Data Mining Approach for Quality Prediction and Improvement of Injection Molding Process

-Data mining techniques are gaining importance in extracting hidden relationships, associations and patterns from manufacturing process data for prediction of quality of the products. Data mining models are built on historical injection molding process dataset using Decision Tree, k-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) techniques to predict the quality of product for a specific setting of process parameters. These models are evaluated against test cases, and found that Decision Tree and k-NN models have less rate of misclassification than other models. The key factors that are causing flash, sink marks, short shot and burn marks in the product are identified by Decision Tree and presented as explicit rules.


Classification by Decision Tree Model
The classified data by Decision Tree Predictor of Knime software and observed values are shown in Fig.4. The model predicted class labels of test cases correctly except in two cases, wherein short shot is wrongly predicted as sink marks and burn marks as flash.
IV. k-NEAREST NEIGHBOR k-Nearest Neighbor algorithm has been used to classify the test dataset. Numeric columns along with Euclidean distance are used in the implementation of algorithm. The work flow incorporating K-Nearest Neighbor and input nodes has been presented in Fig.4. Number of nearest neighbors is selected as 2 to classify new instance. Weight by distance option has been chosen so that closer neighbors have more influence on the class [8].  V. SUPPORT VECTOR MACHINE The work flow involving support vector leaner and predictor nodes, and XLS reader nodes supplying training and test datasets has been shown in Fig.5. Support Vector Machine is trained by SVM Learner node on input data.RBF (Radial Basis Function) kernel has been chosen with value as 0.2. Overlapping penalty is selected as 2 through SVM dialog box as shown in Fig.6.

Classification by Support Vector Machine
The classification by SVM Predictor showing predicted class labels of test cases is shown in TABLE III. This model is able to classify the labels correctly of all test cases representing acceptable products, but misrepresented in some cases of products with sink marks, flash and burn marks.   Fig.7.

Classification by Neural Network Model
Shrink after commit option has been selected to avoid conflicts with other rules of different classes. Use class with coverage option is selected to ensure the maximum degree of coverage of target columns during training [8][9]. The options dialog box for PNN Learner node is shown in Fig.8. The predicted data by PNN Predictor for class labels of test cases has been presented in TABLE IV. The learner statistics produced by PNN learner is given in Fig.9. This model is not able to classify the labels of most of the test cases representing acceptable products and products with sink marks, flash, and burn marks.

VII. CONCLUSION
The data mining models that are built on the injection molding dataset for analysis by Knime software shall be used in predicting acceptable products and products rejected due to short shot, flash, sink and burn marks. Data mining models are built on dataset by applying Decision Tree, K-Nearest Neighbor, Support Vector Machine and Probabilistic Neural Network (PNN) algorithms.
Prediction accuracies of these Decision Tree and KNN models are found to be satisfactory with misclassification in two cases and one case out of 20 test cases respectively by each model. But prediction accuracies of SVM and PNN models are not satisfactory with misclassification instances of four and eight respectively out of 20 by each model. Sink marks are caused by low hold time (<= 2.5 s), nozzle temperature (<= 230.5 o C), and high molding temperature (> 227.5 o C and <=242.5 o C). High barrel temperature at Zone 5 (> 223.5 o C) is resulting in burn marks. Short shot is caused by low injection pressure (<= 147.5 bar), injection speed (<= 73%), mould temperature (<= 212.5 o C) and barrel temperature at Zone 5 temperature (<= 213.5 o C).
High injection pressure (>154 bar), nozzle temperature (> 230.5 o C), hold time (> 5 s), injection speed (< 93.5%) and mould temperature (<= 227.5 o C and > 242.5 o C) and low clamping force (<= 117.5 ton) are responsible for flash to occur. These are few causes mainly responsible for the above mentioned defects to occur in the products. These models are used in avoiding the production of defective products by eliminating causes of defects and thereby improving the quality of products.