Instructional Objectives

Transcription

1 WORKSHOP n PRODUCT AND PROCESS DESIGN LECTURE 08: INTRODUCTION TO PRODUCT DESIGN AND MANUFACTURING Daniel R. Lewin Department f Chemical Engineering Technin, Haifa, Israel Ref: Seider, Seader and Lewin (2004), Chapter 19 1 Instructinal Objectives When this part f the curse is cmpleted, the student will: Have an understanding f business decisin making (BDM) in the prcess industries Be able t define the Sigma Level f a manufacturing prcess Knw the steps fllwed in prduct design and manufacture (DMAIC) Be able t qualitatively analyze a prcess fr the manufacture f a prduct and knw hw t identify the critical-t-quality (CTQ) step using DMAIC 2 Daniel R. Lewin, Technin 1

2 Prduct Design Design f chemical prducts begins with the identificatin and creatin f ptential pprtunities t satisfy scietal needs and t generate prfit Natural Resurces Prcess Basic chemical prducts (cmmdity and specialty chemicals, bi-materials, plymeric materials) Basic Chemical Prducts Industrial Prducts Manufacturing Prcess Cnsumer prducts (dialysis devices, Pst-it ntes, transparencies, drug delivery patches, csmetics,...) Basic chemical prducts Prcess Industrial prducts (films, fibers, paper, ) 3 BDM in Design Curses Can intrduce students t: Input creatin. Input prcessing Idea stimulatin Challenge: In framewrk f lectures and HW assignments t include aspects f business decisin-making in student design prjects. usually time limitatins and the academic setting place severe cnstraints 4 Daniel R. Lewin, Technin 2

3 Example Market IC Industry What d the fllwing have in cmmn? Discman VCR Vide camera Digital Camera Cell phne Laptp 5 The Electrnics Fd Chain Electrnic Equipment and Systems > $1,000 B Semicnductrs $175 B Semicnductr Materials and Equipment $72 B Surce: Gartner Dataquest 2003 data 6 Daniel R. Lewin, Technin 3

4 IC Prductin Capability Mre s Law Transistrs per chip Industry Average Year Semicnductr Grwth Implicatins f Mre s s Law Year Demand rise time: 3-6 mnths Prductin rise time: 2-3 years Average Grwth 8 Daniel R. Lewin, Technin 4

5 Semicnductr Grwth Average Semicnductr Grwth Rate: 18% Average Electrnics Grwth Rate: 9% Hw lng will this disparity last? 9 Semicnductr Cntent f Electrnics 40 Semicnductr Cntent (%) Where will this level ff? Surce: Semicnductr Internatinal Year 10 Daniel R. Lewin, Technin 5

6 Industry Drivers Push vs. Pull Market requires (push): Smaller feature sizes desired Larger chip area desired Imprved IC designs lead t innvatins IC industry delivers (pull): Lwer cst per functin (higher perfrmance per cst) New applicatins are enabled t absrb chips with new capabilities Higher vlumes prduced 11 Class Exercise: Help Sell Chips! Discuss the IC cntent f the fllwing list f prducts, and suggest hw the prprtin f IC cmpnents can be increased in the next prduct release (three suggestins per prduct): Discman VCR Vide camera Digital Camera Cell phne Laptp 12 Daniel R. Lewin, Technin 6

7 Cnfigured Prduct Design Cnfigured Cnsumer Prduct Design Much design activity centered n 3-D structure f prduct, e.g., Integrated circuits Hemdialysis devices Espress cffee machines Slar desalinatin devices Drug delivery patches Autmtive fuel cells Hand warmers Pst-it ntes Ink-jet cartridges Detachable wall hangers Laundry detergents 13 Example: Hemdialysis Device Seek t design a dispsable, sterilized membrane mdule sells fr under $10 e.g., C-DAK 400 artificial kidney 60 MM units sld/yr 10,000 hllw fibers/unit 22cm Bld frm bdy Bld t bdy 4 cm 10µm 200 µm Dialysate Slutin 24 cm 14 Daniel R. Lewin, Technin 7

8 1. Pstulate a design cnfiguratin, including: Bld flw rate = 200 ml/min Dialysate flw rate = 500 ml/min 2. Estimate mass-transfer cefficients fr transfer f urea acrss membrane. 3. Estimate pressure drp. Design Prcedure 4. Slve mass-transfer mdel (ODEs) fr urea cncentratin in bldstream as functin f time. Adjust the design t reduce urea nitrgen level (BUN) frm mg/dl in 4 hr. 15 Example: CVD f Plysilicn In manufacture f integrated circuits, amrphus silicn is cmmnly depsited n a silicn substrate by the rapidthermal chemical vapr depsitin (RTCVD) prcess, invlving parallel electrdes at lw pressure (1 trr). Feed Gas e.g., Desire t depsit a 500 Å Electrdes Shwerhead n an 8 cm wafer L Effluent Gas Plasma Reactr Substrate 10% SiH 4 (silane) in He is fed thrugh shwerhead. RF pwer surce (13.56 MHz) generates a plasma dw dt 16 Daniel R. Lewin, Technin 8

9 Prduct Design Objective Frmulate the 1. Navier-Stkes equatins 2. Species mass balances fr SiH 4, SiH 3, SiH 2 Slve using FEMLAB t determine the film thickness adjust the design t achieve unifrmity This design prject was addressed by Penn undergraduates in Imprved chamber design increases unifrmity frm 5% t 2% and thus prfitability. 17 Six-sigma in Prduct Manufacture Definitin: 6σ = Six Sigma a structured methdlgy fr eliminating defects, and hence, imprving prduct quality in manufacturing and services. aims at identifying and reducing the variance in prduct quality, and invlves a cmbinatin f statistical quality cntrl, data analysis methds, and the training f persnnel. 18 Daniel R. Lewin, Technin 9

10 Six-sigma in Prduct Manufacture Definitin: σ is the standard deviatin f the value f a quality variable, x, a measure f its variance, assumed t be nrmally distributed: f ( x ) x µ = exp σ 2π 2 σ Average Standard Deviatin Assume LCL = µ -3σ, and UCL = µ + 3σ: x µ + 3σ µ µ -3σ Shewart Chart 19 Six-sigma in Prduct Manufacture At SD = σ, the number f Defects Per Millin Opprtunities (DPMO) belw the LCL in a nrmal sample is: µ + 3σ 6 6 DPMO = 10 f x dx = 10 1 f x dx = 1,350 1 ( ) ( ) ( ) µ 3σ 2 µ 3σ The same DPMO will be abve the UCL is a nrmal sample. The plt shws f(x) fr σ = Daniel R. Lewin, Technin 10

11 Six-sigma in Prduct Manufacture In accepted six-sigma methdlgy, a wrst-case shift f 1.5σ in the distributin f quality is assumed, t a new average value f µ + 1.5σ In this case, the DPMO abve the UCL = 66,807, with nly DPMO = 3 belw the LCL (σ = 2). 21 Six-sigma in Prduct Manufacture Hwever, if σ is reduced by ½ (σ = 1), s that LCL = µ -6σ, and UCL = µ + 6σ, the DPMO fr nrmal and abnrmal peratin are nw much lwer: 22 Daniel R. Lewin, Technin 11

12 Sigma Level vs. DPMO Sigma Level DPMO 697, ,770 66,810 22,750 6,210 1, Cmputing the Sigma Level Example: On average, the primary prduct frm a distillatin clumn fails t meet its specificatins during five hurs per mnth f prductin. Cmpute its sigma level. Slutin: 6 5 DPMO = 10 = 6, The chart n slide 20 gives the Sigma level as Daniel R. Lewin, Technin 12

13 Cmputing Thrughput Yield Fr n steps, where the number f expected defects in step i is DPMO, i the defect-free thrughput yield is: n DPMOi TY = 1 6 i = 1 10 If the number f expected defects in each step is n identical, then TY is: DPMO TY = e.g., in the manufacture f a device invlving 40 steps, each perating at 4σ ( DPMO=6,210), then: ( ) 40 TY = = i.e., 22% f prductin lst t defects! (verall 2.3σ) 25 Mnitring and Reducing Variance A five-step prcedure is fllwed - Define, Measure, Analyze, Imprve, and Cntrl - DMAIC: Define: A clear statement is made defining the intended imprvement. Next, the prject team is selected, and the respnsibilities f each team member assigned. T assist in prject management, a map is prepared shwing the suppliers, inputs, prcess, utputs and custmers (referred t by the acrnym, SIPOC). 26 Daniel R. Lewin, Technin 13

14 Mnitring and Reducing Variance Fr example, a cmpany prducing PVC tubing by extrusin needs t imprve quality. A SIPOC describing its activities might lk like this: 27 Mnitring and Reducing Variance Measure: The CTQ variables are mnitred t check their cmpliance with the UCLs and LCLs. Mst cmmnly, univariate statistical prcess cntrl (SPC) techniques, such as the Shewart chart, are utilized. The data fr the critical quality variables are analyzed and used t cmpute the DPMO and the sigma level. Cntinuing the PVC extrusin example, suppse this analysis indicates peratin at 3σ, with a target t attain 5σ perfrmance. 28 Daniel R. Lewin, Technin 14

15 Mnitring and Reducing Variance Analyze: T increase the sigma level, the mst significant causes f variability are identified, assisted by a systematic analysis f the sequence f manufacturing steps. This identifies the cmmn rt cause f the variance. In the PVC extrusin example, make a list f pssible causes fr prduct variance: Variance in quality f PVC pellets Variance in vlatiles in pellets Variance in steam heater perating temperature 29 Mnitring and Reducing Variance Imprve: Having identified the cmmn rt cause f variance, it is eliminated r attenuated by redesign f the manufacturing prcess r by emplying prcess cntrl. Cntinuing the PVC tubing example, suggest hw the variance in prduct quality can be reduced. Redesign the steam heater. Install a feedback cntrller t manipulate the steam valve t enable tighter cntrl f the perating temperature. Cmbinatin n the abve. 30 Daniel R. Lewin, Technin 15

16 Mnitring and Reducing Variance Cntrl: After implementing steps t reduce the variance in the CTQ variable, this is evaluated and maintained. Thus, steps M, A, I and C in the DMAIC prcedure are repeated t cntinuusly imprve prcess quality. Nte that achieving 6σ perfrmance is rarely the gal, and seldm achieved 31 Six Sigma fr Design The DMAIC prcedure is cmbined with ideas specific t prduct design t create a methdlgy that assists in applying the sixsigma apprach t prduct design. Again, a fivestep prcedure is recmmended: Step 1: Define Prject: The market pprtunities are identified, a design team is assigned, and resurces are allcated. Typically, the prject timeline is summarized in a Gantt chart. 32 Daniel R. Lewin, Technin 16

17 Six Sigma fr Design Step 2: Identify Requirements: As in DMAIC, the requirements f the prduct are defined in terms f the needs f custmers. Step 3: Select Cncept: Innvative cncepts fr the new design are generated, first by brainstrming. These are evaluated, with the best selected fr further develpment. 33 Six Sigma fr Design Step 4: Develp Design: Often several teams wrk in parallel t develp and test cmpeting designs, making mdificatins as necessary. The gal is t prepare a detailed design, tgether with a plan fr its management, manufacture, and quality assurance. Step 5: Implement Design: The detailed designs in Step 4 are critically tested. The mst prmising design is pilt-tested and if successful, prceeds t full-scale implementatin. 34 Daniel R. Lewin, Technin 17

18 Example: Design Espress Machine Espress cffee is prepared in a machine that injects high-pressure steam thrugh a cake f grund cffee. In a cnventinal machine, the user manually lads grund cffee int a metal filter cup, lcks the cup under the steam head, and then pens the steam heater. Identify all f the surces f variance in the quality f the cffee prduced using the abve machine. Suggest imprvements in the design t reduce the variance in the quality. 35 Example: Design Espress Machine Fr example, this is a SIPOC fr a cmpany making espress machines that seeks t release a new mdel that guarantees higher quality prduct. 36 Daniel R. Lewin, Technin 18

19 Example: Design Espress Machine Measure: The CTQ variables are mnitred t check their cmpliance with the UCLs and LCLs. Mst cmmnly, univariate statistical prcess cntrl (SPC) techniques, such as the Shewart chart, are utilized. The data fr the critical quality variables are analyzed and used t cmpute the DPMO and the sigma level. Cntinuing the espress machine example, the CTQ variables easily quantified wuld be the slids cntent in the cffee, and the extractin yield. Suppse analysis indicates peratin at 3σ, with a target t attain 5σ perfrmance. 37 Example: Design Espress Machine Analyze: T increase the sigma level, the imprtant causes f variability are identified, assisted by a systematic analysis f the manufacturing sequence. This identifies the cmmn rt cause f the variance. In the espress machine example, the fllwing is a partial list f pssible causes fr prduct variance: Variance in freshness f cffee beans Variance in degree f grinding f beans Variance in packing and amunt f grund cffee used Variance in water pressure Variance in quality and quantity f water used 38 Daniel R. Lewin, Technin 19

20 Example: Design Espress Machine Surce: Cffee Brewing Institute 39 Example: Design Espress Machine Imprve: On identifying the cmmn rt cause f variance, eliminate r attenuated it by redesign f the r by emplying prcess cntrl. 40 Cntinuing the espress machine example, hw the variance in prduct quality can be reduced? Install a water filter Install a water flw metering system t cntrl the quantity f water used Install a pressure cntrl lp t reduce the variance in pressure The ther surces f variance that are nt under the cntrl f the manufacture f the espress machine as described abve. This suggests a new prduct Daniel R. Lewin, Technin 20

21 Class Exercise: Frtune Ckies The mvie shws the prductin line at the Glden Gate Frtune Ckie Cmpany. Nte that final assembly is manual, with the frtune slip placed inside the wafer, which is then flded. Identify all f the surces f variance in the quality f the frtune ckies prduced using the abve prcedure. Suggest imprvements in the design t reduce the variance in the quality. 41 Summary This part f the curse prvides: An understanding f business decisin making (BDM) in the prcess industries Mstly transmitted in the class (brainstrming), but can be expected in design reprts A definitin f the Sigma Level f a manufacturing prcess Increased lsses [DPMO] means decreased sigma level). Instructin n applying DMAIC in prduct design and manufacture, with examples. 42 Daniel R. Lewin, Technin 21