• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.5
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• pp.31-39
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• 2017

Research in the machine tool industry has focused on ICT-based smart machines rather than hardware technologies related to machine tools. Real-time tool-status monitoring is representative of this type of technology and has become important for measuring sensors during cutting processes. In this paper, we studied several research areas and used a round bar to conduct fundamental research into the axial displacement of the main spindle of a tool when it was subjected to a machining load. We were able to use the gap sensor to detect the axial displacement indirectly by using grooves with various shapes on the round bar and sensing the gaps between the grooves. We then determined the optimal groove shape for monitoring the tool state.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.259-262
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• 1998

DNC(Distributed Numerical controller)는 제품을 생산하기 위해 현장용 컴퓨터를 이용하여 공작기계 및 주변장치를 제어하고 감시한다. 본 논문에서는 SFC/OP(Shop Floor Control/point of Production) 시스템과 연계시키기 위한 DNC 시스템의 구조를 제시한다. 본 시스템은 도면 정보를 유기적으로 연결하여 작업절차서를 조회하고, 작업절차서 및 NC 프로그램/공구 보정 데이타를 인덱스 데이터베이스화하고, 소재·지그·공구를 연계한다. 또한 생산정보를 빠르고 능동적으로 현장 작업자에게 전달하고, 기계의 상태정보를 기계로부터 직접 수집함으로써 실시간 모니터링이 가능하다. 분석 및 설계단계에서는 실시간으로 발생하는 이벤트를 처리하기 위해 상태전이도(State Transition Diagram)를 사용한다. 서버 DBMS(Database Management System)로 관계형 데이터베이스를 채택한 Oracle을 사용하였고, 프로그램 개발도구로는 Developer 2000, Microsoft Visual C++ 5.0을 사용하여 구현한다.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.3
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• pp.1-7
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• 2016

The cutting processes used for monitoring engineering includes analysis and feedback about strange conditions, tools collision and tools wear in real time, for improving the working ratio of equipment and productivity. In this study, we proposed monitoring using profibus to increase the reliability as the most important factor for cutting monitoring. The profibus can increase the reliability of cutting monitoring for cutting torque of a main spindle motor and a feed motors through PLC-based interface.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.155-159
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• 1993

최근 많은 생산 시스템의 자동화에 있어서 기계의 상태 진단 및 감시는 설비의 중요도 및 특수성를 고려할때 매우 중요한 비중을 차지하게 되며, 생산 작업을 최적화할 수 있는적당한 제어기술의 필요성과 그에 대한 관심이 날로 증가 하고있는 실정이다. 특히 가공분야에서 많은 부분을 차지하고 있는 절삭가공작업은 기구의 구성이 복잡하고 불확정한 요인을 포함하고있으며 공구의 파손이나 채터진동에 의한 공작물의 정도의 변화가 급속히 발생하기 때문에 이를 위하여 인프로세서 감시가 절실히 요구되고 있다. 그러므로 비정상적인 절삭을 사전에 감지하여 대처함으로써 최적의 작업조 건하에서 안정된 절삭을 할 수 있고 공작기계의 유지, 보수에 경제적인 절감을 기대할 수 있다. 본 연구에서는 2차원 절삭과정중에 발생하는 채터진도에 있어서 절삭 파라메타와 AE 신호와의 관계를 실험적으로 규명하며, AE를 이용한 절삭과정을 모니터링 할 수 있는 방법에 대하여 연구한다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.47-51
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• 1997

The high speed machining which can improve the production and quality in machining has been adopted remarkably in dietmold industry. As the speed of machine tool spindle increases, the machinability evaluation and monitoring of high speed machining is necessary. In this study, the machinability of 30, 000rpm class spindle was evaluated by using the developed tool dynamometer and the machining properties of high hardened and toughness materials in high speed were examined. Finally, the in-process monitoring technologies of tool wear were presented through the prediction by the experimental formula and pattern recognition by the neural network.