A non-parametric model was proposed for modelling the influence of different technological and chemical parameters on the mechanical properties of the 6082 aluminium alloys during the hot extrusion process with a special consideration of human factor. It was shown that human factor (influence of process engineers) was important and that it could be efficiently modeled and taken into account by the proposed Conditional Average Estimator (CAE) method. Production might be improved (optimized) by a proper education and/or by eliminating critical process engineers. It was found that the highest values for elongation and yield stress did not coincide with the range of the most frequent combinations of input parameters.Predložen je neparametarski model za modeliranje utjecaja različitih tehnoloških i kemijskih parametara na mehanička svojstva aluminijske legure 6082 tijekom istiskivanja na toplo s posebnim razmatranjem na ljudskom faktorom. Pokazano je, da je ljudski faktor (utjecaj proces inženjera) važan i da se može efikasno uzeti u obzir s predloženom CAE metodom. Proizvodnja se može poboljšati (optimirati) uz odgovarajuće obrazovanje i/ili uklanjanje kritičnih proces inženjera. Zapaženo je, da se najviše vrijednosti produljenja i naprezanja tečenja ne podudaraju s područjem najčešćih kombinacija ulaznih parametara

G. Kugler

I. Peruš

M. Fazarinc

P. Fajfar

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I. PERU[, M. FAZARINC, G. KUGLER, P. FAJFARON THE INFLUENCE OF HUMAN FACTOR ON MECHANICALPROPERTIES IN ALUMINIUM HOT EXTRUSION PROCESSReceived – Prispjelo: 2009-02-06Accepted – Prihva}eno: 2009-03-20Original Scientific Paper – Izvorni znanstveni radINTRODUCTIONIt is well known that various parameters influencethe mechanical properties of aluminium alloys (i.e.elongation, yield stress, tensile strength) 1, 2. Next tochemical composition that is widely recognized to bethe most influential parameter 1, 3, also other processparameters, i.e. casting temperature, casting rate, ho-mogenization, extrusion ratio, ram speed, temperaturein different phases of melt preparation, etc. as well ashuman factor influence the scatter of obtained mechani-cal properties of final product. Various influences ofprocess parameters (i.e. geometry of extruded section)4, 5 on mechanical properties were examined usingsimple statistical methods 6. The majority of examina-tions deal with the comparison of different parameters inone process 6-11, but the analysis taking into accountthe entire process course are very rare in the availablereferences 12. The latter usually exhibit complex rela-tionships between parameters and final properties ofaluminium alloys. Especially the human factor has notbeen examined so far as an equal part to the other tech-nological parameters and chemical composition. But itis well known from experiences that human factor mayhave a large influence on properties of the final product.Therefore analysis of mutual influence of several in-put parameters, including human factor, on mechanicalproperties of extruded sections of the AA6082 alloy hasbeen presented in this paper. Thus the CAE method thatcould be treated as a special type of probabilistic neuralnetwork 13, 14 was employed.CAE METHOD AS A MATHEMATICALTOOL FOR MODELLING MATERIALPROPERTIESDetailed description of the CAE method from the en-gineering point of view is given in Peru{ et al. (2006)15. For better understanding and further explanations,the basic equations for determining any mechanicalquantity in the hot extrusion process, i.e. yield stress,are repeated here: ,q q A AaannNn nniiN 11andMETALURGIJA 49 (2010) 2, 87-90 87A non-parametric model was proposed for modelling the influence of different technological and chemical pa-rameters on the mechanical properties of the 6082 aluminium alloys during the hot extrusion process with aspecial consideration of human factor. It was shown that human factor (influence of process engineers) wasimportant and that it could be efficiently modelled and taken into account by the proposed Conditional Avera-ge Estimator (CAE) method. Production might be improved (optimized) by a proper education and/or by elimi-nating critical process engineers. It was found that the highest values for elongation and yield stress did notcoincide with the range of the most frequent combinations of input parameters.Key words: hot deformation, neural network, human factor, AA6082 alloy, CAE methodO utjecaju ljudskog faktora na mehani~ka svojstva u istiskivanju aluminija na toplo. Predlo`en jeneparametarski model za modeliranje utjecaja razli~itih tehnolo{kih i kemijskih parametara na mehani~ka svoj-stva aluminijske legure 6082 tijekom istiskivanja na toplo s posebnim razmatranjem na ljudskom faktorom. Po-kazano je, da je ljudski faktor (utjecaj proces in`enjera) va`an i da se mo`e efikasno uzeti u obzir s predlo`enomCAE metodom. Proizvodnja se mo`e pobolj{ati (optimirati) uz odgovaraju}e obrazovanje i/ili uklanjanje kri-ti~nih proces in`enjera. Zapa`eno je, da se najvi{e vrijednosti produljenja i naprezanja te~enja ne podudaraju spodru~jem naj~e{}ih kombinacija ulaznih parametara.Klju~ne rije~i: deformacija na toplo, neuralne mre`e, ljudski faktor, legura AA6082, CAE metodaISSN 0543-5846METABK 49(2) 87-90 (2010)UDC – UDK 620.192.47:004.032.26:628.802:669.715:004.0=111I. Peru{, M. Fazarinc, G. Kugler, P. Fajfar, Faculty of Natural Science andEngineering, University of Ljubljana, Ljubljana, Sloveniaawb bwn D DnliD 	12 221221( )exp( )/(1)In the above equations q is an estimate of yield stress,qn is the same output variable related to the n-th modelvector in the database, N is the number of model vectorsin the database, bnl is the l-th input variable of the n-thmodel vector in the database, and bl is the l-th input vari-able related to the prediction vector. The model vector isdefined on the basis of industrial measurements. D is thenumber of input variables that define the dimension ofthe sample space. Note that equation for an requires theinput parameters to be normalized, generally in therange between 0 and 1, if the same width w of theGaussian function for all the input variables is to beused. An intermediate result in the computational pro-cess is essential in modelling the influence of humanfactor in the presented study. This intermediate result isgiven by the parameter  that represents a measure ofhow the influence of all the model vectors in the data-base is spread over the sample space. It is strongly de-pendant on the smoothing parameter w and is defined as:11NannN (2)It is used for detection of possible less accurate pre-dictions (indicated by small values of ) due to the datadistribution in the database and due to local extrapola-tions outside the data range. Moreover, in case of humanfactor, it helps to detect the most frequent range of pa-rameters of the hot extrusion process used by processengineers.The average prediction error E1 for yield stress thatdetects the quality of the prediction is determined as:Eq Nq qn nnN1121 1  ( ) (3)where q is the mean experimental yield stress, and qnand qn are the n-th estimated and the experimental yieldstress, respectively.INDUSTRIAL DATABASE ANDINPUT/OUTPUT PARAMETERSSix different industrial databases that describe theentire course of the hot extrusion process of the 6082 al-loys were created by the process engineers duringone-year period. They include data on mechanical andcasting parameters, parameters of homogenization,technological and analytical parameters during the ex-trusion process, and the quality and quantity of scrapmaterial. For each extruded section 84 different parame-ters were measured. Based on the work order for the fi-nal product that was the key parameter of all the six da-tabases, an effective database that was used in this studywas created. Due to human errors during the data inputonly data for 3969 sections were usable (approx. 6 %) ofall the initial data of 65 536 extruded sections. Otherdata were either missing or were not correctly put in.In order to demonstrate the influence of human fac-tor only few input parameters were selected. As alreadymentioned, the chemical composition had however thegreatest influence on final properties. Only six chemicalelements (Fe, Si, Mn, Mg, Cu, and Cr) that characterisethe 6082 alloy next to the aluminium content were con-sidered as input parameters. The extrusion ratio and ramspeed were less influential, but nevertheless important.Human factor was taken into account as the influence ofprocess engineers, indicated by discrete values as com-ponents of model vectors.Two output parameters were selected as a representa-tion of mechanical properties of the 6082 alloy, i.e. elon-gation and yield stress. Since there was a strong correla-tion between the yield stress and the tensile strength, onlyyield stress was thus chosen as an output parameter.RESULTS AND DISCUSSIONInfluence of different parameters on the mechanicalproperties of the 6082 alloys was indicated by the termE1 (Eq. (3)), described in the previous section. Figure 1shows the comparison between the predicted and the ex-perimental values for yield stress. It was evident that therelation between the extrusion ratio plus ram speed,when taking into account the influence of process engi-neers, and the yield stress was poor (Figure 1.a) whereasthe corresponding average prediction error El was large.When the influence of chemical composition was takeninto account instead of the influence of process engi-neers, a much better relation was obtained (Figure l.b)and the average prediction error E1 was reduced.Finally, when all the input parameters were taken intoaccount (Figure l.c), prediction improved and the errorE1 was further reduced. The same observation could benoticed also for the linear fit that approached the linepredicted value = measured value which defined the ex-act prediction.The difference between the Figures. 1.b and 1.c wasattributed to the influence of process engineers. It amoun-ted 6 % in terms of the average prediction error E1. Theinfluence of process engineers was much smaller thanthat of chemical composition which amounted approxi-mately 35 % and 43 % for the elongation and the yieldstress, respectively. Nevertheless, the results showed thatinfluence of process engineers could not be neglected.In order to assess the influence of process engineersquantitatively, the yield stresses as a function of ramspeeds and extrusion ratios were analysed. Figs. 2 and 3present a comparison of the results obtained by the CAEmethod for one »highly efficient« (Figures 2.b and 3.b)and for one »less efficient« (Figures 2.c and 3.c) processengineer as well as the results where influence of pro-88 METALURGIJA 49 (2010) 2, 87-90I. PERU[ et al.: ON THE INFLUENCE OF HUMAN FACTOR ON MECHANICAL PROPERTIES IN ALUMINIUM...cess engineers was excluded (Figures 2.a and 3.a) (onlyram speed and extrusion ratio were taken into account).The results are presented with the equal-value linesthat represent constant values of the predicted elonga-tion and the yield stress (Figures 2 and 3). As additionalinformation, equal-value lines of the  -values (Eq. (2))that are related to the reliability of predictions have beenplotted as thinner lines with smaller fonts. The largerwas the  -value, the more reliable were the results, andvice versa. In addition,  -values indicated a range of pa-rameters within which the major part of the hot extru-sion process took place.Figures 2.a and 3.a show that optimal (i.e. highest)values for elongation and yield stress were not obtainedwith the same values of ram speed and extrusion ratio. Ingeneral, larger elongation values could be obtained withmedium values of extrusion ratio and lower ram speeds(approx. 18 and 5 to 15 mm/s, respectively), and higherMETALURGIJA 49 (2010) 2, 87-90 89I. PERU[ et al.: ON THE INFLUENCE OF HUMAN FACTOR ON MECHANICAL PROPERTIES IN ALUMINIUM...(a) extrusion ratio + ram speed + (b) extrusion ratio + ram speed + (c) extrusion ratio + ram speed +process engineers chemical composition chemical composition + process engFigure 1. Influence of different groups of parameters on the yield stress (wn=0,1). Diagonal black line indicates exactprediction while dashed line indicates linear fit.(a) process engineers excluded (b) “highly efficient” process eng. (c) “less efficient” process eng.Figure 2. Influence of ram speed and extrusion ratio on the elongation represented by lines of equal values. The equal-value lines are also indicated (thin lines).(a) process engineers excluded (b) “highly efficient” process eng. (c) “less efficient” process eng.Figure 3. Influence of ram speed and extrusion ratio on the yield stress represented by lines of equal values. The equal-value lines are also indicated (thin lines).yield stresses with medium values of extrusion ratio andram speeds (approx. 15 and 15 mm/s, respectively).Note that thinner lines indicated two main areas of ramspeeds and extrusion ratios: (a) low extrusion ratio andhigh ram speed, and (b) medium extrusion ratio and me-dium ram speed.Figures 2.b and 3.b show the same functional rela-tionships as presented in Figures 2.a and 3.a with the ex-ception that the results for »highly efficient« process en-gineer are presented in Figures 2.b and 3.b while the re-sults for »less efficient« process engineer are presentedin Figures 2.c and 3.c. In general, the same trends couldbe observed for all the process engineers. Some differ-ences could be found only in details and in the parame-ter's range (see the  equal-value lines in Figures 2.b and3.b compared to Figures 2.c and 3.c). It could be noticedalso that the results for »highly efficient« process engi-neer (Figures 2.b and 3.b) matched very well with thegeneral influence shown in Figures 2.a and 3.a wherehuman factor was excluded from the analysis. On theother hand, a »less efficient« process engineer (Fig-ures 2.c and 3.c) achieved typically the best results for adifferent range of input parameters than a »highly effi-cient« one, and they were worse than those with the»highly efficient« process engineer. It was interestingthat a »less efficient« process engineers worked lessthan the »highly efficient« one (see the  density ofequal-value lines). Though not presented here, it had tobe mentioned that some high yield stresses were ob-tained by one of the process engineers at a low extrusionratio and a high ram speed, and high elongation values ata relatively low ram speed and medium extrusion ratioby another process engineer.It is important to emphasize that the optimal (high-est) values for elongation and yield stress did not coin-cide with the optimal  -values. There were two reasonsfor that: (1) the optimal values for elongation and yieldstress could not be obtained with the same values of ramspeed and extrusion ratio; (2) since the production pro-cess was not yet optimized, »trial and error« procedurewas evident and shifted both peaks apart.CONCLUSIONSA non-parametric model was proposed for modellingthe influence of different technological and chemical pa-rameters on the mechanical properties of the 6082 alloysduring the hot extrusion. Special consideration was givento the influence of human factor. It was found that humanfactor could be efficiently modelled and taken into ac-count by the proposed CAE method. From the obtainedresults the following conclusions might be derived:1. Human factor (influence of process engineers) isimportant and in general can not be neglected dueto oscillation of mechanical properties and actualdata collection.2. The highest (»peak«) values for elongation andyield stress do not coincide with the »peaks« ofthe  values (the area of the most frequent com-bination of input parameters).3. Lack of optimization is clearly recognized fromthe mutual shifts of both peaks.4. The optimal values for elongation and yieldstress can not be obtained with the same values ofram speed and extrusion ratio.It was shown that production could be improved byproper education and/or by eliminating critical processengineers. Further research, if the industry would ex-press such an interest, would be aimed to determinemore accurately the influence of individual process en-gineers. Namely, some input parameters in the hot ex-trusion process are mutually interdependent and thoserelations can not be influenced by process engineers.REFERENCES1 Jin Man, Li Jing, Shao Guang Jie, Journal of Alloys andCompounds, 437 (2006), 146-150.2 M.S. Ozerdem, S. Kolukisa, Materials and Design, 30(2009), 764-769.3 J.S. Robinson, T.Y. Liu, A.K. Khan, M.J. Pomeroy, Journalof Materials Processing Technology, (2008), 10 pages, ava-ilable on-line.4 H.A. Clausen, T. Tryland, S. Remseth, Materials and De-sign, 22 (2001), 267-275.5 I. Grabec, W. Sachse, Synergetics of Measurement, Predic-tion and Control, Springer-Verlag, 1997.6 S.H. Hsiang, Y.W. Lin, Journal of Materials ProcessingTechnology, 192-193 (2007), 292-299.7 S. Norasethasopon, Journal of Materials Processing Tec-hnology 203 (2008), 137-146.8 T. Tryland, O.S. Hopperstad, M. Langseth, Materials andDesign, 21 (2000), 477-492.9 M. Knap, J. Falkus, A. Rozman, J. Lamut, (In Press), RMZ -Materials and Geoinvironment, 2 (2009).10 M. Knap, J. Falkus, A. Rozman, J. Lamut, Archives of Me-tallurgy and Materials, 53 (2008) 3, 761-766.11 D. ^esnik, V. Bratu{, B. Kosec, J. Rozman, M. Bizjak, RMZ- Materials and Geoinvironment, 55 (2008) 2, 163-172.12 Z. Jan~íková, V. Roubí~ek, D. Juchelková, Metalurgija, 47(2009) 1, 339-342.13 D.F. Specht, IEEE Transactions on Neural Networks, 2(1991), 568-576.14 M. Ter~elj, R. Turk, G. Kugler, I. Peru{, Computational Ma-terials Science, 42 (2008) 4, 625-637.15 I. Peru{, P. Fajfar, K. Poljan{ek, Earthquake engineeringand structural dynamics, 35 (2006) 12, 1453-1470.Note: Responsible for English language is A. Paulin.90 METALURGIJA 49 (2010) 2, 87-90I. PERU[ et al.: ON THE INFLUENCE OF HUMAN FACTOR ON MECHANICAL PROPERTIES IN ALUMINIUM...

O utjecaju ljudskog faktora na mehanička svojstva u istiskivanju aluminija na toplo

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