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.

A new method for workpiece error analysis and compensation in turning is introduced. It is known that the workpiece error consists of two parts: machine tool error (including the geometric error and thermal-induced error) and cutting induced error. The geometric error of the machine tool is independent on machining operation and, hence, can be measured o. -line using a

In this paper, an effective algorithm based on improved time-domain averaging is proposed to detect tool flute breakage during end milling using feed-motor current signatures. The algorithm proposed is demonstrated to be effective in detecting tool flute breakage in real time through a series of milling experiments, and is also demonstrated to be insensitive to the effects for transients, such as cutter runout, entry/exit cuts, and noise in the feed-motor current signals. Results indicated that the approach showed excellent potential for practical, on-line application for tool flute breakage detection during end milling.

It is very important to use a reliable and inexpensive sensor to obtain useful information about manufacturing processing, such as cutting force for monitoring automated machining. In this paper, the feed-cutting force is estimated using inexpensive current sensors installed on the ac servomotor of a computerized numerical control (CNC) turning center, with the results applied to the intelligent tool wear monitoring system. The mathematical model is used to disclose the implicit dependency of feed-cutting force on feed-motor current and feed speed. Afterwards, a neurofuzzy network is used to identify the cutting force with current measurement only. This hybrid math-fuzzy approach will reduce the modeling uncertainty and measurement cost. Finally, the estimated cutting force is applied in the tool-wear monitoring process. Successful experiments demonstrate robustness and effectiveness of the suggested method in the wide range of tool-wear monitoring 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.