ACTA TECHNICA NAPOCENSIS

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1 231 TEHNIA UNIVERSITY OF UJ-NAPOA ATA TEHNIA NAPOENSIS Series: Applied Mthemtics, Mechnics, nd Engineering Vol. 61, Issue II, June, 2018 THE SEETION OF INDUSTRIA ROBOTS FOR MIITARY INDUSTRY USING AHP METHOD: A ASE STUDY Mihel SIMION, vini SOAIU, On GIURGIU, Silviu Mihi PETRIŞOR Abstrct: Given the multitude of industril robots mnufcturing compnies, the complex structurl configurtion nd the utomtion degree, the selection of industril robots in order to crry out specific tsk, is becoming more nd more difficult. In this reserch pper, the AHP method ws pplied to select the most fvorble configurtion of n industril robot tht must perform the technologicl process of AR welding of the trcked mini-robots housing, used in militry pplictions. For this purpose, severl industril robots mnufcturing compnies tht re producing industril robots with different technicl specifictions were tken into considertion. The study highlights the usefulness in pplying decisionmking methods in utomted technologicl processes, in order to fcilitte nd simplify the selection process of the industril robot, to obtin the best version of industril robot, from set of lterntives tht crry out one or more specific tsks. Key words: selection of industril robots, welding, militry industry, AHP method. 1. INTRODUTION The improved performnce of the industril robots ws determined by the fst development, in the lst yers, of informtion technology nd engineering science, mking them incresingly used in most common work res, such s: engineering, medicl domin, militry, mintennce, food industry, etc., s well s in industril environments nd in hzrdous environments such s: explosive, nucler, or qutic domin, nd mny others [1-8]. A wide rnge of industril robots is offered by the interntionl mrket, from lightweight robots, for which precision, repetbility nd high working speed re importnt requirements, respectively hevy duty industril robots for which high loding cpcity is required [9-10]. Thus, the selection of industril robots for specific field of work or for specific tsk becomes incresingly difficult, considering the structurl vritions of industril robots vilble on interntionl mrket. Selecting n industril robot for specific ppliction consists in identifying the robot configurtion, tking into ccount certin fctors (technicl, economic, mintennce), so the selected robot will correspond to the functionl requirements. In the selection process of industril robots, the objective (tngible) fctors tht cn be tking into considertion re: the control resolution, velocity, ccurcy, repetbility, lod crrying cpcity, degrees of freedom, mnipultor rech (rm, body nd wrist), mximum tip speed, memory cpcity nd supplier s service qulity [9,11]. The subjective (intngible) fctors re: relibility, progrming flexibility, mn mchine interfcing bility, mounting type, vendor s service qulity [9]. Selection of the best suited industril robot from mny vilble lterntives nd/or to rnking the entire set of lterntives is typicl Multi-riteri Decision-Mking (MDM) problem [12-13]. Ech decision tble in MDM methods consists of four min stges: setting up the globl objective, the lterntives, the criteri, the ttributes, finding out the reltive importnce of ech ttribute, estblishing the globl weight of ech lterntives, tking into ccount ll considered criteri.

2 232 For decisionl spects, mthemticl models hve been developed, bsed on some influence fctors, which llow the identifiction of the best solution, from techniclly, economiclly nd mintennce point of view, from wide rnge of finished products, like industril robots. The reserch focuse on selecting industril robots, in order to ccomplish certin tsk, by using different decision methods: Anlytic Hierrchy Process (AHP), Technique for Order Preference by Similrity to Idel Solution (TOPSIS), Vlse Kriterijumsk Optimizcij Kompromisno Resenje (VIKOR), Anlytic Network Process (ANP) nd Fuzzy models [1, 3-5, 14]. More studies regrding industril robots selection re given in [10-13, 15-20]. The im of this reserch pper ws to select the best vrint of n industril robot from 15 lterntives, tht cn perform the welding process. The tsk for the selected industril robot ws to perform the welding of the trcked minirobots for militry pplictions housing, while complying some requirements like: high speed, ccurcy, lrge rm rech, reduced dimensions of the robot s body nd rm, high flexibility nd reduced cost. For this purpose, the AHP method ws used to obtin the weights of the criteri tht influence the selection decision, nd lso to hierrchize the proposed industril welding robots (the lterntives). 2. MATERIA AND METHOD Anlyticl Hierrchy Process (AHP) is powerful multi-criteri decision-mking tool especilly in hierrchicl decision-mking where the decision problem is structured into components of different levels. Decision-mkers elicit pirwise comprisons, bsed on their vlue judgments, of the elements in the sme level, respecting n element in higher immedite level. The strength of the AHP consist in cpturing subjective judgments of decision-mkers nd integrting them into the decision-mking process [21]. According to [20-23] the min steps in computing the AHP method re: Step 1: onstructing of the model. Problem should be clerly defined, estblished the generl objective nd then decomposed into criteri nd sub criteri for ech studied lterntive. Subsequently, the hierrchicl tree would be relized, bsed on criteri nd sub criteri previously defined. The Generl Objective of this study ws to select the best lterntive of industril robot with AHP method, from set of industril robots. The tsk for the industril robots is to perform the welding of the trcked mini-robots for militry pplictions housing mde of 4 15 mm thickness high qulity steels. Thus, for this cse study were selected 15 industril robots (A 1 A 15) tht execute AR welding (the most commonly used metl welding process) from three mnufcturing compnies. Some of these 15 selected industril robots re not limited to AR welding, they cn lso perform PASMA nd/or MIG welding. From ech compny where chosen five AR welding industril robots, with different technicl specifictions. In figure 1 is illustrted the hierrchicl tree for this cse study. Fig. 1. The hierrchicl tree for the selection the best lterntive of industril robots for militry industry, where S..1, S..2 nd S..3 mens sub criteri level 1, 2 nd 3

3 233 The hierrchicl tree used for the selection of the best lterntive of industril robots for militry industry ws divided into three min criteri: 1 Technicl criterion, 2 Size nd dimensionl criterion, 3 Other criterion. The technicl criterion ( 1) is divided into sub-criteri: 1.1 Motion (with: (Speed of xis) grouped into Min nd Mx; (Rnge) grouped into Min nd Mx); 1.2 Repetbility; 1.3 Allowble loding moment (with: Wrist grouped into J4, J5 nd J6; Inerti grouped into J4, J5 nd J6). The size nd dimensionl criteri ( 2) is divided into following sub-criteri: 2.1 Pylod; 2.2 Robot mss nd 2.3 Robot rech (with V-rech nd H-rech). The 3 Other criteri, is grouped s following: 3.1 Power rte; 3.2 ost; 3.3 Flexibility (with: Mounting type grouped into: Floor, eiling; Angle nd Wll; Welding type, grouped into: AR, Plsm, MIG; Other pplictions, grouped into: Mteril Hndling nd No other ppliction. For this specific ppliction, the type of welding ws considered level 2 subcriterion (S..2) indicting the degree of flexibility of the industril robots from the welding process (criteri 3.3) point of view. In tble 1 (prt 1 3) re given the chrcteristics of the lterntives for ech criterion. riteri /Alterntives The chrcteristics of the lterntives for ech criterion (prt 1) A (Axis J2) 625 (Axis J6) 250 (AxisJ2) 720 (Axis J6) ± 0, A (Axis J1,2,3) 720 (Axis J6) 230 (Axis J2) 720 (Axis J6) ± 0, A (Axis J1) 630 (Axis J6) 250 (Axis J2) 720 (Axis J6) ± 0, A (AxisJ2) 1000 (AxisJ6) 245 (AxisJ2) 720 (AxisJ6) ± 0, A (AxisJ1,2) 630 (AxisJ6) 255(AxisJ2) 900 (AxisJ6) ± 0, A (Axis1,2) 700(Axis6) (Axis2) ±360 (AxisJT6) ± 0, A (Axis J3) 650 (Axis J6) (Axis J2) ±360 (AxisJT6) ± 0, A (Axis J3) 700 (Axis J6) (Axis J2) ±360 (AxisJT6) ± 0, A 9 300(Axis1,2,3) 740(xis6) (Axis3) ±360 (AxisJT6) ± 0, A (Axis1) 610(Axis6) (Axis2) ±360 (AxisJT6) ± 0, A (Axis2) 600 (Axis6) (Axis2) ±360 (Axis6) ± 0, A (Axis J2) 560 (Axis6) (Axis J2) ±200 (Axis6) ± 0, A (Axis J2) 610 (Axis6) (Axis J2) ±360 (Axis6) ± 0, A (AxisJ2) 630 (Axis J6) (Axis2) ±10 (Axis6) ± 0, A (Axis1,2) 520 (Axis6) ±150 (Axis4) ±200 (Axis6) ± 0, Tble The chrcteristics of the lterntives for ech criterion (prt 2). riteri /Alterntives A YES A YES A YES A YES A YES A YES A YES A YES A YES A YES A Not vilble YES A Not vilble YES A Not vilble YES A Not vilble YES A Not vilble YES

4 234 The chrcteristics of the lterntives for ech criterion (prt 3). riteri /Alterntives A 1 Yes Yes Yes Yes A 2 Yes Yes Yes Yes Yes Yes A 3 Yes Yes Yes Yes A 4 Yes Yes Yes Yes A 5 Yes Yes Yes Yes A 6 Yes Yes Yes A 7 Yes Yes Yes A 8 Yes Yes Yes A 9 Yes Yes Yes A 10 Yes Yes Yes A 11 Yes Yes Yes Yes A 12 Yes Yes Yes Yes A 13 Yes Yes Yes A 14 Yes Yes Yes Yes A 15 Yes Yes Yes References [24-27] Step 2: Generting the pirwise comprison mtrices. The reltive importnce for ech lterntive, criteri or sub criteri ws estblish by using Sty s nine-point scle (tble 2) to convert the verbl judgments in numericl quntities from 1 (equl importnce) to 9 (extreme importnce) [21]. Then building the pirwise comprison mtrices for ech lterntives, sub criteri nd criteri, where the ij (i,j = 1 n) element indicte the reltive importnce for lterntive i respecting the lterntives j using the reltion (1). A = M ii n1 = 1, ji = M n 2 1 ij, O ij 1n 2n, M nn 0. (1) Step 3: Developing normlized mtrix by dividing ech number from the column of the pirwise comprison mtrix by its column sum. Rting scle Definition Then, verging ech row of the normlized mtrix to obtin the reltive weight Wi (i=1 n) of the lterntive preferences respecting ech criterion/ sub-criterion. Step 4: onstructing the n dimensionl column vector, which represents the weighted sum of ech lterntive considered seprtely nd considering ech criterion: X = A W = M n M n 2 O 1n w1 c1 2n w 2 = c2.(2) M M M nn w n cn Step 5: Obtining the priority vectors (w) from the pirwise comprison mtrix (A) by solving n eigenvlue problem in the following eqution: A w = λ w (3) mx Step 6: lculting the consistency index (I), rndom inconsistency (RI) nd consistency rtio (R) using the reltions (4)-(6): Sty`s fundmentl scle [21] Explntion Tble 2 1 Equl importnce Two elements contribute eqully to the objective 2 Wek Between equl nd moderte 3 Moderte importnce Experience nd judgment slightly fvor one element over nother 4 Moderte plus Between moderte nd strong 5 Strong importnce Experience nd judgment slightly fvor one element over nother 6 Strong plus Between strong nd very strong 7 Very strong An element is fvored very strongly over nother 8 Very, very strong Between very strong nd extreme 9 Extreme importnce The evidence fvoring one element over nother is one of the highest possible order or ffirmtion

5 235 = ( λ n) /( n 1) (4) I mx RI = [ ( n 2) ]/ n (5) R = I / RI. (6) If R < 0.1, then the mtrix is consistent, nmely the vector of the weights is well determined [22, 23], thus the lgorithm is reiterted form step 2 until this condition is fulfilled. 3. RESUTS AND DISUSSION Becuse the mthemticl model obtined fter running the AHP lgorithm is complex, in tbles 3 5 re illustrted, s exmples, decision mtrix, normlized mtrix nd the results obtined for the generl objective, for criterion 2, respectively for sub criteri level 3 (S..3) Tble 3 Steps nd results obtined for the criterion level 3 (model). Decision mtrix A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9 A 10 A 11 A 12 A 13 A 14 A 15 A A A A A A A A A A A A A A A Decision mtrix A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9 A 10 A 11 A 12 A 13 A 14 A 15 A A A A A A A A A A A A A A A Results for Results for W X V λ I RI R W X V λ I RI R R<0.1 A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A TRUE FOR nd

6 236 Tble 4 Steps nd results obtined for the criterion 2 (model). Decision mtrix W X V λ I RI R R< Decision mtrix W X V λ I RI R R< TRUE TRUE Steps nd results obtined for the generl objective (model). Decision mtrix Normlized mtrix W X V λ I RI R Tble 5 R <0.1 TRUE In figure 2 is presented the locl weights of criteri from level 1 bsed on the generl objective. ocl weights for criteri level % 5.26% 47.37% Fig. 2. The locl weights of criteri from level 1 After nlysis with AHP method, results tht 1 (technicl criteri) nd 3 (other criteri) hve obtined the gretest priority (47.37 %), followed by 2 (size nd dimensionl criteri) with score (5.26 %). This mens tht in order to choose the best vrint of AR welding industril robot for the welding process of the trcked mini-robot for militry pplictions housing, it is necessry tht the technicl chrcteristics (like working speed, repetbility) to be fulfilled, s well s other chrcteristics like mounting type. From this AHP first level criteri nlysis, results tht for the welding process for trcked mini-robots it is necessry to cquire industril robots with high technicl performnces. The low vlue for 2, regrding the size of the industril robot, cn be explined by the fct tht the selection of the optiml vrint of robot is not constrined by the working spce, in militry industry spcious industril hlls re used. In figure 3 5 re illustrted the locl weights of criteri from level ocl weights for criteri level 2, riterion % 74.50% 5.46% Fig. 3. The locl weights of criteri from level 2, riterion 1 In figure 3 re illustrted the locl weights of criteri from level 2 with respect to technicl criteri ( 1), the sub-criteri 1.2 (repetbility) with vlue 74.5% hs the highest rnking, followed by sub-criteri 1.1 (motion) with 20 %, nd the lst rnking with 5.46 % corresponding to sub-criteri 1.3 (llowble loding). From this level 2 criteri clssifiction, results tht it is importnt for the welding robot to work repetbility in certin limits. For exmple, in militry industry, it is necessry to perform the spot welding in precise spot, or on

7 237 certin spot line, under identicl welding conditions. The evlutors hve tken into considertion, s well, the sub-criteri 1.1 (motion of the industril robot), in which it ws considered the speed of xis of joints of the robot, respectively the rnge of robot s rm fetures with high importnce in the welding process in the militry industry. ocl weights for criteri level 2, riterion % 5.26% 47.37% Fig. 4. The locl weights of criteri from level 2, riterion 2 In the cse of min criteri 2, the subcriteri 2.1 (pylod) nd 2.3 (robot rech) hve the highest priority (47.37 %), followed by 2.2 (robot mss) with only 5.26 %. Anlyzing the obtined weight, ensue tht it is necessry tht the vrint of industril robot to hve high loding cpcity, respectively mximum rm rech, to ensure n extensive workspce, in both verticl nd horizontl plne. For the lst min criteri ( 3), the sub criteri 3.2 (cost) nd 3.3 (flexibility) hve the sme weight (47.37 %), nd for 3.1 (power rte) hve obtined only 5.26 %. In this cse, both technic nd economic fctors re considered in the selection of the suitble industril robot for AR welding process in militry industry. ocl weights for criteri level 2, riterion % 47.37% 47.37% Fig. 5. The locl weights of criteri from level 2, riterion 3 Techniclly, the robot must offer high flexibility, nmely the possibility of reprogrmming for nother tsk in the militry industry (for exmple pick nd plce tsk), mounting the robot in different positions (like wll, ceiling, ngle), respectively setting vrious welding devices on the free end of the robot s rm for different welding techniques (MIG, PASMA, AR). On the other hnd, the economicl fctor is eqully importnt s the technicl fctor, becuse it is desirble tht the proper robot, with the best technicl performnce, it is bought t n ffordble price. In figure 6 re illustrted the locl weights of criterion from level % 35.00% 25.00% 15.00% 5.00% ocl weights for criteri level 3, riterion % 5.00% 5.00% 45.00% Fig. 6. The locl weights of criteri from level 3, riterion The chrt from figure 6 represents the distribution of the locl weights of criteri from level 3 of the sub-criteri (Mounting type). riteri (Floor) nd (Wll) hve erned the highest weights (45%) thus to perform the AR welding procedure of the trcked mini-robots housing, the chosen industril robots, beside stndrd mounting on the floor, hve lso the mounting option on the wll, where the robot`s bse is in the plne. Mounting the welding robot on the wll or on the lifting crne, mkes the end-effector (the welding hed) progrmble to execute complex pths in the work spce, by using the robot`s rm extension closest to its mximum opening, to cncel the singulrities in the kinemtic chin, or to obtin lrger work spce for the welding robot. In figure 7 it is shown the globl weights of lterntives considering ech criterion.

8 % 16.00% 14.00% 12.00% 8.00% 6.00% 4.00% 2.00% A 1 A 2 A 3 A 4 A 5 A 6 A 7 A 8 A 9 A 10 A 11 A 12 A 13 A 14 A 15 Globl weights 2.48% 10.09% 3.82% 15.98% 15.22% 5.05% 4.71% 5.34% 9.70% 5.37% 7.04% 7.09% 2.01% 2.76% 3.33% Fig. 7. Globl weights of lterntives tking into ccount ll the criteri Of the 15 industril robots lterntives nlyzed using the AHP method, tking into ccount ll the identified criteri nd sub criteri, respectively by the tngible nd intngible chrcteristics, it hs emerged tht the lterntives tht hve obtined the highest weight re: on the first rnk is A 4 with 16%, followed by A 5 ( 15%), nd then next lterntives (A 2 nd A 9) hve the sme vlue 10%. The digrm from Figure 8 represents the distribution of the locl weights of some criteri from level 1 for the well-rnked four lterntives A % 6.06% 18.92% 15.87% 8.27% A % 8.14% 2.96% 15.87% 44.92% A % 1.77% 18.92% 15.87% 1.53% A % 1.77% 18.92% 5.24% 8.27% Fig. 8. The distribution of the locl weights of some criteri from level 1, for the lterntives A4, A5, A2, A9 From globl nlysis of the digrm presented in Figure 8, it ws found tht the lterntive A 4 obtined the fvorble weight to the criteri 1.2 (Repetbility), 2.2 (Robot mss), 3.1 (Power rte), compred to the other three lterntives (A5, A2, A9). oclly, the criteri 1.2 hs obtined the highest score (30.42%), followed by 2.2 with 18.93% nd the lowest scores ws obtined by 3.2 (ost) with 8.27% nd 2.1 (Pylod) with 6.06%. It cn be noticed tht in cse of lterntive A5 (rnked in position 2 fter nlysis with AHP), the criteri ost (3.2) hs the highest weight (44.92%), then 3.1 (15.8%), 3.2 (8.14%), 2.2 (2.96%). Although, for both lterntives A4 nd A5, the technicl criteri hve comprble vlues, but from the cost point of view, the lterntive A4 hs obtined the lower score, which mkes it to be the fvorble choice for the proposed ppliction. 4. ONUSIONS In literture it is proven tht the AHP technique is powerful tool for selection problem solving in order to chieve the best solution from multitude of lterntives for specific gol (the generl objective), tking into ccount tngible nd intngible fctors, respectively vrious criteri nd sub criteri. The pplicbility of AHP technique is lrge, being successfully used in different domins with different pplictions, such s the militry industry. In this reserch pper, is presented cse study for solving decisionl problem using the AHP method. With AHP method ws obtined the best vrint of AR welding industril robot from 15 lterntives bsed on estblished generl objective, respectively tngible nd intngible

9 239 fctors (criteri nd sub criteri level 1, 2 nd 3). The industril robot tsk considered for this cse study is to crry out the AR welding of the metl components from mechnicl housing structure of the trcked mini-robots for militry pplictions. For AHP method, the criteri nd sub criteri re defined considering both technicl nd economic fctors. Even if for this specific ppliction the welding procedure is AR, for AHP ws tken into considertion the welding type s sub criteri level 2. Thus, the chosen industril robot cn be lso used for welding of other types of mterils, such s luminum, incresing the degree of flexibility of the chosen robot. Also, with AHP method ws obtined the rnking of the lterntives by globl weights of lterntives considering ech criterion. In the cse of the technicl criteri ( 1.2, 2.1, 2.2, 3.1) the lterntives A 4, A 5 nd A 9 hve obtined the highest scores, but from the cost spect, the lterntive A 5 chieved the highest score nd lterntive A 2 the smllest vlue (1.53%). This expresses the fct tht the industril robot corresponding to the lterntive A 5 hs good technicl chrcteristics t n cceptble cost, which mkes it good choice for the chosen ppliction. Future reserch will be performed using the AHP method results by pplying more complex decision-mking process, like AHP TOPSIS in fuzzy environment. Fuzzy TOPSIS is employed to determine the priorities of the lterntives bsed on the weights obtined with AHP method. 8. REFERENES [1] Özgüler, Ș., Güneri, A.F., Gülsün, B., Yilmz, O. Robot selection for flexible mnufcturing system with AHP nd TOPSIS methods. 15th Interntionl Reserch/Expert onference Trends in the Development of Mchinery nd Associted Technology September, Prgue, zech Republic [2] Dneshmnd, M., Bilici, O., Bolotnikov, A., Anbrjfri, G. Medicl robots with potentil pplictions in prticiptory nd opportunistic remote sensing: A review. Robotics nd Autonomous Systems, 2017 [3] Nzmul Hud, M., Yu. H., ng, S. Robots for minimlly invsive dignosis nd intervention. Robotics nd omputer-integrted Mnufcturing, vol 41, pp , [4] Steopn, M., Şteopn, A., oste, A. Mobile pltform for indoor use. Act Technic Npocensis Series: Applied Mthemtics nd Mechnics Vol. 55, Issue I, pp , [5] Bicn, O., Isps, V., Steopn, M., Design of mobile micro-robot for detecting metl objects. 1st Interntionl onference on Qulity nd Innovtion in engineering nd Mngement, 2011 [6] Aoym, H., Ishikw, K., Seki, J., Okmur, M., Ishimur, S., Stsumi, Y. Development of Mine Detection Robot System. Interntionl Journl of Advnced Robotic Systems, volume 4, number 2, [7] yons, D. M., Arkin, R.., Jing, S., O brien, M., Tng, F., Tng, P. Performnce Verifiction for Robot Missions in Uncertin Environments. Robotics nd Autonomous Systems, [8] Qi, Y., Tng, D., Wng, J. Spce robot ctive collision voidnce mneuver under thruster filure. Aerospce Science nd Technology, volume 67, pp , [9] Thriri, F., Dwl, S.Z.B. Integrted decision mking nd optimiztion model for robot technology from plnning to implementtion. Proceedings of the 41st Interntionl onference on omputers & Industril Engineering, os Angeles, liforni, pg , [10] Birgi, B., Dey, B., Srkr, B., Snyl, S. A novel multiplictive model of multi criteri nlysis for robot selection. Interntionl Journl on Soft omputing, Artificil Intelligence nd Applictions (IJSAI), volume 1, number 3, pp. 1-9, [11] Gorbe, D., Pwr, D., Pwr, N.. Selection of industril robots using omplex Proportionl Assessment method. Americn Interntionl Journl of Reserch in Science, Technology, Engineering & Mthemtics, pp , [12] Athwle, V. M., htterjee, P., hkrborty, S. Selection of industril robots using ompromise Rnking Method. Proceedings of the 2010 Interntionl onference on Industril Engineering nd Opertions Mngement, Jnury 9 10, Dhk, Bngldesh, [13] Adkne, R. V., Nrkhede, A.R. Multi Attribute Decision Mking: A Tool for Robot Selection. Interntionl Journl of Engineering Development nd Reserch, volume 2, number 1, pp , [14] Krimird, M. A subjective-objective decision mking in fuzzy environment. World of Sciences Journl, Austri, 2013.

10 240 [15] Hleh, H., Nezu, K. A multi-criteri pproch to robot selection for computer integrted mnufcturing system. 8th IEEE Interntionl onference on Intelligent Engineering Systems, vol 151, pp [16] htterjee, P., Mondl, S., hkrborty, S. A comprtive study of preference dominnce-bsed pproches for selection of industril robots. Advnces in Production Engineering & Mngement, vol 9, no 1, pp. 5-20, [17] Thriri, F., Th, Z. riticl success fctors for technology selection specificlly ROBOTS. Journl of Business Mngement nd Economics vol 2, nr 3, pp , [18] Ro Venkt, R., Pdmnbhn, K.K. Selection, identifiction nd comprison of industril robots using digrph nd mtrix methods. Robotics nd omputer-integrted Mnufcturing, vol. 22, pp , [19] Athwle, V. M., hkrborty, S. A comprtive study on the rnking performnce of some multicriteri decision-mking methods for industril robot selection. Interntionl Journl of Industril Engineering omputtions, volume 2, pp , [20] Bhttchry, A., Srkr, B., Mukherjee, S.K. Integrting AHP with QFD for robot selection under requirement perspective. Interntionl Journl of Production Reserch, volume 43, number 17, pp , [21] stillo.n., D. F. K., Gitgno F.T., oo.a., Pcns S.M., Toroy N., Ocmpo., Si., Ocmpo.O. Approprite criteri set for personnel promotion cross orgniztionl levels using nlytic hierrch process (AHP). Interntionl Journl of Production Mngement nd Engineering, volume 5, number 1, pp , [22] Pinto, D., Shresth, S., Bbel, M.S., Ninswt, S. Delinetion of groundwter potentil zones in the omoro wtershed, Timor este using GIS, remote sensing nd nlytic hierrchy process (AHP) technique. Appl. Wter Science, vol. 7, pp , [23] Achry, V., Shrm, S.K., Gupt, S.K. Anlyzing the fctors in industril utomtion using nlytic hierrchy process. omputers nd Electricl Engineering, pp. 1-10, [24] Fnuc Robots, eric.com/home/industril-solutions/mnuf cturing-pplictions/spot-welding-robots. [25] Robot Worx, pplictions/rc-welding. [26] Yskw, /spot-welding#robots. [27] Kwsky, /en1/pplictions/robotic-rc-welding. SEETAREA ROBOTIOR INDUSTRIAI IN INDUSTRIA MIITARĂ FOOSIND METODA AHP: STUDIU DE AZ Abstrct: Avnd in vedere multitudine compniilor cre produc roboti industrili, configurți structurlă complexă și grdul de utomtizre l roboților industrili, selectre roboților industrili pentru îndeplini o numită srcină devine din ce în ce mi dificilă. În cestă lucrre de cerectre, s- plict metod AHP pentru select ce mi fvorbilă configurție unui robot industril cre trebuie să efectueze sudre AR crcselor miniroboţilor şenilţi utilizţi în plicţii militre. În cest scop, u fost lute în considerre mi multe compnii cre produc roboți industrili cu diferite specificţii tehnice. Studiul evidențiză utilitte plicării metodelor de lure deciziilor în procesele tehnologice utomtizte, pentru fcilit și simplific procesul de selecție robotului industril, pentru obține vrint potrivită robotului industril dintr-un set de lterntive, cre relizeză un su mi multe srcini specifice. Mihel SIMION, Ph.D, Eng, ecturer, Technicl University of luj-npoc, Mechnicl Engineering Deprtment, E-mil: mihel.simion@rezi.utcluj.ro, No , Muncii Blvd., , luj-npoc vini SOAIU, Ph.D, Eng, ecturer, Technicl University of luj-npoc, Mechnicl Engineering Deprtment, E-mil: lvini.socciu@termo.utcluj.ro, No , Muncii Blvd., , luj-npoc On GIURGIU, Ph.D, Eng, Techer Assistnt, Technicl University of luj-npoc, Mechnicl Engineering Deprtment, E-mil: on.giurgiu@termo.utcluj.ro, No , Muncii Blvd., , luj-npoc Silviu Mihi PETRIŞOR, Ph.D, Eng, Associte Professor, Nicole Bălcescu nd Forces Acdemy, Technicl Science Deprtment, E-mil: robmilcp@gmil.com, No. 3-5, Revoluţiei Street, , Sibiu