SENSORLESS DETECTION OF TOOL BREAKAGE IN MILLING

One of the most important research topics in the area of Intelligent Manufacture Systems (IMS) is the automatic detection of tool breakage, wear of chipping during the cutting process. Sensor-based techniques are available for cutting force measurements, but there are drawbacks in this approach in cost and idle times. This work proposes a sensorless monitoring system for tool monitoring in order to detect breakage and chipping by exploiting the wavelet transform and a neural network. Previous works have made use of these tools for monitoring several machining parameters, but we propose an integrated low-cost approach to detect quickly the changes in the tool integrity for monitoring. The system output produces an accurate detection of the tool integrity that enables the system to prevent damage due to tool breakage. This approach allows for an industrial solution to be developed.     

Tool breakage detection in CNC high-speed milling based in feed-motor current signals

Tool condition monitoring, mainly tool breakage detection for high-speed machining (HSM), is an important problem to solve; however, the techniques or types of sensors applied in other research projects present certain inconveniences. In order to improve tool breakage monitoring systems, a simple, effective, and fast method is presented herein. This method is based on the discrete wavelet transform (DWT) and statistical methodologies. The effectiveness of the method is based on the measurements of the feed-motor current signals using inexpensive sensors. It is well-known that during the cutting process, the motor current is related to the tool condition. The current consumption changes when the tool is broken as compared to when the tool is in normal cutting condition. This difference can be obtained from the waveform variances between the signals in order to ascertain the tool condition. The algorithms of this research project consist of obtaining compressed signals from the I _rms feed-motor current signals applying the DWT. Then from these compressed signals, we detect the asymmetries between them. The arithmetic mean value is applied to asymmetries of consecutive machining lengths to reduce noise in the data having a mean value of a series of asymmetries; also, a normal cutting threshold is set up in order to make decisions regarding the tool conditions so as to detect tool breakage. Therefore, this research project shows a low-cost monitoring system that is simple to implement.     

Driver current analysis for sensorless tool breakage monitoring of CNC milling machines

Tool wear and breakage detection is one of the most important problems found during manufacture in automated CNC machines. From several techniques devoted to sense tool condition, driver current monitoring has been used for a sensorless approach. In order to efficiently use the driver current monitoring technique an exhaustive analysis on the nature of the real components of the signal is required. The novelty of this paper is to present a driver current signal analysis to estimate the influence of the most important spurious signal components in order to determine the optimal parameters for signal conditioning. Beside the cutting force signal, the spurious signals considered in the analysis are high-frequency noise, current control commutation and ball screw effects. The analysis is compared with experimental data in order to validate the model and a case study is presented to show the general procedure.     

Analysis of compliance between the cutting tool and the workpiece on the stability of a turning process

Chatter is a common vibration problem that limits productivity of machining processes, since its large amplitude vibrations causes poor surface finishing, premature damage and breakage of cutting tools, as well as mechanical system deterioration. This phenomenon is a condition of instability that has been classified as a self-excited vibration problem, which shows a nonlinear behavior characterized by the presence of limit cycles and jump phenomenon. In addition, subcritical Hopf and flip bifurcations are mathematical interpretations for loss of stability. Regeneration theory and linear time delay models are the most widely accepted explanations for the onset of chatter vibrations. On the other hand, models based on nonlinearities from structure and cutting process have been also proposed and studied under nonlinear dynamics and chaos theory. However, on both linear and nonlinear formulations usually the compliance between the workpiece and cutting tool has been ignored. In this work, a multiple degree of freedom model for chatter prediction in turning, based on compliance between the cutting tool and the workpiece, is presented. Hence, a better approach to the physical phenomenon is expected, since the effect of the dynamic characteristics of the cutting tool is also taken into account. In this study, a linear stability analysis of the model in the frequency domain is performed and a method to construct typical stability charts is obtained. The effect of the dynamics of the cutting tool on the stability of the process is analyzed as well.     

Characterization of machining chattering dynamics: An R/S scaling analysis approach

Self-excited vibrations in machining, well known as chatter, are a kind of dynamical instability that represents a serious productivity issue because of a poor surface finishing, early damage, and breakage of cutting tools they tend to. Chatter presents a highly nonlinear nature characterized by subcritical Hopf and period-doubling bifurcations, as well as limit cycles, quasiperiodic, and even chaotic behavior. Several efforts on modeling, monitoring, and control of chatter have been developed, such as linear and nonlinear predictive analyses based on the regenerative theory, analysis of signals in frequency domain or time domain, and control strategies based on pairs of sensors and actuators. Monitoring signals is particularly important since the real behavior of the process can be measured, by displacement, acceleration or audio transducers. In this work, an analysis of the predictability of a long-term signal, based on the rescaled range (R/S) analysis and the dynamical behavior of the Hurst exponent, is presented as an effective method to identify and monitor correlations and nonlinear behavior of machining. This method was validated experimentally with acceleration signals from a milling process. Fractal nature of vibrations was confirmed. Results were in agreement with a theoretical stability analysis of different conditions of machining, e.g., unstable conditions presented a Hurst exponent value of about 0.5 and even lower (anti-correlated dynamics), whereas stable conditions were characterized by a Hurst exponent larger than 0.5 (correlated dynamics). The results lead to the conclusion that R/S scaling analysis is an effective method to monitor and predict the emergence of chatter behavior in machining.     

SENSORLESS DETECTION OF TOOL BREAKAGE IN MILLING

One of the most important research topics in the area of Intelligent Manufacture Systems (IMS) is the automatic detection of tool breakage, wear of chipping during the cutting process. Sensor-based techniques are available for cutting force measurements, but there are drawbacks in this approach in cost and idle times. This work proposes a sensorless monitoring system for tool monitoring in order to detect breakage and chipping by exploiting the wavelet transform and a neural network. Previous works have made use of these tools for monitoring several machining parameters, but we propose an integrated low-cost approach to detect quickly the changes in the tool integrity for monitoring. The system output produces an accurate detection of the tool integrity that enables the system to prevent damage due to tool breakage. This approach allows for an industrial solution to be developed.