Tool wear monitoring and alarm system based on pattern recognition with logical analysis of data

CORE SEMINAR 1
Tool Wear Monitoring and AlarmTool Wear Monitoring and Alarm
System Based on PatternSystem Based on Pattern
Recognition With LogicalRecognition With Logical
Analysis of DataAnalysis of Data
Presented By:Presented By:
Name: Nehem TuduName: Nehem Tudu
Roll No.: M150360MERoll No.: M150360ME
Branch: Manufacturing TechnologyBranch: Manufacturing Technology

CORE SEMINAR 2
CONTENTCONTENT
 IntroductionIntroduction
 Logical Analysis of DataLogical Analysis of Data
 Working of LADWorking of LAD
 Design of ExperimentDesign of Experiment
 Knowledge Extracting and LearningKnowledge Extracting and Learning
 The Statistical Proportional Hazards Model (PHM)The Statistical Proportional Hazards Model (PHM)
 LAD Online Alarm System DevelopmentLAD Online Alarm System Development
 Comparison With the PHM Alarm FunctionComparison With the PHM Alarm Function
 DiscussionDiscussion
 ConclusionConclusion
 ReferenceReference

CORE SEMINAR 3
INTRODUCTIONINTRODUCTION
 Tool wear in machining is analyzed by:Tool wear in machining is analyzed by:
1.1. Theoretical & numerical approachTheoretical & numerical approach
2.2. Data driven approachData driven approach
 Our objective is to report & discuss the results obtained experimentallyOur objective is to report & discuss the results obtained experimentally
 The work-piece material is TiMMCsThe work-piece material is TiMMCs
 Differentiate between two types of covariates;Differentiate between two types of covariates;
1.1. Internal (diagnostic) covariates, which carry direct information about the wear processInternal (diagnostic) covariates, which carry direct information about the wear process
2.2. External (environmental or/& machining conditions) covariates, which affect the wear processExternal (environmental or/& machining conditions) covariates, which affect the wear process
 Internal covariates are observed by online monitoring of time dependent factorsInternal covariates are observed by online monitoring of time dependent factors
 Combination of internal & external covariates is used before in order to develop accurateCombination of internal & external covariates is used before in order to develop accurate
modelmodel
 Tool wear monitoring system based on LAD during turning TiMMCs under variableTool wear monitoring system based on LAD during turning TiMMCs under variable
conditions were implementedconditions were implemented
 The platform of PXI & LABVIEW were used to develop the tool wear alarm systemThe platform of PXI & LABVIEW were used to develop the tool wear alarm system

CORE SEMINAR 4
TOOL WEARTOOL WEAR
 The rate at which the cutting edge of aThe rate at which the cutting edge of a
tool wears away during machiningtool wears away during machining
 In cutting process, produced by contactIn cutting process, produced by contact
& relative sliding between& relative sliding between
 Cutting tool & the work-pieceCutting tool & the work-piece
 Between the cutting tool & the chipBetween the cutting tool & the chip
under the extreme conditions of cutting areaunder the extreme conditions of cutting area
 Type of tool wearType of tool wear
 Crater wear on rake faceCrater wear on rake face
 Flank wear on flank faceFlank wear on flank face
 Tool wear mechanisms in metal cuttingTool wear mechanisms in metal cutting
includeinclude
 Abrasive wearAbrasive wear
 Adhesive wearAdhesive wear
 Diffusion wearDiffusion wear
 Oxidation wear, etc.,Oxidation wear, etc.,

CORE SEMINAR 5
LOGICAL ANALYSIS OF DATA (LAD)LOGICAL ANALYSIS OF DATA (LAD)
 LAD is a data-driven combinatorial optimization technique that allows theLAD is a data-driven combinatorial optimization technique that allows the
classification of phenomena based on pattern recognitionclassification of phenomena based on pattern recognition
 LAD is applied in two consecutive stages:LAD is applied in two consecutive stages:
1.1. Training or learning stage, part of the data is used to extract special patterns of someTraining or learning stage, part of the data is used to extract special patterns of some
phenomenaphenomena
2.2. Testing or the theory formation stage, the remainder of the data is used to test theTesting or the theory formation stage, the remainder of the data is used to test the
accuracy of the previously learned knowledgeaccuracy of the previously learned knowledge
 LAD is based on supervised learningLAD is based on supervised learning
 In this work, we have two classes of cutting toolIn this work, we have two classes of cutting tool::
1.1. Worn-out toolWorn-out tool
2.2. A fresh toolA fresh tool
 Has certain advantages over other techniques:Has certain advantages over other techniques:
 It is a non-statistical approachIt is a non-statistical approach
 It does not need any prior assumptions regarding the posteriori class probabilitiesIt does not need any prior assumptions regarding the posteriori class probabilities
 User are able to track back any results (phenomena or effects) to its possible causesUser are able to track back any results (phenomena or effects) to its possible causes

CORE SEMINAR 6
Working of LADWorking of LAD
 Each observation carries the values of the internal & external covariates & aEach observation carries the values of the internal & external covariates & a
labellabel
 Internal covariates are the radial force (fx), the feed force (fy), and theInternal covariates are the radial force (fx), the feed force (fy), and the
cutting force (fz)cutting force (fz)
 External covariates are the cutting speed (v) and the feed rate (f )External covariates are the cutting speed (v) and the feed rate (f )
 After accomplishment of the two phases worn patterns & fresh patterns areAfter accomplishment of the two phases worn patterns & fresh patterns are
found by LADfound by LAD
 Worn patterns are used in order to develop tool wear monitoring modelWorn patterns are used in order to develop tool wear monitoring model
 Model is later incorporated in the platform of PXI & LABVIEW in order toModel is later incorporated in the platform of PXI & LABVIEW in order to
monitor the tool wear online & to give an alarm when the tool worn patternsmonitor the tool wear online & to give an alarm when the tool worn patterns
are detectedare detected
 Observations are classified as either +ve (fresh, πObservations are classified as either +ve (fresh, π++
, class 1) or -ve (worn-, class 1) or -ve (worn-
out,out,ππ--
, class 2), class 2)
 LAD generates collections of patterns which characterizes each classLAD generates collections of patterns which characterizes each class
 Patterns represent interactions between variables separatelyPatterns represent interactions between variables separately

CORE SEMINAR 7
First steps involved in LADFirst steps involved in LAD
 Data binarization is the process of transformation of data into a BooleanData binarization is the process of transformation of data into a Boolean
databasedatabase
 This technique substitutes each numerical variable by at least one binaryThis technique substitutes each numerical variable by at least one binary
attributeattribute
 For e.g, binarization of a continuous numerical variableFor e.g, binarization of a continuous numerical variable AA is done by ranking, inis done by ranking, in
ascending order, all the distinct values of the numerical variableascending order, all the distinct values of the numerical variable AA as follows:as follows:
uuAA
(1)(1)
< u< uAA
(2)(2)
<..... <u<..... <uAA
(q)(q)
(q≤Q) (1)(q≤Q) (1)
where q is the total number of distinct values of the variablewhere q is the total number of distinct values of the variable AA, &, & QQ is the totalis the total
number of observations in the training set.number of observations in the training set.
 The cut-pointsThe cut-points δδA,,jA,,j, where, where jj is the number of cut-points for each variable, areis the number of cut-points for each variable, are
found between each pair of values that belong to different classes.found between each pair of values that belong to different classes.
 By using Eq. (2), the cut-points are calculated as follows:By using Eq. (2), the cut-points are calculated as follows:
δδA,jA,j =(u=(uAA
(k)(k)
+u+uAA
(k+1)(k+1)
)/2 (2))/2 (2)
wherewhere uuAA
(k)(k)
πϵ πϵ ++
&& uuAA
(k+1)(k+1)
πϵ πϵ --
or vice versa.or vice versa.
 A binary attributeA binary attribute bb is then formed from each cut-pointis then formed from each cut-point
 Each cut-pointEach cut-point δδA,jA,j has a corresponding binary attributehas a corresponding binary attribute bbδA,jδA,j with defined valuewith defined value
bbδA,jδA,j = 1 if u= 1 if uAA ≥ δ≥ δA,jA,j
0 if u0 if uAA < δ< δA,jA,j (3)(3)

CORE SEMINAR 8
Second steps involved in LADSecond steps involved in LAD
 Pattern generation, the key building blockPattern generation, the key building block
in LAD knowledge extractionin LAD knowledge extraction
 Linear programming is used to generateLinear programming is used to generate
patternspatterns
 It is assumed that each generated patternIt is assumed that each generated pattern
p is associated with a Boolean patternp is associated with a Boolean pattern
vectorvector W=(wW=(w11,w,w22,...,w,...,wqq,w,wq+1q+1,....,w,....,w2q2q )) with sizewith size
n wheren where n=2qn=2q,, qq is the size of binaryis the size of binary
observation vectorobservation vector
 IfIf wwj+qj+q=1=1 then literalthen literal xxjj is included in patternis included in pattern
pp
 Y=(yY=(y11, y, y22,....., y,....., yDD
++
)) is Boolean coverageis Boolean coverage
vector whose number of elements =vector whose number of elements =
number of +ve observationnumber of +ve observation DD++
& where& where yyii ==
00 if a patternif a pattern pp++
covers +ve observationcovers +ve observation ii &&
11 otherwiseotherwise
 Each +ve observationEach +ve observation i πϵi πϵ ++
is representedis represented
as a Boolean observation vectoras a Boolean observation vector aai,ji,j =1=1 ifif
the binary attributethe binary attribute bbjj =1=1 && aai,j+qi,j+q=1=1 ifif bbjj =0=0

CORE SEMINAR 9
Third steps involved in LADThird steps involved in LAD
 Theory formation or testing stage is the final step in the LAD decision modelTheory formation or testing stage is the final step in the LAD decision model
 Zero value means that LAD cannot classify the observation whereZero value means that LAD cannot classify the observation where NN++
(N(N--
) is) is
the number ofthe number of +ve (-ve)+ve (-ve) patterns that are generated,patterns that are generated, ZZii
++
(o)(Z(o)(Zii
--
(o))=1(o))=1if patternif pattern
(i)(i) covers observation O, & zero otherwisecovers observation O, & zero otherwise
 σσii
++
(σ(σii
--
)) is the weight of theis the weight of the +ve (-ve)+ve (-ve) patternpattern
 The calculated value ofThe calculated value of Δ(o)Δ(o) of any new observation gives an indication toof any new observation gives an indication to
whether the observation belongs to fresh or worn-out classwhether the observation belongs to fresh or worn-out class
 To measure the accuracy, the quality of classificationTo measure the accuracy, the quality of classification vv is usedis used
 wherewhere aa && bb are the proportion of observations,are the proportion of observations, +ve+ve && -ve-ve, which are, which are
correctly classifiedcorrectly classified
 cc && ee are the proportion of observations,are the proportion of observations, +ve+ve && -ve-ve, which are unclassified, which are unclassified

CORE SEMINAR 10
Design of ExperimentDesign of Experiment
 TThe experiment was conducted in thehe experiment was conducted in the
machining laboratory at Ècolemachining laboratory at Ècole
Polytechnique de MontrèaPolytechnique de Montrèa
 Cylindrical bar of Ti-6Al-4V alloyCylindrical bar of Ti-6Al-4V alloy
reinforced with 10-12% volume fractionreinforced with 10-12% volume fraction
of TiC ceramic particle is usedof TiC ceramic particle is used
 The wear is measured at discreteThe wear is measured at discrete
points of time through inspections usingpoints of time through inspections using
an Olympus SZ-X12 microscopean Olympus SZ-X12 microscope
 The procedure continues until the toolThe procedure continues until the tool
wear reached predefined thresholdwear reached predefined threshold
(VB(VBBmaxBmax =0.2mm)=0.2mm)
 This procedure is repeated for 28 toolsThis procedure is repeated for 28 tools

CORE SEMINAR 11
Knowledge Extraction and LearningKnowledge Extraction and Learning
 Cutting tool is failed when the tool is getting dull & no longer operates withCutting tool is failed when the tool is getting dull & no longer operates with
acceptable qualityacceptable quality
 Cutting tool fails after reaching the worn-out stageCutting tool fails after reaching the worn-out stage
 Classification limits is consideredClassification limits is considered
 To distinguish between fresh & worn-out toolsTo distinguish between fresh & worn-out tools
 In some cases where the velocity is high, progressive wear is rapidly evolving &In some cases where the velocity is high, progressive wear is rapidly evolving &
there is just one observation for wear value above 0.15mmthere is just one observation for wear value above 0.15mm

CORE SEMINAR 12
 This classification procedure is repeated for the 28 toolsThis classification procedure is repeated for the 28 tools
 Our objective is to use the data presented in Table 2 to train LAD to detectOur objective is to use the data presented in Table 2 to train LAD to detect
automatically the worn patterns & without human interferenceautomatically the worn patterns & without human interference
 The software CBMLAD is used, in order to extract the knowledge from theThe software CBMLAD is used, in order to extract the knowledge from the
collected data, and then to train LADcollected data, and then to train LAD

CORE SEMINAR 13
 Set O of the 273 observations is also divided into two sets of training, L,Set O of the 273 observations is also divided into two sets of training, L,
and testing, Tand testing, T
 Tenfold cross validation procedure is conductedTenfold cross validation procedure is conducted
 The quality of classification is calculated on the testing setThe quality of classification is calculated on the testing set
 Thus, the learning procedure is repeated ten times with different trainingThus, the learning procedure is repeated ten times with different training
setssets
 The results show that the quality of classificationThe results show that the quality of classification vv =97.2%=97.2%
 The obtained five worn patterns do not cover any observation in fresh toolThe obtained five worn patterns do not cover any observation in fresh tool
spacespace
 Patterns will lead us to build the online tool wear alarm systemPatterns will lead us to build the online tool wear alarm system

CORE SEMINAR 14
The Statistical Proportional Hazards Model (PHM)The Statistical Proportional Hazards Model (PHM)
 A proportional hazards model (PHM) of the wear process is developed fromA proportional hazards model (PHM) of the wear process is developed from
the obtained experimental datathe obtained experimental data
 We calculate the time to failure (TTF) by interpolating between twoWe calculate the time to failure (TTF) by interpolating between two
measurements around the failure thresholdmeasurements around the failure threshold
 TTF is calculated when tool wear threshold is reachedTTF is calculated when tool wear threshold is reached
 The concept of a PHM is that the failure rate of the cutting tool is not onlyThe concept of a PHM is that the failure rate of the cutting tool is not only
dependent on the age of the tool but also is affected by the internal &dependent on the age of the tool but also is affected by the internal &
external covariatesexternal covariates

CORE SEMINAR 15
 We consider the Weibull distribution as a baseline functionWe consider the Weibull distribution as a baseline function
 The failure hazard rate is written asThe failure hazard rate is written as
 The conditional survival function can thus be given asThe conditional survival function can thus be given as
 The conditional survival functionThe conditional survival function R(t;Y,Z)R(t;Y,Z) & its derivative& its derivative R(t;Y,Z)=R(t;Y,Z)=
h(t;Y,Z)R(t;Y,Z)h(t;Y,Z)R(t;Y,Z) are used to estimate the parametersare used to estimate the parameters (β,η,α(β,η,α11,γ,γ11,γ,γ22)) by using theby using the
maximum likelihood functionmaximum likelihood function
 EXAKT software estimates the PHM parameters as shownEXAKT software estimates the PHM parameters as shown
Proportional Hazards Model (PHM)Proportional Hazards Model (PHM)

CORE SEMINAR 16
 The PHM model with all significantThe PHM model with all significant
variables is found by eliminatingvariables is found by eliminating
the variables whose impact on thethe variables whose impact on the
probability of failure is lowprobability of failure is low
 It is concluded that the effects ofIt is concluded that the effects of
the radial force and the cuttingthe radial force and the cutting
force are higher than the effect offorce are higher than the effect of
the feed force on the progressivethe feed force on the progressive
flank tool wearflank tool wear
 EXAKT produces theEXAKT produces the
Kolmogorov–Smirnov test whichKolmogorov–Smirnov test which
evaluates the model fitevaluates the model fit

CORE SEMINAR 17
 EXAKT gives a control-limit,EXAKT gives a control-limit, d > 0d > 0 which is used in order to find the minimumwhich is used in order to find the minimum
expected machining cost per unit timeexpected machining cost per unit time

CORE SEMINAR 18
LAD Online Alarm System DevelopmentLAD Online Alarm System Development
 The platform of PXI and LABVIEW were used to develop the online alarmThe platform of PXI and LABVIEW were used to develop the online alarm
systemsystem
 For each transmitted set of measurements, the system search for wornFor each transmitted set of measurements, the system search for worn
patterns until color-coded lamp turns to red, when worn pattern is detectedpatterns until color-coded lamp turns to red, when worn pattern is detected

CORE SEMINAR 19
Comparison With the PHM Alarm FunctionComparison With the PHM Alarm Function
 In order to compare the results, the recommended optimal time toIn order to compare the results, the recommended optimal time to
replacement is calculated by using the covariate's valuesreplacement is calculated by using the covariate's values
 The recommended optimal replacement time according to certain covariate'sThe recommended optimal replacement time according to certain covariate's
values using PHM are calculated using the below equationvalues using PHM are calculated using the below equation

CORE SEMINAR 21
DiscussionDiscussion
 PHM decisions are based on the assumption of a statistical goodness of fitPHM decisions are based on the assumption of a statistical goodness of fit
of a suitable hazard function & the cost's ratioof a suitable hazard function & the cost's ratio
 LAD alarm points are based on pattern recognitionLAD alarm points are based on pattern recognition
 LAD replacement decision gave warning alarm before the tool wear reachedLAD replacement decision gave warning alarm before the tool wear reached
the maximum flank wearthe maximum flank wear VBVBBmaxBmax =0.2mm=0.2mm & without losing valuable resource& without losing valuable resource
due to early replacementdue to early replacement
 LAD can detect worn patterns online & in real time by monitoring covariatesLAD can detect worn patterns online & in real time by monitoring covariates
over timeover time
 Important requirement for using LAD is the availability of a database thatImportant requirement for using LAD is the availability of a database that
represents accurately the phenomena under studyrepresents accurately the phenomena under study

CORE SEMINAR 22
ConclusionConclusion
 A new online tool wear alarm system based on LAD is developedA new online tool wear alarm system based on LAD is developed
 Alarm system is constructed based on data collected during turningAlarm system is constructed based on data collected during turning
TiMMCs, under changeable machining conditionsTiMMCs, under changeable machining conditions
 Platform of PXI and LABVIEW were used to develop the alarm systemPlatform of PXI and LABVIEW were used to develop the alarm system
 LAD alarm system is validated by comparing it to the PHM warningLAD alarm system is validated by comparing it to the PHM warning
functionfunction
 Results show that the proposed alarm system detects the worn patternsResults show that the proposed alarm system detects the worn patterns
and gives “warning alarm” in order to replace the cutting tool at aand gives “warning alarm” in order to replace the cutting tool at a
working age that is relatively closer to the actual observed failure timeworking age that is relatively closer to the actual observed failure time

CORE SEMINAR 23
Future Scope of WorkFuture Scope of Work
 The performance of the alarm system will be improved by includingThe performance of the alarm system will be improved by including
additional variables, such as vibration signal, AEs, & cutting temperaturesadditional variables, such as vibration signal, AEs, & cutting temperatures
 In order to distinguish between different tool wear phases, a multiclass LADIn order to distinguish between different tool wear phases, a multiclass LAD
technique will be testedtechnique will be tested
 The quality of the detected patterns will be improved, & nonpure patternsThe quality of the detected patterns will be improved, & nonpure patterns
which can cover more than one class will be used, & give more details aboutwhich can cover more than one class will be used, & give more details about
the characteristics of LAD’s patternsthe characteristics of LAD’s patterns
 CBMLAD and our alarm system will be incorporated in a CNC machineCBMLAD and our alarm system will be incorporated in a CNC machine
 The learning stage can be done online thereby eliminating the need forThe learning stage can be done online thereby eliminating the need for
offline analysisoffline analysis

CORE SEMINAR 24
ReferenceReference
 Shaban, Y., Yacout, S., and Balazinski, M., 2015, "Tool Wear MonitoringShaban, Y., Yacout, S., and Balazinski, M., 2015, "Tool Wear Monitoring
and Alarm System Based on Pattern Recognition With Logical Analysis ofand Alarm System Based on Pattern Recognition With Logical Analysis of
Data," ASME J. Manuf. Sci. Eng., 137(4), p. 041004.Data," ASME J. Manuf. Sci. Eng., 137(4), p. 041004.
 Li, B., 2012, “A Review of Tool Wear Estimation Using Theoretical AnalysisLi, B., 2012, “A Review of Tool Wear Estimation Using Theoretical Analysis
and Numerical Simulation Technologies,” Int. J. Refract. Met. Hard Mater.,and Numerical Simulation Technologies,” Int. J. Refract. Met. Hard Mater.,
35, pp. 143–151.35, pp. 143–151.
 Ryoo, H. S., and Jang, I. Y., 2009, “MILP Approach to Pattern Generation inRyoo, H. S., and Jang, I. Y., 2009, “MILP Approach to Pattern Generation in
Logical Analysis of Data,” Discrete Appl. Math., 157(4), pp. 749–761.Logical Analysis of Data,” Discrete Appl. Math., 157(4), pp. 749–761.
 Makis, V., 1995, “Optimal Replacement of a Tool Subject to RandomMakis, V., 1995, “Optimal Replacement of a Tool Subject to Random
Failure,” Int. J. Prod. Econ., 41(1), pp. 249–256.Failure,” Int. J. Prod. Econ., 41(1), pp. 249–256.
 Chik, Z., Aljanabi, Q. A., Kasa, A., and Taha, M. R., 2014, "Tenfold crossChik, Z., Aljanabi, Q. A., Kasa, A., and Taha, M. R., 2014, "Tenfold cross
validation artificial neural network modeling of the settlement behavior of avalidation artificial neural network modeling of the settlement behavior of a
stone column under a highway embankment," Springer Arab J Geosci.,stone column under a highway embankment," Springer Arab J Geosci.,
7:4877–4887.7:4877–4887.

Tool wear monitoring and alarm system based on pattern recognition with logical analysis of data

Recommended

Recommended

More Related Content

Similar to Tool wear monitoring and alarm system based on pattern recognition with logical analysis of data

Similar to Tool wear monitoring and alarm system based on pattern recognition with logical analysis of data (20)

Recently uploaded

Recently uploaded (20)

Tool wear monitoring and alarm system based on pattern recognition with logical analysis of data