Modeling of CNC Machining Process - Artificial Neural Networks Approach

Transcription

1 Modelig of CNC Machiig Process - Artificial Neural Networks Approach DAVID SAMEK, ONDREJ BILEK 2 Departmet of Productio Egieerig, Faculty of Techology Tomas Bata Uiversity i Zli am. T. G. Masaryka, 76 Zli Czech Republic samek@ft.utb.cz, bilek@ft.utb.cz 2 Abstract: - CNC machiig is kow as a advaced machiig process icreasigly used for moder materials. This paper outlies modelig methodology applied to optimize cuttig parameters durig CNC millig with ball ed mill tool. The parameters take ito accout were radial depth of cut ad feed per tooth. A predictive model was based o artificial eural etwork approach. Key-Words: - Modelig, artificial eural etworks, CNC machiig, surface roughess, feed-forward etworks Itroductio CNC (Computer Numeric Cotrol) machiig is oe of the most popular approaches of covetioal machiig by tools with defied geometry. Computer cotrol of machiig program brigs sigificat advatages i compariso to huma upredictable machiig. What is more, the CNC machiig leads to a uique possibility to process plaig. Kowledge of the machiig process ad optimal settigs of the iput parameters are essetial for the quality ad accuracy of the machied parts. Despite the fact that the machiig process is affected by large amout of factors ad the process itself is time-variat the modelig of the output process parameters is curretly becomig more available []. For machiig of sculptured fuctioal surfaces the millig process is usually used. The millig is mai machiig techology that do ot have comparable alterative i the area of covetioal machiig. The millig process is beig successfully applied for various operatios from roughig to fiishig operatios. The process of millig is described by the use of differet cuttig strategies, multi-edge tools ad i most cases creatio of part program usig Computer Aided Maufacturig (CAM) software [2, 3]. The surface quality of the milled products iflueces vast factors. Namely, there are wearig, frictio, heat trasmissio, light reflectio, fatigue stregth, corrosio resistace ad lubricat distributio [4, ]. The most importat parameter Ra is derived as the arithmetic mea of the roughess profile. This parameter is widely used ad is commo i maufacturig practices, although gives limited iformatio about machied surface []. Parameter Ra is ofte appeded by the maximum height of the profile Rz. Despite it is depedet o Ra it carries importat iformatio about the surface quality ad therefore it is cosidered i the followig measuremets. Several methodologies for process optimizatio of surface roughess ad accuracy were developed [6, 7]. There have bee published may approaches to surface roughess modelig that comprises kiematic model, experimetal ivestigatio ad aalysis, implemetatio of artificial itelligece (AI) ad methods that use desiged experimets. The modelig ad predictio of surface roughess is a crucial step to improve maufacturig process while reducig the cost of productio. Oly few recet studies have bee published i this area ad they are mostly focused o the millig with ball ed mill tool. The paper presets modelig ad predictio of techological parameters of CNC millig usig artificial eural etworks (ANN), while multilayered feed-forward eural etwork (MFFNN) was applied. The studied iput parameters of the millig process are as follows: radial depth of cut a e, feed per tooth f z. The obtaied results are verified o the experimetal measuremet. 2 Iitial Experimets The millig operatios were performed o the three axis vertical millig ceter Mikro HSM 8. For ISBN:

2 the purpose of experimet were selected ball mill tools from sitered carbide with PVD coatig. Cuttig speed was costat 2 m/mi, tool rake agle was egative -4, surface icliatio agle was 3. For the tool clampig was used the shrik fit holder HSK E o a modular system Easyshrik 2. The tool overhag was mm durig millig. The ivestigated iput parameters radial depth of cut a e, feed per tooth f z were set from the rage.6 mm.6 mm ad. mm.7 mm respectively. The complete list of process parameters is show i the Table. The process parameters settigs selected for this experimetal work were chose optimal accordig to maufacturig practices. Table - Process parameters for experimets Cuttig speed (v) 2 m/mi Radial depth of cut (a e ).6 mm,.2 mm,.32 mm,.4 mm,.6 mm Feed per tooth (f z ). mm,.2 mm,.3 mm,. mm,.7 mm Surface icliatio 3 agle (α) Tool rake agle (γ) 2 Tool diameter 2 mm Tool overhag mm Machied workpieces were from stailess steel X3CrMoV2- with hardess of 63 Rockwell Hardess (HRC), whereas the chemical compositio was guarateed by the supplier. Fiftee specimes were machied for each iput parameters combiatio directio was parallel to y axis ad climb cuttig strategy was cosidered for the better surface roughess. Nevertheless, the machiig of ope areas such as stadaloe surfaces i described experimet sigificatly icrease machiig time due to a larger umber of ocuttig movemets. Surfaces of the all fiftee workpieces were measured usig Mitutoyo SJ-3 after machiig. Accordig to stadard, surface roughess was measured i the perpedicular directio to the feed rate, providig higher values of Ra ad Rz. The parameter Ra was withi the experimet cosidered as surface characteristic that is i additio typical parameter of surface roughess i maufacturig practices [8, 9]. 3 Data Modelig The model desig results from the problem defiitio two observed iput parameters (a e ad f z ) ad two desired output parameters (Ra ad Rz). Thus, the model has to cotai two iputs ad two outputs. The experimetally obtaied data were loaded ito Matlab, where all computatios were performed. After the statistical aalysis it was decided to use artificial eural etwork as the process model, because the data were oised, multidimesioal ad the expected fuctio is oliear. Usig Matlab Neural Network Toolbox various structures of MFFNN were created i order to fid optimal solutio for the give problem. The tested structures are listed i the Table 2, while NN stads for umber of euros, TF for trasfer fuctio, T for hyperbolic taget fuctio ad L for liear fuctio. Table 2 Tested eural etwork structures Iput layer Hidde layer Hidde layer 2 Output layer NN TF NN TF NN TF NN TF et 2 - T L et2 2 - T L et3 2-2 T L et4 2 - T L et 2 - T L et6 2-2 T T 2 L et7 2 - T T 2 L Fig. Ball ed millig geometry NX CAM software was used to create CNC part program for the fiishig operatio withi the ±. tolerace, usig the Costat Z strategy. The feed The experimetal data had to be trasformed ito the iterval <-, ->. After that, the created artificial eural etworks were traied to the trasformed measured data usig Leveberg-Marquart Algorithm. Certaily, after the predictio all output ISBN:

3 data had to be trasformed back before validatio to real experimetal data. For the all computatios the batch programs were created usig Matlab stadard programig eviromet (M-Files). 4 Testig Experimets ad Verificatio The prepared models were tested by ew experimetal data. The models were used for predictio of resultig surface quality. The, fiftee of workpieces were milled ad measured for each combiatio of ispected parameters. The machiig ad other coditios remaied the same as i the part 2 of this paper. The obtaied experimetal results were compared to the predicted values. I order to umerically compare predictio accuracy of all predictors followig criteria were defied. Average absolute value of predictio differece for Ra J Ra ad for Rz J Rz : () i -y() i t i= J Ra = () ( i) -y() i t i= J Rz =, (2) where is umber of measuremets, t stads for target (measured value) ad y is predicted value. Because it is very importat to observe also extremes i predictio iaccuracy, the maximal predictio error for Ra E Ra ad E Rz for Rz are used. The resultig criteria are preseted i the Table 3. As ca be see, it is complicated to distiguish what model provides the best results, because there are four wiers from the poit of view of each of the criteria the differet etwork is the best. Therefore, it was used multiple attribute decisio makig method, while it was applied same weight for all four criteria. The ordial method was utilized for assigig poits to the idividual etworks/criteria. Therefore, the lowest sum of the poits results to the wier. As ca be see from Table 4, the best score gives the et7. Due to space limitatios all results caot be show. Cosequetly, i the Fig. 2 are preseted predictios ad predictio errors for the et7 oly. I the box plots (Fig. 2 to ) ca be see the measured values of roughess parameters Ra ad Rz (target data). The middle lie i the box with the x symbol represets the media. Boudaries of boxes represet the first ad third quartile, which is % of all measured values. The area betwee first ad third quartile idicate the iterquartile rage (IQR). Extreme values (. iterquartile rage) are the limit lie, which is 2% of the values of the lowest ad highest values. Poits that are located at a distace greater tha. IQR from the media are show as +. These poits represet the possible devious measuremet. The predicted values of Ra ad Rz are depicted by asterisks ad they are coected by thick lie. Table 3 Compariso of predictors J Ra J Rz E Ra E Rz et et2 et3 et4 et et6 et Table 4 Multiple attribute decisio makig J Ra (-) J Rz (-) E Ra (-) E Rz (-) Σ et et2 et3 et4 et et6 et ISBN:

4 =.6 mm =.2 mm Fig. 2 Predictio of Ra usig et7, a e =.6 mm Fig. Predictio of Rz usig et7, a e =.2 mm =.6 mm =.32 mm Fig. 3 Predictio of Rz usig et7, a e =.6 mm Fig. 6 Predictio of Ra usig et7, a e =.32 mm =.2 mm =.32 mm Fig. 4 Predictio of Ra usig et7, a e =.2 mm 4 Fig. 7 Predictio of Rz usig et7, a e =.32 mm ISBN:

5 =.4 mm =.6 mm Fig. 8 Predictio of Ra usig et7, a e =.4 mm Fig. Predictio of Rz usig et7, a e =.6 mm =.4 mm Ra predictio error Fig. 9 Predictio of Rz usig et7, a e =.4 mm Number of testig vector (-) Fig. 2 Predictio error for Ra i µm =.6 mm Rz predictio error Fig. Predictio of Ra usig et7, a e =.6 mm Number of testig vector (-) Fig. 3 Predictio error for Rz i µm ISBN:

6 Ra predictio error (%) Number of testig vector (-) Fig. 4 Predictio error for Ra i % Rz predictio error (%) Number of testig vector (-) Fig. Predictio error for Rz i % Discussio ad Coclusios As ca be see from the Fig. 2 ad from the Table 3, the predictor et7 provided reasoable good results. The maximum predictio error for Ra ad Rz was.92 ad.222 µm respectively. The predictio error i percet was.2 % for Ra ad 3. % for Rz at the most. The average value of the predictio error was.69 µm for Ra ad.986 µm for Rz. O the other had, the other predictors are worth of oticig too. For example et6 is excellet for the predictio of Ra it has the best values of the criteria J Ra ad E Ra, but at the same time it has the worst predictio accuracy for Rz. The lowest average predictio differece J Rz imparted et2 with the oe hidde layer, o the cotrary et3 had the lowest E Rz. It ca be cocluded that desig of models / predictors of techological processes is complex ad at all cases the model has to be verified by ew experimets i order to get the proof that predictor is appropriate. The proposed predictor based o multilayered feedforward eural etwork ca be supposed for obtaiig optimal settigs of the CNC millig machie for desired surface quality. What is more, the predictor eables the predictio outside the measured data. Refereces: [] P. G. Beardos, G. C. Vosiakos, Predictig surface roughess i machiig: a review, It. J Mach. Tool. Ma., Vol. 43, 23, pp [2] M. Iliescu, P. Spau, M. Rosu, B. Comaescu, Simulatio of Cylidrical-Face Millig ad Modelig of Resultig Surface Roughess whe Machiig Polymeric Composites. I Proceedigs of the th WSEAS Iteratioal Coferece o Automatic Cotrol, Modellig ad Simulatio. WSEAS, 29, pp [3] J. Chladil, Aalysis of CNC Machiig i CAM System Applicatios. I: M. Maas (ed.), Tools 29, Zli, 29. [4] M. A. Afifah, Y. T. A. Erry, A. Delvis, B. M. N. Siti, A. F. H. Muataz, Developmet of surface roughess predictio model for high speed ed millig of hardeed tool steel, Asia J. Sci. Res. Vol. 4. No. 3, 2, pp [] M. Folea, D. Schlegel, N. Lupulescu, L. Parv, Modelig Surface Roughess i High Speed Millig: Cobalt Based Superalloy Case Study. I Proc. of the st It. Cof. o Maufacturig Egieerig, Quality ad Productio Systems. WSEAS, 29, pp [6] G. Varga, J. Kudrak, Effect of Evirometally Coscious Machiig o Machied Surface Quality. App. Mech. Mat., No. 39, 23, pp [7] C. Felho, A method for calculatio of theoretical roughess i face millig. I: M. Maas (ed.), Proceedig of 8th Iteratioal Tools Coferece, Zli, 2. [8] J. Jersak, N. Gaev, J. Kovalcik, S. Dvorackova, J. Karasek, A. Hotar, Surface itegrity of hardeed bearig steel after millig, Mauf. Techol. Vol., 2, pp [9] M. Kasia, K. Vasilko, Experimetal verificatio of the relatio betwee the surface roughess ad the type of used tool coatig, Mauf. Techol. Vol. 2, 22, pp [] S. Al-Zubaidi, J. A. Ghai, C. H. C. Haro, Applicatio of artificial eural etworks i predictio tool life of PVD coated carbide whe ed millig of TI6aL4v alloy, It. J. of Mechaics. Vol. 6, No 3, 22, pp ISBN: