Int J Adv Manuf Technol (1999) 15:730–736 1999 Springer-Verlag London Limited

Sensor Fusion for Monitoring and Controlling GrindingProcesses

I. InasakiDepartment of System Design Engineering, Keio University, Yokohama, Japan

A monitoring and controlling system for the cylindrical grind-ing process is introduced with experimental verification. Thesystem uses two different types of sensor, the acoustic emission(AE) and the power sensor, so that most of the problems inthe grinding process can be detected and furthermore, thegrinding cycle can be automatically optimised. The concept ofan intelligent grinding database, which is capable of auto-matically establishing the database by using some practicalgrinding results, is also introduced.

Keywords: Grinding; Monitoring; Sensor fusion

1. Introduction

There is a strong demand to develop an intelligent grindingsystem, which can replace skilled operators with sophisticatedmonitoring and controlling devices. Some reasons behind suchrequirements are:

1. There is a shortage of skilled operators in industry whichrequires the automation of the grinding process.

2. The increasing application of CBN wheels, which are moreexpensive than the conventional wheels, requires thereduction of abrasive consumption through monitoring andcontrolling the process.

Three important roles of the monitoring system are to detectproblems, which occur during the process, to provide infor-mation to optimise the process and to contribute to establishingthe database, which is necessary to determine the set-up para-meters (Fig. 1). The major problems in the grinding processesare, for example, chatter vibration, grinding burn and surfaceroughness deterioration. They are mostly observed after grind-ing for some time therefore, they are regarded as wheel-lifecriteria. As far as the grinding process using the CBN wheelsis concerned, the surface roughness deterioration is the biggestproblem, in other words, it is the most common criterionapplied for judging the wheel life. In order to meet the

Correspondence and offprint requests to: Professor I. Inasaki, Depart-ment of System Design Engineering, Keio University, 3-14-1 Hiyoshi,Kohoku-ku, Yokohama, 223, Japan. E-mail: inasakiKsd.keio.ac.jp

Fig. 1. Tasks of the monitoring system.

requirement for detecting those problems, sensor fusion, i.e.,using the multiple sensors, appears to be promising. In thispaper, a proposal will be made to fuse the AE sensor and thepower sensor for assessing the wheel life [1].

If we take an example of cylindrical plunge grinding, thereare many set-up parameters in addition to the grinding wheeland grinding fluid selection to be predetermined, such as wheelsurface speed, workpiece speed and infeed-rate. Among thoseparameters, the most influential one on the grinding result isthe infeed-rate and how to establish the grinding cycle, whichconsists of rough, finish, and spark-out grinding. The infor-mation obtained through the monitoring system can be usedfor minimising the grinding cycle time while meeting therequirements for the ground part quality. An algorithm forautomatically adapting the grinding cycle to meet such demandswill be proposed in this paper.

The appropriate selection of the initial set-up parameters tomeet the demand is a difficult task for the grinding operator,particularly when a new task is given. The conditions not onlyfor the grinding process but also for the dressing process, shouldbe determined. What is necessary to achieve this is the operator’sskill and experience or a database, which makes it possible forthe operator to determine the set-up parameters. Establishing thegrinding database requires time and money. Therefore, a proposalwill also be made in this paper to establish automatically thegrinding database by effectively using the information obtainedthrough the monitoring system [2].

Monitoring the conditioning process, i.e., truing and dressing,is very important, particularly when the CBN wheel is used[3]. Most of the abrasives are consumed during the conditioningprocess instead of during the grinding process. Therefore, this

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process should also be monitored carefully so that the abrasiveconsumption can be minimised. The proposed monitoringsystem based on an AE sensor integrated CBN wheel alsomakes it possible to monitor the conditioning process.

2. Sensor Fusion Based on the AE andPower Sensors

Let us now consider why multiple sensors are necessary formonitoring and controlling the grinding process taking theexample of the wheel-life assessment. The life of a grindingwheel is judged in various ways depending on the operations.The representative criteria for the wheel life are the occurrenceof the chatter vibration, grinding burn on the ground part, andthe surface roughness deterioration. Most of those problems,which determine the wheel life, are closely related to thechange of the grinding wheel surface as the grinding timeadvances (Table 1).

The surface characteristics of the grinding wheel change duringthe process in such ways as the change of the geometricalconfiguration, which is the macroscopic change, and the cuttingedge distribution and the cutting-edge wear, which are rathermicroscopic changes. Self-excited chatter vibration in grinding iscaused by a regenerative effect on the grinding wheel surface

Table 1.Wheel life criteria and sensors for detecting them.

Change on the Wheel life criteria Sensorwheel surface

Change in geometrical Chatter vibration AE Sensorconfiguration

Change in surface Deterioration of surface Power Sensortopography roughness

Burn

Fig. 2. Intelligent grinding system.

[4]. Non-uniform wear of the wheel surface generates waves asthe grinding time elapses and consequently causes chattervibration. The chatter frequency is related to the natural frequencyof the mechanical system and is normally in the range of somekHz. The vibration in such a frequency range can be detectedwith an accelerometer or other vibration sensors. The monitoringsystem introduced in this paper uses an AE sensor, which hasthe following advantages: small in size, ease of attachment, androbustness. The power sensor does not possess a good enoughdynamic response to detect such a high-frequency phenomenon.

Grinding burn is closely related to the wear of the graincuttings, which can be detected by the power sensor [5]. Thesurface roughness deterioration is, needless to say, caused bycutting-edge breakage and dislocation, which changes the cutting-edge distribution on the wheel surface. It is, therefore, conceivablethat the AE signal from the grinding process provides somenecessary information for estimating the surface roughness.

As discussed above, major problems in grinding thatdetermine the wheel life are detected through combiningtwo different types of sensor; AE and power sensors. Ofcourse, it is also conceivable to use the signal from twodifferent kinds of sensor for detecting a single problem,aiming at an increase in reliability. Sensors are combinedfor two different purposes: complementation and interaction.Grinding wheel life, which may be determined by differentcriteria, requires complementary usage of multiple sensorsto deal with the detection of different types of wheel life.Sensors may be combined interactively to improve thereliability of the sensing system.

3. Architecture of the Intelligent GrindingSystem

The architecture of the intelligent cylindrical plunge grindingsystem so far developed is shown in Fig. 2. The main functionof the system is to judge the wheel life automatically in termsof the chatter vibration, grinding burn, and the surface rough-ness deterioration, and to optimise the grinding cycle time.

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The system has two different types of sensor: the AE sensorand power sensor. The AE sensor is mainly used for the detectionof contact between the grinding wheel and the workpiece, andthe chatter vibration, which must include the high-frequencycomponent of the signal, and the surface roughness deterioration.The power sensor is mainly used for detecting grinding burn,which is closely related to the dullness of the grain cutting edges.Furthermore, signals obtained through both sensors are effectivelyfused for optimising the grinding cycle time.

A characteristic feature of the system is that the AE sensor isintegrated into the grinding wheel by taking advantage of theCBN wheel, which has a hub made of metal as shown in Fig. 3[6]. This wheel has several built-in devices, such as batteries, anFM transmitter, and signal processing circuits, so that the signalcan be sent by radio to the receiver located outside the wheel.By mounting a sensor in the grinding wheel, it becomes possibleto detect the AE signal from the grinding point as closely aspossible. The adverse influence of the mechanical noise generatedat the spindle bearing can also be minimised.

The detected AE signal is first pre-amplified and processedwith a full-wave rectifier to obtain the envelope of the AE wave.Then the enveloped signal is transmitted to the receiver outsidethe wheel. At the time when the grinding process is monitored,the signal is further processed with two different filters, i.e. alow-pass filter (10 Hz) and a high-pass filter (1 kHz). The formerfilter is used to obtain the static component of the enveloped AEsignal, the latter filter is used to obtain the dynamic componentof the enveloped signal.

4. Wheel-Life Judgement

4.1 Detection of Chatter Vibration

When CBN wheels are used in grinding, the most importanttask of the monitoring system is to judge the wheel life, andconsequently to reduce the number of conditioning runs tominimise the wheel consumption due to conditioning. Chattervibrations that appear as the grinding time advances are theself-excited vibration caused by the regenerative effect on thewheel surface and the forced vibration caused by out-of-roundness of the wheel. In both cases, the grinding wheelloses its roundness, and conditioning becomes necessary. This

Fig. 3. Sensor integrated CBN grinding wheel.

type of wheel life can be successfully detected with the AEsensor, which is capable of detecting the high frequency signalfrom the grinding process.

The frequency analysis of the AE signal envelope whengrinding is continuously carried out shows clearly that noparticularly noticeable peak can be seen at grinding immedi-ately after conditioning, but as the grinding time advances, aparticular frequency component increases in a noticeable man-ner. The characteristics of the spectral pattern of the AE signalenvelope are differentiated by means of a neural network forthe automatic detection of the wheel life defined by chatter.For this, a three-layered back-propagation model has beenadopted. In the first place, the frequency in the spectrum ofthe AE signal envelope that gives the maximum level isdetected. Then, the data are normalised by division by thepeak level so that they have values between 0 and 1. Thesenormalised values are considered as input data for the inputlayer of the neural network. It follows that when no particularlyprominent peak exists, almost all the values are close to 1and, conversely, when the spectrum of a chatter frequencyshows up prominently, only the input values near the peaktake on values close to 1. The reason why the frequency rangeunder observation was not fixed as described above is that anetwork has to be constructed in such a way as to be capableof coping with changes in the chatter frequency owing to thealteration of grinding conditions.

4.2 Estimation of Surface Roughness

The most generally used index for judging the life of a CBNwheel is the deterioration of the ground part surface roughness.Direct measurement of the surface roughness during the processis extremely difficult. Therefore, indirect measurement of thesurface roughness by means of an AE sensor is proposed inthis system. The surface roughness is the function of the wheelsurface topography, i.e. the cutting-edge distribution on thewheel surface. Therefore, it is envisaged that the surface rough-ness can be indirectly assessed through monitoring the AEsignal from the grinding process, which is affected by thewheel surface topography.

As the stock removal increases, the AE amplitude decreasesand the surface roughness increases. This phenomenon can beexplained as follows. As the grinding time advances, thenumber of cutting edges on the wheel surface decreases owingto the chipping and dislodging of the grains from the bondingmaterial. Thus, the signal level of the AE decreases and thesurface roughness increases. The change in the AE signalis much more significant than the change in the power inmost cases.

The change in the DC and AC components of the AE signaland the cumulative stock removal of the material are used forestimating the surface roughness by applying the fuzzy-neuroalgorithm. Some results of the estimation after the learningprocess has been completed are illustrated in Fig. 4. In thisexample, all parameters used for the estimation and the surfaceroughness are normalised with respect to their initial values sothat the number of the necessary learning processes can beminimised. Therefore, it is necessary to measure the initial

Sensor Fusion for Monitoring and Controlling Grinding Processes 733

Fig. 4.Estimation of surface roughness.

surface roughness at the beginning of grinding to obtain anabsolute value of the roughness.

4.3 Detection of Grinding Burn

Another type of wheel life is characterised by grinding burn,which is closely related to the power consumption during thegrinding process. Therefore, as far as the burning detectionis concerned, monitoring of the grinding power appears tobe desirable.

5. Optimisation of Grinding Cycle Time

The cylindrical grinding cycle applied in this system consistsof rough and finish grinding and does not have any spark-outgrinding period. In order to optimise the grinding cycle time,a rapid infeed and a retraction are used before roughing andbetween roughing and finishing, respectively, as shown inFig. 5. The optimisation of the grinding cycle in this casemeans that the cycle time is minimised without any seriousgrinding problems. Some necessary parameters to determinethe grinding cycle, such as the amount of rapid infeed and

Fig. 5.Applied grinding cycle.

Fig. 6. Parameters in grinding cycle.

retraction, maximum permissible infeed-rate for roughing andfinishing and necessary time for finishing to meet the surfaceroughness requirement, are automatically determined in thesystem taking into account the following demands (Fig. 6):

The time constant due to the elastic deformation in the mecha-nical system should be minimised for reducing the grindingtime.

The infeed-rate for roughing should be maximised within therange where grinding burn does not occur. This is also neces-sary for minimising the grinding cycle time.The infeed-rate for finishing should meet the requirement interms of the surface roughness as well as the form accuracyof the ground part.

Consequently, the performance index for optimising the grind-ing cycle is the grinding time under the constraint of therequired part accuracy and the surface quality.

The AE signal and the power signal are effectively used foroptimising the grinding cycle time based on the above men-tioned strategies. For example, the AE signal is used forminimising the time constant, whereas the power signal isused for maximising the infeed-rate of the roughing process.Optimisation of the cycle time is performed between grindingof each workpiece by modifying the grinding conditions. Somerules for modifying the grinding condition have been developedso that fuzzy logic could be applied, as shown in Fig. 7.

734 I. Inasaki

Fig. 7. Fuzzy reasoning.

Figure 8 shows an example of the minimisation process,which illustrates that the grinding cycle time is successfullyreduced through the increase of the roughing infeed-rate andthe decrease of the time constant.

6. Intelligent Grinding Database

For obtaining suitable grinding conditions, many experimentalapproaches which try to establish a quantitative relationbetween set-up parameters and grinding results have beenemployed so far [7]. However, as grinding is a very compli-cated process with a large number of characteristic quantities,most of the proposed relations have restrictive assumptions,hence the reliability of the relations seems not to be sufficientfor practical usage. Based on this consideration, a newapproach, which tries to imitate the decision-making processof a skilled operator, is proposed. The characteristic feature ofthe proposed method is that the set-up parameters, not onlyfor the grinding process but also for the conditioning process,are taken into account.

Fig. 8.Reduction of grinding cycle time.

6.1 Concept and Structure of the IntelligentDatabase [2]

A skilled operator empirically understands causalities betweenthe input and output quantities which can be obtained throughpost-process measurement and extracts the relationship betweenthe set-up parameters and the results through trial and error.The intelligent database introduced here uses genetic algorithmsand fuzzy reasoning so that the system can learn the relation-ship between the input and the output from the grinding resultsand transform them into explicit rules which can meet thequality requirements for the workpiece.

Figure 9 shows the structure of the proposed system thatconsists of two data storages and two functional modules. Agrinding operation generates a series of given or set parametersand subsequent results such as surface roughness. The grindingdatabase stores these data one by one. Once the store is filledup with data, new data will be integrated according to the“first in first out” strategy. The grinding rule base stores fuzzy

Fig. 9. Structure of the intelligent grinding database.

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production rules that represent relationships between parametersand results. The learning module evaluates the individual datain the grinding database, and generates fuzzy productionrules inductively.

In order to realise these functions, genetic algorithms areused. The reasoning module accepts the request of the para-meter set-up for an individual grinding task. Then, it inferssuitable values for dressing as well as for grinding parameters.

The fuzzy production rules have to be coded as strings ofbinary code for the application of genetic algorithms. Thecoding method adopted in the system is illustrated in Fig. 10.The length of strings is equal to the number of fuzzy sets inan element, and each bit corresponds to one of the basic fuzzysets. According to this coding method, the rule, for example,

If ( fd is very small) and (ad is medium)then (Rz is small)

can be transformed as

10000 00100 01000

where, fd is dressing feed,ad is dressing depth of cut, andRzis surface roughness of the workpiece.

The general procedure of the genetic algorithms is depictedgraphically in Fig. 11.

6.2 Experimental Verifications

In order to confirm the effectiveness of the proposed system,cylindrical plunge grinding using a conventional wheel wasconducted. The setting parameters are dressing depth of cutad, dressing feedfd, grinding depth of cutaw, and workpiecerotational speednw. Outputs from the grinding process, surfaceroughnessRz, and specific grinding energyec, which must beclosely related to grinding burn, are measured after the process(post-process measurement) and during the process (in-processmeasurement), respectively.

After the learning process, the performance of the systemwas evaluated by giving the required value of the surfaceroughness and the specific grinding energy. The dressing andgrinding are performed using the values of the set-up para-meters provided from the intelligent database and the grindingresults were compared with the required values to see if thereasoning was correct or not.

One of the results, when the number of learning data setswas 10 is illustrated in Fig. 12. The open circles represent the

Fig. 10.Fuzzy sets and strings.

Fig. 11.Genetic algorithms.

Fig. 12.Experimental verification of the intelligent database.

data used for learning and the solid circles represent thecombinations of the required surface roughness and the specificgrinding energy to be achieved. The results obtained throughthe grinding tests are represented by the triangles, which areclose to each required value. Although further improvement isnecessary for practical application, it can be said that theeffectiveness of the proposed system can be confirmed.

736 I. Inasaki

7. Conclusions

To achieve the reliable and flexible condition monitoringsystem is a key issue for establishing any machining processeswith minimum human intervention. In this paper, an approachfor establishing an intelligent grinding system has been intro-duced. For succeeding in this development, sensor fusion tech-nology and some artificial intelligent technologies were used.

The monitoring system should be more effectively integratedthan at present into the grinding machines in such a way thatthe grinding condition is controlled in a real-time mode inaccordance with the information from the monitoring system.Establishment of such a feedback control will lead to the realadaptive control of the grinding process.

References

1. I. Inasaki, “Monitoring technology for achieving intelligent grind-ing system”, ASME Paper, MED 6(1), 1997.

2. M. Sakakura and I. Inasaki, “Intelligent data base for grindingoperations”, Annals CIRP, 42(1), pp. 379–382, 1993.

3. I. Inasaki, “Monitoring of dressing and grinding processes withacoustic emission signals”, Annals CIRP, 34(1), pp. 277–280, 1985.

4. I. Inasaki, “Selbsterregte Schwingungen beim Aussenrundeinstech-scheifen”, Annals CIRP, 23(1), pp. 117–118, 1974.

5. I. Inasaki, “Monitoring and optimization of internal grinding pro-cess”, Annals CIRP, 40(1), pp. 359–362, 1991.

6. T. Matsumoto, I. Inasaki and K. Ogawa, “Monitoring of grindingprocess with a sensor integrated CBN wheel”, SME TechnicalPaper, MR 93–367.

7. H. K. Tonshoff, J. Peters, I. Inasaki and T. Paul, “Modeling andsimulation of grinding process”, Annals CIRP, 41(2), p. 677, 1992.