• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.810-813
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• 2000

Burr makes trobles on manufacturing process due to deburring cost, quality of products and productivity. This paper described the results of experimental study on the influence of the cutting parameters on the formation of exit burrs in face milling. The cutting parameters were investigated changing exit angle, rake nagle , lead angle in tool geometry as well as feed per tooth. Also we carried out experimets on several materials. Using the result of experimental study, burr types are classified according to appearance and formation mechanism in exit burr and we are considered the burr formation in each type of burr.

• Design & Manufacturing
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• v.15 no.3
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• pp.1-6
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• 2021

An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.3
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• pp.114-121
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• 2000

A new concept drill was developed recently (or increasing accuracy and productivity in drilling operation. The burr formation in drilling causes many problems in deburring operation because burrs are formed inside holes and it is difficult to remove them. Burr formations are observed in drilling operation with a new concept drill and are compared with conventional HSS drill. Several workpieces with different materials are drilled with several cutting conditions, velocity and feed rate. The burr in drilling can be classified into three types according to the location of crack. To observe the burr formation mechanism, the cap which is formed with the new concept drill is observed and measured.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.2
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• pp.133-139
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• 2002

A number of experimental studies on burr formation in face milling operations have been pursued. They usually focus on machining parameters such as depth of cut, leed rate, spindle speed and in-plane exit angle. But it if difficult to set the correlation between burrs and the parameters on burr when such parameters are considered at the same time. Therefore, in this paper, acoustic emission (AE) is considered as an alternate way to predict burr types regardless of machining conditions. AE signals during face milling were sampled and processed, then fed into an artificial neural network to classify burr types \\\"on-line\\\".\\\".uot;.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.2
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• pp.199-205
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• 2001

Burr makes troubles on manufacturing process due to deburring cost, quality of products and productivity. This paper described the results of experimental study on the influence of the cutting parameters on the formation of exit burrs in face milling. Using the results of experimental study, burr types are classified and data bases are developed to predict burr formation result. From the CAD file for work geometry and the NC data for tool path, the exit angles are calculated at every edges. This program predicts the burr geometry at exit edges using the prediction algorithm and data bases which are developed experimentally. Simulation results on deformation strain and temperature are also available in specific 2-dimensional cutting conditions. Also algorithm which can determine the exit angle is proposed.