ANALYSIS OF ALUMINIUM PROFILE MANUFACTURING INDUSTRIES BY USING TOPSIS METHOD

Transcription

1 Int. J. ech. Eng. & Rob. Res Prashant B alve and Shrkant Jachak 2014 Research Paper ISSN Vol. 3 No. 3 July IJERR. All Rghts Reserved ANALYSIS OF ALUINIU PROFILE ANUFACTURING INDUSTRIES BY USING TOPSIS ETHOD Prashant B alve 1 * and Shrkant Jachak 1 *Correspondng Author: Prashant B alve pbmalve@redffmal.com Ths paper deals wth the selecton of optmum manufacturng frm for the manufacturng of Alumnum profle wth the help of AD (mult-attrbute decson makng) technque. Varous choces of ndustres are generally avalable that are producng the same type of the product. Selecton of ndustry s done based on varous alternatves and attrbutes such as product cost cycle tme weght of profle percentage converson of metal number of tral run number of work staton etc. For the research data from four Alumnum profle manufacturng ndustres are selected. The product of these ndustres are same.e. Alumnum profle. Consderng sxteen dfferent attrbutes whch affects the performance of the ndustry. Selecton s done by usng Technque for Order Preference by Smlarty to Ideal Soluton (TOPSIS) method. Keywords: Alumnum profle ult-attrbute Decson akng (AD) Technque for Order Preference by Smlarty to Ideal Soluton (TOPSIS) INTRODUCTION Introducton to Decson akng n the anufacturng Envronment anufacturng s the backbone of any ndustralzed naton. Its mportance s emphaszed by the fact that as an economc actvty t comprses approxmately 20 to 30% of the value of all goods and servces produced. A country s level of manufacturng actvty s drectly related to ts economc health. In general the hgher the level of manufacturng actvty n a country the hgher the standard of lvng of ts people. anufacturng can be defned as the applcaton of mechancal physcal and chemcal processes to modfy the geometry propertes and/or appearance of a gven startng materal n the makng of new fnshed parts or products. Ths effort ncludes all ntermedate processes requred for the producton and ntegraton of a product s 1 Department of echancal EngnnerngYeshwantrao Chavan College of Engneerng Hngna Road Wanadongr Nagpur Inda. 195

2 Int. J. ech. Eng. & Rob. Res Prashant B alve and Shrkant Jachak 2014 components. The ablty to produce ths converson effcently determnes the success of the company. The type of manufacturng performed by a company depends on the knds of products t makes. anufacturng s an mportant commercal actvty carred out by companes that sell products to customers. In the modern sense manufacturng nvolves nterrelated actvtes that nclude product desgn and documentaton materal selecton process plannng producton qualty assurance management and marketng of products. These actvtes should be ntegrated to yeld vable and compettve products The selecton decsons are complex as decson makng s more challengng today. Necessary condtons for achevng effcent decson makng consst n understandng the current and upcomng events and factors nfluencng the whole manufacturng envronment n explorng the nature of decson-makng processes and the reach of dfferent typologes of methods and technques and fnally n structurng approprately the decson-makng approach based on a wde range of ssues related to manufacturng systems desgn plannng and management. LITERATURE REVIEW The effectve optmzaton of machnng process parameters affects dramatcally the cost and producton tme of machned components as well as the qualty of the fnal products. Ths paper presents optmzaton aspects of a mult-pass mllng operaton. The objectve consdered s mnmzaton of producton tme (.e. maxmzaton of producton rate) subjected to varous constrants of arbor strength arbor deflecton and cuttng power. Varous cuttng strateges are consdered to determne the optmal process parameters lke the number of passes depth of cut for each pass cuttng speed and feed. The upper and lower bounds of the process parameters are also consdered n the study. The optmzaton s carred out usng three non-tradtonal optmzaton algorthms namely Artfcal Bee Colony (ABC) Partcle Swarm Optmzaton (PSO) and Smulated Annealng (SA). An applcaton example s presented and solved to llustrate the effectveness of the presented algorthms. The results of the presented algorthms are compared wth the prevously publshed results obtaned by usng other optmzaton technques. Several knds of metallc bpolar plates for PEFCs are currently beng developed n order to meet the demands of cost reducton stack volume lower weght and enhanced power densty. Ths work shows an applcaton of the Technque of rankng Preferences by Smlarty to the Ideal Soluton (TOPSIS) ultple Attrbute Decson akng (AD) method for solvng the materal selecton problem of metallc bpolar plates for Polymer Electrolyte Fuel Cell (PEFC) whch often nvolves multple and conflctng objectves. The proposed methodologcal tool can ad the materal desgner n the modelng and selecton of sutable materals accordng to a set of predefned crtera. After ntroducng the theoretcal background a case study s presented for the materal selecton of a bpolar plate n a PEFC. A lst of all possble choces from the best to the worst materals s obtaned by takng nto account all the materal selecton crtera ncludng the cost of producton. A userdefned coden athematca has been 196

3 Int. J. ech. Eng. & Rob. Res Prashant B alve and Shrkant Jachak 2014 developed to facltate the mplementaton of the method. It was shown that the optmum value of each crteron s ndependent of other crtera values (.e. no nteracton s allowed). The proposed approach may be appled to other problems of materal selecton of fuel cell components. An ever ncreasng varety of materals s avalable today wth each havng ts own characterstcs applcatons advantages and lmtatons. In choosng the rght materal there s not always a sngle defnte attrbute of selecton and the desgners and engneers have to take nto account a large number of materal selecton attrbutes. Ths paper presents a logcal procedure for materal selecton for a gven engneerng applcaton. The procedure s based on an mproved compromse rankng method consderng the materal selecton attrbutes and ther relatve mportance for the applcaton consdered. Two examples are ncluded to llustrate the approach. IDENTIFICATION OF PROBLE There are dfferent types of manufacturng ndustres. Four dfferent Alumnum profle manufacturng ndustres are selected whch are stuated around 40 km radus of Nagpur. These are Ama Extruson New Era Extruson Falcon Extruson and Pennar Alumnum Pvt. Lmted varous attrbutes and alternatves from these ndustres are selected. The attrbutes are select wth there producton lne locaton of plant cycle tme etc. In alumnum profle ndustry tme requred for every operaton s very mportant to get qualty product and wastage should be mnmum. TOPSIS s a comprehensve structured frame work. It s used for selectng the best ndustry by comparng the varous alternatves and attrbutes n t. ETHODOLOGY AND CALCULATION We have used one methodologes to optmze and selectng of Alumnum profle manufacturng ndustres. They are Technque for Order Preference by Smlarty to Ideal Soluton (TOPSIS ethod). TOPSIS ETHOD The TOPSIS method was developed by Hwang and Yoon (1981). Ths method s based on the concept that the chosen alternatve should have the shortest Eucldean dstance from the deal soluton and the farthest from the negatve deal soluton. The deal soluton s a hypothetcal soluton for whch all attrbute values correspond to the maxmum attrbute values n the database comprsng the satsfyng solutons. The negatve deal soluton s the hypothetcal soluton for whch all attrbute values correspond to the mnmum attrbute values n the database. TOPSIS thus gves a soluton that s not only closest to the hypothetcally best that s also the farthest from the hypothetcally worst. PROCEDURE The man procedure of TOPSIS ETHOD s as follows: Step 1: The frst step s to determne the objectve and to dentfy the pertnent evaluaton attrbutes. Step 2: Ths step represents a matrx based on all the nformaton avalable on attrbutes. 197

4 Int. J. ech. Eng. & Rob. Res Prashant B alve and Shrkant Jachak 2014 Ths matrx s nothng but the decson table. Each row of ths matrx s allocated to one alternatve and each column to one attrbute. Therefore an element m j of the decson table D gves the value of the j th attrbute n orgnal real values that s non-normalzed form and unts for the th alternatve. In the case of a subjectve attrbute (.e. objectve value s not avalable) a ranked value judgment on a scale s adopted. Once a subjectve attrbute s represented on a scale then the normalzed values of the attrbute assgned for dfferent alternatves are calculated n the same manner as that for objectve attrbutes. Step 3: Obtan the normalzed decson matrx R j m j 1 2 mj 1/ 2 Step 4: Decde on the relatve mportance (.e. weghts) of dfferent attrbutes wth respect to the objectve. A set of weghts w =1 may be decded upon. Step 5: Obtan the weghted normalzed matrx V j (for j = 1 2 ) such that w. Ths s done by the multplcaton of each element of the column of the matrx R j wth ts assocated weght w j. Hence the elements of the weghted normalzed matrx V are expressed as: V j = w j x R j Step 6: Obtan the deal (best) and negatve deal (worst) solutons n ths step. The deal (best) and negatve deal (worst) solutons can be expressed as: V V max mn V / j J mn V j / j J / 1 2 N j V / j J max V j / j J / 1 2 j N = {V 1 V 2 V 3... V } where J = (j = )/j s assocated wth benefcal attrbutes and J = (j = )/j s assocated wth non benefcal attrbutes. Step 7: Obtan the separaton measures. The separaton of each alternatve from the deal one s gven by the Eucldean dstance n the followng equatons: S S 0.5 j j J 1 2 V V 1 2 N 0.5 j j J 1 2 V V 1 2 N Step 8: The relatve closeness of a partcular alternatve to the deal soluton P j can be expressed n ths step as follows: P S S S Step 9: A set of alternatves s generated n the descendng order n ths step accordng to the value of P ndcatng the most preferred and least preferred feasble solutons. P j may also be called the overall or composte performance score of alternatve A Lst of Attrbutes 1. Cycle tme. 2. Dead cycle tme. 3. Product cost 4. Workng temperature. 5. Pre heatng temperature. 6. Sockng tme.xc 7. Weght of profle 198

5 Int. J. ech. Eng. & Rob. Res Prashant B alve and Shrkant Jachak

6 Int. J. ech. Eng. & Rob. Res Prashant B alve and Shrkant Jachak 2014 Industres CT DT PC WT PHT ST WP C Pennar AL Falcon Ex Ama Ex New Era Ex Industres PC NTR FIT NWS HT APC TCY TP Pennar AL Falcon Ex Ama Ex New Era Ex Industres CT DT PC WT PHT ST WP C Pennar AL Falcon Ex Ama Ex New Era Ex Sum of square Square root Industres PC NTR FIT NWS HT APC TCY TP Pennar AL Falcon Ex Ama Ex New Era Ex Sum of square Square root Industres CT DT PC WT PHT ST WP C Industres PC NTR FIT NWS HT APC TCY TP

7 Int. J. ech. Eng. & Rob. Res Prashant B alve and Shrkant Jachak 2014 CT DT PC WT PHT ST WP C PC NTR FIT NWS HT APC TCY TP Industres CT DT PC WT PHT ST WP C Industres PC NTR FIT NWS HT APC TCY TP Type of procrdure. 9. antenances cost. R j m j 1 2 mj 1/ Percentage converson of metal. 11. Number of tral run. 12. Fnal nspecton tme. 13. Number of work staton. 14. ateral handlng tme 15. Average power consumpton. 16. Toolng cost per year. Lst of Attrbutes wth Abbrvaton and Values: Decson atrx of Alumnum Profle anufacturng Industres for Tpopss.e. (m j ) To obtan the normalsed matrx = 1 2 m j 1/ 2 Normalzed matrx of alumnum Profle Industres for TOPSIS (R j ) Weghts of attrbutes of alumnum profle ndustres for TOPSIS W j Normalzed matrx of alumnum profle ndustres V j = R j x W j for TOPSIS Ideal best (V j +) and deal worst (V j -) of alumnum profle manufacturng ndustres for TOPSIS Separaton measure of alumnum profle ndustres for TOPSIS Rankng of alumnum profle ndustres usng TOPSIS CONCLUSION Usng AD models n selecton of Alumnum profle manufacturng ndustry can be consdered an effcent and sutable tool. The decson matrx s ntroduced for selectng the approprate ndustry for the manufacturng of 201

8 Int. J. ech. Eng. & Rob. Res Prashant B alve and Shrkant Jachak 2014 Attrbutes Ideal Best (Vj+) Ideal Worst(Vj-) CT DT PC WT PHT ST WP C PC NTR FIT NWS HT APC TYC TP S. No. Industres Sa + Sa P 1. Pennar Alumnum Falcon Extruson Ama Extruson New Era Extruson Pennar Alumnum Ama Extruson Falcon Extruson New Era Extruson Alumnum profle based on desgn crtera and possble canddate materals. The weghted coeffcents are obtaned for every attrbute by makng use of radcal root method (also called the geometrc mean method). The decson matrx and weghted coeffcents are taken as the nput for Ordnary TOPSIS and Block TOPSIS. These models lst ndustres from the best to the worst takng nto account all ndustry selecton crtera ncludng cost. ethods that determne both the score and the rank of each ndustry may be preferred over methods that provde only the rank of ndustry. In order to enhance the accuracy of the fnal decson usng the TOPSIS method can be consdered an effcent tool. Usng ths method t s we have sorted out the Pennar Alumnum Profle anufacturng Industry s havng hghest rank as compare to other three ndustres. Thus Pennar Alumnum Profle anufacturng Industry s the best Choce. REFERENCES 1. Agrawal V P Kohl V and Gupta S (1991) Computer Aded Robot Selecton: The ultple Attrbute Decson akng Approach Internatonal Journal of Producton Research Vol. 29 pp Bhangale P P Agrawal V P and Saha S K (2004) Attrbute Based Specfcaton Comparson and Selecton of a Robot echansm & achne Theory Vol. 39 pp Booth D E Khouja and Hu (1992) A Robust ultvarate Statstcal Procedure for Evaluaton and Selecton of Industral Robots Internatonal Journal of Operatons & Producton anagement Vol. 12 pp Rao R V (2007) Decson akng n the anufacturng Envronment Usng Graph Theory and Fuzzy ultple Attrbute Decson akng ethods Sprnger- Verlag London. 202

9 Int. J. ech. Eng. & Rob. Res Prashant B alve and Shrkant Jachak Rao R V and Padmanabhan K K (2006) Selecton Identfcaton and Comparson of Industral Robots usng Dgraph and atrx ethods Robotcs and Computer-Integrated anufacturng Vol. 22 pp Rao R V and Patel B K (2009) Decson akng n the anufacturng Envronment Usng an Improved PROETHEE ethod Internatonal Journal of Producton Research Vol. 48 pp Rao R V Patel B K and Parnchkun (2011) Industral Robot Selecton Usng a Novel Decson akng ethod Consderng Objectve and Subjectve Preferences Robotcs and Autonomous Systems. 203