Let JMP Work for You - Analyzing Complex Process Data with the Help of JMP Scripting Language

Transcription

1 Let JMP Work for You - Analyzing Complex Process Data with the Help of JMP Scripting Language Dr. Katharina Lankers, SCHOTT AG, Germany JMP Discovery Summit, Denver September 14, 2011 Slide 1

2 Outline Outline SCHOTT AG Company Overview Example of a Manufacturing Process Typical Problems : JMP Approaches and JSL Script examples for: Process and Machine Analysis Detection and Ranking of Influences Simulation of Classification Summary / Questions Slide 2

3 Company Overview SCHOTT AG Company Overview Slide 3

4 Company Overview The SCHOTT Group Home Tech Pharmaceutical Systems Advanced Materials Flat Glass Electronic Packaging Lighting and Imaging Solar Slide 4

5 Company Overview SCHOTT Products Home Tech: CERAN glass-ceramic cooktop panels, Transparent ROBAX glass-ceramic for fireplaces and stoves Pharmaceutical Systems: Special glass tubing for technology and pharmaceuticals (syringes, cartridges, vials and ampoules) Advanced Materials: High-quality products for optics, electronics, biotech and research; ZERODUR glass-ceramic mirror substrates for use in astronomy Flat Glass: Processed flat glasses, glass components and systems; SCHOTT Termofrost door systems for the food display industry Electronic Packaging: Hermetic housings for reliable protection of sensitive electronics; Glass powders for use in electronics and healthcare Lighting and Imaging: High-tech solutions for lighting and image transmission; LED and fiber optic machine vision lighting systems Solar: Receivers for solar power plants based on parabolic trough technology; PV solar power components: wafers, cells and modules Slide 5

6 Company Overview SCHOTT s Main Plant and Headquarters SCHOTT in Mainz Slide 6

7 Company Overview Research & Development Development projects concentrate on the following areas: New and improved glasses and glass-ceramics Process development melting and hot forming Coating technologies Application development for the target markets SCHOTT serves More than 600 R & D staff worldwide New product rate: over 30% of sales Slide 7

8 Company Overview Close to Customers - All over the World Canada USA Mexico Colombia Brazil Argentina Europe: Austria Bulgaria Croatia Czech Republic Denmark France Germany Greece Hungary Italy Netherlands Poland Romania Russian Federation Serbia Spain Sweden Switzerland Turkey Ukraine United Kingdom Tunisia Israel Egypt Dubai South Korea Japan China Hong Kong Taiwan Thailand India Malaysia Singapore Indonesia Australia Production plants ( ) and sales offices in 42 countries 17,500 employees (6,400 in Germany) 2.85 billion euros global sales (FY 2009/2010) Slide 8

9 SCHOTT in North America (NAFTA) Company Overview Backup 11 production plants / 5 sales offices / 2,800 employees Slide 9

10 Process Example Example of a Production Process and Tasks Slide 10

11 Process Example Example of a Manufacturing Process Process steps and collected data Machine parameters Machine parameters Machine parameters Machine parameters Machine parameters Machine parameters Machine parameters Machine parameters Machine parameters Step 1 Step 2 Step 3 Quality Control Machine 1.1 Machine 1.2 Machine 1.3 Machine 2.1 Machine 2.2 Machine 2.3 Machine 3.1 Machine 3.2 Machine 3.3 Sample Inspection Sample Inspection Sample Inspection Sample Inspection Automatic inspection and classification Climate Data Slide 11 SQL-Database Logbook Handwritten Notes Special testings Analyses with JMP

12 Tasks Tasks in process control and optimization Machine Monitoring Evaluation of sample inspections and special testings Readjust machine parameters Find reasons for process and quality changes Find best settings Improve quality continually! Visualization and Analyses with JMP Day-to-day business requires fast reactions Let JMP prepare the basic analyses for competent discussions and decisions Slide 12

13 Process and Machine Analysis A Typical Problem and Approach with JMP Problem: Quality fluctuations have to be decreased Probable source: Manufacturing step 2 (high maintainance machines) Step 1 Step 2 Step 3 Quality Control Machine 1.1 Machine 1.2 Machine 1.3 Machine 2.1 Machine 2.2 Machine 2.3 Machine 3.1 Machine 3.2 Machine 3.3 Task: Display all relevant data clearly arranged for decision-making Slide 13

14 Process and Machine Analysis To do - Step 1 Collect and merge data Machine parameters Machine parameters Machine parameters tracing Step 1 Step 2 Step 3 Quality Control Machine 1.1 Machine 1.2 Machine 1.3 Machine 2.1 Machine 2.2 Machine 2.3 Machine 3.1 Machine 3.2 Machine 3.3 Sample Inspection Sample Inspection Automatic inspection and classification Entries from LogBook Slide 14

15 Process and Machine Analysis To Do Step 2 Evaluate maintainance information Detect machine runs, preprocess data Maintainance intervals Machine Run Exclude data Slide 15

16 To Do Step 3 Analyze data, calculate characteristics w.r.t. machine runs, detect significant trends Process and Machine Analysis Machine Run Statistics Trends Offsets Duration of runs Slide 16

17 Process and Machine Analysis To Do Step 4 Visualize / Generate Summary Report Machine Analysis ProcessStep 2 Prod.Line 001 Time period: aa.bb.cc - xx.yy.zz Summary in table form Machine runs Characteristics Quality Characteristics Machine Summary in charts Oneway Quality Overlay and Regression Quality Trends Quality Offsets Quality Duration of Runs Material Information Machine Parameters Sample inspection results Slide 17 Discuss results and decide

18 How JSL helps speeding up a JMP analysis Process and Machine Analysis 1. Collect and merge data 2. Evaluate maintainance information; Detect machine runs preprocess data JSL Open (database) Adapt and match time stamps (new columns, formulas (Round, Lag, )) Messages for data tables (join, concatenate, stack, split, summary, subset, ) Messages for columns and rows (add formulas, select where, exclude, hide, color / mark by column) 3. Analyze data, calculate characteristics w.r.t. machine runs, detect significant trends 4. Visualize / Generate Summary Report Slide 18 Analyze menu (Oneway analysis, Linear regression/ bivariate) read data from reports Again: Messages for data tables, Summary statistics Display trees: New window, Display boxes (TextBox, OutlineBox, BorderBox, TableBox,NumberColBox, StringColBox, ) Graph menu

19 Process and Machine Analysis Process and machine analysis in a JSL script JMP Slide 19

20 Feature Selection If you need more. Find reasons for unexpected quality variations Many possible influences Typical situation: Which process parameters might be relevant? Problem: feature selection Slide 20

21 Feature Selection Best practice for feature selection Combination of Decision trees (partitioning) Multilinear regression and Clustering Slide 21

22 Feature Selection Best practice for feature selection step by step Step 1: Use decision trees for determining possibly relevant factors from a large pool of potential influences Slide 22

23 Feature Selection Best practice for feature selection step by step Step 2: Take selected factors as input for stepwise modelling (backward search) to reduce further and rank influences Slide 23

24 Feature Selection Best practice for feature selection step by step Step 3: Build model and validate Step 4: Rank model inputs, evaluate w.r.t. plausibility 54 53, ,5 208,389 Input ,114 Input 5 109,35569 Input 14 Slide 24

25 Feature Selection Best practice for feature selection step by step Step 5: Find related features via correlation analyses and/or clustering Step 6: Report Results Selected feature Related / similar features Slide 25

26 Feature selection procedure in a JSL script User interface Decision tree Stepwise modelling Model evaluation (Display tree commands) JSL scetch of used commands Feature Selection table << partition ; << split best ; << small tree view ; << Column Contributions (Result: Report partitioning) << make data table ; << get values (Result: list of selected influences) table << Fit Model( Personality(Stepwise),Run Model () ); << enter all ; << direction(backward) ; << go (Result: further reduction of possible influences) << Make model ; << Personality( Standard Least Squares ) << Emphasis( Effect Screening ) << run model; << Save columns (prediction formula); (Result: model formula for selected inputs) << make data table ; << Sort(By( Prob.> t "), order(ascending), << get values; (Result: sorted list of selected inputs) Related inputs Report of results Slide 26 Hierarchical Cluster(..Two Way Clustering); Diagram(X(:Parent), Y(:Child), Change Type(Nested))) (Result: scheme / list of similar inputs) (commands from graph menu) << journal window (Result: Output journal with all results)

27 Feature Selection Feature Selection Procedure in a JSL Script JMP Slide 27

28 Classification Example: Questions of Classification Distribution of Quality Parameters Classification What changes in distribution are needed for better classification? Class C B A Slide 28 $ $$ $$$$

29 Classification Simulation of Classification Scripting interactive graphs: Slide 29

30 Summary Summary JMP applications in process control and optimization: Analyses serve as a basis for competent discussions and decisions Preparation of analyses and reports takes time Day-to-day business requires fast reactions When you are in a hurry walk slowly write a script! JSL helps to speed up the basic analyses get yield data, machine settings, inspection results and possible correlations with a few mouseclicks Stabilize and optimize processes continually without loss of time Slide 30

31 Questions? Slide 31