• 한국기계가공학회지
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• 제2권3호
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• pp.13-19
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• 2003

The high speed machining technology has been improved remarkably in die/mold industry with the growth of parts and materials industries. Though the spindle speed of machine tool increases, the condition monitoring techniques of the machine tool, tool and workpiece in high speed machining ate incomplete. In tins study, efficient sensing technology in high speed machining is suggested by observing the characteristics of cutting force, gap sensor and accelerometer signal also, machinability of high-speed machining is experimentally evaluated sensing technique to monitor the machine tool and machining conditions was performed.

• 소성∙가공
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• 제18권2호
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• pp.144-149
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• 2009

Recently, the monitoring technology of machining process is very important to improve productivity and quality in manufacturing filed. Such monitoring technology has been performed to measurement using vibration signal, acoustic emission signal and tool dynamometer. However, micro machining is limited small-scale parts machining because micro tool is very small and weakness to generate signal in micro machining process. Therefore, this study has efficient sensing technology for real monitoring system in micro machine that is proposed to supplement a disadvantage of single-sensor by multi sensor. From experimental result, it was evaluated tool wear and cutting situation according to repetitive slot cutting condition and changing cutting condition, and it was performed monitoring spindle rpm and condition according to compare acceleration signal with current signal.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.125-130
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• 1994

Recently,manufacturing work been transformed to small acale production from with various items to act up to user's expectation from mass production with a little items required in the past. The FMS using NC type mother machinaries has been applied actively also in domestic manufacturing line to meet thus tendancy, but there are many machining troubles occured in work process not be settled yet. Nowdays high efficiency has been required no less than high precision in grinding work for the improvement of productivity. In this study, to represent more advanced FMS can be adapted to thus situation In-process type monitoring method using AE and Current sensors is suggested to investigatethe machining condition in grinding process. As results from this experimental study, is is recoqnized well that grinding conditions and dressing point of in time can be estimated effectively using monitoring method suggested. Furthermore, surface shape of grinding wheel on voluntary point of in time can be predicted indirectly through the observation and comparison of AE signal waveform obtained as performance of continuous dressing work.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.1069-1074
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• 2008

Tool condition monitoring (TCM) is crucial for improvement of productivity in manufacturing process. However, TCM techniques have not been applied to monitor tool failure in an industrial gear shaving application. Therefore, this work studied a statistical TCM method for monitoring gear shaving tool condition. The method modeled the shaving process using beta probability distribution in order to extract the effective features. Modeling includes rectifying for converting a bi-modal distribution into a unimodal distribution, estimating parameters of beta probability distribution based on method of moments. The usefulness of features obtained from the proposed method was evaluated and discussed.

• 한국생산제조학회지
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• 제7권3호
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• 한국기계가공학회지
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• 제15권4호
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• pp.134-140
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• 2016

Main Spindle System has effect on performance of machine tools and working quality as well as is required of high reliability. Especially, it takes great importance in producing automobiles which includes a large number of working processes. However, main spindle unit in Machine tools are often cases where damage occurs do not meet the design life due to driving in harsh environments. This is when excessive maintenance and repair of machine tools or for damage stability has resulted in huge economic losses. Therefore, this studying propose a method of accelerated life test for diagnosing and prognosis the state of life assessment main spindle system. Time status monitoring of diagnostic data - through the analysis of the frequency band signals were carried out inside the main spindle bearing condition monitoring and fault diagnosis.

• 한국기계가공학회지
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• 제10권5호
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• pp.13-19
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• 2011

In this paper, the active monitoring and control system is developed. This system can monitor the status of high the speed spindle in real time during its processing, and can analyze its influence of dimensional accuracy and processing if any, and can control the machining condition to realize the machining system equipped with active monitoring and self-diagnostic features. Machining experiment was performed on 3 materials Al, Brass and S45C in order to derive the relation between active monitoring and control algorithm by the machining load. In addition, we measured surface roughness of processing specimen along with the data change of spindle rotating speed and conveying speed according to variation of machining load. Based on these experiments, we derived relations for each material that can be applied to the control algorithm to allow self control of the rotating speed and conveying speed according to the machining load.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 춘계학술대회 논문집
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• pp.82-87
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• 1997

In order to make unmanned machining systems with satisfactory performances, it is necessary to incorporate appropriate condition monitoring systems in the machining workstation to provide the required intelligence of the expert. This paper deals with condition monitoring for tool wear and fracture during turning operation. Developing economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique are proposed. The validity of the proposed system is confirmed through the large number of cutting tests.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 추계학술대회 논문집
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• pp.51-52
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• 2007

This paper compared wired Acoustic Emission (AE) signals with wireless AE signals. According to the material and process condition, each process signal has distinguishable characteristic to show each removal phenomenon. Therefore, wired and wireless AE sensors having different bandwidth are complementary for CMP process monitoring. Especially, the AE sensor was used to investigate abrasive and molecular-scale phenomena during CMP process, which was compatible to acquire high level frequency. In experiment, wireless AE system was used to get signals in rotary system, using bluetooth. But, it is possible to acquire only RMS signals, which can not analyze abrasive and molecular-sale phenomena. Second, wired AE system was installed using mercury slip-ring, which is suitable not only for rotation equipment but also for acquiring original signals. The acquired signals were analyzed by FFT for understanding of abrasive and molecular revel phenomena in CMP process, finally, we verified that two types of AE sensor with different bandwidth were complementary for CMP process monitoring.

• Tribology and Lubricants
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• 제36권6호
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• pp.297-306
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• 2020

This paper reviews studies on the types and functions of oil sensors used for machine condition monitoring. Machine condition monitoring is essential for maintaining the reliability of machines and can help avoid catastrophic failures while ensuring the safety and longevity of operation. Machine condition monitoring involves several components, such as compliance monitoring, structural monitoring, thermography, non-destructive testing, and noise and vibration monitoring. Real-time monitoring with oil analysis is also utilized in various industries, such as manufacturing, aerospace, and power plants. The three main methods of oil analysis are off-line, in-line, and on-line techniques. The on-line method is the most popular among these three because it reduces human error during oil sampling, prevents incipient machine failure, reduces the total maintenance cost, and does not need complicated setup or skilled analysts. This method has two advantages over the other two monitoring methods. First, fault conditions can be noticed at the early stages via detection of wear particles using wear particle sensors; therefore, it provides early warning in the failure process. Second, it is convenient and effective for diagnosing data regardless of the measurement time. Real-time condition monitoring with oil analysis uses various oil sensors to diagnose the machine and oil statuses; further, integrated oil sensors can be used to measure several properties simultaneously.