• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.1
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• pp.113-120
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• 1994

The dynamic characteristics of turning processes are complex, non-linear and time-varying. Consequently, the conventional techniques based on crisp mathematical model may not guarantee cutting force regulation. This paper presents a fuzzy controller which can regulate cutting force in turning process under varying cutting conditions. The fuzzy control rules are extablished from operator experience and expert knowledge about the process dynamics. Regulation which increases productivity and tool life is achieved by adjusting feedrate according to the variation of cutting conditions. The performance of the proposed controller is evaluated by cutting experiments in the converted conventional lathe. The results of experiments show that the proposed fuzzy controller has a good cutting force regulation capability in spite of the variation of cutting conditions.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.2
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• pp.123-130
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• 2000

Fuzzy control is proposed to regulate cutting force in turning operations under varying cutting conditions. The traditional linear controllers based on crisp mathematical model cannot effectively control cutting force becasue of the nonlinear dynamics of turning operations. The proposed fuzzy controller is based on operator experience and expert knowledge. The membership functions for the inputs and the output of the controller are designed. Cutting force is regulated by adjusting feedrate according to the variation of cutting conditions. The performance of the proposed controller is evaluated by experiments. The results of experiments show that the proposed fuzzy controller has a good cutting force regulation over a wide range of cutting conditions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.74-77
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• 2001

One of the most important technology in PA(Factory Automation) is to construct the diagnostic system for manufacturing process. To improve the productibility in the factory, the state of tools such as bite, drill, endmill should be monitored continuously. In this study, fuzzy logic was used to check the wear of drill in drilling process. The input variables to construct the fuzzy rules are cutting force and the rate of cutting force's change. The experiment was done with the fixed spindle speed and feed rate in cutting condition.

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.1-13
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• 2016

The increase of consumer needs for quality metal cutting related products with more precise tolerances and better product surface roughness has driven the metal cutting industry to continuously improve quality control of metal cutting processes. In this paper, two different approaches are discussed. First, design of experiments (DOE) is used to determine the significant factors and then fuzzy logic approach is presented for the prediction of surface roughness. The data used for the training and checking the fuzzy logic performance is derived from the experiments conducted on a CNC milling machine. In order to obtain better surface roughness, the proper sets of cutting parameters are determined before the process takes place. The factors considered for DOE in the experiment were the depth of cut, feed rate per tooth, cutting speed, tool nose radius, the use of cutting fluid and the three components of the cutting force. Finally the significant factors were used as input factors for fuzzy logic mechanism and surface roughness is predicted with empirical formula developed. Test results show good agreement between the actual process output and the predicted surface roughness.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.69-73
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• 2001

The dynamic characteristics of turning processes are complex, non-linear and time-varying. Consequently, the conventional techniques based on crisp mathematical model may not guarantee surface roughness regulation. This paper presents a fuzzy controller which can regulate surface roughness in milling process using end-mill under varying cutting condition. The fuzzy control rules are established from operator experience and expert knowledge about the process dynamics. regulation which increases productivity and tool life is achieved by adjusting feed-rate according to the variation of cutting conditions. The performance of the proposed controller is evaluated by cutting experiments in the converted CNC milling machine. The result of experiments show that the proposed fuzzy controller has a good surface roughness regulation capability in spite of the variation of cutting conditions.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.2
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• pp.138-144
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• 1992

Conventionally, model equation for cutting process has been used at adaptive control. But in this paper, the cutting force is discerned by piezo electric dynamometer and is controlled adaptively using fuzzy inferance so that the constant load feeding is possible. Main conclusions are as follows : (1) with proper design of fuzzy label, more active cutting force control is possible. (2) adaptive control is possible with only qualitative knowledge instead of model equation of cutting process.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.833-836
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• 2001

One of the most important technology in Factory Automation and Unmanned Automation is to construct the diagnostic system for manufacturing process. To improve the productivity in cutting process, the state of tools such as bite, drill, endmill should be monitored continuously. In this study, fuzzy logic was used to check the wear of drill in drilling process. The input variables to construct the fuzzy rules are cutting force and the rate of cutting force's change. The experiment was done with the fixed spindle speed and feed rate in cutting condition. The proposed algorithm is verified by comparing Fuzzy wear with real wear measured.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.798-801
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• 1993

This paper presents the decision support system using fuzzy knowledge to adapt the cutting conditions chosen by a conventional expert system to a particular machine tool, workpiece and clamping system. These preliminary results demonstrate the capability of fuzzy logic to adjust cutting parameters taking into account parameters difficult to quantify.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.95-103
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• 1995

Recently the trends toward reduction in size of industrial products have increased the application of micro drilling. But micro drilling has still much difficulty so that the needs for active control which give adaptation to controller are expanding. In this paper initial cutting condition was determined for some sorkpieces by experiment and GA-based Fuzzy controller was devised by genetic algorithms and fuzzy inference. The fuzzy inference has been applied to the various prob- lems. However the determination of the membership function is one of the difficult problem. So we introduce a genetic algorithms and propose a self-tuning method of fuzzy membership function. Based on this intelligent control, automation of micro drilling was carried out like the cutting process of skilled machinist.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.28-38
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• 1995

The flank wear at the minor cutting edge significantly affects the geometric accuracy and surface roughness in finish machining. A fuzzy estimator based on a fuzzy inference algorithm with a max-min composition rule is introduced to evaluate the minor flank wear length. The features sensitive to minor flank wear are extracted from the dispersion analysis of a time series AR model of the feed directional acceleration signal. These features, dispersions, are used for constructing linguistic rules, and then the fuzzy inferences are carried out with test data sets collected under various cutting conditions. The proposed system turns out to be effective for estimating minor flank wear length.