金屬帶鋸床設(shè)計

喜歡這套資料就充值下載吧。資源目錄里展示的都可在線預(yù)覽哦。下載后都有，請放心下載，文件全都包含在內(nèi)，有疑問咨詢QQ:414951605 或 1304139763 journal of materials processing technology 2 0 9 ( 2 0 0 9 ) 23022313journal homepage: adaptive control and monitoring of bandsawing using a neural-fuzzy systemIlhan Asilt urka, AliUn uvarbaFaculty of Technical Education, Selcuk University, Konya 42250, TurkeybFaculty of Mechanical Engineering, Selcuk University, Konya 42250, Turkeya r t i c l ei n f oArticle history:Received 27 June 2007Received in revised form11 March 2008Accepted 14 May 2008Keywords:Intelligent manufacturingFuzzy controlNeural-fuzzy controllerAdaptive control of band sawingBand sawinga b s t r a c tIn bandsaw machines, it is desired to feed the bandsaw blade into the workpiece with anappropriate feeding force in order to perform an efficient cutting operation. This can beaccomplishedbycontrollingthefeedrateandthrustforcebyaccuratelydetectingthecuttingresistance against the bandsaw blade during cutting operation. In this study, a neural-fuzzy-based force model for controlling band sawing process was established. Cutting parameterswerecontinuouslyupdatedbyasecondaryneuralnetwork,tocompensatetheeffectofenvi-ronmental disturbances. Required feed rate and cutting speed were adjusted by developedfuzzy logic controller. Results of cutting experiments using several steel specimens showthat the developed neural-fuzzy system performs well in real time in controlling cuttingspeed and feed rate during band sawing. A material identification system was developed byusing the measured cutting forces. Materials were identified at the beginning of the cuttingoperation and cutting force model was updated by using the detected material type. Con-sequently, cutting speed and feed rate were adjusted by using the updated model. The newmethodology is found to be easily integrable to existing production systems. 2008 Elsevier B.V. All rights reserved.1.IntroductionIn band sawing, the power rating of the machine limits thethickness and hardness of the metal to be cut. In band sawingprocess, metal removal is accomplished by forcing a multi-toothed tool against the workpiece. The depth of cut in sawingcannotbepresetlikeothermetalcuttingprocessesandcontrolcan only be exercised over the thrust load applied between theblade and workpiece material. The amount of metal removedby each tooth is dependent primarily on how well the bladetransmits the applied pressure to the workpiece and alsoon the penetration ability of the cutting teeth. Machiningforces generated during sawing process are therefore foundto have greater significance than in other chip removal pro-cesses. It has been found that thrust and cutting loads pertooth per unit thickness reduced with an increase in cuttingCorresponding author. Tel.: +90 332 223 3344; fax: +90 332 241 2179.E-mail address: iasilturkselcuk.edu.tr (I. Asilt urk).speed. A reduction in the thrust force will cause a reductionin the depth of cut taken by the engaged teeth. An increasein the feed rate causes a substantial increase in both cut-ting and thrust loads per tooth. Geometry of the workpiecedoes also have a considerable influence on cutting perfor-mance. In band sawing, the thrust load is normally constantalongtheworkpiecebreadth.Whensawingroundsectionsthewidth of the workpiece changes within the cut, the cutwidthincreasesastheblademovestowardsthecentreanddecreasesas the cut is being finished. Band saw machines that operateon a pressure feed principle maintain a constant chip loadper tooth as described while the blade saws through varyingsections.Artificial intelligence (AI) methods are widely used in solu-tion of complex engineering problems. Some of the mostcommonlyusedAItechniquesareneuralnetworks(NN),fuzzy0924-0136/$ see front matter 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.jmatprotec.2008.05.031journal of materials processing technology 2 0 9 ( 2 0 0 9 ) 230223132303NomenclatureDdiametereoutput errorffeed ratefiinstantaneous feed rateFoutprocess outputFmeasmeasured forceFrefreference forceGmaterial group noHhardnessIintegral of errorIAM 1intelligent adaptive moduleLinstantaneous heightMRRmaterial removal ratePIperformance indexTWRtool wear rateyprocplant outputyrefreference model outputviinstantaneous cutting speedlogics (FL), expert systems (ES) and models using hybrids ofthese.Artificial intelligence methods are used in every stage ofmanufacturing. Machining is one of the basic manufacturingmethods used in the industry. Manufacturers must minimizecostandprocesstime,andadditionallytheproductmustcom-ply with the required dimensions and quality criteria for abetter competition.Increasing the productivity of metal cutting machine toolsis a principal concern for manufacturing industry. In tra-ditional machining systems, cutting parameters are usuallyselected prior to machining according to machining hand-books or the users experience. The selected machiningparameters are usually conservative to avoid machining fail-ure. To ensure the quality of machining products, to reducethe machining costs and increase the machining efficiency,it is necessary to adjust the machining parameters in real-timeandtooptimizemachiningprocessatthattime.Adaptivecontrol of the machining process is preferable to solve aboveproblems.Since band sawing process is non-linear and time-varying,it is difficult for traditional identification methods to providean accurate model. Adaptive control methods provide on-lineadjustment of the operating conditions. Therefore, parame-ter adaptive control techniques for machining processes weredeveloped to adjust the feed rate automatically to maintain aconstant cutting force. Applications of these techniques suc-cessfully increased both the metal removal rate and tool life.In this paper, an intelligent neural-fuzzy adaptive controlscheme is proposed for band sawing process. The proposedadaptive control system can be applied effectively in variouscutting situations.2.Literature surveyTherearealotofworksexistingintheliteratureonmonitoringand controlling of the machining operation.Groover pointed out that conventional control theory couldbe inefficient and unstable due to disturbing variations in themachining conditions. It is stated that fixed cutting forceswouldbeausefulapproachforincreasingtoollifeandmaterialremoval rate (Groover, 1987).The conventional PID feedback control system has beenusedincontrollingmachiningprocessesbynumerousresearchers (Masory and Koren, 1980, 1985; Lauderbaugh andUlsoy, 1989; Koren, 1988). The main problem with the fixedgain Adaptive Control Constraint (ACC) system is the one thatproduce poor performance and may become unstable duringthe time-varying machining process. The use of various formsof adaptive control in an ACC system has been examined byadjusting the gain of the controller.Model reference adaptive control-based ACC systems(MRAC)havebeendevelopedbysomeresearchers(MasoryandKoren, 1980, 1985; Lauderbaugh and Ulsoy, 1989). These stud-ies found that MRAC perform control duties better than fixedgain controllers. A typical MRAC incorporates the parameterestimation of the cutting process.Recently, many studies have been devoted to the theoryof fuzzy control and its application to machining processes.Tarng et al. developed a fuzzy logic-based controller (FLC) foradaptive control of turning operations. The developed FLC canadjust feed rate on-line so as to reduce machining time andmaintain constant force (Tarng and Cheng, 1993; Tarng andWang, 1993).IntheexperimentalstudiesofZhangandKhanchustambham (1993), it is shown that process opti-mization is possible by online monitoring and controllingof the machining process. This eliminates the effect ofdisturbances caused by operator.An online monitoring system was designed by Ordonezet al. (1997) by using artificial intelligence based on sensors.Signals which were taken from sensors are used in AI deci-sion making during the cutting process. The real time signalsobtained through force transducers and estimated cuttingforces obtained by using NN were compared. Consequently,estimated model was implemented to surface roughness,tool wear and geometric tolerances. Feed forward and backpropagation algorithms were used as architecture and train-ing algorithm of NN model, respectively. Direct and indirectadaptive fuzzy techniques and simulations of conventionalcontrols were compared.An adaptive control approach was suggested by Rodolfo etal. (1998) for maintaining the cutting force constant, in themilling process. The constant force feed rate was investigatedwithout delay time.Tsai et al. (1999) observed that, surface roughness canexperimentally be determined by one or more quantita-tive measurements. Estimated surface roughness model wasbased on relative vibrations between the tool and the workpiece. Estimated surface roughness was improved by usingsignals that are taken from vibration and proximity sensors.System accuracy was observed as 9699%.An adaptive controller with optimization was designedbased on two kinds of NN by Liu and Wang (1999) formilling process. A modified back propagation NN was pro-posed adjusting its learning rate and adding dynamic factorin the learning process, and was used for the online modeling2304journal of materials processing technology 2 0 9 ( 2 0 0 9 ) 23022313of the milling system. A modified Augmented Lagrange Multi-plier (ALM) neural network model was proposed adjusting itsiteration step, and was used for the real time optimal controlof the milling process. In this study, the simulation and exper-imental results show that not only does the milling systemwith the designed controller have high robustness and globalstability, but also the machining efficiency of the milling sys-tem with the adaptive controller is much higher than for thetraditional CNC milling system.Adaptive control constraint is one of the methods usedin controlling machining processes. Force control algorithmshave been developed and evaluated by numerous researchers.Among the most common is the fixed gain proportional inte-gral (PI) controller, originally proposed for milling by Kim etal. (1999). The gain of the controller is adjusted in response tovariations in cutting conditions in the proposed controller.The essential aim of the neural network-based controller istoconstructareversefunctionforthemachiningsystemusingtheNNsothattheoutputofthemachiningsystemapproachesto the desired output. Machining process can usually be con-trolled by adjusting the feed rate or spindle speed. The neuralnetwork-based ACC system has been applied to machiningprocess control by (Liu et al., 1999; Hang and Chiou, 1996).In a study by Liu et al. (2001), the major adaptive controlconstraintsystemswerediscussedbasedonthefeedbackcon-trol, parameter adaptive control/self-tuning control, modelreference adaptive control, variable structure control/slidingmodecontrol,neuralnetworkcontrol,andfuzzycontrol.Theirtypical applications to constant cutting force control systemare also described, and some recent experiments results werepresented.Online method of achieving optimal settings of a fuzzy-neural network has been developed by Sandak and Tanaka.Results of the cutting experiments using several wood speciesshow that the fuzzy-neural system developed performs wellin online feed rate optimization during band sawing, whilemaintainingsawdeviationwithinspecifiedlimits(SandakandTanaka, 2003).Zuperl et al. (2005) discussed the application of fuzzy adap-tive control strategy to the problem of cutting force control inhighendmillingoperations.IntheirACsystem,thefeedrateisadjusted on-line in order to maintain a constant cutting forcein spite of variations in cutting conditions. They developed asimplefuzzycontrolstrategyintheintelligentsystemandcar-riedoutsomeexperimentalsimulationswiththefuzzycontrolstrategy.The effect of cutting speed, feed rate and work piece geom-etry in band sawing were investigated by Ahmad et al. (1987).In the experimental studies, reduction in the thrust force andcutting force per teeth for unit thickness were observed, as thecutting speed increased.Ko and Kim observed that, in order to create a mechanis-tic model of cutting force, specific cutting pressure should beobtained through cutting experiments. The band sawing pro-cess is similar to milling in that it involves multi-point cutting,so it is not an easy matter to evaluate specific cutting pres-sure. The cutting force is predicted by analyzing the geometricshape of a saw tooth. They stated that the predicted cuttingforce coincided well with those measured in validation exper-iment. Therefore, the predicted cutting forces in band sawingcan be used for the adaptive control of saw-engaging feed ratein band sawing (Ko and Kim, 1999).Anderson et al. (2001) pointed out a mechanical cuttingforce model for band sawing. The model describes the vari-ation in cutting force between individual teeth and relates itto initial positional errors, tool dynamics and edge wear. Bandsawing is a multi-tooth cutting process, and the terminologyof the cutting action is discussed and compared with othercutting processes.3.Adaptive control of machining processesIntelligent machining system applications include monitor-ing and control technologies. This system also improves themachining operations. In this system, process related datais acquired, and then process is controlled. Manufacturingindustries are affected by computer technologies. Recently,automation works were made on the material handling,quality monitoring, motion control, source planning, pro-cess control, etc. The sensing of the machining process ismuch more comprehensive and complex. Many papers havebeen prepared about monitoring and control of the machinetool.Researchers and industrialists concerned with tool mon-itoring and adaptive control. One of the most importantfunctions of the intelligent control is the provision of requiredaction in the unknown or indefinite ambient processes.Machine tool and cutting tools are protected by the moni-toring system. Tool changing cost, scrap rate and productioncost is reduced by real time tool wear measurement. Thus, fullcapacities of the machine tools are maintained.In the machining processes, feed rate is continuouslyadjusted for keeping on the process with constant referenceforce in the adaptive control systems. Thanks to adaptivecontrol, since it aims to minimize the production time toadjust the feed rate to optimal values in the high capacityworking conditions. Consequently, tool life increases with therestricted load application.3.1.Necessities of the adaptive controls in themachining operationIn the case of depth or width of cut, feed rate are usuallyadjusted to compensate for the variability. This type of vari-ability is often encountered in profile milling or contouringoperations (Groover, 1987).When hard spots or the areas of difficulty to machine areencountered in the workpiece, either speed or feed is reducedto avoid premature failure of the tool.If the work piece deflects as a result of insufficient rigidityinthesetup,thefeedratemustbereducedinordertomaintainaccuracy in the process.As the tool begins to dull, the cutting forces increase. Theadaptive controller will typically respond to tool dulling byreducing the feed rate.The workpiece geometry may contain shaped sectionswhere no machining needs to be performed. If the tool were tocontinue feeding through these so-called air gaps at the samerate, time would be lost. Accordingly, the typical procedure isjournal of materials processing technology 2 0 9 ( 2 0 0 9 ) 230223132305Fig. 1 Adaptive control optimization in milling system.to increase the feed rate, by a factor of two or three, when airgaps are encountered.These sources of variability present themselves as time-varying and, for the most part, unpredictable changes in themachining process. It should be examined how adaptive con-trol can be used to compensate for these changes.3.2.Adaptive control methods in the machiningprocessThe methods that are mentioned below are used in machiningoperations. These are namely Adaptive Control with Opti-mization, Geometric Adaptive Control and Adaptive ControlConstraint.3.2.1.Adaptive control optimization (ACO)In this type of adaptive control, a performance index is spec-ified for the system. This performance index is a measureof overall process performance, such as production rate orcost per volume of metal removed. The objective is to opti-mize the index of performance by controlling speeds and/orfeeds.A system with the adaptive controller for machining pro-cess can be constructed based on NNs as shown in Fig. 1. Thesystem is modeled on-line by the modified BP learning algo-rithm. The feed rate is adjusted and the process is optimizedin real-time by the modified ALM NN. In the process the differ-ence between measured cutting force and estimated cuttingforce is (e), which is used as back propagation NN and is toadjust the weights of the NN. Feed rate is adjusted in the senseof object function constraints (Liu et al., 1999).3.2.2.Geometric adaptive control (GAC)Geometricadaptivecontrolisusuallyusedinfinishmachiningoperations, where the objective is to achieve a desired surfacequality and/or accurate part dimensions despite tool wear ortool deflection (Liang et al., 2004). Owing to the relationshipbetween feed rate and surface quality, surface roughness ordimensional accuracy of the part is continuously measuredby the sensor by means of feed-back.Therefore in most GAC systems, the cutting speed is con-stant and the machining feed is manipulated to achieve thedesired surface quality (Masory and Koren, 1980).The dimensional precision in turning is usually achievedby measuring the part diameter at various points after themachining. Ultrasonic sensors were used in turning operationfortheestimationandthecontrollingofthesurfaceroughness(Coker and Shin, 1996). Offset distance is manually adjustedto compensate for inaccuracy.3.2.3.Adaptive control constraint (ACC)The objective in this method is to manipulate speeds and/orfeeds to maintain the measured variables below their con-straint limit values. A typical configuration of the adaptivecontrol is illustrated in Fig. 2 for machining process. Adaptivecontrol constraint is one of the effective methods of solvingthe above problems. ACC controls the machining parame-ters to maintain the maximum working conditions during theFig. 2 The integral ACC system of the turning process.2306journal of materials processing technology 2 0 9 ( 2 0 0 9 ) 23022313Fig. 3 Self-tuning control-based ACC system.time-varying machining process. Cutting force, power, surfacequality, etc. are constant parameters.Where Foutis the process output, Fmeasis the measuredforce, Frefis the reference force, e is the output error, f is feedrate.The performance index is generally economic function,andalsomaximizessubjecttoprocessandsystemconstraints.In this form of adaptive control, constraint limits are imposedon the measured process variables. Performance index ismaterial removal rate (MRR) to tool wear rate (TWR) as in Eq.(1). This ratio must be maximized.Performance index:PI =MRRTWR(1)where PI=performance index; MRR=material removal rate;TWR=tool wear rate.The determination of the performance index as real timeis rather difficult with nowadays technologies. Because of thisreason TWR is not a measurable value as real time. ConstraintAdaptive Control was classified by Liu et al. (2001) as below,- A feedback controller-based ACC system,- Self-tuning control-based ACC system,- A model reference adaptive control-based ACC system,- A variable structure system-based ACC system,- A neural network-based ACC system,- A fuzzy control-based ACC system.Frequently used methods mentioned above are explainedbelow.3.2.3.1. Self-tuning control-based ACC system.The early ver-sions of parameter adaptive control-based ACC systems weredeveloped using a simple on-line estimator for the processgain and an integral strategy to adjust the gain of an integralcontroller.Thedefectsofthisstrategyarethatthed