A Multiple Regression Model to Predict In-process Surface Roughness in Turning Operation Via Accelerometer

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Chen KEYWORD SEARCH CIM Manufacturng Machne Tools Research Qualty Qualty Control Revewed

1 Volume 7, Number 2 - February 200 to Aprl 200 A Multple Regresson Model to Predct In-process Surface Roughness n Turnng Operaton Va Accelerometer By Mr. Luke Huang & Dr. Joseph C. Chen KEYWORD SEARCH CIM Manufacturng Machne Tools Research Qualty Qualty Control Revewed Artcle The Offcal Electronc Publcaton of the Natonal Assocaton of Industral Technology 200

. Hs research nterest s n CAD/CAM/ CNC process, fuzzy system-related ntellgent control, and manufacturng system desgn and control. Dr. Joseph C.

2 Mr. Luke H. Huang s an Assstant Professor n the Department of Industral Technology at the Unversty of North Dakota. He teaches course n Manufacturng Automaton, Computer Integrated Manufacturng, Facltes plannng, Manufacturng Materals, etc.. Hs research nterest s n CAD/CAM/ CNC process, fuzzy system-related ntellgent control, and manufacturng system desgn and control. Dr. Joseph C. Chen, PE s an assocate professor n the Department of Industral Educaton and Technology at Iowa State Unversty. Recently, he receved the Early Achevement n Teachng Award from Iowa State Unversty. Hs research nterests are n cellular manufacturng systems, machne tool dynamcs, and ntellgent control as t relates to neural networks and fuzzy control. A Multple Regresson Model to Predct In-process Surface Roughness n Turnng Operaton Va Accelerometer By Mr. Luke Huang & Dr. Joseph C. Chen Introducton Although the lathe s the oldest machne tool, t s stll the most commonly used machnng operaton n the manufacturng ndustry. Many cylndrcal parts are products of turnng operatons. Some of these cylndrcal parts, such as shaft, axs, and bearng, are crucal n machnng motons. The tradtonal way to montor the surface qualty of a machned part s to measure the surface roughness by usng a surface gauge. The most used surface gauge s the stylus type surface gauge. It has a damond stylus draggng along the test surface, of whch, the up and down movement s recorded and calculated for the surface roughness. Snce ths measurng method requres that the stylus have drect contact to the measured surface, measurement cannot be conducted unless the test surface s n a statonary mode. In other words, the stylus measurng method cannot be appled to an n-process work pece on a lathe when the work pece s spnnng. Other measurement technques must be used to obtan n-process surface roughness n turnng operatons. Snce there s no actual n-process measurng avalable, the surface roughness s predcted by the use of other technologes, such as optcal, acoustc, electromagnetc, force, and vbraton. However, the optcal, acoustc, and electromagnetc technologes are not practcal n the machnng envronment because chps and coolant nterfere wth the travel of these sgnals. Cuttng force and machnng vbraton can be used to predct the surface roughness of a machned surface. Practcally, a dynamometer (the force sensor) s expensve and dffcult to mount to a lathe. On the other hand, an accelerometer (the vbraton sensor) s nexpensve and easy to mount. Therefore, an accelerometer has the potental to be appled n collectng vbraton nformaton for the predcton of a machned surface. Machnng vbraton exsts throughout the cuttng process. Whle nfluenced by many sources, such as machne structure, tool type, work materal, etc., the composton of the machnng vbraton s complcated. However, at least two types of vbratons, force vbraton and self-excted vbraton, were dentfed as machnng vbratons. (Kalpakjan, 995). Force vbraton s a result of certan perodcal forces that exst wthn the machne. The source of these forces can be bad gear drves, unbalanced machne-tool components, msalgnment, or motors and pumps, etc. Self-excted vbraton, whch s also known as chatter, s caused by the nteracton of the chpremoval process and the structure of the machne tool, whch results wth dsturbances n the cuttng zone. Chatter always ndcates defects on the machned surface (Rakht, Osman, and Sankar, 973; Jang s et al., 996). Therefore, vbraton, especally selfexcted vbraton, s assocated wth the machned surface roughness. Attempts have been made to use vbraton sgnals n predctng tool wear and tool lfe n turnng operatons and other machnng operatons (Fang, Yao, and Arndt, 99; Yao, Fang, and Arndt, 99; Bonfaco and Dnz, 994; Fernandes and Dnz, 997). Results showed that vbraton sgnals were promsng n the predctons of these 2

3 applcatons. Vbraton sgnals were also employed to predct the surface roughness of machned parts usng mllng operaton (Lou and Chen, 999). Lou and Chen found that the predcton accuracy was as hgh as 96%. However, no work has been done n the predcton of surface roughness n turnng operaton by usng vbraton sgnals. Purpose of Study Based on the above analyss, the purpose of ths study was to set up a multple regresson model that was capable of predctng the n-process surface roughness of a machned work pece usng a turnng operaton. The model was expected to have the followng features:. Use machnng parameters, such as feed rate, spndle speed, and depth of cut, as predctors. 2. Apply vbraton nformaton that was collected wth an accelerometer as another predctor. 3. The predcton accuracy s hgh to above 90%. Expermental Setup The hardware setup s shown n Fgure. Two sensng systems were set on a lathe (Enterprse 550, Mysore Krloskar Inc., Karnataka, Inda). The accelerometer (PCB356B08, PCB Pezotroncs, Depew, NY) was secured at the tool holder below the nsert. The vbraton sgnal that was generated by the accelerometer was sent to a sgnal condtoner, n whch the sgnal s voltage was amplfed to between V and + V. A multfuncton data acquston board (OMB-Daqbook/00, Omega Engneerng Inc., Stamford, CT) receved the condtoned sgnals and had them stored n a dedcated computer. Smultaneously, a proxmty sensor (Honeywell 922AC08YI mcroswtch, Honeywell Inc., Mnneapols, MN) was mounted over the chuck and counted the spndle s rotatons by detectng the holes on the chuck. The sgnals from the proxmty sensor were also sent to the multfuncton data acquston board. Wrng of these devces s shown n Fgure 2. The proxmty sensor was a mcro swtch. It was turned on and released a hgh voltage when there was ferrous materal detected. When there was no ferrous materal n the hole postons close to t, t was turned off and released a low voltage. Therefore, sgnals from the proxmty sensor were pulses that ndcated the poston of the chuck rotaton wth holes on the chuck as references (Fgure 3). Snce there were sx holes around the chuck, sx pulses represented a revoluton. The proxmty sgnals were graphed along wth the vbraton sgnals, and they serve as dentfcatons of revolutons for the vbraton sgnals. Ths made t possble to separate the vbraton sgnals revoluton by revoluton. The lathe was modfed wth an addtonal dgtal postonng devce (Wzard, Allam Electronc Corp, Mam, FL). Wth ths devce, the poston of the tool could be dsplayed dgtally to ten-thousandth of an nch. power supply load (MΩ) Prox. sensor sgnal condtoner accelerometer Lathe Chuck Proxmty Sensor Fgure. Hardware set up. Work pece Tool & Tool holder The work materal was alumnum 606T2, dmensoned n φ 2 nches. The lathe chuck held ths work pece wth about.5 nches extendng out the chuck as shown n Fgure 4. Before the data-collectng cut, each pece was cut to 0.98 nches n dameter to elmnate varance n raw materal sze. Vbraton data was collected when the tool cut to about.25 nches (measurng zone) from the chuck. The surface roughness was measured mmedately after the work pece was cut wth a pocket surface gauge (PocketSurf, Mahr Federal Inc. Provdence, RI). The measurng length was fve mcronches. Each pece was randomly measured around the measurng zone ten tmes, as shown n Fgure 4. Data were arranged n two groups. One group was desgned for tranng the model, and another was desgned to only test the model for accuracy. These Multfuncton data acquston board Fgure 2. Wrng of the sgnal system termnal board Accelerometer Sgnal Condtoner multfuncton data acquston board personal computer 3

4 two groups of data were ndependent from each other. Data were also collected from tools of dfferent nose szes. One tool had a nose radus of 0.06 nches, and the other had a nose radus of 0.03 nches. As combnatons, there were four groups of data, Tran 6, Tran 3, Test 6, and Test 3. The organzaton of data s llustrated n Fgure 5. All data sets have four ndependent varables, whch are spndle speed (S), feed rate (F), depth of cut (D), vbraton ampltude average (V), and one dependent varable, surface roughness average (R). In the tranng data set, there are three samplng levels for spndle speed and depth of cut and sx for feed rate. In the testng data set, there are three for spndle speed and feed rate and two for depth of cut. Vbraton and surface roughness respond to these ndependent varables. The samplng level of vbraton and surface roughness vared. Wth three duplcates, there are 62 sets of data n total for each tranng data set and 54 for each testng data set. Processng Vbraton Sgnals The vbraton sgnals were voltages generated by the accelerometer n response to vbraton and were converted from analogue to dgtal. Wth the help of the proxmty sgnals, the vbraton sgnals were separated revoluton by revoluton. Next, the voltage sgnals of each revoluton were averaged (Equaton ). The mean served as the centerlne of vbraton. Then, the absolute dfferences of the centerlne and each voltage value were averaged to obtan the Vbraton Ampltude Average (V) (Equaton 2). Fgure 3. Identfcatons of revoluton n the vbraton sgnals (Data came from cuttng condtons of 630rpm spndle speed, 2pm feed rate, and 0.05n depth of cut.) Voltage Sgnals from the proxmty sensor One revoluton Sgnals from the accelerometer Tme Fgure 4. Setup of the work pece and the measurng zone..5 n.25 n 2 n Fgure 5. Samplng data structure DATA FOR SYSTEM MODELING φ0.98 Measurng zone Data of Tool 0.06 Data of Tool 0.03 V = c c ave v, () Tranng Data Testng Data Tranng Data Testng Data V ave the centerlne value of vbraton voltages n a revoluton, V an ndvdual vbraton voltage, c the number of voltage value a revoluton has. S - 3 levels: 630, 840, 000 rpm D - 3 levels: 0.00, 0.020, n F - 6 levels:.5, 2.4, 3.4, 4.4, 5.4, 6.7 n/mn V- samplng levels vared. R dependent varable S - 3 levels: 630, 840, 000 rpm D - 2 levels: 0.05, n F - 3 levels: 2.0, 3.8, 6.2 n/mn V- samplng levels vared. R - dependent varable 4

5 V = c c v V ave, (2) V - vbraton ampltude average. Multple Regresson Modelng The goal of the multple regresson analyss was to determne the dependency of surface roughness to vbraton and other selected machnng parameters. In addton to the man effects of these varables, effects of the nteractons of them were ncluded n the analyss. The model was expressed as: R = β 0 + β S S + β F F + β D D + β V V + β SF SF + β SD SD + β SV SV + β DF DF + β DV DV + β FV FV + β SDF SDF + β SDV SDV + β SFV SFV + β DFV DFV + β SDFV SDFV, (3) R surface roughness average, F feed rate, S spndle speed, D depth of cut, V vbraton ampltude average, β lnear constants. Wth the sgnfcance level set to 0.0 (α = 0.0), the null hypothess and alteratve hypothess for the model were: H 0 : β S = β F = β D = β V = β SF = β SD = β SV = β DF = β DV = β FV = β SDF = β SDV = β SFV = β DFV = β SDFV = 0. H a : at least one of the βs does not equal to zero. A statstcal software program, SPSS verson 8.0, was employed n model tranng. Two tranng data sets, Tran 6 and Tran 3, were appled to tran the above model to obtan two resulted models, MR6 and MR3, respectvely. For the nvolvement of the nteractve predctor varables, a total of 5 predctor varables were used n the tranng of the model, as shown n Equaton 3. Correlatons of the predctor varables wth the predcted varable from two data sets were reported as Pearson correlaton coeffcents by the lnear multple regresson analyss wth SPSS 8.0 (Table ). They both showed that feed rate had the greatest correlaton coeffcent. Other prmary varables were much smaller than feed rate. Interactve varables assocated wth feed rate have greater correlaton coeffcents as well. Among the prmary varables, vbraton had the second greatest correlaton coeffcent, whch suggested that vbraton nformaton should not be gnored n the predcton. As shown n Table 2, both MR6 and MR3 models had hgh regresson coeffcents (0.970 and 0.966, respectvely). The square values of the regresson coeffcents were and 0.933, respectvely, whch ndcated hgh assocaton of the regresson coeffcents wth varances n the predctor values. All these evdences showed a strong lnear relatonshp between the predctor varables (S, F, D, and V) and the predcted varable (R) for both models. The results of analyss of varance (ANOVA) of the models also supported strong lnear relatonshps n the models (Table 3). The F values of regresson were and for MR6 and MR3, respectvely. These hgh F values ndcated a great sgnfcance (α = 0.000) for both models n rejectng the null hypothess (H 0 ) that every coeffcent of the predctor varables n the model was zero. Instead, the alteratve hypothess, at least one of these coeffcents dd not equal to zero, was accepted. Therefore, the lnear relatonshp between the predcted varable (R) and Table. Correlatons of predctor varables to the predcted varable Predctor Varables Pearson Correlaton Coeffcents* Tran 6 Tran 3 S F D V SF SD SV FD FV DV SFD SFV SDV FDV SFDV *The predcted varable s R Table 2. Model Summares Model r r square MR MR

6 predctor varables sgnfcantly exsted. The coeffcents of all predctor varables and the constants of the model are lsted n Table 4. Accordng to these coeffcents, the multple regresson models are bult as shown n Equatons 4 and 5 for MR6 and MR3, respectvely. There were four groups of data avalable for testng the model accuracy because of the samplng desgn (Fgure 5). They were Tran 6, Tran 3, Test 6, and Test 3. Table 5 lsts the calculated accuracy values n percentage as well as the accuracy dfferences between the tranng and testng data sets. The tranng data gave hgher and consstent model accuracy, whch were 94.98% and 94.53% for Tran 6 and Tran 3, respectvely. The model accuracy from the testng data vared. Whle the accuracy (92.8%) of Test 3 was close (2.35% less) to that of Tran 3, the accuracy of Test 6 MR6: R = S F D V SF SD SV FD FV DV SFD SFV SDV FDV SFDV (4) MR3: R = S F D V SF SD SV FD FV DV SFD SFV SDV FDV SFDV (5) The Model Accuracy Accuracy s a measure of the closeness of the predcted value to the measured one. For each sngle data set, the accuracy s the rato of the absolute dfference of the predcted and the measured R-values to the measured value. The accuracy s expressed n percentage (Equaton 6). The model accuracy s the average of the accuracy values of all data sets (Equaton 7). δ = o R R R 00%, (6) d predcton accuracy of data set, R predcted R by data set, R o measured R correspondng to data set. δ, (7) model predcton accuracy, n number of data sets n the tranng data set. = n n Table 3. The ANOVA Table of the regresson models Model Item Sum of Squares df Mean Square F Sg. MR6 Regresson Resdual Total MR3 Regresson Resdual Total Dependent Varable: R Table 4. Coeffcents of the model MR6 MR3 Predctor varable Coeffcents Predctor varable Coeffcents (Constant) (Constant).426 S S F F D D V 6.66 V SF SF SD 4.32 SD SV 0.03 SV FD FD FV FV DV DV SFD SFD SFV SFV SDV SDV FDV FDV SFDV SFDV Dependent Varable: R 6

7 (8.55%) dropped far away (3.4% less) from that of Tran 6. Snce the model was traned wth the tranng data set, t was reasonable that the model better ftted the tranng data set than the test data sets. Therefore, t was understandable that the tranng data set generated hgher model accuracy than the testng data set dd. Nevertheless, wth the evdence that three accuracy values out of four ranged from 92% to almost 95%, the model accuracy was consdered hgh. Verfcaton of Usng Vbraton Informaton The necessty of usng vbraton nformaton n the model was verfed wth two other models, MR6-noV and MR3-noV (Equatons 8 and 9), whch were also generated wth SPSS 8.0. Data sets Tran 6 and Tran 3 wth the vbraton part subtracted were the tranng data for these two models, respectvely. These models served as controls to verfy the effectveness of the vbraton nformaton n the model. MR6-noV: R = S F D SF SD FD SFD (8) MR3-noV: R = S F D SF SD FD SFD (9) Accuracy values wth vbraton nformaton and wthout vbraton nformaton are compared by applyng a t-test. The results are lsted n Table 6. Sgnfcant dfferences between accuracy values wth vbraton nformaton and wthout vbraton nformaton were found when the tranng data sets were appled; as, bascally no statstcal sgnfcance was found from the testng data sets. However, t seemed a trend that the accuracy values from data sets wth vbraton nformaton were numercally larger than those from wthout. On average, the accuracy value from wth vbraton nformaton s.55% greater than that from wthout vbraton nformaton. From these evdences and the prevous correlaton analyss (Table ), the use of vbraton nformaton was found valuable. Concluson and Dscusson The expermental desgn and setup to develop a multple regresson model for an on-lne, real-tme surface roughness predcton system have been demonstrated. The experment of collectng data for tranng and testng have been conducted. Usng the tranng data, a multple regresson model has been developed to be ntegrated n the predcton system. A group of testng data are also conducted to evaluate the accuracy of ths proposed surface roughness predcton model. Wth these data and results, one could conclude: Table 5. Model accuracy. Wth lnear correlaton coeffcents of and for models MR6 and MR3, respectvely, usng the experment data, the predctor varables, such as feed rate, vbraton ampltude average, spndle speed, and depth of cut, have strong lnear correlaton wth the predcted varable. The ANOVAR results also show that both models are vald at a hgh sgnfcance (a = 0.000). Therefore, the proposed regresson approach for an n-process surface roughness predcton model s reasonably adapted. 2. The vbraton ampltude average has Pearson correlaton coeffcents of 0.8 and 0.24 for data obtaned by usng two dfferent tools, respectvely. These coeffcents rank vbraton ampltude average the second among the four predctor varables n havng strong correlaton wth surface roughness. Wthout the vbraton data, the predcton accuracy of the proposed multple regresson Model Data Data Model Standard Acc. Dff. sources sets accuracy Devaton by sources MR6 Tranng % % Testng % MR3 Tranng % % Testng % Table 6. Comparson of accuracy values from wth and wthout vbraton nformaton by pared t-test. Pared Accuracy Standard p Values Dfference Sources Devaton of accuracy Tran Tran6-noV Tran Tran3-noV Test Test6-noV Test Test3-noV

8 model declnes by about.55%. Therefore, the use of the accelerometer s valuable. 3. Establshed by usng 62 data sets and tested by usng 54 data sets for each tool condton, the proposed regresson model bascally possesses accuracy of above 90% on predctng the nprocess surface roughness from feed rate, vbraton ampltude average, spndle speed, and depth of cut. Ths s consdered as enough to be appled n most manufacturng shops. Takng these conclusons as the foundaton, further research wll be conducted to develop other predcton systems that could enhance the accuracy for surface roughness predcton n an on-lne, real-tme fashon, whch could eventually be adapted by ndustry. References Bonfaco, M. E. R. and Dnz, A. E., (994). Correlatng tool wear, tool lfe, surface roughness and tool vbraton n fnsh turnng wth coated carbde tools, Wear, 73(- 2), pp Fang, X. D., Yao, Y. and Arndt, G., (99). Montorng groove wear development n cuttng tools va stochastc modelng of threedmensonal vbratons, Wear, 5(), pp Fernandes, R. and Dnz, A. E. (997). Usng wavelet transform to analyze tool vbraton sgnals n turnng operatons, Journal of the Brazlan Socety of Mechancal Scences, 9(3), pp Jang, D. Y., Cho, Y., Km, H. and Hsao, A. (996). Study of the correlaton between surface roughness and cuttng vbratons to develop an on-lne roughness measurng technque n hard turnng, Internatonal Journal of Machne Tools Manufacturng, 36(3), pp Kalpakjan, S., (995). Manufacturng Engneerng and Technology (3 rd Ed.), Addson-Wesley, Readng, MA. Lou, M. S. and Chen, J. C., (999). In-process surface roughness recognton (ISRR) system n endmllng operatons, Internatonal Journal of Advanced Manufacturng Technology, 5(3), pp Rakht, A. K., Osman, M. O. M. and Sankar, T. S., (973). Machne tool vbraton; Its effect on manufactured surfaces, Proceedngs of the Fourth Canadan Congress of Appled Mechancs, June 973, pp Yao, Y., Fang, X. D. and Arndt, G., (99) On-lne estmaton of groove wear n the mnor cuttng edge for fnsh machnng, CIRP Annals, 40(), pp

International Journal of Industrial Engineering Computations

International Journal of Industrial Engineering Computations Internatonal Journal of Industral Engneerng Computatons 4 (2013) 51 60 Contents lsts avalable at GrowngScence Internatonal Journal of Industral Engneerng Computatons homepage: www.growngscence.com/jec