Multisensor approaches for chatter detection in milling
Introduction
In recent years, the competitiveness of international markets is increasing the interest in unmanned machining systems or intelligent machining systems, where most of the productive activities are performed without the operator.
In the new conditions, the machine tool should be able to perform automatically several activities, such as: detection and suppression of vibrations and chatter, tool condition monitoring, optimization of cutting parameters, collision detection and prevention and others.
Chatter is a vibrational phenomenon which arises in machining processes for specific combinations of cutting parameters. It is a very complex phenomenon characterized by unstable, chaotic motions of the tool and by strong anomalous fluctuations of cutting forces. The onset of chatter may cause abnormal tool wear or tool breakage, damage of both the tooling structure and the spindle bearings, poor surface roughness and poor dimensional accuracy of the workpiece. In the last decades, many researchers have focused on the development of analytical and numerical methods for the prediction of chatter [1], [2]. However, these methods are difficult to apply for chatter prevention in industrial conditions since a good estimation of the machining system dynamics and cutting forces is required.
An automatic system for chatter detection and suppression is an alternative solution to this problem. There are several methodologies that can be applied effectively for the suppression of chatter, for instance: mechanical dampers, actuators, spindle speed regulation or modulation systems [2], [3]. The spindle speed regulation system is the most practical approach for chatter suppression since it does not require any modification of the machine tool or tool holder. For instance, as soon as the chatter onset is identified by an adequate chatter identification system, the control system can automatically vary the cutting speed until a stable condition is reached, as proposed by Liao et al. [4] and by Tarng et al. [5]. However, there is a need for a reliable method for automatic detection of chatter in industrial conditions.
The characteristics of the identification system are similar to those proposed by Tarng [6] in 1988 for tool breakage detection systems, as follows: reliability, robustness, responsiveness, flexibility and practicality. In addition, the application in industrial conditions implies the following requirements:
- 1.
it should not modify the modal parameters of the machining system, in particular it should not reduce the stiffness of the machine tool;
- 2.
it should be compatible to pallet changer and to tool changer;
- 3.
it should not put constraints on the selection of cutting parameters and on any other machining condition (tool dimensions, workpiece dimensions, tool geometry, and others);
- 4.
the functioning of the chatter detection system should not rely on the knowledge of the actual cutting conditions and on a priori knowledge of the machining system dynamics;
- 5.
the system should be insensitive to environmental noise.
In this work, the application of several different types of sensors—rotating dynamometer, accelerometers, acoustic emission sensor and electrical power sensor—and the methodologies for chatter detection in face milling are discussed and tested with experimental data, in order to determine which sensor type or which combination of sensors is more suitable for industrial application.
Section snippets
Chatter identification systems
In recent years, many researchers have investigated the application of sensors for chatter detection in machining operations.
Table 1 illustrates some research works focused on chatter identification systems. The table is organized to indicate the machining process considered in the research, the sensor or sensors used, the applied signal processing techniques or classification methods and the authors’ reference.
Several contributions have investigated chatter identification systems in milling.
Design of experiments and experimental procedures
In order to investigate the application of sensors for detection of chatter in milling, several tests in face milling were performed. The general set up of the face milling tests is in Fig. 1, Fig. 2. Two tests configurations had been used, as given in Table 2.
For each test configuration, the frequency response of the system and the first natural frequency were estimated by applying the pulse test method [19]. In both configurations, the extension was included in the tooling structure in order
Analysis of data
The analysis of data was performed in two steps. First, preliminary analysis was carried out in order to determine the characteristics of the sensor signals, then the chatter indicators were designed and their characteristics were compared.
Validation
In order to validate the approach, this methodology was applied to a new test configuration very different from those described in Section 3.
Dry milling tests were performed on an EX–CELL–O High Speed Machining center (Fig. 14).
The tooling system was composed of an HSK 63 spindle adaptor, a mechanical extension of 181 mm length and an end milling cutter. The cutter was a Stellram face milling cutter, diameter D=25 mm, tool cutting edge angle χ=90°, number of teeth z=3, inserts ADGT12T3PDFR-721
Conclusions
According to the considerations presented in this work, it is possible to draw the following conclusions:Regenerative chatter has a strong influence on the cutting force signals measured by a rotating dynamometer in face milling, and it produces variations on the signals features both in time domain and in frequency domain. The characteristic of the signals measured by accelerometers mounted on the spindle housing of the machine tool are influenced by regenerative chatter even if their
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