classification of defects in casting surface using … · segmentation, artificial neural networks,...

CLASSIFICATION OF DEFECTS IN CASTING SURFACE USING

FLUFFY ALGORITHM

J.PRIYA, Asst .Professor, Deparment of Computer Science, NGM College, Pollachi mail id:[email protected]

E.BANUMATHI, Mphil. Research Scholar, Deparment of Computer Science, NGM College, Pollachi

mail id:[email protected]

Abstract: The objective of proposing a new methodology was to develop an automated computer aimed mechanism in

which human intervention is reduced and the process is carried out in unsupervised manner. The proposed

method undergoes the process of defect spotting by including the techniques like image processing and data

mining. The implementation of the proposed methodology is done in MATLAB by generating a Graphical User

Interface. The tested results of the proposed methodology were satisfactory with accuracy rate of 98% and

classification rates were also remarkable.

Keywords: Data Mining, Image processing, Fluffy Algorithm, Clssification

Introduction Automatic detection and diagnosis of casting defects will help to increase the productivity in meal industries.

The emerging trends in data mining techniques will help to predict and diagnosis the defects in advance and it

reduces the manual work. This paper analysis the various data mining techniques used to predict the defects

from 2004 onwards. The various data mining techniques have been used in researcher are K-mean

segmentation, Artificial neural networks, Pareto chart, Fuzzy based system, machine-learning techniques,

decision trees, Association rules and Bayesian network. As per the analysis these techniques are used to detect,

diagnosis, minimizing the casting defects percentages and improve the quality and productivity. It has been

proved. When comparing the accuracy of these techniques K-Mean segmentation gave 95% of accuracy than

other data mining techniques.

Research Methodology

In foundry industries the metals are molded into a fine finished product by undergoing many different

steps. There are possibilities for the product to get attached with some defects these defects are numerous in

numbers. The defects are examined by human eye to verify the defects in the finished product. This traditional

method very time consuming and it always has to depend on the expert. Also the efficiency of finding faults is

completely based on the experience of the person who is involved in the process.

JASC: Journal of Applied Science and Computations

Volume VI, Issue I, January/2019

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To identify these defects it highly required to develop an automated mechanism which can detect the

defects in a flawless manner. It can be done by implementing the advanced techniques like image processing

and data mining algorithms in MATLAB simulator. This chapter discusses functioning of proposed GUI to

defect identification methodology with its output. Graphical user interface contains the following seven steps: Step: 1: To the load the defective image from the database. The data base contains more than 200 images which

are combination of both defective and non-defective images.

Step: 2: As the image is loaded into the processor the images are converted into Contrast images by enhancing

and resizing the images.

Step: 3: The images are segmented into various cluster regions and the clusters are selected from it.

Step: 4: Among the cluster the required features are selected to extract them from the segmented image. Were

these features are compared with various segments of the induced image.

Step: 5: Once the feature of the defective image is identified the defective image is segmented.

Step: 6: As the defected region is identified from the original image it classified into different classes to form a

group.

Step: 7: Hence, in the last stage the defected region in the casted surface is identified and it is left for further

decision of personals in foundry industry.

The following figure: 4.1 show the outlook of GUI which developed for processing the cased surface image. It

has features for contrast enhancement, image segmentation, and classification, and also to display of the

accuracy in percentage of the identified defective area.

Figure: 4.1: GUI Outlook

Preprocessing

The initial process in the proposed method is to load the image from the database. The images in the database

are not restricted to any type of detects. It is mixture of defective and non-defective images. Input image taken

from database contains the following types of defects such as Blowhole, Coldshuts, uneven harshness,

shrinkage and cold shut. Figure 4.2 shows the image of blowhole defects form the database.



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Figure: 4.2: Images in Database

The figure 4.3 shows that the Selection of defected images from the dataset. Here image with blow hole is

selected as input to the GUI. Once the image is loaded into the simulator the image is converted into system

readable image. The images are passed for further process.

Figure: 4.3: Loading image into GUI

As the image is loaded into the GUI – MATLAB simulator the image is converted into a gray scale image.

Where the image is contrast enhanced which will help in identifying the defective areas in the image. The

enhanced areas are color converted. The figure 4.4 shows the output for the gray scale and contrast enhanced

images.



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Figure 4.4: Gray Scale and Contrast Enhancement

The gray scale image is segmented for identifying the defective areas. The images are marked as small portion

of dissimilar patterns. The cluster of segmented portion of image is selected to mark the similar patterns. This

segmentation helps in gathering knowledge about similar and similar patterns that are present in the image. The

figure 4.5 and 4.5.1 shows the clusters that are chosen from the segmentation.

Figure 4.5.1: Cluster selection in segmentation



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Segmented image K-Means clustering technique is applied in the proposed methodology to identify the defective region in the

casted surface of any metal used in the foundry industry. The application of this clustering technique starts the

process by partitioning the data collection into number of k groups. The data set is classified into numerous set

of dissimilar groups. These clusters are disjoint in nature. The k-means algorithm is carries two different stages.

In the first step the k-centroid for every cluster groups are calculated and in the next step it considers the data

point is nearest to the centroid in the same cluster group. Although there are number of methods to specify the

centroid in the cluster group, the most familiar and efficient technique is Euclidean distance.

Figure 4.6 Image Segmentation

Classification

The images that are segmented and clustered in the previous stage are passed to the next stage where the defects

that are marked in the casted surface are classified using the naïve Bayes and neural network classifiers. The

classified regions in the images are paralleled with the data set to group them into similar defects. The figure 4.7

and 4.7.1 shows the process of classifiers.



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Figure: 4.7.1 Classification of Defetive Image

Accuracy of classificaiton The figure shows that the proposed method achieved 96.77% accuracy. Proposed fluffy algorithm also

calculates the affected region of casting surface. So we easily get the information about the defected casting

product. This figure shows the classification result as blow hole image.

Figure 4.8: Accuracy Calculation

Features Extraction

The amount of recourse required is simplified by involving feature extraction process in the data set. The

greatest problem involved in handling larger data set is about dealing with the large number of variables. To

analyses the large number of variable consumes larger amount of memory and the amount of power consumed

by processor is also higher which will affect the performance of the algorithm with training sample data.



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The Feature extraction of a texture is computed using statistical distribution of the pixels that are found in the

image. The Gray Level co-occurrence Matrix (GLCM) method is the feasible method to extract the textures that

are scattered in the image.

The following features are extracted from this segmented image: Mean, Standard Deviation (S.D), Entropy,

RMS, Variance, Smoothness, Kurtosis, Skewness, IDM, Contrast, Correlation, Energy, Homogeneity.

Figure 4.9: Selected Features

The following figure 4.10 deploys the image of casting surface which has a uneven hardness in it. The image

has under done various steps in the simulator to detect the defect in the surface. The proposed method has

identified the defect in the image as uneven hardness. The identification accuracy of the proposed system is

about 59.67%

4.10: Gray Scale Conversion of Casted Surface Image



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4.10.2: Segmentation of Casted Surface Image with unevenhardness The image is segmented and the defect is

labeled as unevenhardness in casted surface of a metal. The proposed method is very efficient in predicting the

defects in the image. The figure 4.7 shows that the prediction accuracy of this unevenhardness is 98.38%.

Figure 4.11: Accuracy in Defect Identification



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COMPARISON OF EXISTING WITH PROPOSED METHODS The proposed method was compared with various other existing methods like Fuzzy logic, Window based

classification, Multi-class Support Vector Machine, feed-forward network, k Nearest Neighbours, Vector

Quantization Neural Network, Intelligent Decision Support Systems by considering the parameters like time

consumption, accuracy, speed, error rate, efficiency and many other aspects.

S.no Data

Mining Techniques

Accuracy

1 Rotation technique 90%

2 Fuzzy logic 90.8%

3 Window based classification 92%

4 Multi-class Support Vector

Machine

93%

5 feed-forward network 85%

6 Naive Bayes 91%

7

k Nearest Neighbours

89%

8 Vector Quantization Neural

Network.

95%

9 Intelligent Decision Support

Systems (IDSS)

90.2%

10 Particle Swarm Optimization 94%

11 Fluffy algorithm(Proposed) 98.38%

Table: 4.1: Comparison of accuracy with other methods

The proposed method is the efficient method in identifying the defects in the casting surface metals. This is

carried out in six steps were the data set is loaded into the GUI. The loaded image is converted into gray scale in

addition to it the images are segmented into various partitions and later they are clustered any finally the

clusters are classified to identify the type of defect automatically with the support of feature extraction

techniques.



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Chart 4.1: Comparison Chart of Proposed Method with Existing Methods

Conclusion The proposed method undergoes the process of defect spotting by including the techniques like image

processing and data mining. The proposed method is tested with the training data which includes both the

defective images but also with the images that are without defects. The tested results of the proposed

methodology were satisfactory with accuracy rate of 98% and classification rates were also remarkable.

References: 1. Lanzetta, M., & Sachs, E. (2003). Improved surface finish in 3D printing using bimodal powder

distribution. Rapid Prototyping Journal, 9(3), 157-166.,

2. Rooks, B. (2002). Rapid tooling for casting prototypes. Assembly Automation, 22(1), 40-45.,

3. Armillotta, A. (2006). Assessment of surface quality on textured FDM prototypes. Rapid Prototyping

Journal, 12(1), 35-41.

4. Wang, W., Conley, J. G., & Stoll, H. W. (1999). Rapid tooling for sand casting using laminated object

manufacturing process. Rapid Prototyping Journal, 5(3), 134-141 introduces.



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5. Dengiz, O., Smith, A. E., & Nettleship, I. (2006). Two-stage data mining for flaw identification in

ceramics manufacture. International journal of production research, 44(14), 2839-2851.

6. Gunasegaram, D. R., Farnsworth, D. J., & Nguyen, T. T. (2009). Identification of critical factors

affecting shrinkage porosity in permanent mold casting using numerical simulations based on design of

experiments. Journal of materials processing technology, 209(3), 1209-1219.

7. Doctor, Y. N., Patil, B. T., & Darekar, A. M. (2015). Review of optimization aspects for casting

processes. International Journal of Science and Research (IJSR), 4(3), 2364-2368.

8. Wang, Q., & Wang, Y. (2014). U.S. Patent No. 8,706,283. Washington, DC: U.S. Patent and Trademark

Office

9. Umezawa, K., Toh, T., Tanaka, M., Takeuchi, E., & Inomoto, T. (1999). U.S. Patent No. 5,884,685.

Washington, DC: U.S. Patent and Trademark Office.

10. Wang, Q., Backer, G. P., & Metzger, D. L. (2013). U.S. Patent No. 8,355,894. Washington, DC: U.S.

Patent and Trademark Office.



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