Development of an intelligent system for tool wear monitoring applying neural networks

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1 of Achevements n Materals and Manufacturng Engneerng VOLUME 14 ISSUE 1-2 January-February 2006 Development of an ntellgent system for tool wear montorng applyng neural networks A. Antć a, J. Hodolč a, M. Sokovć b, * a Faculty of Techncal Scences, Unversty of Nov Sad, Trg D. Obradovća 6, Nov Sad, Serba & Montenegro b Faculty of Mechancal Engneerng, Unversty of Ljubljana, Askerceva 6, SI-1000 Ljubljana, Slovena * Correspondng author: E-mal address: mrko.sokovc@fs.un-lj.s Receved ; accepted n revsed form Manufacturng and processng ABSTRACT Purpose: The objectve of the researches presented n the paper s to nvestgate, n laboratory condtons, the applcaton possbltes of the proposed system for tool wear montorng n hard turnng, usng modern tools and artfcal ntellgence (AI) methods. Desgn/methodology/approach: On the basc theoretcal prncples and the use of computng methods of smulaton and neural network tranng, as well as the conducted experments, have been drected to nvestgate the adequacy of the settng. Fndngs: The paper presents tool wear montorng for hard turnng for certan types of neural network confguratons where there are precondtons for up buldng wth dynamc neural networks. Research lmtatons/mplcatons: Future researches should nclude the ntegraton of the proposed system nto CNC machne, nstead of the current separate system, whch would provde synchronsaton between the system and the machne,.e. the approprate reacton by the machne after determnng excessve tool wear. Practcal mplcatons: Practcal applcaton of the conducted research s possble wth certan restrctons and supplement of adequate number of expermental researches whch would be drected towards certan combnatons of machnng materals and tools for whch neural networks are traned. Orgnalty/value: The contrbuton of the conducted research s observed n one possble vew of the tool montorng system model and t s desgnng on modular prncple, and prncple buldng neural network. Keywords: Manufacturng and processng; Machnng; Artfcal Intellgence methods; Tool wear, Montorng 1. Introducton Modern procedures for part manufacturng mpose cost reductons that can be realzed n the followng ways: ncreasng turnng regme, reducng manufacturng tme and number of rejects. In order to accomplsh that n varous processng procedures, extreme efforts of tools and machnes are requred. Processng tool condton s very nfluental on reducng rejects and standstlls n manufacturng, whch can drectly be seen through geometrc, surface and structural propertes and characterstcs of a processed part. The ncrease of cuttng forces s drectly lnked to the wear condton of a processng tool whch leads to heat ncrease and hence to structure change of the processed surface of the workpece and ts dmensons. Tmely and adequate tool replacement presents a very mportant component n processng, and therefore n turnng, to whch a sgnfcant attenton wll be gven n ths paper. Many 146 Research paper Copyrght by Internatonal OCSCO World Press. All rghts reserved. 2006

2 Manufacturng and processng authors have consdered mechansms nfluencng the tool wear process n turnng. For example, Scheffer et al [1] beleve there are two prncpal characterstcs nfluencng the relablty of turnng: cuttng speed and value of the force appearng n turnng. Researches dealng wth ths topc have shown that, from the pont of optmal tme of tool lfe expectancy, large varatons n speed and cuttng force are not allowed. Based on experence, t s known that flank wear drectly nfluences the qualty of processed surface, and that nsert breakage s nfluenced by crater wear appearng because of dffuson chemcal reacton n processng. The experence says that tool wear process moves on contnually and gradually,.e. that the nsert wear degree can be determned and one can react on tme, whle the breakage comes suddenly and contnual tool montorng s essental for breakage detecton. The concluson s that other methods can be used for tool breakage montorng and collsons n relaton to tool wear montorng Tool wear wear montorng Systems for tool wear montorng, both old and new generaton, as ts measurng value utlzes process parameters that are ndrectly lnked to tool wear, those beng: force or vbraton, Acoustc Emssons (AE) etc. The process s also nfluenced by condtons under whch the processng s takng place, lke tool geometry, tool materal and product, etc. For modellng non-lnear dependences that are separated from the measurng sgnal, processng condtons, tool wear or tool breakage, neural networks, fuzzy logc systems or the combnaton of both methods are used. Balaznsk et al [2] state that ntellgent neural networks and neural fuzzy technques are ntensely studed and they present the most selected ntellgence neural network methods for mergng montorng propertes. However, wth commercally avalable systems, the approach one sensor/one tool per process s domnant and the applcaton of AI method can rarely be found. In hs survey paper, Sek [3] establshed that, n the prevous perod, most researchers elaborated on the tasks of classfyng wear or breakage. Tool wear s a term not unformly defned and t has to be defned clearly before statng the montorng task. Tool breakage s always defned and classfed by two states, broken or not broken. Tool wear classfcaton has to use more than two tool states, that s, t should be contnual evaluaton of wear condton [4, 5]. Parameters defnng wear are average and maxmal wdth of flank wear, as well as depth, length and wdths of crater wear. Crtera that should defne wear as unform, needs to be fxed n order to present the state of tool wear. If wear s defned n two groups (wear wdth), t becomes qute wde and one can recognze only new and sgnfcantly worn tools. To montor wear n practce, t s necessary to establsh several wear groups, whch practcally presents very promsng montorng strategy. It can be sad that wear s a contnual and monotonously ncreasng process; therefore, contnual evaluaton would most approprately sut the physcal processng. Last years have seen the ntensve work to apply artfcal ntellgence (AI) method for montorng tool wear. Thus Balaznsk et al [2] and [6] compare the applcaton of three AI methods: a feed forward back-propagaton (FF-BP) neural network, a fuzzy decson support system (FDSS) and an artfcal neural network-based fuzzy nference system (ANNBFIS). The focus s not only on the accuracy of the tool wear predcton, but also on practcal usablty of the presented methods. Ozel and Nadgr [7] propose the use of back-propagaton neural networks for predctng flank wear durng hard turnng. Force measurng tests that appear n cuttng processes are performed usng a dynamometer that could measure three force components. In ths case, the force rato and processng condtons are ncluded as characterstcs of the neural network nput layer; the hdden layer had 30 neurons, and the output layer conssted of eght neurons, whch was a bnary representaton of the expermentally measured flank wear,.e. eght condton characterstcs of tool wear. For flank wear predctons, good results were acqured usng ths neural network method. Kothamasu and Huang [8] and Scheffer et all [1, 9] also propose another method based on a combnaton of statc and dynamc neural networks. 2. Proposal for for a a montorng system model system model The proposed tool montorng system model can bascally be observed through four segments unted n a whole that s, usng back propagaton, connected wth the machne-managng unt, as shown n (Fg. 1). Specal segments of the system are: sensor part part for data acqurng, processng and analyzng part for tranng neural network part for presentng results. Sensor part of the machne tool s made of measurng bearng placed n the front bed of the machne tool man spndle. Besdes the measurng bearng, there s also another sensor workng on the prncple of measurng stran gauge, placed on the processng tool holder and desgned specally for ths case for measurng cuttng forces appearng on the tool tself [10, 11, 12, 13]. Part for data acqurng, processng and analyzng contans standard A/D card ED 300, whch receves nput data from the exstng sensors, converts them to dgtal nformaton, and sends them to computer database. For nformaton flow, the composte software named ED LINK s responsble, allowng the possblty for programmng condtonng speed and type of nput data [10, 11]. Neural network bult nto the system s a mult-layer percepton network wth sgnal spreadng n one drecton (feedforward topology), and one of the best-known types of feed forward neural networks. The network has three layers: the nput layer contans three neurons, ntermedate hdden layer contans m neurons, and the output layer has one neuron. Software system s desgned to acqure and process nformaton n on-lne work regme and to manage the work of hardware components, so t can be based on set lmtatons for montor processng and tool wear. To establsh the degree of tool wear, we can utlze comparatve analyss of wear curves obtaned by system tranng usng neural networks. Determnng the leftover tool duraton s set on the bass of wear trend ganed by comparatve analyss wth the wear curve and real condton. READING DIRECT: 147

3 Journal of Achevements n Materals and Manufacturng Engneerng Volume 14 Issue 1-2 January-February 2006 unt value. For -th value of the nput vector from the varable regstered by the measurng bearng FRprom, normalzaton formula can be wrtten n the followng way: pˆ where p psr. (1) s p N 1 p sr p. (2) N 1 s an average value, and s p 1 N 1 N 1 ( p p sr ) 2 (3) Fg. 1. Algorthm of the developed tool montorng system based on neural networks 3. Neural network network for tool for wear tool wear montorng montorng 3.1. Pre-processng and tranng set 3.1. Pre-processng and tranng set As already stated, neural network has three nputs to whch the force values from the sensor on the tool holder, measured force from measurng bearng, and cuttng speed are drected. Usng these three values, the neural network at ts output evaluates the values of flank wear VB n the same tme moment. For the need of tranng process, a set was formed contanng 30,900 nput vectors and the same number of precse values of output varable. In creatng the set, specal attenton was gven to data representatves, that s, data was selected to cover all the nterval of possble values of nput varables and to be adequate to real change condtons. The tranng set formed n such a manner ensured that neural network correctly approxmated the dependence of nput values and output varable on the whole range of nput szes. In order to have effcent tranng, all the values n the tranng set were prevously normalzed. Normalzaton was performed n a way that every nput and output sze n the tranng set had the average value equal to zero, and standard devaton reduced to s a standard devaton of the process varable nput vector defned over the whole tranng set (N = 30,900). The formulas for other varables n the tranng set can be wrtten smlarly. Before the tranng process, apart from data normalzaton, t was necessary to perform the selecton of neural network topology. Snce the values of the output varable VB depended solely on momentary values of nput varables, a mult-layer percepton network wth sgnal spreadng n one drecton (feedforward topology) was selected for network topology. In addton, the theory stated that the functon that the resultant neural network had to approxmate was dstnctly non-lnear; hence, for the output functon of neurons n the hdden layer a sgmod functon was selected [14, 15]: 2 ( net ) 1. net 1 e y (4) where net M j1 w j x j b, (5) s the sum of nput network szes multpled wth approprate neuron weght coeffcents. The network used, as already sad, had three layers: nput, hdden and output, as shown n (Fg. 2); t presented a suffcent number of layers for the problem under observaton, consderng the fact that mult-layer percepton wth one hdden layer could wth arbtrary accuracy 0 unformly approxmate any real contnual functon on the real fnal axs. Fg. 2. Neural network topology 148 Research paper A. Antć, J. Hodolč, M. Sokovć

4 Manufacturng and processng In the nput as well as the output layer, the number of neurons was determned by the number of nputs, and outputs, so that nput layer contaned three neurons that corresponded to nput varables (FRtool, FRprom, Vm/mn), and the output layer contaned one neuron whose output gave the value of the estmated sze of flank wear VB. The number of neurons n the hdden layer was determned by experments comparng network performances wth dfferent number of neurons n the hdden layer. Durng the experment, networks were tested wth two to seven neurons n the hdden layer, and for every topology several tranngs wth the same tranng set were performed so that the performances of every topology could be estmated as objectvely as possble. Networks wth a small number of neurons (two and three neurons) n the hdden layer dd not present satsfactory results, whch can be attrbuted to nsuffcently rch network structure that mpled small capacty for functon approxmaton. Networks wth fve or more neurons n the hdden layer successfully approxmated nput-output dependence, so any of those topologes was adequate for mplementaton. In selectng fnal topology, a general drecton was used sayng that the total number of neurons n a neural network should be as small as possble, snce n that way the generalzaton network abltes were ncreasng and the appearance of over fttng was avoded. Consderng all mentoned a network wth fve neurons n the hdden layer was selected for the fnal network structure. Neural network fnal topology was traned several tmes wth the same tranng set, yet each tme wth the new, randomly generated, ntal values of weght coeffcents (Fg. 3). For the maxmal value of teratons, the value 1,000 was adopted, snce t was notced that n the later teratons the neural network error was not reduced by any sgnfcant value. Neural network tranng fnshed wth the ntermedate square error (calculated over the entre tranng set) n the nterval between 10-3 and Ths error was calculated wth the normalzed data; so, to get real value of ntermedate square error t was necessary to multply the ganed value wth the value of standard devaton of flank wear VB. On our tranng set, the standard devaton of flank wear had the value , so the real value of ntermedate square error was between 10-6 and Experment setup setup Machne parameters were selected n order to respond to ndustral applcaton n real manufacturng. Machne condtons for every experment are presented n Table 1. The experments were repeated under the same condtons for the possbltes of tranng, verfyng and testng neural network model. Bascally, cuttng speed and tranng number vared. There were ten experments n total, all of them wth the same basc confguraton. However, some of the expermental condtons were beng changed to solate dsturbances and dentfy the propertes of adequate montorng sgnal. Specal focus was on ensurng that all expermental condtons remaned the same, except for the parameters that were changed under control. The basc confguraton of the expermental measurng setup s shown n (Fg. 4), and t contans CNC lathe equpped wth sensors for measurng cuttng force, those beng: promess sensor and specally desgned sensor wth measurng stran gauge placed on the tool holder. Fg. 3. The change of ntermedate square error durng neural network tranng Tranng ANN was performed wth reslent modfcaton of the basc back propagaton algorthm that was desgned for ANN wth squashng actvaton functons, that s, functons that compress the nfnte nput area nto the fnal output nterval (lke sgmod functon). These functons could cause problem whle usng basc back propagaton algorthm, snce the gradent could have very small values and therefore cause small changes n weght coeffcents, whch led to long-term tranng. Thus, the reslent algorthm utlzed only the sgn of partal nference n order to determne the drecton of weght coeffcent changes, whle the change sze was determned by a specal parameter whose value was, durng the tranng, changed followng the specal algorthm. Fg. 4. Basc confguraton of the expermental measurng setup Development of an ntellgent system for tool wear montorng applyng neural networks 149

5 Journal of Achevements n Materals and Manufacturng Engneerng Volume 14 Issue 1-2 January-February 2006 Table 1. Experment parameters Exp. 1 Exp Exp. 10 Machne INDEX GU 600 Holder PTGNL 25x25 Insert TNMG Cuttng depth [mm] 1 mm Speed [m/mn] 200 Materal workpece.4730 Number of passng Total tme [mn] Dameter [mm] Passng length [mm] Results The selected approprate model was establshed to be relatvely relable method for montorng tool wear durng hard turnng. Durng the research, several dfferent network confguratons were used and studed for ther applcaton n tool wear montorng durng hard turnng. It s known that statc cuttng forces are good tool wear ndcators; however, adequate dynamc analyss of cuttng forces can also gve satsfactory propertes for wear montorng. (Fg. 5) presents cuttng force components measured durng tool wear montorng. 300 m a) 300 m b) 300 m c) Fg. 6. Tool nsert for expermental measurements F(x,z) force tme s Fg. 5. Cuttng forces measured durng montorng Fg. 7. Neural network exact value and estmated value Tool wear (VB) was measured after each turnng and the value sutng one passng was lnearly put nto the table. Wear measurng was performed usng tool mcroscope havng the 30 tmes enlargement. Inserts used n the experments were coated by TN. Ther lfe expectancy had the tendency to termnate suddenly after the coatng dsappeared from the cuttng part, whch could be seen n swft and sudden jump of cuttng force. Fg. 6 shows the appearance of worn nserts durng the experment, a) tool nsert from experment 1, b) tool nsert from experment 3, c) tool nsert from experment 5. Fg. 7 presents the agreement between the model of the estmated value of the traned neural network and the exact value ganed by measurng. For better survey, Fg. 8 presents normalzed ntermedate value of the estmated value ganed results and measured results. Wear measurng results used for tranng were gven n Fg. 9 for experments 1-5. In each case, the model was tested on the prevously unseen data snce these parameters remaned constant durng every ndvdual test of tool lfe expectancy. Fg. 8. Normalzed ntermedate value of real measurng and estmated values 150 Research paper A. Antć, J. Hodolč, M. Sokovć

6 Manufacturng and processng VB [mm] model estmated values tme [mn] Fg. 9. Real measurng results Table 2. Parameters of nput szes and standard devaton Intermedate value of Intermedate value of nput szes output sze Standard devaton of nput szes Standard devaton of output sze Table 2 presents ntermedate value parameters of nput szes and wth standard devaton. 6. Future work work The lack of neural networks (same as many other expermental models) requres long-term tranng wth data normalzaton wth the values expected to be workng n the real condtons. The network cannot work wthout prevous tranng. To expand one s work, t s necessary to utlze both numercal and expermental methods. Consderng the fact that the network should be re-traned from tme to tme, tranng perod can be consdered as a major drawback n the applcaton of neural networks n real manufacturng. However, future research could nclude the ntegraton of the exstng system nto CNC-machne, nstead of currently separated devce; ths would ensure that montorng system and machne could react synchroncally,.e. the machne could react by stoppng once the over-worn tool s detected. More precsely, dynamc neural networks to ensure addtonal correcton of the traned statc network n on-lne work regme could also enlarge the exstng model. 7. Conclusons The paper presented that neural networks (NN) can be used for effcent wear montorng durng hard turnng, wth the lsted lmtatons. After consderng many possble setups for wear montorng model usng dfferent confguraton types of neural networks, and based on nput and output parameters, the one selected performed wth optmal results for the selected number of network layers and neurons. The model was set so t could be upgraded rather easy by dynamc neural network, whch s one of relatvely new research drectons n ths feld. References [1] C. Scheffer, H. Kratz, P.S. Heyns, F. Klocke: Development of a tool wear-montorng system for hard turnng, Internatonal Journal of Machne Tools & Manufacture 43, (2003), [2] M. Balaznsk, E. Czogala, K. Jemelnak, J. Lesk: Tool condton montorng usng artfcal ntellgence methods, Engneerng Applcatons of Artfcal Intellgence 15, (2002), [3] B. Sek: On-lne ndrect tool wear montorng n turnng wth artfcal neural networks, a revew of more than a decade of research, Mechancal Systems and Sgnal Processng 16(4), (2002), [4] D.E. Dmla Sr., P.M. Lster: On-lne metal cuttng tool condton montorng. II: tool-state classfcaton usng multlayer percepton neural networks, Internatonal Journal of Machne Tools & Manufacture 40, (2000), [5] Shang-Lang Chen, Y.W. Jen: Data fuson neural network for tool condton montorng n CNC mllng machnng, Internatonal Journal of Machne Tools & Manufacture 40, (2000), [6] U. Zuperl, F. Cus, M. Mlfelner: Fuzzy control strategy for adaptve force control n end mllng, COMENT Worldwde Congress on Materals and Manufacturng Engneerng and Technology, Glvce-Wsla, Poland, 2005, pp [7] T. Ozel, A. Nadgr, Predcton of flank wear by usng back propagaton neural network modellng when cuttng hardened H-13 steel wth chamfered and honed CBN tools, Internatonal Journal of Machne Tools & Manufacture 42, (2002), [8] R. Kothamasu, S.H. Huang, Intellgent tool wear estmaton for hard turnng: Neural-Fuzzy modellng and model evaluaton, Proceedngs of the Thrd Internatonal Conference on Intellgent Computaton n Manufacturng Engneerng, Ischa, Italy, 2002, [9] C. Scheffer, P.S. Heyns: An ndustral tool wear montorng system for nterrupted turnng, Mechancal Systems and Sgnal Processng 18 (2004), [10] A. Antc, J. Hodolc, R. Gatalo, M. Stevc, Contrbuton to the development of the mult-sensor system for tool montorng, Annals of DAAAM & Proceedngs of the 12th nternatonal DAAAM Symposum, Venna, Austra, 2001, [11] A. Antc: A Contrbuton to the development of tool montorng system n flexble manufacturng systems, Master s thess, Faculty of Techncal Scences, [12] F. Cus, M. Mlfelner, J. Balc An overvew of data acquston system for cuttng force measurng and optmzaton n mllng Contemporary, COMENT Worldwde Congress on Materals and Manufacturng Engneerng and Technology, Glvce- Wsla, Poland, 2005, pp [13] J.S. Son, D.M. Lee, I.S. Km, S.G. Cho: A Study on On-lne learnng Neural Network for Predcton for Rollng Force n Hot-rollng Mll, COMENT Worldwde Congress on Materals and Manufacturng Engneerng and Technology, Glvce- Wsla, Poland, 2005, pp [14] P. D. Lppman, "Neural Computng theory and practce", Van Nostrand Renhold N. Y., [15] M. Redmller, and H. Braun, A drect adaptve method for faster back-propagaton learnng: The RPROP algorthm, Proceedngs of the IEEE Internatonal Conference on Neural Networks, Development of an ntellgent system for tool wear montorng applyng neural networks 151