DMAIC refers to a data-driven cycle of analysis steps used for improving, optimizing and stabilizing business processes with the steps Define, Measure, Analyze, Improve, and Control (DMAIC). In this paper, we will demonstrate the Define, Measure, and Analyze steps for the calendering step in Lithium-Ion-Battery Production. It appears that the measurement system is at least questionable. Moreover, we identified influential factors for thickness and porosity of the foil

Meyer, Oliver

Schnackenberg, Sarah

Weihs, Claus

English

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DMAIC in Lithium-Ion-Battery ProductionClaus Weihs, Oliver Meyer and Sarah SchnackenbergAbstract DMAIC refers to a data-driven cycle of analysis steps used forimproving, optimizing and stabilizing business processes with the steps Define,Measure, Analyze, Improve, and Control (DMAIC). In this paper, we willdemonstrate the Define, Measure, and Analyze steps for the calendering step inLithium-Ion-Battery Production. It appears that the measurement system is atleast questionable. Moreover, we identified influential factors for thickness andporosity of the foil.Claus WeihsTU Dortmund University, D-44221 Dortmund  claus.weihs@tu-dortmund.deOliver MeyerTU Dortmund University, D-44221 Dortmund  oliver.meyer@tu-dortmund.deSarah SchnackenbergTU Dortmund University, D-44221 Dortmund  sarah.schnackenberg@tu-dortmund.deArchives of Data Science, Series A(Online First) DOI 10.5445/KSP/1000085951/09KIT Scientific Publishing ISSN 2363-9881Vol. 4, No. 1, 20182 Claus Weihs, Oliver Meyer and Sarah Schnackenberg1 Introduction and OverviewDMAIC refers to a data-driven cycle of analysis steps used for improving,optimizing and stabilizing business processes with the steps Define,Measure,Analyze, Improve, and Control (see Figure 1). Define identifies the problem,Measure produces measurements, Analyze looks for relationships betweenquality characteristics and influential factors, Improve utilizes methods likedesign of experiments for finding the optimal factor levels for improving theprocess, and Control implements tools for controlling whether the improvedprocess works in a stable manner.DefineProblem definitionand limitationMeasureMeasurement SystemAnalysisAnalyzeProcessmodelingImproveProcessimprovementControlImplementation andmonitoringDMAICFigure 1: DMAIC cycle..DMAIC is a combination of management methods and statistical tools. Thefollowing list gives examples for such methods (statistical methods emphasized):• Define: VoC (Voice of the Costumer), VoB (Voice of the Business),SIPOC (Supplier, Inputs, Process, Outputs, Costumer), project charter,process map, Pareto diagram, Kano diagram, CTQ (Critical To Quality),RACI (Responsible, Accountable, Consulted, Informed) diagram, . . .• Measure: sampling, data exploration, process capability, control charts,Measurement System Analysis (MSA, Gauge R&R), . . .DMAIC in Lithium-Ion-Battery Production 3• Analyze: Ishikawa diagram, uni-/multivariate data analysis, DoE (Designof Experiments for factor reduction), regression, statistical testing, . . .• Improve: NGT (Nominal Group Technique), FMEA (Failure Mode andEffects Analysis), Poka Yoke (error prevention), DoE (for optimization),regression, pilot study, . . .• Control: control charts, process capability, process documentation, . . .For more information on the approach and the methods see, e.g., Pyzdek andKeller (2014). In this paper, we will demonstrate such methods for one step inLithium-Ion-Battery production (cp., e.g., Siemens (2017)).2 DMAIC: DefineIn the Define step we aim to understand the problem and define the exact task.2.1 Project QS-Zell in the ProZell-ClusterFigure 2: Coating Line (Source: ZSW)..TheProZellResearch Cluster is financed by theFederal Ministry of Educationand Research (BMBF), founded to pool the know-how of the leading batteryresearch facilities in Germany. The goal is to build a basis for the establishmentof a competitive state-of-the-art Lithium-Ion-Battery production. In ProZell,QS-Zell is the project for Quality Assurance. The core of QS-Zell is the research4 Claus Weihs, Oliver Meyer and Sarah Schnackenbergproduction line (Forschungs-Plattform, FPL) at the Zentrum für Sonnenenergie-und Wasserstoff-Forschung Baden-Württemberg (Center for Solar Energy andHydrogen Research, ZSW) (see Figure 2 for the coating part of the FPL). Finally,the optimized process steps implemented on the FPL should be used as a blueprint for industrial production lines.2.2 The Battery Production ProcessThe production of Lithium-Ion-Batteries can be divided into 4 major productionstages which again consist of several minor sub-processes:1. The production of coated foils that serve as electrodes (anode andcathode).2. The casing of the coiled foils in a metal box.3. The filling of the battery case with an electrolyte.4. The formation (first charge and discharge) of the battery cell.2.3 Statistical Quality Management in ProZellIn the Research Cluster, statistical tasks are the analysis and modeling of singleprocess steps of the production chain, the modeling of the whole process chainincluding the development of quality uncertainty along the chain, and thedevelopment and implementation of statistical quality control. The main goal isto better understand and improve the production process.DMAIC in Lithium-Ion-Battery Production 52.4 The Calendering Processheatedsteel rollscoated foiladjustedthicknessFigure 3: Calendering scheme.Let us look more closely into the calendering process of the already coatedfoils in order to define the task: Calendering starts after the slurry is applied tothe foil. The slurry needs to be compressed in order to adjust its porosity (cp.Figure 3).Porosity influences the ions’ ability to move through the slurry and the anode’sability to store these ions after charging. Thus, porosity of the coat influencesthe electrochemical characteristics of the final battery, like loading capacity andpossible loading rate (C-rate). The following Project Schedule was defined:1. Measuring the quality before optimization of the coated foil, in partic-ular validation of relationship between coat thickness (non-destructivemeasurement) and porosity (destructive). However, thickness beforecalendering was not tracked, yet.2. DoE for identifying important process factors for thickness/porosity andfinding optimal factor levels to reduce waste.3. Measuring quality after optimization of the coated foil.4. Defining a baseline for industrial production.5. Process control after optimization to ensure constant quality levels.6 Claus Weihs, Oliver Meyer and Sarah Schnackenberg3 DMAIC: MeasureIn theMeasure step we determine how to measure quality characteristics andanalyze the quality of the measurement system.3.1 DataPorosity data is obtained by destructive measurement after calendering. Thick-ness is measured on-line at three points of the foil (left/center/right). Sincemeasurement of porosity is destructive and time consuming, we analyze thick-ness as a substitute since it is regarded as highly correlated with porosity. Onethickness measurement per second is obtained for each position. The foil is cutin the middle after calendering.3.2 Measurement System AnalysisFor Measurement System Analysis (MSA), open questions are how to get anidea of thickness before calendering and how to do MSA? MSA characterizesreproducibility and repeatability (R & R). For reproducibility, note that thereis only one machine and one user for the calendering process, but threemeasurement positions. For repeatability, note that variation in time shouldbe small. For the proposed MSA procedure the coated foil is sent through thecalender with only the thickness gauge activated, but no compression(!). Thisstep is repeated 3 times and results are compared.Figure 4: Thickness measurement results of coated foil from 3 runs (Data: ZSW).DMAIC in Lithium-Ion-Battery Production 7Results of the 3 runs are pretty different (especially results marked in blue,see Figure 4). One problem is the matching of single measurements since themeasurement position is not tracked and starting/ending points are unknown.However, speed of foil is measured every second. Thus, we identify the startingpoint and use the speed to match data.Figure 5: Transformed thickness measurements (center) of coated foil from 3 runs.However, even after transformation, results are pretty different for the centerposition (see Figure 5). Thickness measurements of the first run (blue) aresystematically lower. Also, there are differences in the variation of the runs.Since the foil is cut in the middle after calendering the left and right positionshould be compared in greater detail. As we know from an older result, the rightposition is traditionally subject to greater uncertainty. This also seems to betrue in our case (bigger standard deviation (stdev), see Figure 6). Moreover, thedifferences between the runs are quite large for both positions. This again is abigger problem on the right side with the standard deviation of the 3rd run (red)being more then 4 times larger than the others.8 Claus Weihs, Oliver Meyer and Sarah SchnackenbergMean Left Rightblue 137.20 132.40green 134.20 135.40red 135.10 127.70Std.Dev. Left Rightblue 6.04 4.03green 3.16 3.19red 3.31 17.20Figure 6: Transformed thickness measurements (Right, Left) and corresponding means, standarddeviations (stdev).Let us finish MSA by a formal analysis of variance (anova). For this, wedetermine the influence of the factors position with levels left, center, rightand parts with levels corresponding to 60 consecutive measurement regionson the foil. As can be seen in Table 1 an anova on the mean measurementsin these regions for the 3 above replicates shows that position has a (highlysignificant) effect which causes, together with the replicates, > 99% of thestandard deviation of thickness. So, the difference between the measurements inthe 3 positions is much too high to be acceptable, i.e. either the measurementsystem has to be improved or calendering systematically does not lead to thesame thickness in the 3 positions which does not appear to be acceptable either.Table 1: Anova for Parameters Position and Parts.position parts ResidualsSum of Squares 74208.7 6684.1 1087987.6Deg. of Freedom 2 1 7748DMAIC in Lithium-Ion-Battery Production 94 DMAIC: AnalyzeTable 2: Factor Levels.Abbr. Parameter LevelsRT Roller Temperature in ◦C 30 40 50IR IR-pre-heating Temp. in ◦C 24 70 110RG Roller Gap in mm 100 120FS Foil Speed in m/min 4 6FT Foil Tension in Newton 40 60In the Analyze step, we try to find relationships between quality characteristicsand influential factors. Before creating a DoE, possibly important process factorsand their settings were identified in cooperation with ZSW researchers (seeTable 2).For DoE we identified 3 factors with 2 levels (linear effect over Region ofInterest (RoI)) and 2 factors with 3 levels (possible quadratic effect over RoI).Because of limited amount of material, no interactions between factors wereconsidered. For the construction and analysis of the DoE, the levels of theparameters were coded to −1 ("low"), 0 ("mean") and 1 ("high").To construct the DoE, we split each 3-level factor X into 2 surrogate factorsX1 and X2 with 2 levels each (Montgomery, 2017, Chapter 9.4.1, pp. 422),where X = 1 implies X1 = X2 = 1, X = 0 implies X1 , X2, and X = −1 impliesX1 = X2 = −1. Then, the sum of the effects of X1 and X2 equals the effect ofX and the product (interaction) of the effects of X1 and X2 the effect of X2. Afull factorial design with only the surrogate factors is created and the unusedcolumns are utilized for the 2-level factors. If there are not enough columns left,the full factorial is expanded. The advantage of a plan designed in this manneragainst others like D-optimal plans is that all (including linear and quadratic)effects are uncorrelated. This was an issue of importance for our project partner,because they felt that being able to identify the impact of the process factorswould be of value in further research and optimization projects.10 Claus Weihs, Oliver Meyer and Sarah SchnackenbergTable 3: Full Factorial Design with Surrogates.RT1 RT2 IR1 IR2 RT1RT2 IR1IR2 RT1IR1 RT1IR2 RT2IR1 RT2IR2 RT1RT2IR1 RT1IR1IR2 RT2IR1IR2 RT1RT2IR1IR2RT2 IR2 RG FS FT-1 -1 -1 -1 +1 +1 +1 +1 +1 +1 -1 -1 -1 +1-1 -1 -1 +1 +1 -1 +1 -1 +1 -1 -1 +1 +1 -1-1 -1 +1 -1 +1 -1 -1 +1 -1 +1 +1 +1 +1 -1-1 -1 +1 +1 +1 +1 -1 -1 -1 -1 +1 -1 -1 +1-1 +1 -1 -1 -1 +1 +1 +1 -1 -1 +1 -1 +1 -1-1 +1 -1 +1 -1 -1 +1 -1 -1 +1 +1 +1 -1 +1-1 +1 +1 -1 -1 -1 -1 +1 +1 -1 -1 +1 -1 +1-1 +1 +1 +1 -1 +1 -1 -1 +1 +1 -1 -1 +1 -1+1 -1 -1 -1 -1 +1 -1 -1 +1 +1 +1 +1 -1 -1+1 -1 -1 +1 -1 -1 -1 +1 +1 -1 +1 -1 +1 +1+1 -1 +1 -1 -1 -1 +1 -1 -1 +1 -1 -1 +1 +1+1 -1 +1 +1 -1 +1 +1 +1 -1 -1 -1 +1 -1 -1+1 +1 -1 -1 +1 +1 -1 -1 -1 -1 -1 +1 +1 +1+1 +1 -1 +1 +1 -1 -1 +1 -1 +1 -1 -1 -1 -1+1 +1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 -1 -1+1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1However, since the amount of material available for our experiments washighly limited, we needed to find a way to reduce the number of experiments ifpossible. Considering the full factorial 24 design for the surrogates including theremaining 2-level factors (see Table 3), 4 level combinations exist twice (onlythe signs of the surrogates IR1 and IR2 are interchanged, e.g., in experiments2 and 3). Therefore, the number of experiments was reduced from 16 to 12by leaving out experiments 3, 6, 11, and 15. This left us with a still nearlyD-optimal design and also gave us the opportunity to repeat the 4 experimentsin question at the end if there was still enough material left.Table 4: First Design.No. RT IR RG FS FT1 -1 -1 -1 -1 +12 -1 0 +1 +1 -13 -1 +1 -1 -1 +14 0 -1 -1 +1 -15 0 -1 +1 -1 -16 0 0 -1 +1 +17 0 +1 +1 -1 -18 0 +1 -1 +1 -19 0 0 +1 -1 +110 +1 0 -1 -1 -111 +1 -1 +1 +1 +112 +1 +1 +1 +1 +1DMAIC in Lithium-Ion-Battery Production 11For practical reasons, the Roller Temperature was not randomized. Therefore,the surrogate design leads to the experiments in Table 4.For Data Collection, choose the correct level combination, start the machineand wait until the previous foil has passed thickness measurement (dependingon speed). Wait 30 more seconds for the process to adjust (just to be sure). Markthe foil right before the thickness measurement unit. And collect thickness datafor 60 seconds (around 50 measurements for each level combination).The first 5 experiments went through without major problems. However, thefoil started to rupture in experiment number 6. Such a problem never occurredbefore with other types of foils. After several attempts, in order not to wastemore material, the experiments were stopped. The maximum foil tension (FT)appeared to be too high. A new DoE had to be constructed.Table 5: Second Design.No. RT IR RG FS1 -1 0 +1 +12 0 -1 -1 +13 0 -1 +1 -14 0 0 -1 +15 0 0 +1 -16 +1 0 -1 -17 +1 -1 +1 +18 -1 -1 -1 -1By the first DoE, a lot of material was already used/lost. Running 12 newexperiments appeared to be impossible. Therefore, the idea was to reduce thenumber of experiments needed and incorporate old experiments in the newdesign. This is realized by excluding foil tension as an influential factor fixingits level to -1 and by reducing the number of levels for IR-temperature to 2. Thenew DoE was constructed in the same manner as the first one, but needed only8 experiments (see Table 5), 3 of which match experiments already performed(marked in red). This means that all considered factors, including the quadraticeffect of the roller temperature (RT2), can be estimated without any correlations.With the remaining material we were barely able to run the 5 new experimentsneeded.12 Claus Weihs, Oliver Meyer and Sarah SchnackenbergTable 6: Linear Model for the Left Part of the Foil.Estimate Std. Err. t-value p-valueIntercept 101.89 0.25 403.08 < 0.001IR 1.45 0.25 5.72 0.029RG 7.73 0.25 30.56 0.001FS 0.86 0.25 3.38 0.077RT2 −1.15 0.25 −4.56 0.045RT −3.41 0.25 −9.55 0.011RMSE 0.7149 on 2 degrees of freedomR2 0.9982Regression is applied on the means of the observations for each levelcombination. In the model for the left part of the foil (see Table 6), all factorsare significant. For all three models, i.e. for the left/center/right parts of the foil,the fit is > 0.99. Note that such a high goodness of fit is facilitated by usingobservation means. Nevertheless, linear dependence on the selected features isimpressively shown. Moreover, coefficient signs are the same for all factors inall the thickness models and all signs make sense. However, only factors RG andRT are significant for the center and the right part of the foil, mean thickness issignificantly higher in the center, and variation is lower on the left.4.1 Models for Thickness and PorosityComparing the results for thickness and porosity (see Table 7) in the center ofthe foil, the same factors are identified as significant and thickness appears to bea good surrogate for porosity, which is even harder to predict (lower R2).5 DMAIC: Improve - What to do nextUp to now, we considered the Define,Measure, and Analyze DMAIC steps. Inthe next steps, in order to improve the process, first the measurement system hasto be adapted to avoid different results in different positions (left/center/right).Moreover, optimal factor levels are to be found for future production. Also,DMAIC in Lithium-Ion-Battery Production 13Table 7: Linear Model of Porosity (Left) and Thickness (Right) for the Center.Estimate Std. Err. t-value p-value Estimate Std. Err. t-value p-valueIntercept 41.50 0.37 111.09 < 0.001 104.71 0.51 207.28 < 0.001IR 0.61 0.37 1.63 0.245 1.00 0.51 1.97 0.187RG 3.59 0.37 9.61 0.011 7.53 0.51 14.91 0.004FS −0.05 0.37 −0.12 0.915 0.78 0.51 1.55 0.261RT2 0.12 0.37 0.33 0.774 −1.11 0.51 −2.19 0.160RT −2.17 0.53 −4.11 0.054 −3.16 0.71 −4.42 0.047RSE 1.056 on 2 degrees of freedom 1.429 on 2 degrees of freedomR2 0.9824 0.9922optimized process results have to be compared with the present ones andspecification limits for thickness and porosity have to fixed.Acknowledgements The authors thank the German Federal Ministry of Education and Research(BMBF) under the Grant 03XP0076B (project QS-Zell) for financial support. The authors also thankStefan Rößler, Stefan Mähr, Hai Yen Tran and Wolfgang Braunwarth (ZSW) for realizing the experi-ments and for fruitful discussions in analyzing the data.ReferencesMontgomery DC (2017) Design and Analysis of Experiments. Wiley, New Jersey.ISBN: 978-1-119113-47-8.Pyzdek T, Keller PA (2014) The Six Sigma Handbook. McGraw-Hill, New York.ISBN: 978-0-071840-53-8.Siemens (2017) Battery Manufacturing Process. URL: http://w3.siemens.com/markets/global/en/battery-manufacturing/applications/process/pages/default.aspx. Accessed 2017-10-26.

DMAIC in Lithium-Ion-Battery Production

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DMAIC in Lithium-Ion-Battery Production

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