International Journal of Machine Tools and Manufacture
A cutting power model for tool wear monitoring in milling
Introduction
In metal cutting operations, tool condition must be monitored either by operators or by on-line tool condition monitoring systems (TCMS) to avoid possible damage to both products and machine tools. With the ever-increasing demand for cost saving and quality improvements, on-line tool condition monitoring is becoming more and more important in modern manufacturing. In the last three decades [1], [2], [3], [4], many efforts have been made to develop a reliable and cost-effective TCMS for applications on the shop floor. The suggested techniques for tool condition monitoring can be grouped into two main categories [1], [2], [3]: direct methods and indirect methods. The direct methods can be implemented using devices such as touch trigger probes, optical sensors, and proximity sensors to measure the geometry and/or the morphology of cutting edge. The indirect methods are based on the acquisition of process variables from which tool condition can be deduced according to certain known signal patterns. The former methods are reliable, but they cannot provide continuous in-process measurements because cutting edges are generally inaccessible during cutting; on the contrary, the latter methods can take measurements while cutting tools are actively engaged in cutting, which makes it possible to monitor the cutting tool condition monitoring on-line.
In these indirect TCMSs, process variables such as cutting forces, acoustic emission, temperature, vibration, spindle motor power (current), etc. are measured continuously and tool states are estimated according to certain signal patterns which are correlated to tool wear or breakage [4]. Among many on-line TCMSs, the spindle motor power monitoring system is considered to be one of the most applicable systems for shop floor applications because it is relatively simple and its mounting hardly affects the machining operations. Like many other monitoring systems, most power monitoring systems are usually based on the constant threshold monitoring strategy where the measured power signals are continuously compared with a preset monitoring threshold that is assumed to be correlated to tool breakage or certain level of tool wear [3], [5], [6]. Although the constant threshold monitoring strategy is easy to apply, it is only valid for a particular set of machining conditions. Indirectly measured power signals are generally affected by work-piece material variation, geometry and material of the cutting tool and cutting condition. In particular, the constants or thresholds suggested to relate power signals to tool condition are specific to a certain set of cutting conditions. This necessitates the development and storage of a set of thresholds as well as many other parameters for each process condition of interest. In addition, extensive wear tests must be carried out for the conditions or sets of conditions desired in order to obtain the various constants or parameters needed to predict tool breakage or tool wear level. These costly drawbacks make industry very reluctant to accept the technology for practical applications on their shop floors. Clearly, it is very demanding for new monitoring approaches capable of circumventing these drawbacks, especially the ones that can deal with variable cutting conditions.
The main objective of this study is to develop a cutting power model in which cutting conditions (such as cutting speed, feed rate, depth of cut, work-piece material and tool material) as well as tool flank wear will be taken into account. Based on the power model, a threshold updating monitoring strategy, which can deal with variable cutting conditions, is presented.
Section snippets
Cutting power modeling in face milling
Power monitoring of a cutting tool is based on the fact that less power is consumed when using a sharp tool than a worn tool. Because the power consumption of a spindle drive motor is determined by the cutting torque, the tangential component of cutting force will be of interest in the current study. In this section, the cutting power model will be developed based on a modified mechanical cutting force model.
Experimental procedures
The cutting experiments were carried out on a horizontal column–knee type milling machine (X62W). The data acquisition system was composed of a motor power transducer, an A/D conversion card and a personal computer. Flank wear of the carbide insert was measured using a microscope.
Milling experiments were carried out under different cutting conditions (Table 1). Based on the measured power signals and the least mean square procedure, the power model constants obtained are K=1.54 MN/m, c=0.23,
A threshold updating strategy for tool condition monitoring
In this section, based on the mean cutting power model, a tool wear monitoring strategy, which can deal with variable cutting operations, by continuously updating monitoring threshold of mean cutting power signal will be presented. It includes the following steps:
- (1)
Input data of the machine tool, the cutting tool and the work to be machined;
- (2)
Select a tool wear criterion [VB] according to the requirements of product quality such as surface roughness and dimensional accuracy;
- (3)
Make on-line
Conclusions
A cutting power model for tool wear monitoring in variable cutting conditions has been developed. The intermittent cutting load on a spindle motor in milling operation has been simulated. It was found with the experiments that there are inherent fluctuations in measured cutting power signals due to the intermittent cutting load in milling operation. These fluctuations make it very difficult to use the power model to predict instantaneous power signal. However, the mean cutting power of measured
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