Condition monitoring of wind turbines: Techniques and methods

Manufacturing of cylindrical gears by generating cutting processes: A critical synthesis of analysis methods

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.

The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation

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.

In situ monitoring of FDM machine condition via acoustic emission

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.

Tool Wear Monitoring Using Acoustic Emissions by Dominant-Feature Identification

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.

Byung-Jin Ryu

Papers

Development of an on-line tool-life monitoring system using acoustic emission signals in gear shaping