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Transputer-based tool monitoring system
Transputer-based tool monitoring system T. Pfeifer and P. Plapper Institute of Machine Tools and Production-Engineering, Chair of Metrology and Quality Assurance, Steinbachstrasse 53B, 5100 Aachen, Germany The recent development of computer technology has created processors based on completely new computer structures. Transputers represent the most promising development in this field. An enhancement of the communication and computation performance makes possible the bypassing of bottlenecks which may occur in the sequential processing of the algorithms. On the basis of these processors operating in parallel, a multi-sensor system has been built up which can perform a real-time analysis of transient signals. This article describes the structure of the modular transputer system and its application in the monitoring of a drilling process. The efficiency of real-time analysis methods is discussed. Keywords:Drilling, multi-sensorsystem, statistical analysis, tool wear
1. Fields of application of the system The system realised (Pfeifer and Plapper, 1989a) was designed for multi-purpose application in production measuring technology. In this field, it is intended for use in the monitoring of machining processes such as turning, drilling or milling. In the monitoring of the drilling process, the transputer system has already been applied successfully as described below. The hardware concept is also designed flexibly for further processes. In this case only the software modules need to be adapted. The production processes shown in Fig 1 represent a selection of the possible manufacturing processes. If appropriate sensors are connected, other measuring tasks such as, for example, the monitoring of ball bearings can be performed by the multi-sensor system.
2. Analysis of the damages to be expected According to Takeyama (1970) the most significant proportion of the downtime is made up by the end of service life of the tools, accounting for approximately 30%, and the fracture or partial break-out accounting for 9-17%. Such cost-intensive damage is to be identified in real-time by the transputer system. The breakage of a cutting blade can cause very high costs, such as damage to the tool or to the workpiece, and should be avoided for this reason also. The remaining causes for downtime are divided up among others in adjustment work for eliminating inaccuracies, rest times, removal of shavings and safety requirements, according to Takeyama (1970). These reasons for downtimes are not the subject of the described research work so the system was not designed for this application. The transputer is in a position to identify the following parameters during the production process:
134
• Identification of cut - commencement of monitoring and check whether tool or workpiece are missing. • Tool breakage - causes the machine to stop immediately. • Tool wear - effects tool change after completion of the current operation.
3. The sensors The parameters listed above can be recorded with a variety of sensors. Thus, for instance, the expansions of a machine tool proportional to the forces can be registered among others with capacitive, inductive, piezoelectric or optical pickups or with strain gauges. In order to achieve a uniform analogue interface of the modular transputer system, an attempt was made firstly to reduce the number of measuring pickups as far as possible to a few universally usable pickups. Beyond this background, Fig 1 summarises the measuring parameters and sensors. The measuring parameters for stock-removal production processes with a geometrically defined cutting edge are discussed below. Since the parameters to be monitored - c u t , tool breakage and w e a r - c a n n o t be measured directly during the production process, they must be obtained by the analysis of secondary signals. Due to this it is necessary to measure the cutting force components (Kluft, 1983), the torque (D6rrenberg, 1973) and the solid-borne sound (Christoffel, 1984). Piezoelectric sensors are available for these measurements. The impedance is transformed in standard commercial preamplifiers. Depending on the requirements of the process under review, these pickups may be combined in any quantity. This also assures flexibility in terms of various production processes.
MeasurementVol 9 No3, Jul-Sep1991
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4. T h e p r o c e s s o r s In order to be able to intervene quickly in the production process in the case of the aforementioned damage occurring, a real-time analysis of the data of the multisensor system is urgently necessary. Defining real time analysis as the time needed for signal processing which has to be less than or at most the same as the sampling rate. For this reason there is a need for either very fast algorithms or extreme fast processors. Having the opportunity to augment the calculating capacity, several parallel processors should be employed for the analysis of the digitalised signals. Communication bottlenecks can occur in conventional multiprocessor systems based on bus structures due to the bus used in multiplex. For this reason the tool momtoring system described here was built up on a transputer basis. The core point of the transputer concept consists of parallel processing microcomputers. These processors can exchange data simultaneously via four serial interfaces, the links, with the neighbouring processors. The links work with transmission rates of up to 20 MBaud. Because of the 32-bit structure, the transputers can address a maximum memory capacity of 4 GByte. The large computing performance of one single transputer can be simply augmented by adding further processors to the transputer network. Programming is carried out ill O C C A M , a special high-level parallel programming language. 5. T h e a n a l o g u e - t o - d i g i t a l c o n v e r t e r m o d u l e s In order to couple the multi-sensor system to the transputer network, a modular intelligent analogue-todigital (ADC) converter unit is used (Fig 2). In one module in each case a sensor signal is digitised with subsequent data reduction. The modules are designed flexibly to allow possible expansion with additional MeasurementVol 9 No 3, JuI-Sep 1991
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sensors. Since each transputer has only four interfaces, one of which is required for transmitting the data on to the host, one additional processor is necessary for every three A D C modules. This is why the limits for additional modules are set solely by the costs for the connected transputer network. The boundary values of piezoelectric sensors have been taken as a basis for the selection of the conversion depth and the scanning rate. This resuited in a conversion depth of the signals of 8 bit, scanned with 50 kHz. The adaptation of the signal to the input area of the converter of +10 V takes place in an operation amplifier which can be set by the software. A Tchebycheff low-pass filter of the 8th order was realised as an anti-aliasing filter. This is necessary to achieve the lowest possible over-scanning (3.33 fold). The data flow to be processed would otherwise rise beyond proportion and exceed both the memory and the computing capacity of the transputers. In order to be able to evaluate cross-correlations between the various signals, data must be recorded exactly at the same time for all channels. For this all sample-and-hold components are triggered from a central control unit. In order to relieve the processors, the option for digital preprocessing of data has been kept open. For this reason an own analogue-to-digital converter is necessary for each A D C module. The galvanic separation of analogue and digital parts is effected via separate voltage supplies and optical couplers. A transputer in which on-line data reduction takes place is integrated on each A D C module. It passes the reduced data to the transputer network in which the actual analysis of the signals takes place. The network consists of four levels of transputers (Fig 3): The processors of the lowest level (T 0.x) are sited piggy-back on the A D C modules. They reduce the incoming data with problem adapted algorithms. These transputers are connected to a ring, allowing fastest 135
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triggering of the other units as soon as manufacturing start or end is recognised. The parameters extracted out of the signals of the multi-sensor system are discussed in the following chapters. At the next level (T l.x) each transputer achieves the data of three ADCs. They are correlated and sent to the third level (T 2.1). This processor decides whether the process is all right or it has to be interrupted. The transputers of the fourth level (T 3.x) are provided as interface between the monitoring system on one side and the user and the CNC of the manufacturing cell on the other side. To communicate with the user a G E M based software was developed. To interrupt the manufacturing process, if necessary, another processor is linked to the CNC.
6.1 Start of drilling process
For the identification of the start of production it proved appropriate to monitor the passive force in time domain. This signal must exceed a threshold of, for example, 4% of the measuring range for at least 1 ms. If there is just a single peak dropping below this value during this period, the monitoring period starts anew. This mode of procedure is expedient to prevent misinterpretation of peaks caused by non-process-conditioned faults- e g, from other machines. The end of the production process is identified if the passive force drops below a threshold of 1% of the measuring range. 6.2 Tool fracture
6. First e x p e r i e n c e g a i n e d with t h e s y s t e m
The transputer system described here was used to monitor a drilling process. Heat-treated Ck 45 (800 N/mm 2) was machined with double-cutting tools with diameters between 6.8 and 17.5 mm. The maximum cutting speed was 20 to 28.5 m/min with a feed of 1 to 1.5% of diameter. In order to have reproducible data records available for off-line testing of the efficiency of differing algorithms, the signals resulting from the drilling process were recorded with a multi-channel tape recorder. The signals recorded simultaneously were the process forces, the torque and the solid-borne sound picked up at three different positions. There are four fields of signal evaluation (Pfeifer and Plapper, 1989b): • • • •
Time domain analysis Statistical analysis Frequency analysis Cepstral analysis
The complexity of the algorithms, and due to this the time needed to calculate the parameters, extends from the first field of analysis to the last one. For this reason, in this paper time domain and statistical methods are preferred for real time analysis. 136
Generally, a distinction is made between two types of drill fracture. On one hand the tool can fail suddenly, on the other hand the fracture may be caused by progressive wear. In contrast to the failure caused by wear, the sudden failure cannot be forecast via secondary process parameters; during the final drilling process, only the dynamic of the accelerations increases. The reliable post-mortem identification of the fracture is based on calculation of the maximum difference of the passive force within a time window at the end of the production process. This makes it possible to switch off the machine before major damage occurs. For this purpose an interface corresponding to RS 232 is created at the transputer network which allows direct response of the CNC. 6.3 Tool wear
The overwhelming majority of tool breakages is caused by progressive wear. Therefore the object of actual research aims to identify the end of drill life prior to the wear-caused fracture by analysing the accelerations. Modern real-time analysis methods are used here helping to analyse the "life' of the tool. For effective toolwear monitoring it is necessary to analyse signals altering significantly as a function of wear. This is true for the accelerations. This chapter describes tool-wear anMeasurement Vol 9 No 3, JuI-Sep 1991
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Monitoring the accelerations in the course of advanced service life, the normal drilling sound is increasingly superimposed by 'squeaks' (Fig 4). They are differing in time and intensity. These characteristics do not increase constantly with increasing service life. Periods of remarkable 'squeaks' are followed by rather moderate drilling. Because of this a simple comparison with limits could certainly not cope satisfactorily with the problem. The first 'squeaks' appeared at the beginning of the drilling. This corresponds with the weak guidance of the drill at the beginning of each hole before the heels are finally locating the tool. Experiments with artificially
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Vol 9 No 3, JuI-Sep 1991
Fig 4 "Squeaks' announcing the close end of service life 137
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As a method of data reduction, the transputers calculate the amplitude distribution of the accelerations (Fig 6). This leads to a histogram for each borehole or, if desired, for each drill rotation. This procedure is a very effective method for data reduction. Calculating the histogram of the signals measured during the drilling process for 30 s, the effective reduction is better than 99.98%. Based on this histogram several promising parameters can be calculated easily. These evaluations can be executed very fast, so that they are suited for real-time analysis. The parameters are, for example, dynamic, standard deviation and joint central moments of 4th order. As described below, the increasing wear can be detected out of these parameters. We also studied other parameters which were not predictory in view of tool wear, such as mean value, skewness and maximum accumulation of the histogram. The efficiency of these parameters is discussed in the following sections. With progressive tool wear the amplitudes of the accelerations grow, which is seen in a broadening of the histogram. This change can be observed by analysing the dynamic of the solid-borne sound. This value is the difference between the maximum and the minimum amplitude. It is quite easily evaluated out of the extreme points of the histogram. Although this parameter is a
Analysing the intensity and cumulation of the 'squeaks', some well-founded parameters may be obtained. During the ongoing service life, the length of time of the modulations increases significantly. The number of 'squeaks'-per-drilling and the duration of each modulation rises. At the closing end of the service life the duration of these characteristics grows and the period between them decreases. Finally, it may appear that the modulations merge to one long 'squeak'. In this case the number of 'squeaks'-per-drilling will drop combined with a significant increase of their duration. Using these parameters to monitor the tool wear it has to be considered that this fusion of the modulations does not appear at the end of every drill's service life. The duration of the "squeaks' in relation to the time needed for each complete drilling gives the percentile of the "squeaks" (Fig 5). This parameter provides a reliable prediction of failure.
6.3.2 Statistical analysis If a new drill is used, the width of the wear mark is slight and a relatively small solid-borne sound level is measured. The intensity of the vibrations only rises as the wear increases. In this field statistical analysis gives several promising parameters. Acceleration
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Pfeifer and Plapper function of the distance between the drilling point and the transducer position, it is an extremely sensitive characteristic parameter. After digital filtering of the superpositioned effects, the dynamic indicates very clearly the increasing tool wear. The joint central moments rnz are calculated in a similar manner to the moments of inertia out of the histogram: The number f(x) of a certain signal amplitude x is multiplied with the distance of this amplitude from the mean value (x-p,~). For higher orders this distance is raised to the corresponding power z. This is done for all amplitudes: m~ = ~ ( ( x - ~ , , y . f(x) dx J
The histogram of a whole borehole has a gaussian shape (Pfeifer and Plapper, 1989b). Monitoring a fixed drilling time representing a fixed number of samples, this form is determined by the mean value (joint central moment of 1st order) and the standard deviation (joint central moment of 2nd order). The mean of the accelerations measured at a fixed workpiece is always zero, otherwise the structure would move. In our studies this parameter was zero _+1 digit caused by the accuracy of its calculation. So the mean gives no information about the tool wear. More promising for this application is the analysis of the standard deviation. Comparing this parameter with the optical measured tool wear, a correlation between the increase of the standard deviation and the huge increase of the tool wear can be shown. Though it varies with the distance between the drilling point and the position of the transducer it can be used as a parameter to detect the end of the tool life. Corresponding to the mean, the joint central moment of 3rd order (skewness) is usually zero. This parameter considers the sign of the accelerations whereby the number of positive and negative amplitudes are normally equivalent. It does not change significantly with increasing tool wear. This corresponds with the results in the literature (Lange, 1983), The joint central moment of 4th order reacts in a similar way to the standard deviation upon tool wear. Due to the higher exponent, this parameter is more sensitive to higher amplitudes; therefore it indicates changes in the state of the tool earlier than the standard deviation. The joint central moments of higher order than 4 do not give significantly more information about the remaining tool life. Indeed, their evaluation time rises excessively according to the order of the exponent. The maximum of the histogram corresponds with the number of the most frequent amplitudes of the acceleration. In accordance with Lange (1983), this parameter becomes large at weak signals and small at loud solidborne sounds. Due to the gaussian shape of the histogram the maximum should be reciprocal to the standard deviation. In reality the most frequent amplitude may vary in a small range about the mean. But these undulations do not correlate with tool wear. Because this parameter gives no additional information it is not considered helpful. The monitoring methods explained analyse the accelerations measured during the machining of an entire
MeasurementVol 9 No 3, Jul-Sep 1991
hole. Short changes in the characteristic of the signals like the described 'squeaks' do not influence these parameters significantly. Though there are modifications at the end of tool-life, they can't be recognised because their percentile is too low. The huge computing performance of the transputer system already allows the real-time calculation of the above explained statistical parameters for each drill rotation. Analysing the amplitude distribution of each drill rotation, a lower dynamic level at the beginning of the process can be observed. This is caused by the depth of cut at the start of the drilling cycle. In this state the cutting area has not reached its maximum. Increasing wear causes higher dynamics throughout the machining process. Integrating the filtered dynamics of an entire bore cycle results in another successful parameter to predict failure caused by wear.
7. Summary A modular multi-sensor system is presented which allows real-time processing of signals recorded simultaneously. For this purpose the measuring data are evaluated in parallel on a transputer network. In accordance with the original design of the hardware the system is used to monitor a drilling process. Effective on-line analysis methods forecasting tool failure are discussed. In order to monitor the tool wear, statistical methods are successfully applied. The research led to significant parameters which permit the safe real-time monitoring of drills. The transputer system is in a state to prevent tool failures caused by tool wear and recognises nonannounced tool breakages to help avoid severe damage. The object of present work is establishing a feature extraction system based upon this knowledge. This intelligent system will use the described parameters as coefficients, It is intended to be flexible and smart for quick adaption to a great variety of machines.
8. Literature Christoffel, K. 1984. Werkzeugfiberwachung beim Bohren und Fr~isen. Thesis R W T H Aachen. D6rrenberg, R. 1973. Untersuchungen an Spiralbohrern mit innenliegenden Kfihlkan~ilen. Thesis T U Berlin. Kluft, W. 1983. Werkzeugfiberwachungssysteme ffir die Drehbearbeitung. Thesis R W T H Aachen. Lange, J. 1983. M6glichkeiten der Analyse von Ger~uschsignalen beim Drehen. Thesis T U Berlin. Pfeifer, T. and Plapper, P. 1989a. Transputergestfitztes Multisensorsystem zum Oberwachung von Werkzeugmaschinen, hTdustrie Anzeiger. 111 (57/58), 29-31. Konradin Verlag, Leinfelden. Pt'eifer, T. and Plapper, P. 1989b. Werkzeuge zur Echtzeitanalyse der Signale eines Multisensorsystems, lnformatik Fachberichte Nr. 237, 'Parallele Datenverarbeitung mit dem Transputer', p. 152 ft. Springer Verlag, Berlin, Heidelberg. Takeyama, H. 1970. Optimierende Steuerung bei der Drehbearbeitung. Werkstatt und Betrieb, 103, 627637. Carl Hanser Verlag, Munich.
139
1. Fields of application of the system The system realised (Pfeifer and Plapper, 1989a) was designed for multi-purpose application in production measuring technology. In this field, it is intended for use in the monitoring of machining processes such as turning, drilling or milling. In the monitoring of the drilling process, the transputer system has already been applied successfully as described below. The hardware concept is also designed flexibly for further processes. In this case only the software modules need to be adapted. The production processes shown in Fig 1 represent a selection of the possible manufacturing processes. If appropriate sensors are connected, other measuring tasks such as, for example, the monitoring of ball bearings can be performed by the multi-sensor system.
2. Analysis of the damages to be expected According to Takeyama (1970) the most significant proportion of the downtime is made up by the end of service life of the tools, accounting for approximately 30%, and the fracture or partial break-out accounting for 9-17%. Such cost-intensive damage is to be identified in real-time by the transputer system. The breakage of a cutting blade can cause very high costs, such as damage to the tool or to the workpiece, and should be avoided for this reason also. The remaining causes for downtime are divided up among others in adjustment work for eliminating inaccuracies, rest times, removal of shavings and safety requirements, according to Takeyama (1970). These reasons for downtimes are not the subject of the described research work so the system was not designed for this application. The transputer is in a position to identify the following parameters during the production process:
134
• Identification of cut - commencement of monitoring and check whether tool or workpiece are missing. • Tool breakage - causes the machine to stop immediately. • Tool wear - effects tool change after completion of the current operation.
3. The sensors The parameters listed above can be recorded with a variety of sensors. Thus, for instance, the expansions of a machine tool proportional to the forces can be registered among others with capacitive, inductive, piezoelectric or optical pickups or with strain gauges. In order to achieve a uniform analogue interface of the modular transputer system, an attempt was made firstly to reduce the number of measuring pickups as far as possible to a few universally usable pickups. Beyond this background, Fig 1 summarises the measuring parameters and sensors. The measuring parameters for stock-removal production processes with a geometrically defined cutting edge are discussed below. Since the parameters to be monitored - c u t , tool breakage and w e a r - c a n n o t be measured directly during the production process, they must be obtained by the analysis of secondary signals. Due to this it is necessary to measure the cutting force components (Kluft, 1983), the torque (D6rrenberg, 1973) and the solid-borne sound (Christoffel, 1984). Piezoelectric sensors are available for these measurements. The impedance is transformed in standard commercial preamplifiers. Depending on the requirements of the process under review, these pickups may be combined in any quantity. This also assures flexibility in terms of various production processes.
MeasurementVol 9 No3, Jul-Sep1991
Pfeifer and Plapper
Tool.ear I/,
- Turning
TOO1 b r e a k a g e P a r t l y breakage Machine v l b r a t Ions
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4. T h e p r o c e s s o r s In order to be able to intervene quickly in the production process in the case of the aforementioned damage occurring, a real-time analysis of the data of the multisensor system is urgently necessary. Defining real time analysis as the time needed for signal processing which has to be less than or at most the same as the sampling rate. For this reason there is a need for either very fast algorithms or extreme fast processors. Having the opportunity to augment the calculating capacity, several parallel processors should be employed for the analysis of the digitalised signals. Communication bottlenecks can occur in conventional multiprocessor systems based on bus structures due to the bus used in multiplex. For this reason the tool momtoring system described here was built up on a transputer basis. The core point of the transputer concept consists of parallel processing microcomputers. These processors can exchange data simultaneously via four serial interfaces, the links, with the neighbouring processors. The links work with transmission rates of up to 20 MBaud. Because of the 32-bit structure, the transputers can address a maximum memory capacity of 4 GByte. The large computing performance of one single transputer can be simply augmented by adding further processors to the transputer network. Programming is carried out ill O C C A M , a special high-level parallel programming language. 5. T h e a n a l o g u e - t o - d i g i t a l c o n v e r t e r m o d u l e s In order to couple the multi-sensor system to the transputer network, a modular intelligent analogue-todigital (ADC) converter unit is used (Fig 2). In one module in each case a sensor signal is digitised with subsequent data reduction. The modules are designed flexibly to allow possible expansion with additional MeasurementVol 9 No 3, JuI-Sep 1991
, zl.~v,//l~,>,v; c, ./>/~//,>;;x,;~%l
Fig I Selection o f the sensors in accordance with the damage to be expected - depending on the machine being monitored
sensors. Since each transputer has only four interfaces, one of which is required for transmitting the data on to the host, one additional processor is necessary for every three A D C modules. This is why the limits for additional modules are set solely by the costs for the connected transputer network. The boundary values of piezoelectric sensors have been taken as a basis for the selection of the conversion depth and the scanning rate. This resuited in a conversion depth of the signals of 8 bit, scanned with 50 kHz. The adaptation of the signal to the input area of the converter of +10 V takes place in an operation amplifier which can be set by the software. A Tchebycheff low-pass filter of the 8th order was realised as an anti-aliasing filter. This is necessary to achieve the lowest possible over-scanning (3.33 fold). The data flow to be processed would otherwise rise beyond proportion and exceed both the memory and the computing capacity of the transputers. In order to be able to evaluate cross-correlations between the various signals, data must be recorded exactly at the same time for all channels. For this all sample-and-hold components are triggered from a central control unit. In order to relieve the processors, the option for digital preprocessing of data has been kept open. For this reason an own analogue-to-digital converter is necessary for each A D C module. The galvanic separation of analogue and digital parts is effected via separate voltage supplies and optical couplers. A transputer in which on-line data reduction takes place is integrated on each A D C module. It passes the reduced data to the transputer network in which the actual analysis of the signals takes place. The network consists of four levels of transputers (Fig 3): The processors of the lowest level (T 0.x) are sited piggy-back on the A D C modules. They reduce the incoming data with problem adapted algorithms. These transputers are connected to a ring, allowing fastest 135
Pfeifer and Plapper
sensor systems ~
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triggering of the other units as soon as manufacturing start or end is recognised. The parameters extracted out of the signals of the multi-sensor system are discussed in the following chapters. At the next level (T l.x) each transputer achieves the data of three ADCs. They are correlated and sent to the third level (T 2.1). This processor decides whether the process is all right or it has to be interrupted. The transputers of the fourth level (T 3.x) are provided as interface between the monitoring system on one side and the user and the CNC of the manufacturing cell on the other side. To communicate with the user a G E M based software was developed. To interrupt the manufacturing process, if necessary, another processor is linked to the CNC.
6.1 Start of drilling process
For the identification of the start of production it proved appropriate to monitor the passive force in time domain. This signal must exceed a threshold of, for example, 4% of the measuring range for at least 1 ms. If there is just a single peak dropping below this value during this period, the monitoring period starts anew. This mode of procedure is expedient to prevent misinterpretation of peaks caused by non-process-conditioned faults- e g, from other machines. The end of the production process is identified if the passive force drops below a threshold of 1% of the measuring range. 6.2 Tool fracture
6. First e x p e r i e n c e g a i n e d with t h e s y s t e m
The transputer system described here was used to monitor a drilling process. Heat-treated Ck 45 (800 N/mm 2) was machined with double-cutting tools with diameters between 6.8 and 17.5 mm. The maximum cutting speed was 20 to 28.5 m/min with a feed of 1 to 1.5% of diameter. In order to have reproducible data records available for off-line testing of the efficiency of differing algorithms, the signals resulting from the drilling process were recorded with a multi-channel tape recorder. The signals recorded simultaneously were the process forces, the torque and the solid-borne sound picked up at three different positions. There are four fields of signal evaluation (Pfeifer and Plapper, 1989b): • • • •
Time domain analysis Statistical analysis Frequency analysis Cepstral analysis
The complexity of the algorithms, and due to this the time needed to calculate the parameters, extends from the first field of analysis to the last one. For this reason, in this paper time domain and statistical methods are preferred for real time analysis. 136
Generally, a distinction is made between two types of drill fracture. On one hand the tool can fail suddenly, on the other hand the fracture may be caused by progressive wear. In contrast to the failure caused by wear, the sudden failure cannot be forecast via secondary process parameters; during the final drilling process, only the dynamic of the accelerations increases. The reliable post-mortem identification of the fracture is based on calculation of the maximum difference of the passive force within a time window at the end of the production process. This makes it possible to switch off the machine before major damage occurs. For this purpose an interface corresponding to RS 232 is created at the transputer network which allows direct response of the CNC. 6.3 Tool wear
The overwhelming majority of tool breakages is caused by progressive wear. Therefore the object of actual research aims to identify the end of drill life prior to the wear-caused fracture by analysing the accelerations. Modern real-time analysis methods are used here helping to analyse the "life' of the tool. For effective toolwear monitoring it is necessary to analyse signals altering significantly as a function of wear. This is true for the accelerations. This chapter describes tool-wear anMeasurement Vol 9 No 3, JuI-Sep 1991
Pfeifer and Plapper
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Monitoring the accelerations in the course of advanced service life, the normal drilling sound is increasingly superimposed by 'squeaks' (Fig 4). They are differing in time and intensity. These characteristics do not increase constantly with increasing service life. Periods of remarkable 'squeaks' are followed by rather moderate drilling. Because of this a simple comparison with limits could certainly not cope satisfactorily with the problem. The first 'squeaks' appeared at the beginning of the drilling. This corresponds with the weak guidance of the drill at the beginning of each hole before the heels are finally locating the tool. Experiments with artificially
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worn tools yield to the perception that these characteristics are caused by hardness of the workpiece and heel wear. The accelerations shown in Fig 4 were recorded with a drill rotating at 733 rev/min. The periodicity of the "squeaks' takes 82 ms, which harmonises with the numbers of revolutions. This corresponds to a constant angular position of the tool. Obviously they were always caused by the same heel during contact with a special point of the workpiece. After 380 ms a second modulation appears dephased for about 41 ms. This may be caused by the other heel at the same point of the workpiece. It may be also provoked by the first heel which meets conditions leading to the 'squeaks' at an angular position of 180° . According to further investigations it was proved that only one heel caused the modulations. They were measured at differing angular positions.
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Vol 9 No 3, JuI-Sep 1991
Fig 4 "Squeaks' announcing the close end of service life 137
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As a method of data reduction, the transputers calculate the amplitude distribution of the accelerations (Fig 6). This leads to a histogram for each borehole or, if desired, for each drill rotation. This procedure is a very effective method for data reduction. Calculating the histogram of the signals measured during the drilling process for 30 s, the effective reduction is better than 99.98%. Based on this histogram several promising parameters can be calculated easily. These evaluations can be executed very fast, so that they are suited for real-time analysis. The parameters are, for example, dynamic, standard deviation and joint central moments of 4th order. As described below, the increasing wear can be detected out of these parameters. We also studied other parameters which were not predictory in view of tool wear, such as mean value, skewness and maximum accumulation of the histogram. The efficiency of these parameters is discussed in the following sections. With progressive tool wear the amplitudes of the accelerations grow, which is seen in a broadening of the histogram. This change can be observed by analysing the dynamic of the solid-borne sound. This value is the difference between the maximum and the minimum amplitude. It is quite easily evaluated out of the extreme points of the histogram. Although this parameter is a
Analysing the intensity and cumulation of the 'squeaks', some well-founded parameters may be obtained. During the ongoing service life, the length of time of the modulations increases significantly. The number of 'squeaks'-per-drilling and the duration of each modulation rises. At the closing end of the service life the duration of these characteristics grows and the period between them decreases. Finally, it may appear that the modulations merge to one long 'squeak'. In this case the number of 'squeaks'-per-drilling will drop combined with a significant increase of their duration. Using these parameters to monitor the tool wear it has to be considered that this fusion of the modulations does not appear at the end of every drill's service life. The duration of the "squeaks' in relation to the time needed for each complete drilling gives the percentile of the "squeaks" (Fig 5). This parameter provides a reliable prediction of failure.
6.3.2 Statistical analysis If a new drill is used, the width of the wear mark is slight and a relatively small solid-borne sound level is measured. The intensity of the vibrations only rises as the wear increases. In this field statistical analysis gives several promising parameters. Acceleration
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Pfeifer and Plapper function of the distance between the drilling point and the transducer position, it is an extremely sensitive characteristic parameter. After digital filtering of the superpositioned effects, the dynamic indicates very clearly the increasing tool wear. The joint central moments rnz are calculated in a similar manner to the moments of inertia out of the histogram: The number f(x) of a certain signal amplitude x is multiplied with the distance of this amplitude from the mean value (x-p,~). For higher orders this distance is raised to the corresponding power z. This is done for all amplitudes: m~ = ~ ( ( x - ~ , , y . f(x) dx J
The histogram of a whole borehole has a gaussian shape (Pfeifer and Plapper, 1989b). Monitoring a fixed drilling time representing a fixed number of samples, this form is determined by the mean value (joint central moment of 1st order) and the standard deviation (joint central moment of 2nd order). The mean of the accelerations measured at a fixed workpiece is always zero, otherwise the structure would move. In our studies this parameter was zero _+1 digit caused by the accuracy of its calculation. So the mean gives no information about the tool wear. More promising for this application is the analysis of the standard deviation. Comparing this parameter with the optical measured tool wear, a correlation between the increase of the standard deviation and the huge increase of the tool wear can be shown. Though it varies with the distance between the drilling point and the position of the transducer it can be used as a parameter to detect the end of the tool life. Corresponding to the mean, the joint central moment of 3rd order (skewness) is usually zero. This parameter considers the sign of the accelerations whereby the number of positive and negative amplitudes are normally equivalent. It does not change significantly with increasing tool wear. This corresponds with the results in the literature (Lange, 1983), The joint central moment of 4th order reacts in a similar way to the standard deviation upon tool wear. Due to the higher exponent, this parameter is more sensitive to higher amplitudes; therefore it indicates changes in the state of the tool earlier than the standard deviation. The joint central moments of higher order than 4 do not give significantly more information about the remaining tool life. Indeed, their evaluation time rises excessively according to the order of the exponent. The maximum of the histogram corresponds with the number of the most frequent amplitudes of the acceleration. In accordance with Lange (1983), this parameter becomes large at weak signals and small at loud solidborne sounds. Due to the gaussian shape of the histogram the maximum should be reciprocal to the standard deviation. In reality the most frequent amplitude may vary in a small range about the mean. But these undulations do not correlate with tool wear. Because this parameter gives no additional information it is not considered helpful. The monitoring methods explained analyse the accelerations measured during the machining of an entire
MeasurementVol 9 No 3, Jul-Sep 1991
hole. Short changes in the characteristic of the signals like the described 'squeaks' do not influence these parameters significantly. Though there are modifications at the end of tool-life, they can't be recognised because their percentile is too low. The huge computing performance of the transputer system already allows the real-time calculation of the above explained statistical parameters for each drill rotation. Analysing the amplitude distribution of each drill rotation, a lower dynamic level at the beginning of the process can be observed. This is caused by the depth of cut at the start of the drilling cycle. In this state the cutting area has not reached its maximum. Increasing wear causes higher dynamics throughout the machining process. Integrating the filtered dynamics of an entire bore cycle results in another successful parameter to predict failure caused by wear.
7. Summary A modular multi-sensor system is presented which allows real-time processing of signals recorded simultaneously. For this purpose the measuring data are evaluated in parallel on a transputer network. In accordance with the original design of the hardware the system is used to monitor a drilling process. Effective on-line analysis methods forecasting tool failure are discussed. In order to monitor the tool wear, statistical methods are successfully applied. The research led to significant parameters which permit the safe real-time monitoring of drills. The transputer system is in a state to prevent tool failures caused by tool wear and recognises nonannounced tool breakages to help avoid severe damage. The object of present work is establishing a feature extraction system based upon this knowledge. This intelligent system will use the described parameters as coefficients, It is intended to be flexible and smart for quick adaption to a great variety of machines.
8. Literature Christoffel, K. 1984. Werkzeugfiberwachung beim Bohren und Fr~isen. Thesis R W T H Aachen. D6rrenberg, R. 1973. Untersuchungen an Spiralbohrern mit innenliegenden Kfihlkan~ilen. Thesis T U Berlin. Kluft, W. 1983. Werkzeugfiberwachungssysteme ffir die Drehbearbeitung. Thesis R W T H Aachen. Lange, J. 1983. M6glichkeiten der Analyse von Ger~uschsignalen beim Drehen. Thesis T U Berlin. Pfeifer, T. and Plapper, P. 1989a. Transputergestfitztes Multisensorsystem zum Oberwachung von Werkzeugmaschinen, hTdustrie Anzeiger. 111 (57/58), 29-31. Konradin Verlag, Leinfelden. Pt'eifer, T. and Plapper, P. 1989b. Werkzeuge zur Echtzeitanalyse der Signale eines Multisensorsystems, lnformatik Fachberichte Nr. 237, 'Parallele Datenverarbeitung mit dem Transputer', p. 152 ft. Springer Verlag, Berlin, Heidelberg. Takeyama, H. 1970. Optimierende Steuerung bei der Drehbearbeitung. Werkstatt und Betrieb, 103, 627637. Carl Hanser Verlag, Munich.
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