• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.7
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• pp.1223-1233
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• 1992

Chemical and mechanical properties of workpieces and tools are important factors for selecting machining parameters in machining process planning. As there is no universal rule representing the machinability defined by metal removal rate, the selection of machining parameters still requires experience-oriented methods. In this paper, a new approach is presented to develop mathematical models for generating optimum machinability in turning processes based on chemical and mechanical properties of workpieces. Neural-Network methodology is introduced to identify mathematical models for machinability. It is confirmed by simulations that the proposed methodology can be used for developing numerical controllers with adaptive control performance.

• Journal of Computational Design and Engineering
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• v.2 no.4
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• pp.261-267
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• 2015

Free-form blades are widely used in different industries, such as aero-engine and steam turbine. Blades that are damaged during service or have production deficiencies are usually replaced with new ones. This leads to the waste of expensive material and is not sustainable. However, material and costs can be saved by repairing of locally damaged blades or blades with localized production deficiencies. The blade needs to be further machined after welding process to reach the aerodynamic performance requirements. This paper outlines an adaptive location approach of repaired blade for model reconstruction and NC machining. Firstly, a mathematical model is established to describe the localization problem under constraints. Secondly, by solving the mathematical model, localization of repaired blade for NC machining can be obtained. Furthermore, a more flexible method based on the proposed mathematical model and the continuity of the deformation process is developed to realize a better localization. Thirdly, by rebuilding the model of the repaired blade and extracting repair error, optimized tool paths for NC machining is generated adaptively for each individual part. Finally, three examples are given to validate the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.457-458
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• 2006

A method for obtaining smooth, jerk bounded feed rate profile in high speed machining has been developed. This study proposes a NURBS interpolator based on adaptive feed rate control with a well developed look ahead algorithm which takes into account the machining dynamics as well. Limitation of jerk and proportional torque rate result in smoothened loads on the machine which effectively reduces excitation of the resonant frequencies of the machine. It is found that the values of the feed rate of the down stream sharp corner have profound effect on the feed rate of the upstream sharp corners. By using a windowing scheme the feed rate profile obtained after look ahead method is re-interpolated to reduce the jerk related problems. This is compared with the adaptive NURBS interpolator to show the effectiveness of the proposed method. Simulation results indicate that the consideration of 'ripple effect' is important in avoiding jerk and thereby increasing the machining accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.214-222
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• 2003

Studies on the optimization of machining process can be divided into two different approaches: off-line feedrate scheduling and adaptive control. Each approach possesses its respective strong and weak points compared to each other. That is, each system can be complementary to the other. In this regard, a combined system, which is a feedrate control system fur cutting force optimization, was proposed in this paper to make the best of each approach. Experimental results show that the proposed system could overcome the weak points of the off-line feedrate scheduling system and the adaptive control system. In addition, from the figure, it can be confirmed that the off-line feedrate scheduling technique can improve the machining quality and can fulfill its function in the machine tool which has a adaptive controller.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.2
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• pp.133-145
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• 1996

This paper presents an indirect cutting force measuring system, which uses the current signals from the AC servo drive units of the horizontal machining center, with its applications to the adaptive regulation of the cutting forces in various milling processes and to the on-line monitoring of tool breakage. A typical model for the feed-drive control system of a horizontal machining center is developed to analyze cutting force measurement from the drive motor. The pulsating milling forces can be measured indirectly within the bandwidth of the current feedback control loop of the feed-drive system. It is shown that the indirectly measured cutting force signals can be used in the adaptive controller for cutting force regulation. The whole scheme has been embedded in the commercial machining center and a series of cutting experiments on the face cutting processes are performed. The adaptive controller reveals reliable cutting force regulating capability against the various cutting conditions. It is also shown that the tool breakage in milling can be detected within one spindle revolution by adaptively filtering the current signals. The effect of the cutter run-out has been considered for the reliable on-line detection of tool breakage.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.3
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• pp.713-723
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• 1995

In intelligent machine tools, a computer based control system, which can adapt the machining parameters in an optimal fashion based on sensor measurements of the machining process, should be incorporated. In this paper, the technology for adaptively optimizing the cutting conditions to maximize the material removal rate in face milling operations is proposed using the exterior penalty function method combined with multilayered neural networks. Two neural networks are introduced ; one for estimating tool were length, the other for mapping input and output relations from experimental data. Then, the optimization of cutting conditions is adaptively implemented using tool were information and predicted process output. The results are demonstrated with respect to each level of machining such as rough, fine and finish cutting.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.1
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• pp.49-55
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• 2020

Studies on the optimizations of machining processes use two different methods. The first is feed control in real-time by spindle load in a machine tool. The second is feed scheduling in NC code control by material removal rate using a CAD/CAM system. Each approach possesses its respective merits and issues compared to the other. That is, each method can be complementary to the other. The purpose of the study is to improve the productivity of the bulkhead, an aircraft Duralumin structure. In this paper, acceleration or deceleration of cutting tool by spindle load data is achieved using adaptive feed control macro programming in a machine tool.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.1
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• pp.163-172
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• 1988

An off-line Geometric Adaptive Control Scheme is applied to the milling machine to identify its guideway errors. In the milling process, the workpiece fixed on the bed travels along the guideway while the tool and spindle system is fixed onto the machine. The scheme is based on the exponential smoothing of post-process measurements of relative machining errors due to the tool, workpiece and bed deflections. The guideway error identification system consists of a gap sensor, a, not necessarily accurate, straightedge, and the numerical control unit. Without a priori knowledge of the variations of the cutting parameters, the time-varying parameters are also estimated by an exponentially weighted recursive least squares method. Experimental results show that the guideway error is well identified within the range of RMS values of geometric error changes between machining passes disregarding the machining conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.690-694
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• 1996

In order to regulate the cutting force at a desired level during peripheral end milling processes, a feedrate override Adaptive Control Constant system was developed. This paper presents an explicit pole-assignment PI-control law through spindle motor current monitoring and its application to cutting force regulation for feedrate optimization. An experimental set-up is constructed for the commercial CNC machining center without any major changes of the structure. A data transfer system is constructed with standard interface between an IBM compatible PC and a CNC of the machining center. Experimental results show the validity of the system.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.82-91
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• 1996

In this study, we put our object to carry out adaptive modeling of cutting process in turning system, and to find out the optimal cutting conditions to maximize material removal rate under some constraints. We used a back-propagation neural network to model the cutting process adaptively and a genetic algorithm to find out optimal cutting conditions. The experimental results show that a back-propagation neural network could model the cutting process effciently, and optimized cutting conditions for maximizing the material removal rate were obtained through the adaptive process model and genetic algorithms. Therefore, the proposed approach can be applied to the real machining system.