• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.2
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• pp.103-108
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• 2018

In interrupted cutting, the workpiece has a groove that impacts both the cutting tool and the workpiece. Therefore, cutting tool damage occurs rapidly. In this study, I performed interrupted cutting of carbon steel for machine structures (SM20C) using an uncoated carbide tool (SNMG120404, P20), and observed tool damage, cutting chip shape, and the workpiece surface. Results: Under the specific cutting conditions of feed rate = 0.066 mm/rev, cutting speed = 120 m/min, and depth of cut = 0.1 mm; and feed rate = 0.105 mm/rev, cutting speed = 120 m/min, and depth of cut = 0.2 mm, the observed tool damage was small. Similar chip shape was observed (Expt. No. 1, 3, 7). Workpiece damage was observed (Expt. No. 3, 5, 7, 9).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.982-986
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• 1997

A method using cutting force signal and neural network for detection tool damage is proposed. Cutting force signal is gained by tool dynamometer and the signal is prepocessed to normalize. Cutting force signal is changed by tool state. When tool damage is occurred, cutting force signal goes up in comparison with that in normal state. However,the signal goes down in case of catastrophic fracture. These features are memorized in neural network through nomalizing couse. A new nomalizing method is introduced in this paper. Fist, cutting forces are sumed up except data smaller than threshold value, which is the cutting force during non-cutting action. After then, the average value is found by dividing by the number of data. With backpropagation training process, the neural network memorizes the feature difference of cutting force signal between with and without tool damage. As a result, the cutting force can be used in monitoring the condition of cutting tool and neural network can be used to classify the cutting force signal with and without tool damage.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.75-78
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• 2000

This paper is estimated to enhance yield improvement and device reliability using PDM(plasma damage monitoring) system capable of in-suit detection about plasma nonuniformity. PDM Tool is the non-contact method of wafer and surface potential electrode(kelvin probe). Its tool measures Vox(oxide barrier) with charge created by plasma. It's possible to inspect the wafer damage generated by plasma charge and analysis of in-situ monitoring data. we obtained the good data which is continuously prevented from plasma damage using its tool for 10weeks. This tool is contributed to preventive steps contemporaneously inspecting the difference of inter-chamber.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.4
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• pp.24-29
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• 2017

This paper provides an experimental analysis on the breakage of the coated tool using the face-milling cutter of the machining center due to changes in the cutting speed and the feed rate. The experimental studies were conducted using STS 304 materials and the damage to the tool was analyzed according to the change in machining time. The experiments confirmed that the cutting speed and feed rate affected the tool damage and the mechanical impact and thermal shock were determined to severely damage the tool. From the production engineering point of view, it has been experimentally investigated that the increased feed rate significantly influences the material removal rate more than the increased cutting speed.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.06b
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• pp.141-146
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• 1999

In the stage of process design, many factors affecting tool life should be considered. Wear, Damage Accumulation and excessive die Stress are those. Most Engineer think wear and damage accumulation affection deeply to the cold forging dies and wear for the hot forging dies. In this report, the example that wear and stress distribution affect tool life in hot forging together will be introduced and the way to solve that problem using Finite Element Method.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.48-55
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• 1997

A useful method to detect tool breakage suing neural network of cutting force signal is porposed and implemented in a basic cutting process. Cutting signal is gathered by tool dynamometer and normalized as a preprocessing. The cutting force signal level is continually monitored and compared with the predefined level. The neural network has been trained normalized sample data of the normal operation and cata-strophic tool failure using backpropagation learning process. The develop[ed system is verified to be very effective in real-time usage with minor modification in conventional cutting processes.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.188-191
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• 2001

Generally, main factors of tool damage are cutting speed, feed rate and depth of cut. The increase of those factors can cause tool breakage or worsen product quality such as machining accuracy deterioration. Those three factors are concerned with cutting force. Cutting force reaches at its maximum value when cutter blade cuts away the object directly, and it is the time when tool damages are at high probability. In this study, we detect the maximum cutting force affecting tool damage and control the maximum cutting force based on the measured peak force.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.208-216
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• 2020

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, ν-ASVR (ν-Asymmetric Support Vector Regression), which considers the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that ν-ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, the ratio of the number of data belonging to ⲉ-tube can be adjusted with ν. Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.98-104
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• 2013

The effect of the tool-sheet interaction on formability enhancement in electromagnetic forming is investigated using FEM. A free bulging and a conical forming die with 0.7mm AL1050 sheet are used to evaluate damage evolution based on Gurson-Tvergaard-Needleman plasticity material model. The impact between the tool and sheet results in complex stress states including compressive hydrostatic stresses, which leads to a suppression of void growth and restrain ascending void volume fraction of the sheet. Therefore, the damage suppression due to the tool-sheet interaction can be the main factor contributing to the increased formability in the electromagnetic forming process.

• Advances in concrete construction
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• v.1 no.2
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• pp.187-200
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• 2013

The damage level in structures (global scale), elements (intermediate scale) and sections (local scale) can be evaluated using a single parameter called the "Damage Index". Part of the damage attributed to the local scale relates to the damage sustained by the materials of which the section is made. This study investigates the damage of concrete subjected to monotonic compressive loading using four different damage models - one proposed here for the first time and three other well-known models. The analytical results show that the proposed model is promising yet simple and effective for evaluating the damage of concrete. The proposed damage model of concrete with its promising characteristics indicated, appears to be a useful tool in the damage assessment of structures made of concrete.