In recent years, a number of machining centers have been built using linear motors. These machining centers have great potential for precision and high-speed machining. Nevertheless, a number of problems remain unsolved, such as monitoring and control. This paper presents a new tool breakage monitoring method for this type of machining center using the current signal of the linear motor. First, the relationship between the cutting force and the motor current is analyzed. Then, the new tool breakage method is presented. From a mathematical point of view, the new method uses a nonlinear energy operator to capture the abrupt changes of the motor current signal, which is directly related to the tool breakage. The experiment validation is included.

Most practical industrial process data contain contributions at multiple scales in time and frequency. Unfortunately, conventional statistical process control approaches often detect events at only one scale. This paper addresses a new method, called multiscale statistical process monitoring, for tool condition monitoring in a machining process, which integrates discrete wavelet transform (WT) and statistical process control. Firstly, discrete WT is applied to decompose the collected data from the manufacturing system into uncorrelated components. Next, the detection limits are formed for each decomposed component by using Shewhart control charts. A case study, i.e., tool condition monitoring in turning using an acoustic emission signal, demonstrates that the new method is able to detect abnormal events (serious tool wear or breakage) in the machining process.

This paper presents a new condition monitoring method based on a latent process model. The method consists of three steps. First, a sensor signal is modeled by a latent process model that is a combination of a time-varying auto-regression model and a dynamic linear model, which decomposes the signal into several components, and each component represents a different part of the monitored system with different time-frequency behavior. Based on the latent process model, important features are extracted. Finally, using the generative topographic mapping, the selected features are mapped to a lower (two)- dimension space for classification. The proposed method is tested in condition monitoring of sheet metal stamping processes. A large number of experiments were conducted. In particular, two cases are presented in detail. From the testing results, it is found that the proposed method is able to detect various defects with a success rate around 98%. This result is significantly better than the conventional artificial neural network method. In addition, the new method is a self-organizing method and hence, little training is necessary. These advantages make the method very attractive for practical applications.

The importance of monitoring cutting forces in turning has been well recognized in machine tool communities. This paper proposes a new method to measure the cutting forces in turning using inexpensive current sensors and the cutting force model. First, the relationship between the various factors, which affect the performance of the spindle and feed drive systems, and the models of the spindle and feed drive systems are analyzed. Then, some reliable and inexpensive Hall-effect current transducers are employed to sense the current signals of the ac servomotor in a computer numeric control (CNC) turning center; the tangential (Ft ) and axial (Fa ) cutting forces in turning are estimated by applying a neuro–fuzzy technique. Finally, the normal cutting pressure (Kn ) and effective friction coefficient (Kf ) are calculated through the cutting mechanical model, so the axial cutting forces (Fr ) can also be estimated based on the model of cutting force. Experimental results demonstrate that the method proposed can measure tangential, axial, and radial cutting forces with an error of less than 10%, 5% and 25%, respectively.

This paper presents a new method for the condition monitoring based on the so-called information maximum (InfoMax). First, the InfoMax concept is employed to build a neural network. The neural network is used for independent component analysis to identify the source (input)that causes malfunctions (output). To demonstrate the new method, two application examples were included. First, tool breakage detection in an end milling process. The monitoring signal is the current of the feed-motor, which is used to detect the change of the cutting force and accordingly, to detect tool breakage. Second, is the monitoring of a water pump. In this example, seven acceleration signals were simultaneously acquired and used to identify the location of the fault (bearing crack). The experiment results indicate that the new method is effective.