• 한국산업융합학회 논문집
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• 제22권6호
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• pp.627-635
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• 2019

In this study, we developed for processing complex features using CAM software that satisfies precision for example practice and related qualification tests suiTable for CNC training purposes. In addition, functions such as location control, speed control, and processing path generation, which are the main functions of CNC machining machines, were constructed using small equipment parts, servo motors, inverters, general purpose PCs, and commercial NC software and researched with the goal of developing low-cost education equipment. In the static accuracy inspection, the degree of machine when measuring the parallelism of the X, Y and Z axes and the vibration of the main shaft did not reach the allowable value. However, we have obtained a finished product that satisfies the CNC machine book sample shape machining, detailed functions of the position control function of the CNC machine tool, linear interpolation function, circular interpolation function, and tool offset function. In the qualification test shape processing, a shape with a degree of 1/100 mm was processed to obtain position accuracy that satisfied the tolerance.

• Design & Manufacturing
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• 제16권4호
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• pp.1-6
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• 2022

Fine blanking is a press processing technology that can process most of the product thickness into a smooth surface with a single stroke. In this fine blanking process, shear is an essential step. The punches and dies used in the shear are subjected to impacts of tens to hundreds of gravitational accelerations, depending on the type and thickness of the material. Therefore, among the components of the fine blanking mold (dies), punches and dies are the parts with the shortest lifespan. In the actual production site, various types of tool damage occur such as wear of the tool as well as sudden punch breakage. In this study, machine learning algorithms were used to predict these problems in advance. The dataset used in this paper consisted of the signal of the vibration sensor installed in the tool and the measured burr size (tool wear). Various features were extracted so that artificial intelligence can learn effectively from signals. It was trained with 5 features with excellent distinguishing performance, and the SVM algorithm performance was the best among 33 learning models. As a result of the research, the vibration signal at the time of imminent tool replacement was matched with an accuracy of more than 85%. It is expected that the results of this research will solve problems such as tool damage due to accidental punch breakage at the production site, and increase in maintenance costs due to prediction errors in punch exchange cycles due to wear.

• 한국기계가공학회지
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• 제11권1호
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• pp.13-19
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• 2012

In this study, ultrasonic vibration tool designed and made by using FEM analysis. And machining test was carried out in various machining conditions using ultrasonic vibration capable CNC machine. For work material, alumina ceramic ($Al_2O_3$) was used while for tool material diamond electroplated grinding wheel was used. To evaluate ultrasonic vibration effect, grinding test was performed with and without ultrasonic vibration in same machining condition. In ultrasonic mode, ultrasonic vibration of 20kHz was generated by HSK 63 ultrasonic actuator. The two grinding speeds, 1.67m/s and 3.35m/s, were applied. On the other hand, grinding forces were measured by KISTLER dynamometer.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1995년도 춘계학술대회 논문집
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• pp.111-117
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• 1995

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2011년도 춘계학술대회 논문집
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• pp.551-552
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• 2011

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.28-33
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• 2000

The monitoring of the chatter vibration is necessarily required to do automatic manufacturing system. To this study, we constructed a sensing system using tool dynamometer in order to the chatter vibration on cutting process. And a approach to a neural network using the feature of principal cutting force signals is proposed. with the error back propagation training process, the neural network memorized and classified the feature of principal cutting force signals. As a result, it is shown by neural network that the chatter vibration can be monitored effectively.

• 한국기계연구소 소보
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• 통권15호
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• pp.25-35
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• 1985

During one revolution of a gear, the intensity of the gear-meshing noise or vibration will vary, depending upon the run-out in the pitch diameter of the gear. Here noise can be produced bu excentricity of the shaft with respect to the base circle and by pitch errors. According to the noise generated is clearly dependent on the force and the amount of energy expended in elastically straining the machine member. The purpose of this paper is to discuss the analysis of the noise and technical reduction of gear noise for machine tool.

• 한국공작기계학회논문집
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• 제12권5호
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• pp.1-7
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• 2003

In order to predict vibrations occurred during end-milling processes, the cutting dynamics was modelled by using neural network and combined with structural dynamics by considering dynamic cutting state. Specific cutting force constants of the cutting dynamics model were obtained by averaging cutting forces. Tool diameter, cutting speed, fled, axial and radial depth of cut were considered as machining factors in neural network model of cutting dynamics. Cutting farces by test and by neural network simulation were compared and the vibration displacement during end-milling was simulated.

• 한국기계가공학회지
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• 제20권9호
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.39-45
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• 2000

This paper describes the fundamental investigations on the in-process monitoring techniques focused on Acoustic Emission(AE) based on analytical method. Experiments were conducted on a CNC lathe using conventional carbide insert tools under various cutting conditions. As the result of this study a suggestion is given about the multi-purpose use of AE-signals detected with a single sensor for the monitoring of tool wear, built-up edge and chatter vibration in turning process.