• 한국기계가공학회지
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• 제16권5호
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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.

• 융합신호처리학회논문지
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• 제23권2호
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• pp.84-90
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• 2022

공작기계 상태 진단은 기계의 상태를 자동으로 감지하는 프로세스이다. 실제로 가공의 효율과 제조공정에서 제품의 품질은 공구 상태에 영향을 받으며 마모 및 파손된 공구는 공정 성능에 보다 심각한 문제를 일으키고 제품의 품질 저하를 일으킬 수 있다. 따라서 적절한 시기에 공구가 교체될 수 있도록 공구 마모 진행 및 공정 중 파손 방지 시스템 개발이 필요하다. 본 논문에서는 공구의 적절한 교체 시기 등을 진단하기 위해 딥러닝 기반의 계층적 컨볼루션 신경망을 이용하여 5가지 공구 상태를 진단하는 방법을 제안한다. 기계가 공작물을 절삭할 때 발생하는 1차원 음향 신호를 주파수 기반의 전력스펙트럼밀도 2차원 영상으로 변환하여 컨볼루션 신경망의 입력으로 사용한다. 학습 모델은 계층적 3단계를 거쳐 5가지 공구 상태를 진단한다. 제안한 방법은 기존의 방법과 비교하여 높은 정확도를 보였고, 실시간 연동을 통해 다양한 공작기계를 모니터링할 수 있는 스마트팩토리 고장 진단 시스템에 활용할 수 있을 것이다.

• 한국정밀공학회지
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• 제13권10호
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• pp.112-122
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• 1996

In order to monitior machine tool condition and diagnose alarm states due to electrical and mechanical faults, and expert system using diagnostic parameters of NC machine tools was developed. A model-based knowledge base was constructed via searching and comparing procedures of diagnostic parameters and state parameters of the machine tool. Diagnostic monitoring results generate through a successive type inference engine were graphically displayed on the screen of the console. The validity and reliability of the expert system was rcrified on a vertical machining center equipped with FANUC OMC through a series of experiments.

• 한국공작기계학회논문집
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• 제11권2호
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• pp.103-111
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• 2002

Out of all metal-cutting process, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by computer vision, On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.334-339
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• 2004

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for state monitoring is also presented in the paper.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2000년도 춘계학술대회
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• pp.241-248
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• 2000

Increased complexity of products and their manufacturing processing demans higher quality control and monitoring than ever before. Therefore, flexible automatization or flexible manufacturing systems (FMS) offer numerous advantages over alternative manufacturing methods. In this state, a in-process monitoring is one of the important flexible automation system. And as use of NC and CNC machine tool has been increasing, cutting work has automating and it is necessary to develop the automatic production system combined a couple of machine tool. Thus, in this paper to search examination it can measure the tool wear and the tool life and can be more practical research subject.

• 대한안전경영과학회지
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• 제2권3호
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• pp.185-195
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• 2000

Increased complexity of products and their manufacturing processing demans higher quality control and monitoring than ever before. Therefore, flexible automatization or flexible manufacturing systems (FMS) offer numerous advantages over alternative manufacturing methods. In this state, a in-process monitoring is one of the important flexible automatino system. And as use of NC and CNC machine tool has been increasing, cutting work has automating and it is necessary to develop the automatic production system combined a couple of machine tool. Thus, in this paper to search examination it can measure the tool wear and the tool life and can be more practical research subject.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 춘계학술대회 논문집
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• pp.472-477
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• 2003

Spindle state monitoring is getting more and more important according to the technology trend of spindle that is accurate and automated. Spindle state monitoring is to measure the state of rotation vibrations. The spindle rotation error motion detected by sensing device includes rotation object's unbalance, external forced vibrations, shape error of spindle, as well as measuring error of monitoring device. In this paper, we have inspected the runout characteristics. Also, we introduce the way to exclude the runout element that appear while you monitor a spindle state.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.162-167
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• 2005

Recently, the advanced industries using micro parts are rapidly growing. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro to micro parts. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This study deals with condition monitoring using acoustic emission(AE) signal in the micro-grooving. First, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by using the fuzzy C-means algorithm, which is one of the methods to recognize data patterns. These result is effective monitoring method of micro endmill state by the AE sensing techniques which can be expected to be applicable to micro machining processes in the future.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.393-398
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• 2004

This paper is focused on practical applicability of the optical fiber sensor considering the machine center which is going to use them. Optical fibers may be fluctuated because the machine center operates as column moving type. This causes distortion of the sensor output signal. To reduce this problem, we have improved the sensor structure and its bracket. And we evaluated performances of the sensor.