• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.29-35
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• 2019

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.981-988
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• 2022

The intelligent production plant called smart factories that apply information and communication technology (ICT) are collecting data in real time through various sensors. Recently, researches that effectively applying to these collected data have gained a lot of attention. This paper proposes a method for the tool condition monitoring based on the sound signal generated in machining process. First, it not only detects a fault tool, but also presents various tool states according to idle and active operation. The second, it's to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. Finally, the contrast-enhanced PSD image obtained is diagnosed by using CNN. The results of the experiments demonstrate the high discrimination potential afforded by the proposed sound PSD image + CNN and show high diagnostic results according to the tool status.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.5
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• pp.824-830
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• 2013

In ultra-precision machining, the inside temperature should be controlled precisely. The important factors are environmental conditions (outside temperature, humidity) and temperature conditions (cutting heat, spindle heat). Thus, in this study, an environmental control cell for the ultra-precision machine that could control the inside temperature and minimize effects of the surrounding environment to achieve a thermal deformation of less than 400nm of a specimen was designed and verified through C.F.D. Further, a method that could control the temperature precisely by using a blower, heat exchanger and heater was evaluated. As a result, this study established a C.F.D technic for the environmental control cell, and the specimen temperature was controlled to be within $17.465{\pm}0.055^{\circ}C$.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.24-28
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• 2013

In this paper, a procedure of design and development of a child-sized humanoid robot is described. The design concept for a humanoid robot was proposed and the mechanism of the humanoid robot which is more than 1 meter tall was designed by using 3D design tools. By considering the lightweight of the robot, the hardware for the robot was designed for optimal performance. The frames and links of the robot designed by 3D design tools was manufactured through precision machining with the material which is light and have a good strength. The manufactured child-sized humanoid robot was experimented with basic motions applied inverse kinematics and balance control, and the performance of the motions were verified.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.5
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• pp.7-12
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• 2011

This study covers the optimum design of the 5-axis machine tool. In addition, the intelligent control secures structural stability through the optimum design of the structure of the 5-axis machine center, main spindle, and the tilting index table. The big requirement, like above, ultimately leads to speed-up operation. And this is inevitable to understand the vibration phenomenon and its related mechanical phenomenon in terms of productivity and its accuracy. In general, the productivity is correlated with the operation speed and it has become bigger by its vibration scale and the operation speed so far. Vibration phenomenon and its heat-transformation of the machine is naturally occurred during the operation. If these entire machinery phenomenons are interpreted through the constructive understanding and the interpretation of the naturally produced vibration and heat-transformation, it would be very useful to improve the rapidity and its stability of the machine operation indeed. In this dissertation, the problems of structure through heating, stability, dynamic aspect and safety about intelligent 5-wheel machine tool are discovered to examine. All these discoveries are applied to the structure in order to enhance the density of it. It aims to improve the stability.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.22-35
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• 1992

In highly automated machining centers, intelligent sensor fddeback systems are indispensable on order to monitor their operations, to ensure efficient metal removal, and to initate remedial action in the event of accident. In this study, an on-line tool wear detection system for thrning operations is developed, and experimentally evaluated. The system employs multiple sensors and the signals from these sensors are processed using a multichannel autoegressive (AR) series model. The resulting output from the signal processing block is then fed to a previously tranied artificial neural network (multiayered perceptron) to make a final decision on the state of the cutting tool. To learn the necessary input/output mapping for tool wear detection, the weithts and thresholds of the network are adjusted according to the back propagation (BP) method during off-line training. The results of experimental evaluation show that the system works well over a wide range of cutting conditions, and the ability of the system to detect tool wear is improved due to the generalization, fault-tolearant and self-ofganizing properties of the neural network.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.183-188
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• 2005

Although it is widely used to find an optimum setting of manufacturing process parameters in a variety of engineering fields, the Taguchi method has a difficulty in dealing with multi-response situations in which several response variables should be considered at the same time. For example, electrode wear, surface roughness, and material removal rate are important process response variables in an electrical discharge machining (EDM) process. A simultaneous optimization should be accomplished. Many researches from various disciplines have been conducted for such multi-response optimizations. One of them is a fuzzy logic approach presented by Lin et al. [1]. They showed that two response characteristics are converted into a single performance index based upon fuzzy logic. However, it is pointed out that information regarding relative importance of response variables is not considered in that method. In order to overcome this problem, a desirability function can be adopted, which frequently appears in the statistical literature. In this paper, we propose a novel approach for the multi-response optimization by incorporating fuzzy logic into desirability function. The present method is illustrated by an EDM data of Lin and Lin [2].

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.2
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• pp.64-69
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• 2007

Aerostatic planar XY stages are frequently used as the main frames of precision positioning systems. The machining and assembly process of the rails and bed of the stage is one of first processes performed when the system is built. When the system is complete, the 2D position, motion, and stage flatness errors are measured in tests. If the stage errors exceed the application requirements, the stage must be remachined and the assembly process must be repeated. This is difficult and time-consuming work. In this paper, a method for estimating the errors of a planar XY stage is proposed that can be applied when the rails and bed of the stage are evaluated. Profile measurements, estimates of the motion error, and 2D position estimation models were considered. A comparison of experimental results and our estimates indicated that the estimated errors were within $1{\mu}m$ of their true values. Thus, the proposed estimation method for 2D position and flatness errors of an aerostatic planar XY stage is expected to be a useful tool during the assembly process of guideways.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.1-7
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• 1997

In this paper, a neuro-contouring control scheme for the high precision machining of CNC machine tools is descrihed. The proposed control system consists of a conventional controller for each axis and an additional neuro-controller. For contouring control, the contour error must be computed during realtime motion, but generally the contour error for nonlinear contours is difficult to he directly computed. We, therefore, propose a new contour error model to approximate real error more exactly, and here we also introduce a cost function for better contouring performance and derive a learning law to adjust the weights of the neuro-controller. The derived learning law guarantees good contouring performance. Usefulness of the proposed control scheme is demonstrated hy computer simulations.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.398-403
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• 2004

The demand of high speed machining is increasing because the high speed cutting providers high efficiency of process, short process time, improved metal removal capacity and better surface finish. Active magnetic bearings allow much high surface speed than conventional ball bearings and therefore greatly suitable for high speed cutting. The automatic control concept of magnetic bearing system provides ability of intelligent control of spindle system to increase accuracy and flexibility by means of adaptive vibration control. This paper describes a design and development of a milling spindle system which includes built-in motor with power 5.5㎾ and maximum speed 70,000rpm, HSK-32C tool holer and active magnetic bearing system. Magnetic actuators are designed for satisfying static load condition. The Performances of manufactured spindle system was examined for its static and dynamic stiffness, load capacity, and rotational accuracy. This spindle was run up to 70,000 rpm stably, which is 3.5 million DmN.