Behaviours Indicators of Machine Tools
Knowledge of machine tool behaviour is a key element to improve production, quality and availability as behaviour indicators represent or give an idea of the real state of the machine. Indeed, real conditions can be used to update simulation models’ parameters, detect or anticipate quality fault on workpiece, and to estimate damage state of machine components such as axis and spindle. Behaviour indicators extraction is based on the analysis of specific and known moves or actions of the machine tools. Two kinds of approaches to build these indicators are discussed in this chapter.
KeywordsIndicators Behaviour Characterization Data management Machine condition
The behaviour of a machine tool is the set of actions and operations made by the machine sub-systems in conjunction with themselves and the machine environment. The expected behaviour can be defined as the capacity of a machine tool to achieve its objective: to produce parts with specified quality at high production rates .
These concepts can be monitored through sensor measurements. The characteristics of the sub-systems allow to interpret the expected behaviour from the machine. However, raw data are highly influenced by external and internal conditions. The behaviour of the sub-system can influence the one of another sub-system from the machine tool. By computing contextualized, and comparable over the time, indicators, from sensors measurement and machine operating conditions, it is possible to monitor the machine behaviour and highlight its changes. The quality of the indicators and, consequently, of the monitoring requires a consistent acquisition that is representative of the system dynamics.
Behaviour indicators extraction of a machine tool can be done continuously by the exploitation of the workpiece program and existing machine sensors, or with specific characterization programs using existing sensors and/or additional sensors. Behaviour continuous monitoring using raw measurements is discussed in Sect. 8.2. The characterization tests of machine tools processed occasionally are discussed in Sect. 8.3. Finally, the conclusions are summarized in Sect. 8.4.
8.2 Extraction from Machining Raw Measurements
Minimal raw measurement set for behaviour monitoring
Cycle tool change
A machine is composed by a set of linear and rotating axes, at least one spindle and a set of auxiliary systems to ensure good machining conditions such as lubricant system, cooling system, air system, machining coolant system and hydraulic group. An overview of the machine behaviour is given by merging the results of its sub-systems. Therefore, information about each of them is required. The axis behaviour indicators can be built based on real position, drive current and temperature. In some cases, such as vertical axis, it can be equipped with a compensation system. The axis balance pressure has then to be considered. Behaviour indicators for spindle can be based on speed, current and temperature. The auxiliary systems can be mainly described by the actions of their pumps and tanks where output pressure analysis gives a good representation.
Indicators should be computed from specific operating conditions. The knowledge of workpiece cycle start/end, tool changes and tools in use are then highly recommended for a more accurate analysis. The knowledge of the machine tool environment conditions, such as the workshop inner temperature, is a plus, especially for temperature- and current-based indicators.
8.2.1 Indicator Extraction Process
Collected raw sensors data are, first, consolidated and made reliable, and then, the operating conditions of the machine tool are computed as explained in Sect. 8.1.1. Indicator extraction process from these conditions is detailed in Sect. 8.1.2.
8.2.2 Machine Operating Conditions
The different operating conditions can be collected directly from the machine numerical command. If it is not the case, they should be inferred from the raw sensors measurements such as axis positions. It is suitable to prioritize the first solution as it contains more reliable information describing the machine state. Algorithms based on raw sensors measurement depend on the relevance, the sampling rate and the synchronicity of the crossed data.
The machine tool efforts are different from one condition to another; to study the behaviour of the machine or a specific sub-system, it is recommended to observe sensor measurement independently from one condition to another. Moreover, the behaviour analysis is possible by extracting indicators from the dataset of sensors’ measurements collected in each specific condition as explained in the next section.
8.2.3 Indicator Processing
The central tendency or centre of the distribution given by the mean and the median.
- The dispersion given by the percentiles, extreme values and standard deviation. A percentile is a value below which a given percentage of the data collection falls. The most frequently used percentiles are:
The median or 50th percentile.
The lower and upper box, respectively, the 25th and 75th percentile.
The lower and upper whisker, respectively, the 5th and 95th percentile.
8.2.4 Example of Indicators
In this section, two examples of indicator extraction are presented. The first one is focused on tool behaviour, based on spindle torque observed in a specific machining step. The second one aims at monitoring axis dynamic behaviour.
22.214.171.124 Spindle Torque When Machining at a Specific Step—Tool Behaviour
Tool changes: ruptures are visible each time the tool is replaced.
Tool wearing: for each new tool, the indicator’s value is around 3 Nm and increases with use.
126.96.36.199 Axis Thrust When Axis Is Moving Linear—Axis Dynamic Behaviour
Lower whisker characterizes X-axis thrust required to accelerate.
Upper whisker characterizes X-axis thrust required to decelerate.
Mean gives an indicator of axis balance.
8.3 Machine Tool Characterization Tests
The analysis of indicators obtained from raw measurements during conventional machining operation is sometimes difficult, especially when trying to determine the condition of the machine tool. Perturbations, like the machining process itself, can hide the real performance of the machine tool. In addition, it is sometimes difficult to get repetitive movements from which comparable indicators can be obtained, especially in small batch sectors like aerospace.
In this line, a characterization procedure for machine tools has been defined, validated and implemented in Twin-Control project. The objective is to provide the opportunity to the end-user to perform a very simple and fast characterization of the machine tool, under controlled conditions. This way, a periodic checking is possible, leading to a better track of machine tool condition over time.
Next, the different proposed tests are presented.
8.3.1 Diagonal Positioning Error Measurement
The aim of this test is to determine the volumetric performance of a machine tool through a fast and reduced procedure. To achieve a volumetric performance indicator, diagonal positioning measurement is done in two diagonals of the machine tool. This way, it can be known if machine continues under specifications or not. The measuring procedure is based on an indirect method; it means that not just positioning error of each of the three linear axes is achieved, but perpendicular error between each pair of axes too.
This measuring procedure is suitable for three axes machine tools without moving table (bed type, column type, gantry type) or rotary axis. Moreover, the considered range is between 400 and 20,000 mm for the largest axis length of a machine tool.
As a reference, four measurements per year are suggested, one every three months. However, depending on the results of the volumetric performance indicator, architecture of the machine and workshop ambient conditions, the frequency of the tests could be varied and adapted on each case.
Diagonal positioning measurement in medium-large machine tools requires from an interferometry laser-based measuring system with the capacity to do the tracking of a mirror/retroreflector placed on the machine tool spindle. Either laser tracer or laser tracker measures the relative movement/displacement of a retroreflector from the initial point, based on their interferometry laser-based system. Both measuring devices can track a retroreflector placed on the machine tool´s spindle, allowing the measurement of machine tools movement in a fast and easy way, without special set-ups or fixing tools. This is the main advantage compared with common laser interferometry, which requires a tricky set-up process for this kind of measuring procedures where several axes of the machine tool are interpolated to create a special diagonal.
ETALON AG provides the most suitable software to manage this measure, Track-check . If it is connected to the machine tool and measuring device, it automatically detects machine stops to perform a measurement. When the measurement has been successfully completed, the software calculates the mean bi-directional positional deviation which is graphically represented. A report summarizing the results is also provided.
This test provides a quick view of the geometric condition of the machine tool, but its aim is not to provide quantitative data. If the results show deviation with respect to reference values, a complete volumetric measurement should be performed to map the geometric errors of the machine tool and to be able to compensate them.
8.3.2 Artefact Measurement Using Touch Probe
The main objective is to carry out a fast and reliable “health check” of the machine geometric performance, verifying whether the relative position/orientation between the machine tool coordinate system and the working volume is within the expected tolerances, using an artefact as a reference for the measurements.
The procedure consists of measuring the centre or position of several features (e.g. spheres) of an artefact located in the working volume with the touch probe and the corresponding software that allows doing the measurement. The proposed measuring process is automatic (using a CNC macro) and suitable to have the chance to export the results from the CNC.
As cited above, a calibrated artefact located and fixed on the machine tool volume is measured to analyse the geometrical stability of the machine tool. The artefact is comprised of four spheres, and these geometries are measured each time to assess their position according to machine tool coordinate system and thermal environmental conditions. During the measurements, temperature is monitored to establish a relationship between the dimensional measurements and thermal ones.
For an increase in Tª of approximately 5 °C (comparing to the starting state), the maximum drift of the machine is established in Z-direction (inverse to gravity direction) and is around 60 µm. Moreover, as all the centres present the same behaviour, a lack of stability of the artefact is discarded. The drift in X- and Y-directions is lower than in Z-direction for this kind of machine tool.
With these results, it can be concluded that the indicator, maximum deviation in X–Y–Z of any of the spheres for a certain temperature will be enough to determine the thermal stability of the machine. If the obtained indicator is above the determined threshold, to be characterized before, the machine will need to be examined in deep.
8.3.3 Dynamic Stiffness Measurement of Tool/Part
The objective of this test is to control the dynamics stiffness of the machine tool. A hammer test is proposed to evaluate the dynamic performance of the machine tool .
On the one hand, the force sensor at the hammer serves to provide a measurement of the amplitude and frequency content of the energy stimulus that is applied to a test object. On the other hand, accelerometers are used to measure the machine’s structural response due to the hammer force. A single triaxial accelerometers located at the spindle will be used. A multichannel Fast Fourier Transform (FFT) analyser is needed to carry out the signal acquisition, sensor conditioning and FFT processing.
If possible, both excitation and measurement will be carried at the machine tool spindle. Different resonant frequencies must be identified and characterized with the following indicators: frequency, dynamic compliance, damping ratio and direction of excitation.
8.3.4 Feed Drive and Spindle Auto-Characterization
Back and forth displacement of each linear axis.
Circular interpolation of each pair of axes.
Spindle rotation at constant speed.
Comparison of the indicators obtained in the circularity tests
CNC (linear scales) (µm)
The complete sequence of movements is programmed in ISO code and is fed to the machine. By making use of monitoring capabilities implemented in Twin-Control project , data are continuously acquired during the test. The monitored data are uploaded to the fleet server, where it is processed to obtain relevant indicators like friction, backlash, inversion peaks and maximum power in feed drives, and power consumption in spindles. If an accelerometer is installed in the spindle, vibration analysis can be performed, providing relevant indicators to estimate its condition .
The proposed test is totally automated and does not require special skills to machine tool operator nor special equipment. Test duration can vary depending on machine size, number of axis and dynamics, but it is always below 5 min.
Although the proposed procedure is not aimed for a quantitative characterization of the machine, it is very well suited for a qualitative analysis of machine tool condition. In addition, the short duration and its simplicity make it suitable for a periodic execution, leading to a better control of machine condition during its life cycle.
The behaviour of a machine tool is observable by computing contextualized information from sensors measurements. Two indicator extraction methodologies were described in this section. The first one exploits the in-production sensors’ measurements to extract statistical features from specific machine operating conditions, and the second one exploits the results of specific tests, called characterization tests, performed by the machine.
The raw sensors’ measurements indicator extraction offers high volume of data and is performed in parallel of the production. Nevertheless, the machine capabilities observed depends on the program performed such as indicators that illustrate only a part of the machine capabilities.
The characterization test approach allows to observe the overall machine tool capacities as entire axis moves are performed under controlled conditions. Nevertheless, it requires to perform a specific program interrupting the production for about five minutes.
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