• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.94-100
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• 2000

This paper presents a methodology for In-Process monitoring of tool wear by using ultrasonic sensor in turning operation. An integrated single ultrasonic transducer operation at a frequency of 10MHz is placed in contact with the insert tip. The change in amount of the reflected energy from the nose and flank of the tool can be related to the level of tool wear and the mechanical integrity of the tool. As the results, the tool wear monitoring system based on the ultrasonic pulse-echo method was proposed, it is useful to determine a tool life and tool change time.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.100-108
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• 1995

The In-process minitoring of the chatter vibration is necessarily required to an automatic manufacturing system. In this study, we constructed a multi-sensing system using tool dynamoneter, accelerometer and AE(Acoustic Emission) sensor for a more credible detection of chatter vibration. And a new approach using a multiple neural network to extract the features of multi-sensor for the recognition chatter vibration is proposed. With the Back-propagation training process, the neural network memorize and classify the features of multi-sensor signals. As a result, it is shown by multiple neural network that the chatter vibration can be monitored accurately, and it can be widely used in practical unmanned system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.599-602
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• 2000

A new dressing technique with utilizes electrolytic phenomenon for realizing effective mirror surface grindings with metal bonded super-abrasive wheels is called “Electrolytic In-process Dressing Grinding”. This technique enabled metal bonded micro-grain wheels, such as micro-grain cast iron fiber bonded wheels, to be used for mirror surface finish processes effectively. But this technique requires a lot of knowledge and experience to perform. And the condition of dressing is variable according to the time. Therefore adaptation of Monitoring and Control technique is needed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.1011-1017
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• 1999

Electrolytic in-process dressing grinding technique which enables application of metal bond wheels with fine superabrasives in mirror surface grinding operations has developed. It is possible to make efficient precision machining of hard and brittle material such as ceramic and hard metal by the employment of this technique. However, in order to ensure the success of performances such as efficient machining, surface finish, and surface quality, it is important to sustain the insulating layer that has sharply exposed abrasives in wheel surface. Using AE(Acoustic Emission) sensor, this paper will show whether the insulating layer sustains stably or not in real grinding time. And by comparing AErms value and surface roughness their thresholds for stable electrolytic in-process dressing grinding will be determined.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.70-75
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• 1993

Chatter vibration is an unwanted phenomenon in metal cutting and it always affects surface finish, tool life machine life and the productivity of machining process. The In-process monitoring & control of chatter vibration is necessarily required to automation system. In this study, we constructed the multi-sensing system using Tool Dynamometer,Accelerometer and AE(Acoustic Emission) sensor for the credible detection of chatter vibration. And a new approach using a neural network to process the features of multi-sensor for the recognition of chatter vibration in turning operation is proposed. With the back propagation training process, the neural network memorize and classify the feature difference of multi-sensor signals.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.57-63
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• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.50-56
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• 1995

Chatter Vibration is an unwanted phenomenon in metal cutting and it always affects surface finish, tool life, machine life and the productivity of machining process. The real-time detection of the chatter vibration is is necessarily required to automation system. In this study, we constructed the multi-sensing system using Tool Dynamometer, Accelermeter and AE sensor. Especially, Acoustic Emission(AE) generated during turning was investigated the possibility for real-time detection of chatter vibration. Turning experiments were performed using carbide insert tip under realistic cutting conditions and tapered workpiece of SM45C. Consquently, the real-time detection using multi-sensing system can be used for Inprocess monitoring of chatter vibration.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.6
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• pp.89-97
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• 2022

Ultrasonic metal welding has been widely used for joining lithium-ion battery tabs. Weld quality monitoring has been an important issue in lithium-ion battery manufacturing. This study focuses on the weld quality monitoring in ultrasonic metal welding with the longitudinal-torsional vibration mode horn developed newly. As the quality of ultrasonic welding depends on welding parameters like pressure, time, and amplitude, the suitable values of these parameters were selected for experimentation. The welds were tested via tensile testing machine and weld strengths were investigated. The dataset collected for performance test was used to train the multi-layer perceptron neural network. The three layer neural network was used for the study and the optimum number of neurons in the first and second hidden layers were selected based on performances of each models. The best models were selected for the horn and then tested to see their performances on an unseen dataset. The neural network models for the longitudinal-torsional mode horn attained test accuracy of 90%. This result implies that proposed models has potential for the weld quality monitoring.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.151-156
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• 1995

This paper presents a methodology for real-time detecting and identifying the runout geometry of an end mill. Cutter runout is a common but undesirable phenomenon in multi-tooth machining such as end-milling process because it introduces variable chip loading to insert which results in a accelerated tool wear,amplification of force variation and hence enlargement vibration amplitude. Form understanding of chip load change kinematics, the analytical sutting force model was formulated as the angular domain convolution of three dynamic cutting force component functions. By virtue of the convolution integration property, the frequency domain expression of the total cutting forces can be given as the algebraic multiplication of the Fourier transforms of the local cutting forces and the chip width density of the cutter. Experimental study are presented to validata the analytical model. This study provides the in-process monitoring and compensation of dynamic cutter runout to improve machining tolerance tolerance and surface quality for industriql application.