 40.085 19.965 1050 Ortho. 28 219 54 31.2 677 5.3 64 54 19 60.59 199.790 40.294 20.100 980 Rand. 29 204 55 61.8 364 1.7 55 101 17 56.62 199.775 39.887 19.784 1462 Rand. 30 233 54 147.8 666 6.0 49 147 17 60.06 200.020 40.268 20.125 1404 Rand. 31 225 50 48.1 307 5.6 64 108 18 58.27 199.850 40.035 19.887 1030 Rand. 32 202 53 58.1 413 1.1 53 104 19 56.54 199.887 39.876 19.763 1452 Rand. 33 205 55 34.5 588 7.1 50 66 18 60.28 199.636 40.244 20.014 1316 Rand. 34 233 60 50.4 334 1.8 59 85 12 56.38 199.735 39.852 19.780 959 Rand. 35 223 42 79.3 385 2.6 55 80 17 57.33 200.088 39.960 19.831 1265 Rand. 36 213 57 92.4 491 6.1 58 142 18 59.23 199.913 40.171 19.999 1529 Rand. 37 219 46 139.3 527 6.5 52 132 15 59.72 199.929 40.217 20.015 1598 Rand. 38 204 52 43.7 633 3.4 62 82 17 59.41 199.689 40.218 20.034 1352 Rand. 39 236 46 80.7 320 7.3 61 136 14 58.94 199.970 40.070 19.911 1082 Rand. 40 200 50 64.0 697 3.2 48 95 15 59.30 199.819 40.211 20.230 1524 Rand. 41 240 59 62.9 352 3.8 47 137 17 57.33 199.702 39.948 19.894 967 Rand. 42 211 59 79.7 376 2.1 55 98 12 56.77 199.869 39.905 19.773 1483 Rand. 43 239 47 38.3 653 7.6 55 76 19 60.99 199.965 40.262 20.193 875 Rand. 44 208 60 94.6 568 3.3 64 59 13 58.47 199.975 40.127 19.943 1547 Rand. 45 204 45 38.4 432 5.9 59 92 19 59.59 199.663 40.076 20.076 1387 Rand. 46 220 43 54.7 532 2.0 55 131 20 57.90 199.973 40.038 19.901 1169 Rand. 47 202 45 93.7 376 2.9 48 115 17 57.44 199.953 39.942 19.905 1670 Rand. 48 210 55 136.2 556 2.7 64 111 18 57.96 200.106 40.051 19.953 1691 Rand. 49 226 46 45.9 398 6.6 61 56 17 59.40 199.884 40.084 20.005 1004 Rand. 50 211 54 72.7 541 7.1 56 86 16 60.00 199.894 40.225 20.008 1462 Rand.
, 16). (1) 0.2 0.8 . max min min × (1) , , min max . , 50 Fig. 5 , , . , - (hyper-parameter) , . 1 ~ 40 , 41 ~ 45 , 46 ~ 50 . (input layer), (hidden layer), (output layer)
,
(neuron) . Fig. 6
.
.
(weight), (bias), (activation function)
Fig. 7 1
, Fig. 8 .
Fig. 7 (L) j (L-1) (a) (w) (b) . (σ) , (L+1) . . 17), . (Cost function) , .
(Mean Squared Error MSE)
(2) .
(2) , , . (2) MIMO . , MISO 18). 1 MISO1-2 MIMO1-2 , Fig. 9 . -(hyper-parameter) . -. Table 4 - . Parameter ExplainNumber of neurons Number of neurons placed in the hidden layer Learning rate Rate of updating the weights in the training
Epoch Number of times the model learns the training data Dropout rate Rate of preventing overfitting
-(hyperband) 19). -, 3.2. (epoch) . , . - . Table 5 -, Table 6 .
Parameter Value
Number of neurons 5 - 16
Learning rate 0.001 0.1
Epoch 1 - 1000
Dropout rate 0.1 0.4
Structure Parameter Value
MISO1 Number of neurons 9 Learning rate 0.008 Epoch 633 Dropout rate 0.2 MISO2 Number of neurons 12 Learning rate 0.014 Epoch 562 Dropout rate 0.4 MIMO1 Number of neurons 6 Learning rate 0.005 Epoch 850 Dropout rate 0.2 MIMO2 Number of neurons 11 Learning rate 0.006 Epoch 773 Dropout rate 0.2 -MISO1-2, MIMO1-2 , 3.2. . (3)
(Root Mean Squared Error RMSE) .
(3) , , . Table 7 .Structure Predictor RMSE Ave.
MISO1 mass 0.070
-MISO2 pressure at the end of fill 43.678 -MIMO1 x axis length 0.017 0.027 y axis length 0.025 z axis length 0.040 MIMO2 mass 0.264 pressure at the end of fill 48.314 -x a-xis length 0.035 0.030 y axis length 0.014 z axis length 0.042 MISO1-2 MIMO2 , MISO1-2 MIMO2 . MIMO1 MIMO2 , MIMO1 x z MIMO2 , y MIMO2 . x-y-z , MIMO1 0.027 MIMO2 0.030 . , . , . . 1) . MISO1-2, MIMO1-2 4 , .
2) , MISO1-2 MIMO2 , MIMO1-2 , 3 MIMO1 5 MIMO2 . , . . (Project No. KM200278, 20013311) .
1) Xuehong, L. and Khim, L. S., “A Statictical Experiment Study of the Injection Molding of Optical Lenses”, J. Mater. Process. Technol., Vol. 113, No. 1-3, pp. 189-195, 2001.
2) Gevrey, M., Dimopoulos, L. and Lek, S., “Review and Comparison of Methods to Study the Contribution of Variables in Artificial Neural Network Models”, Ecol. Modell., Vol. 160, No. 3, pp. 249-264, 2003.
5) Choi, G. H., Lee, K. D., Chang, N. and Kim, S. G. “Optimization of Process Parameters of Injection Molding with Neural Network Application in a Process Simulation Environment”, Ann. CIRP., Vol. 43, No. 1, pp. 449-452, 1994.
6) Yarlagadda, P. K. and Khong C. A. T., “Development of Hybrid Neural Network System for Prediction of Process Parameters in Injection Moulding”, J. Mater. Process. Technol., Vol. 118, No. 1, pp. 109-115, 2001.
7) Kenig, S., Ben-David, A., Omer, M. and Sadeh, A. “Control of Properties in Injection Molding by
Neural Networks”, Eng. Appl. Artif. Intell., Vol. 14, No. 6, pp. 819-823, 2001.
8) Lau, H. C. W., Ning, A., Pun, K. F. and Chin, K. S. “Neural Networks for the Dimensional Control of Molded Parts based on Reverse Process Model”, J. Mater. Process. Technol., Vol. 117, No. 1, pp. 89-96, 2001.
9) Manjunath, P. G. and Krishna, P. “Prediction and Optimization of Dimensional Shrinkage Variations in Injection Molded Parts using Forward and Reverse Mapping of Artificial Neural Networks”, Adv. Mater. Res., Vol. 463, pp. 674-678, 2012. 10) Liao, S. J., Hsieh, W. H., Wang, J. T. and Su, Y.
C. “Shrinkage and Warpage Prediction of Injection Molded Thin Wall Parts using Artificial Neural Networks”, Polym. Eng. Sci., Vol. 44, No. 11, pp. 2029-2040, 2004.
11) Chang, F. J., Chiang, Y. M. and Chang, L. C. “Multi-step-ahead Neural Networks for Flood Forecasting”, Hydrol. Sci. J. Vol. 52, No. 1, pp. 114-130, 2007.
12) Petlenkov, E. “Neural Networks based Identification and Control of Nonlinear Systems: ANARX Model Based Approach”, Tallinn University of Technology, Estonia, pp. 67-69, 2007.
13) Shen, C., Wang, L. and Li, Q. “Optimization of Injection Molding Proces Parameters Using Combination of Artifcial Neural Network and Genetic Algorithm Method”, J. Mater. Proces. Technol., Vol. 183, No. 2-3, pp. 412-418, 2007. 14) Altan, M. “Reducing Shrinkage in Injection
Moldings via the Taguchi, ANOVA and Neural Network Methods”, Mater. Des. Vol. 31, No.1, pp. 59-604, 2010.
15) Solvason, C. C., Chemmangatuvalapil, N. G., Eljack, F. T. and Eden, M. R. “Efficient Visual Mixture Design of Experiments Using Property Clustering Techniques”, Ind. Eng. Chem. Res. Vol. 48, No. 4, pp. 245-256, 2009.
16) Leaman, R., Doǧan, R. T. and Lu, Z. “Disease Name Normalization with Pairwise Learning to Rank”, Bioinf. Vol. 29, No. 22, pp. 2909-2917, 2013. 17) Goldberg, Y. “Neural Network Methods for
Natural Language Processing”, Synth. Lect. Hum. Lang. Technol. Vol. 10, No. 1, pp. 1-309. 2017. 18) Selvaraj, R., Deshpande, P. B., Tambe, S. S. and
Kulkarni, B. D. “Neural networks for the identification of MSF desalination plants”, Desalin. Vol. 101, No. 2, pp. 185-193, 1995.
19) Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A. “Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization”, J. Mach. Learn. Res. Vol. 18, No. 1, pp. 1-52, 2018. 2019 2 ~2021 2 : ( ) 2021 3 : ( ), ( ) < > , 2017 2 : ( ) 2017 3 : ( ) 2015 3 : ( ), < > , 2005 8 : ( ) 2006 3 : ( ), < > ,
