• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.553-558
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• 1994

In this paper, we present a new approach for the automation of deburring process. An algorithm for teaching skills of a human expert to a robot manipulator is developed. This approach makes use of TSK fuzzy model that can express a highly nonlinear functional relation with small number of rules. Burr features such as height, width, area, cutting area are extracted from image processing by use of the vision system. Cutting depth, repeative number and normal cutting force are chosen as control signals representing actions of the human expert. It is verified that our processed fuzzy model can accurately express the skills of human experts for the deburring process.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1994년도 특별강연 및 춘계학술발표 개요집
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• pp.186-189
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• 1994

This paper proposes an expert system for the selection of the appropriate laser cutting variables from the available database. Optimum cutting conditions were searched and qualitative comments which are hard to choose as database variables were shown using the binary and fuzzy inference.

• 대한산업공학회지
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• 제23권1호
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• pp.39-54
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• 1997

This paper presents four process models for machining processes : 1) an economical mathematical model of machining process, 2) a prediction model for surface roughness, 3) a decision model for fuzzy cutting conditions, and 4) a judgment model of machinability with automatic selection of cutting conditions. Each model was developed the economic machining, and these models were applied to theories widely studied in industrial engineering which are nonlinear programming, computer simulation, fuzzy theory, and neural networks. The results of this paper emphasize the human oriented domain of a nonlinear programming problem. From a viewpoint of the decision maker, fuzzy nonlinear programming modeling seems to be apparently more flexible, more acceptable, and more reliable for uncertain, ill-defined, and vague problem situations.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.79-82
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• 1997

The current from the feed motor of a machine tool contains substantial information about the machining state. There have been many researches that investigated the current as a measure for the cutting forces. However it has not been reported that indirect measurement of the cutting forces from the current of the feed motor at rest is possible. The cutting force normal to the machined surface influences the machined surface of the workpiece, which makes it necessary to estimate this force to control the roughness of the machined surface. But the unpredictable behavior of the current prevents applying the current to prediction of the cutting state. In this paper, empirical approach was conducted to resolve the problem. Also parametric adaptive and fuzzy logic control strategies are applied to the force regulation problem. As a result, the current is shown to be related to the accumulation of the infinitesimal rotation of the motor, and besides the unpredictable behavior of the current is shown to be caused by the relationship. Subsequently the relationship between the current and the cutting force is identified, and it is presented that control of machined surface using the current of the feed motor at rest is possible.

• Journal of Welding and Joining
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• 제12권3호
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• pp.71-81
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• 1994

Laser cutting is experiencing a rising worldwide acceptance in the industry. Expert systems are necessary for the systematic arrangement and searching of experimental data existing in journal papers, job handbooks, etc.. This paper proposes an expert system for the selection of the appropriate laser cutting variables from the available database. Optimum cutting conditions according to the given cutting velocity were searched and qualitative comments which are difficult to be used as database variables were shown at each velocity using the binary and fuzzy inference. These comments could be informative to unskilled operators of the laser cutting process. Typical cutting conditions of the mild steel and stainless steel were included and the working range and quality variations were displayed graphically. The proposed algorithm was implemented in an IBM compatible personal computer as an expert system for CO$_{2}$ laser cutting to derive the optimum cutting conditions by using the turbo prolog.

• 한국정밀공학회지
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• 제12권5호
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• pp.98-107
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• 1995

The in-process detection of drill wear and breakage is one of the most importnat technical problems in unmaned machining system. In this paper, the monitoring system is developed to monitor abnormal drilling states such as drill breakage, drill wear and unstable cutting using motor current. Drill breakage is detected by level monitoring. Tool wear is classified by fuzzy pattern recognition. The key feature for classification of tool wear is the estimated flank wear which is calculated by the proposed flank wear model. The characteristic of the model is not sensitive to the variation of cutting conditions but is sensitive to drill wear state. Unstable cutting states due to the unsmooth chip disposal and the overload are monitored by the variance/mean ratio of spindle motor current. Variance/mean ratio also includes the information about the prediction of drill wear and drill breakage. The evaluation experiments have shown that the developed system works very well.

• 한국정밀공학회지
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• 제15권4호
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• pp.36-43
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• 1998

We experimented turning SCM440, called difficult-to-cut materials in general, using tungsten carbon tool(PIO) in order to elevate machinability by a new cutting method. The cutting tool designed and made to study was cooled to -17$0^{\circ}C$ in about 1 minute by liquid nitrogen. Then, we operated cryogenic cutting by cooling tool with liquid nitrogen and stuided the effect about cutting force, chip thickness, surface roughness, behavior of tool wear and cutting temperature. In addition, we investigated the possibility that sur face roughness of workpiece can be predicted analyzing cutting characteristics.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 춘계학술대회 논문집
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• pp.262-266
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• 1993

The development of COPS(Computer aided Operation Planning System) needs data mapping paradigm which provides intelligent determonation of cutting conditions from the requirements of process planning side. We proposed the idea of multi-level mapping by the combination of heuristics of domain experts and mathematical abstraction of cutting condition and requirements. Mathematical mathods for the generalization of heuristics were constructed by multi-layer perceptron. DBMS for determination of cutting conditions was constructed by classification and combination of best fitted models. Triangular fuzzy number was used to process the uncertainties in heuristics of experts.

• Advances in materials Research
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• 제1권2호
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• pp.129-146
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• 2012

Polycrystalline diamond is an ideal material for parts with micro-holes and has been widely used as dies and cutting tools in automotive, aerospace and woodworking industries due to its superior wear and corrosion resistance. In this research paper, the modeling and simultaneous optimization of multiple performance characteristics such as material removal rate and surface roughness of polycrystalline diamond (PCD) with ultrasonic machining process has been presented. The fuzzy logic and taguchi's quality loss function has been used. In recent years, fuzzy logic has been used in manufacturing engineering for modeling and monitoring. Also the effect of controllable machining parameters like type of abrasive slurry, their size and concentration, nature of tool material and the power rating of the machine has been determined by applying the single objective and multi-objective optimization techniques. The analysis of results has been done using the MATLAB 7.5 software and results obtained are validated by conducting the confirmation experiments. The results show the considerable improvement in S/N ratio as compared to initial cutting conditions. The surface roughness of machined surface has been measured by using the Perthometer (M4Pi, Mahr Germany).

• Journal of Computational Design and Engineering
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• 제2권3호
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• pp.137-147
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• 2015

In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.