V NIVEL INSTRUMENTAL
3. INSTRUMENTOS Y MECANISMOS ECONOMICOS
The indirect methods often use cutting parameters such as cutting force, vibration, acoustic emission, temperature and power measured during the cutting process. Besides the indirect method used, the selection of parameter is also very important to design an effective condition monitoring system. However, the parameter that is useful for one method could be inappropriate choice for the other. Furthermore, detecting mechanisms including a single sensor could be infrequently making reliable results for the tool condition. Therefore, it is better to employ multiple
sensors to observe the same process in order to detect the tool wear status with high accuracy rates using a sensor fusion model [108].
Indirect methods, which are concerned in this thesis, are usually indirect on-line methods.
The main indirect methods are:
5.3.2.1 Force Sensor
Among the indirect on-line tool wear monitoring methods, cutting force, an indicator of tool condition, is one of the most widely used variables. Indeed, it is noticed that cutting forces increase gradually with tool wear. Exploring the relationship between tool wear propagation and cutting force variation is of great importance to the development of an effective tool condition monitoring strategy. Reference [107] presented an experimental study of variations in the tool wear propagation and cutting force in the end milling process. The experimental results showed that significant wear is the major failure mode affecting the tool life. . The cutting forces have a direct influence on heat generation, tool wear or failure, quality of machined surface and accuracy of the operating parts. Therefore, reference [119] provided in-depth analysis of the work showing that milling dynamometer can measure quasi-static and dynamic cutting forces, and torque by using strain gauge and piezo-electric accelerometer has been designed and constructed.
An online monitoring of the cutting tool wear level is very necessary to prevent any deterioration. However, there is no direct manner to measure the cutting tool wear online. Therefore, reference [120] adopted an indirect method to estimate the wear measurement of one or more physical parameters appearing during the machining process such as the cutting force. The cutting forces are measured by means of a force dynamometer, while the tool wear is measured in an off-line manner using a binocular microscope. In some cases Renishaw contact sensor can be used to measure tool wear.
Despite the importance of micromachining operations in industry and the extensive research conducted in the past, there are few dynamometers capable of measuring the lowest frequencies that exceed the excitation frequency enabling the process
force measurement of micromachining operations. Hence, applications with high spindle speeds require a dynamometer whose lowest frequency value is maximised. Reference [121] contributed an innovative piezoelectric dynamometer (MicroDyn) providing the base for measuring high frequency signals in micro machining processes with rotational speeds of more than 100,000 rpm, resulting in a high excitation frequency. Consequently, the interference of the excitation frequency of those processes with the natural frequency of designed dynamometer makes it impossible to measure machining forces within a wide frequency range.
For development with the on-line monitoring equipment (hardware) and real-time data analysis and optimisation software, reference [122] has presented an intelligent system that commenced with experiments using a force dynamometer. The monitoring system is connected with the PC which including data processing, analysis and optimization.
5.3.2.2 Vibration Sensor
In the condition monitoring of rotating machine, vibration sensors are the most used type of signal, but they do not achieve agreement in the area of the monitoring of cutting tool wear. This is mainly due to the other surrounding sources of vibration. Though, it is clear that cutting with a worn tool leads to higher variations of the effect on the tool; this obviously stimulates the tool to vibrate. The advantages of vibration measurement include ease of implementation and the fact that no modifications to the machine tool or the work piece fixtures are required. Vibration monitoring is mainly used to detect tool condition, surface quality, and dimensional deviations in machining applications. Generally, the vibration amplitude caused by interaction of a new tool and work piece is small compared to worn tool.
Reference [123] developed a reliable monitoring system for industrial application based on the analysis of the structure of the tool vibration signals using singular spectrum analysis (SSA) and cluster analysis. This technique of time series analysis decomposes the acquired tool vibration signals into an additive set of time series. Following this, reference [124] explored the use of data mining techniques for tool condition monitoring in metal cutting using SSA which is performed on vibration signals measured on the tool holder. The main aim is to avoid the lack of large training data set was compensated by application of cross validation. This highlights
vibration components, and benefits of the combining SSA and band-pass filtering to remove undesirable components (noise).
5.3.2.3 Acoustic Emission Sensors
During the cutting process, the workpiece is machined by removing unwanted material (chip) via plastic deformation. The acoustic emission is defined as transient elastic energy released in the deformation, phase transformations and the cracking mechanisms. In rotating machine with very small tool diameters, where the monitoring by cutting forces and motor current is not applicable because of their very low levels, the alternative sensor is of acoustic emissions.
Recently, AE sensors designed for detecting tool breakage have been successful. This result may be explained by the fact that the frequency range of the AE signal is much higher than machine vibrations and environmental noises [125].
It is a simple process to mount the AE sensor on the workpiece side for monitoring the milling and drilling processes which use multipoint rotating cutting tools. However, the difficulty occurs in transmitting the detected signal from the rotating part. Although transmission may be made by radio signal, there are few workable methods that have been developed to transmit the sensor signal from the rotating spindle to the fixed part using optical methods [16]. However, such techniques are still not economically usable due to either the reliability of the system, or the basic cost of the devices and the change in the construction of machine head.
As one of the practical solutions to meet the requirement in terms of the signal transmission, reference [17] has developed the application of the acoustic emission (AE) sensor for monitoring the cutting process. The coolant stream is successfully used as a medium for transmitting the AE wave in the milling process monitoring. This sensor is mounted in the special holder with other necessary devices. Figure 5.2 illustrated the proposal to effectively utilise the cutting fluids as the medium for transmitting the AE signal. The AE sensor is attached to the cutting fluids supply nozzle so that the AE signal generated at the cutting point can be transmitted through the fluids and consequently detected by the sensor. By applying this method, it has become possible to take the AE signal from the rotating tools. But, the concern on this method is the effect of the noise of the fluid flow and machine bearing.
Pre-amplifier 40db Amplifier 40db High pass filter
400KHz Full wave rectifier
AD Converter Micro computer Workpiece AE sensor Cutting fluid Tool holder Cutting tool
Cutting fluid supply system coupled with AE sensor Spindle
Figure 5.2: Monitoring system for the milling process (reproduced from [17]). Micromilling processes can made miniaturised products with high relative accuracy. While micromachining operations are different than conventional macromachining processes, it is important that the modelling of micro end milling forces incorporates the dynamics of the tool, ploughing and elastic recovery. Reference [126] examined the mechanistic modelling of shearing and ploughing domain cutting regimes to accurately predict micro-cutting forces for micromilling with spindle speeds up to 160,000 rev/min with a cooling system that steadies the temperature at high rotations. The tool dynamics are indirectly identified by performing dynamometer and AE coupling analysis.
5.3.2.4 Power Sensor
Apart from these main types of signal used for indirect monitoring, the electric power consumption is often a properly accurate measure of the deterioration of tool condition. It reflects a situation of tool condition change throughout the machining process.The spindle motor power monitoring system is considered one of the most applicable systems for plant floor applications because it is relatively simple and its mounting hardly affects the machining operation. In the last three decades, researchers have utilised many machining variables such as spindle motor power (current) [127-129].
Power sensors are often used in combination with other sensors. For example, if the change in the consumed current would not be sufficient to be detected. Therefore,
modifying a blind sources separation technique has contributed to separating those source signals obtained by milling operations. This method based on wavelet transform and independent component analysis has been developed by [130]. The source signals related to a milling cutter and spindle are separated from a signal of single channel power. The experiments with different tool conditions illustrated that the separation strategy is reliable and encouraging for machining process monitoring.
5.3.2.5 Sound Sensor
When the milling process is stable, the system is controlled by forced vibrations produced by periodic forces which will increase as a result of the interaction between the cutting tool and the workpiece during the machining process [131]. Correspondingly, vibrations arise from this interaction will generate a sound. This sound is a transmission of mechanical energy contains information about the process. Experienced operators can have ability to extract information from it and correct or modify the cutting parameters. Reference [132] developed an approach to collect the milling process sound through a sound sensor (microphone) placed inside the machine-tool enclosure. Frequency range is normally related to the sound range 20 Hz - 20 kHz, but some research has analysed wider ranges 0.5 Hz - 40 kHz Generally, the sound sensor reflects similar behaviour as a accelerometer sensor as both depend on the vibration of the machining process.