Author
Young-Kwon Ji
Bio: Young-Kwon Ji is an academic researcher from Samsung. The author has contributed to research in topics: Acoustic emission & Pinion. The author has an hindex of 1, co-authored 1 publications receiving 37 citations.
Topics: Acoustic emission, Pinion, Tool wear, Gear shaping, Machining
Papers
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TL;DR: In this article, a tool-life monitoring system for gear shaping that uses acoustic emission (AE) signals is presented, which is related to the cutting condition, tool material and tool geometry in the cutting of metals.
Abstract: Sensing techniques for monitoring machining processes have been one of the focuses of research on process automation. This paper presents the development of on-line tool-life monitoring system for gear shaping that uses acoustic emission (AE). Characteristics of the AE signals are related to the cutting condition, tool material and tool geometry in the cutting of metals. The relationship between AE signal and tool wear was investigated experimentally. Experiments were carried out on the gear shaping of SCM 420 material with a pinion cutter having 44 teeth. Root-mean-square (RMS) AE voltages increase regularly according to tool wear. It is suggested that the maximum value of RMS AE voltage is an effective parameter to monitor tool life. In this study, not only the acquisition method of AE signals for rotating objects but also the signal-processing technique were developed in order to realize the in-process monitoring system for gear shaping. The on-line tool-life monitoring system developed has been successfully applied to gear machining processes.
37 citations
Cited by
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TL;DR: A review of the state-of-the-art in the condition monitoring of wind turbines can be found in this article, which describes the different maintenance strategies, condition monitoring techniques and methods, and highlights in a table the various combinations of these that have been reported in the literature.
789 citations
TL;DR: In this paper, empirical, analytical, numerical as well as FEM-based methods describing the chip geometry and predicting the tool life and cutting forces have been developed to capture quantitatively the tool wear progress and the cutting loads.
143 citations
TL;DR: In this article, a tool-wear monitoring procedure in a metal turning operation using vibration features was described, and the measured toolwear forms were correlated to features in the vibration signals in the time and frequency domains.
Abstract: This paper describes a tool-wear monitoring procedure in a metal turning operation using vibration features. Machining of EN24 was carried out using coated grooved inserts, and on-line vibration signals were obtained. The measured tool-wear forms were correlated to features in the vibration signals in the time and frequency domains. Analysis of the results suggested that the vibration signals' features were effective for use in cutting tool-wear monitoring and wear qualification.
129 citations
TL;DR: In this paper, a new method for in situ monitoring of FDM machine conditions, where acoustic emission (AE) technique is applied, is proposed, allowing for the identification of both normal and abnormal states of the machine conditions.
Abstract: Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for fabricating prototypes with complex geometry and different materials. However, current commercial FDM machines have the limitations in process reliability and product quality. In order to overcome these limitations and increase the levels of machine intelligence and automation, machine conditions need to be monitored more closely as in closed-loop control systems. In this study, a new method for in situ monitoring of FDM machine conditions is proposed, where acoustic emission (AE) technique is applied. The proposed method allows for the identification of both normal and abnormal states of the machine conditions. The time-domain features of AE hits are used as the indicators. Support vector machines with the radial basis function kernel are applied for state identification. Experimental results show that this new method can potentially serve as a non-intrusive diagnostic and prognostic tool for FDM machine maintenance and process control.
123 citations
TL;DR: The acoustic emission from an embedded sensor is used for computation of features and prediction of tool wear using a new dominant-feature identification algorithm to reduce the signal processing and number of sensors required.
Abstract: Identification and online prediction of lifetime of cutting tools using cheap sensors is crucial to reduce production costs and downtime in industrial machines. In this paper, we use the acoustic emission from an embedded sensor for computation of features and prediction of tool wear. Acoustic sensors are cheap and nonintrusive, coupled with fast dynamic responses as compared with conventional force measurements using dynamometers. A reduced feature subset, which is optimal in both estimation and clustering least squares errors, is then selected using a new dominant-feature identification algorithm to reduce the signal processing and number of sensors required. Tool wear is then predicted using an Auto-Regressive Moving Average with eXogenous inputs model based on the reduced features. Our experimental results on a ball nose cutter in a high-speed milling machine show the effectiveness in predicting the tool wear using only the dominant features. A reduction in 16.83% of mean relative error is observed when compared to the other methods proposed in the literature.
122 citations