APPLICATION OF TAGUCHI EXPERIMENTAL DESIGN FOR PROCESS OPTIMIZATION OF TABLET COMPRESSION MACHINES AT HLL LIFECARE LIMITED, INDIA

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1 Interntionl Journl of themticl Sciences Vol. 10, No. 3-4, July-December 2011, pp Serils Publictions APPLICATION OF TAGUCHI EXPERIENTAL DESIGN FOR PROCESS OPTIIZATION OF TABLET COPRESSION ACHINES AT HLL LIFECARE LIITED, INDIA Koilkuntl ddulety & Ekkuluri Pdmvthi Abstrct: The problem fced by the HLL Life cre Limited Compny ws high rejections of Core Tblets t compression stge of Tblet mnufcturing. Around 15% btches were reworked in every month due to non ttinment of either required Compression-Strength (Hrdness) Grter-thn 1 Kg per Cm 2, (Higher the Better Cse) or Fribility Less-thn 1% (Lower the Better Cse) re output qulity chrcteristics of Tblets. The uthors hd used the Robust Prmeter Design through Tguchi Approch for compression process prmeter optimiztion nd improved the verge Hrdness from 1.5 Kg per Cm 2 with 0.5 stndrd devition to 3.0 Kg per Cm 2 with stndrd devition of 0.02 nd verge fribility reduced from 0.6 to 0.23 t the sme time the fribility stndrd devition reduced from 0.5 to 0.01, which were brekthrough improvement in both the prmeters. After rriving optiml input prmeter setting i.e. 2% moisture in grnules, compression mchine speed setting t 28 RP, relese pressure t 2.5 Level nd Weight Dozer t 1 st Level, 50 btches hd been produced s prt of vlidtions trils with the mentioned optiml prmeter setting nd proved tht ll the btches hd been pssed for next opertion tht is film coting nd Blister pcking without ny rework. Keywords: Fctors, Fctor levels, en min effect plots, in effect plots for SN rtio, Tguchi pproch. 1.1 Brief Profile of HLL Lifecre Limited 1. INTRODUCTION HLL Life-cre Limited Kngl Fcility, Belgum, Indi: The Kngl plnt in Belgum, Krntk, Indi commenced its opertion with production of condoms in 1985 using Jpnese technology. This unit underwent diversifiction in 1992 with the Tbletting mnufcturing fcility for birth control pills - l-d/n nd the formultion nd Tbletting of Sheli (Centchromn) the indigenous, non-steroidl once--week pill. The Tbletting of Emergency Contrceptive pills ws strted in 2003 (7).

2 172 Koilkuntl ddulety & Ekkuluri Pdmvthi 2. LITERATURE REVIEW The Tguchi s Approch: Genichi Tguchi believed tht qulity should be designed into the products nd not inspected into it. Inspection does not produce good products but only segregtes them from bd products. He lso propgted tht qulity is best chieved by minimizing the devition from trget nd the cost of qulity should be mesured s function of the devition from the stndrd (5). R. A. Fisher in Englnd developed the clssicl methods for design of experiments, in the erly prt of the 20 th century. They include full vriety of sttisticl design techniques bsed on Ltin squres (blnced squre rrngements required for unbised sttisticl experimenttion) nd developed for griculturl industry. While rigorous, mjor problem with pplying Fisher s method in mnufcturing industry is the time nd cost required to lern nd use it. Further, Fisher s methods re often cumbersome to implement in mnufcturing industril experimenttion becuse of certin ssumptions nd procedurl emphsis. Tguchi s pproch to the design of experiments utilizes the concept of robust design. Robust design refers to designing product or process in wy tht it hs miniml sensitivity to the externl noise fctors. Robust design dds new dimension to Fisher s sttisticl experimentl design by explicitly ddressing the concerns fced by ll process nd product designers, nmely (5). How to reduce economiclly the vrition of product s function in the customer s environment, nd How to ensure tht the decisions found to be optimum during lbortory experiments will prove to be so in mnufcturing nd in customer environments. In contrst to Sttisticl Process Control (SPC), which ttempts to control the fctors tht dversely ffect the production, Tguchi methods focus on design-the development of superior performnce designs (for both products nd mnufcturing processes) to deliver qulity. Tguchi methods led to excellence in the selection nd setting of product/process design prmeters nd their tolernces. In the pst decde, engineers hve pplied these methods in over 500 utomotive, electronics, informtion technology nd process-industries worldwide. These pplictions hve reduced crcks in cstings, incresed the life of drill bits, produced VLSI with fewer defects, speeded up the response time of UNIX V, nd even guided humn resource mngement systems design. I. E. Klein pplied the Tguchi methods to the production of thin RF integrted circuits on lumin [1]. The substrtes hd 20 vi holes of 1 mm in dimeter, which led to perturbtion to the homogeneous spred of photo resist (by spinning) djcent to the holes. Consequently, the overll process yield ws 30%. Implementtion of Tguchi methods resulted in substntil increse in production yield to vlue of 90%, which ws obtined repetedly. Thus,

3 Appliction of Tguchi Experimentl Design for Process Optimiztion of Tblet robust cost-effective process ws chieved. The Tguchi s signl-to-noise rtio (SNR) nlysis hs lso been dopted to develop robust design for the Ryleigh surfce coustic wve (SAW) gs sensing device operted in conventionl dely-line configurtion [2]. K. Plnikumr et l., hd mde use of Tguchi s method nd ANOVA nlysis for optimizing the cutting prmeters in turning glss fiber reinforced plstic (GFRP) composites using poly crystlline dimond (PCD) tool for minimizing surfce roughness [3]. Der Ho Wu nd o Sheng Chng hd conducted study which pplies the Tguchi method to optimize the process prmeters for the die csting of thin-wlled mgnesium lloy prts in computer, communictions, nd consumer electronics (3C) industries [4]. Tguchi methods systemticlly revel the complex cuse-effect reltionships between design prmeters nd performnce. These in turn led to building qulity performnce into processes nd products before ctul production begins. The Tguchi s technique of Robust Design is one of the methods for reducing the vrition mong the products. The Tguchi s method of sttisticl design of experiments by using Orthogonl Arrys nd nlyzing the experimentl outcome by in effect plots of mens, in effect plots for S/N rtios for vrious qulity chrcteristics of output (Tblets) re considered nd optimiztion hs been used for reducing the rejections of Lots of Tblet tht re produced by Tblet Compression chines of HLL Lifecre Limited. 2.1 The Problem Sttement The problem fced by the HLL Lifecre Limited Compny ws high rejections of Core Tblets t compression stge of Tblet mnufcturing. Around 15% btches were reworked in every month due to non ttinment of either required Compression-Strength Greterthn 1 Kg per Cm 2, (Higher the Better Cse) or Fribility Less-thn 1% (Lower the Better Cse) tht re output qulity chrcteristics of Tblets. The uthor (1) of the pper ws working s Senior Assistnt nger (Qulity), HLL Lifecre Limited, Kngl Unit, Belgum, interested to deploy the Tguchi Approch for solving the bove problem nd solved the problem by reducing the rework t compression stge from 15% to 0.5%. Which led to round Rs. 10,00,000/- sving. 3. ETHODOLOGY ethodology for deploying Tguchi pproch for process optimiztion (10 step methodology for problem solving) (6) 1. Defining the Sttement of problem 2. Determintion of the objectives

4 174 Koilkuntl ddulety & Ekkuluri Pdmvthi 3. Ensuring correctness of esurement System 4. Identifiction of tblets Qulity Chrcteristics tht re to be optimized 5. Identifiction of the fctors tht re influencing the bove identified performnce chrcteristics nd determintion of the levels nd vlues for ll identified fctors 6. Developing Design for Experimenttion with the help of initb Softwre 7. Conducting the experiments s per Designs, nlyzing the tblets for selected qulity chrcteristics nd posting the vlues in initb worksheet s needed 8. Anlysis of dt of Tblets for selected Qulity Chrcteristics by Tguchi pproch with the help of initb Softwre nd Interprettion of Anlyses nd selection of the optimum levels of the significnt fctors 9. Prediction of the expected results for optiml setting with the help of initb 10. Vlidtion of optiml setting by confirmtion Trils. Step 1: Sttement of the Problem Around 15% btches were reworked in every month due to non ttinment of either required Compression-Strength Greter-thn 1 Kg per Cm 2, (Higher the Better Cse) or Fribility Less-thn 1% (Lower the Better Cse) tht re output qulity chrcteristics of Tblets. Step 2: Objectives of Study Acquiring knowledge of deployment of Tguchi Approch for solving Problem Deploying the Tguchi Approch t Problem re systemticlly in 10 steps s bove Ensuring Rework reduction from 15% to 5% by optimum setting of input prmeters Step 3: esurement System Anlyses Guge R&R ( xx) clculted for ll pplicble mesurement-systems of tblet compression nd found it is well within limits ((1.96* ór&r) < (0.3* one side speck width)) (5) Step 4: Identifiction of tblets Qulity Chrcteristics tht re to be optimized The Brinstorming Technique ws used by involving ll the concerned employees nd executives nd decided to optimize two Qulity Chrcteristics of Tblets tht re: 1. Compression-Strength, & 2. Fribility for reducing rework from 15% to 5%.

5 Appliction of Tguchi Experimentl Design for Process Optimiztion of Tblet Step 5: Identifiction of the fctors nd fctor levels tht re influencing bove two performnce chrcteristics After ppliction of Brinstorming technique with ll the concerned employees nd executives nd fter estblishing cse nd effect reltions between input- prmeters nd output-prmeters of compression process the most significnt four process prmeters re identified s control prmeters long with levels s shown in Tble below. Tble 1 Fctors nd Fctor Levels Level vlues Sl. No. Nme of the fctor Nottion Unit of mesure No. of fctors levels Percentge of oisture % in grnules 2 Speed of the chine S RP Relese Pressure RP Level Weight Dozer WD Level Step 6: Developing & Design for Experimenttion with the help of initb Softwre The bove Fctors nd levels hve been used nd developed the L9 Tguchi Design for experimenttion with the help of initb Softwre is shown in Tble below: Tble 2 L9 Tguchi Design for Experimenttion is Deployed by initb Softwre Sl. No. S RP WD Note Nme of the Fctor Nottion Percentge of oisture in grnules Speed of the chine S Relese Pressure RP Weight Dozer WD

6 176 Koilkuntl ddulety & Ekkuluri Pdmvthi Step 7: Conducting Experimenttion As per bove design 9 experiments with two replictions for ech run nd collected the two vlues tht re Fribility nd Hrdness of Tblets for ech repliction of ech run nd posted in initb worksheet shown below: Tble 3 Experimentl Output Sl. No. S RP WD F1 F2 H1 H Note 1 First Repliction s Fribility Vlue F1 2 Second Repliction s Fribility Vlue F2 3 First Repliction s Hrdness Vlue H1 4 Second Repliction s Hrdness Vlue H2 Step 8: Anlyses of dt of Tblets for selected Qulity Chrcteristics Fribility nd Hrdness optimiztion by ANOVA nd Tguchi Approch with the help of initb Softwre nd Interprettion of Anlyses nd selection of the optimum levels Bsed on the Generl Liner odel ANOVA developed by initb softwre twice (shown below), once Fribility s response vrible nd second time Hrdness s response vrible for investigting significnce effect of four input vribles, S, RP & WD on Fribility nd Hrdness nd concluded tht ll the four input vrible, S, RP & WD (ll the p-vlues re 0.00 i.e less thn 0.05 nd liner model R-squre vlue is more thn 99%) re significntly effecting the both the responses Fribility nd Hrdness (5). The following optiml setting hs been rrived fter developing nd observing Fribility in Effect plots for mens, Hrdness in Effect plots for mens, Fribility in Effect

7 Appliction of Tguchi Experimentl Design for Process Optimiztion of Tblet plots for SN Rtios, Hrdness in Effect plots for SN Rtios, nd by considering ll the delt vlues for ens nd SN Rtios (5). The Optiml Setting is 2% moisture in grnules, 28 RP Speed Relese pressure t 2.5 Level nd Weight Dozer t 1 st Level gives for inimum Fribility nd ximum Hrdness. Tble 4 Optiml Setting is Arrived After Considering in Effect Plots nd Delt Vlues Nme of the fctor Nottion Optiml level Percentge of oisture in grnules 2% Speed of the chine S 28 RP Relese Pressure RP 2.5 Level Weight Dozer WD 1 st Level (The initb generted four min effect plots nd delt vlue for four different scenrios re shown below) Generl Liner odel: F1 versus, S, RP, WD Fctor Type Levels Vlues fixed 3, 2.3, 2.6 S fixed 3 25, 28, 31 RP fixed 3, 2.5, 3.0 WD fixed 3 1, 2, 3 Anlysis of Vrince for Fribility, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj S F P S RP WD Error Totl S = R-Sq = 99.85% R-Sq(dj) = 99.71%

8 178 Koilkuntl ddulety & Ekkuluri Pdmvthi Generl Liner odel: H1 versus, S, RP, WD Fctor Type Levels Vlues fixed 3, 2.3, 2.6 S fixed 3 25, 28, 31 RP fixed 3, 2.5, 3.0 WD fixed 3 1, 2, 3 Anlysis of Vrince for Hrdness, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj S F P S RP WD Error Totl S = R-Sq = 99.84% R-Sq(dj) = 99.70% Fribility 0.7 in Effects Plot for ens Dt ens S e n o f e n s RP WD Figure 1: Fribility in Effects Plot for ens

9 Appliction of Tguchi Experimentl Design for Process Optimiztion of Tblet Hrdness 2.4 in Effects Plot for ens Dt ens S e n o f e n s RP WD Figure 2: Hrdness in Effects Plot for ens Fribility in Effects Plot for SN Rtios Dt ens S e n o f S N 2.3 RP WD 31 R t i o Signl-to-noise: Smller is better Figure 3: Fribility in Effects Plot for SN Rtios

10 180 Koilkuntl ddulety & Ekkuluri Pdmvthi Hrdness in Effects Plot for SN Rtios Dt ens e n o f S S N R t i o RP WD Signl-to-noise: Lrger is better Figure 4: Hrdness in Effects Plot for SN Rtios Tguchi Anlysis: F1, F2 versus, S, RP, WD Response Tble for Signl to Noise Rtios for Fribility (Smller is better) Level S RP WD Delt Rnk Response Tble for ens of Fribility in percentge Level S RP WD Delt Rnk

11 Appliction of Tguchi Experimentl Design for Process Optimiztion of Tblet Tguchi Anlysis: H1, H2 versus, S, RP, WD Response Tble for Signl to Noise Rtios of Hrdness Lrger is better Level S RP WD Delt Rnk Response Tble for ens of Hrdness (Kg per Cm 2 ) Level S RP WD Delt Rnk Step 9: The predicted vlue for optiml setting hs been rrived by initb Softwre for both the prmeters Fribility nd Hrdness re s follows The predicted Fribility for bove optiml setting is 0.223% with Stndrd Devition. The predicted Hrdness for bove optiml setting is Kg per Cm 2 with Stndrd Devition Step 10: Vlidtion of Optiml Setting 50 btches hve been produced with bove optiml setting nd checked the Fribility nd found the verge s 0.23% nd highest vlue is s 0.26% nd verge Hrdness is 3.0 Kg/cm 2 with lowest hrdness in 50 btches is 2.7 Kg/cm 2 nd found no btch is rejected or reworked, ll the btches hd been pssed for next opertion tht is film Coting nd Blister pcking. 4. CONCLUSION The Robust Prmeter Design through Tguchi Approch is shown brekthrough Improvement in compression process prmeter optimiztion i.e verge Hrdness Improved from 1.5 Kg per Cm 2 with 0.5 stndrd devition to 3.0 Kg per Cm 2 with stndrd

12 182 Koilkuntl ddulety & Ekkuluri Pdmvthi devition of 0.02 nd verge Fribility reduced from 0.6 to 0.23 t the sme time the Fribility stndrd devition reduced from 0.5 to 0.01, which were brekthrough improvement in both the prmeters. REFERENCES [1] Ross Philip J., (1989), Tguchi techniques for Qulity Engineering, Prentice Hll. [2] Klien I. E., (1996), Improving the yield in production of thin RF integrted circuits, Interntionl Journl of icroelectronics, 13(3): 12 14, (December). [3] Der Ho Wu, nd Hsin Hu Chen, (2005), Appliction of Tguchi robust design method to SAW mss sensing device, IEEE Trnsctions on Ultrsonic, Ferroelectrics nd Frequency Control, 52(12): , (December). [4] Plnikumr K. Cutting Prmeters Optimiztion for Surfce Roughness in chining of GFRP Composites using Tguchi s ethod, Sthybm Deemed University. [5] ontgomery Dougls C., (2006), Design nd Anlysis of Experiments, (5 th Edition), Wiley Edition. [6] Bgchi Tpn P., (1993), Tguchi ethods Explined Prcticl Steps to Robust Design, Estern Economy Edition. [7] Koilkuntl ddulety Assistnt Professor, Opertions ngement Group, Ntionl Institute of Industril Engineering, umbi , Indi. E-mil: koil@rediffmil.com Ekkuluri Pdmvthi Principl, Asmir Acdemy s English High School umbi , Indi. E-mil: pdmvthi9999@rediffmil.com