Through-Process Optimization

Transcription

1 Through-Process Optimization SEAISI 2018 primetals.com

2 Through-Process Optimization Through-Process Quality Control Through-Process Know-How Full product genealogy Quality control system functionality Deviation & root cause analysis Corrective & compensational actions Automatic product grading KPI evaluation and visualization Know-how rules editor Statistical Process Control and intelligent analysis Interface to data mining platforms Through- Process Optimization Know-how expert service Know-how rules generation Definition and improvement of Key Performance Indicators Product development Quality management Know-how modules, e.g. metallurgy, operations, quality Data based Know-how generation & analysis Trainings Audits & consulting Page 2 6/26/2018 J.F. Plaul CE TPKH

3 Digital landscape of an intelligent steel plant Level 4 (Enterprise Resource Planning System) Level 3 (Production Management System: MES, APS, ODS, etc.) Through-Process Quality Control (TPQC-System) L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 Product yard Management Smart Sensors / Robotics / Tech. Packages / Quality Inspection Systems Condition Monitoring System Maintenance & Asset Technology Page 3 6/26/2018 J.F. Plaul CE TPKH

4 Holistic view on Digitization in Steel Industry and the role of TPQC Through-Process Quality Control Page 4 6/26/2018 J.F. Plaul CE TPKH

5 Through-Process Quality Control System (TPQC) Product quality conformance checks for all process steps Know-how rules to stabilize production and quality Identification of possible root-causes for non-conforming products Suggestion of corrective actions to eliminate root-causes of quality deviations Suggestion of compensational actions to repair non-conforming products as far as possible Benefits Quality management support Reduced manual product inspection, rework and downgrades Support for quality certification (e.g. ISO/TS16949) Reduced influence of human-factor Page 5 6/26/2018 J.F. Plaul CE TPKH

6 TPQC Genealogy and Centralized Process Data Storage Genealogy across the entire production chain Tracking of each parts/segments of the final product across all preceding processing units, including all recorded high-resolution process data. Big-Data data ware house as a basis for product development and gaining new insights TPQC Through-Process Quality Control System Page 6 6/26/2018 J.F. Plaul CE TPKH

7 Conformance checks after each production unit Process Parameter(s) Threshold upper limit Process Parameter(s) Collection Process Parameter(s) Threshold lower limit Process Parameter(s) Evaluation Page 7 6/26/2018 J.F. Plaul CE TPKH

8 Coil- Storage TPQC / Rules monitor Thickness: Center line deviation Directly related quality parameters to this rule Slab geometry Transfer bar camber Centerline deviation Coil telescope Slab, reheated Roughing Mill Coilbox Shear Finishing Mill Cooling Coiling Purchased slab conforming incoming requirements, Temp. OK Temperature OK Thickness OK Centerline deviation too high Risk of cobble or bad coil shape! Flatness OK Root Cause Analysis RCA: Influencing factors for too large centerline deviation at FM: Poor incoming slab geometry Roughing mill pass schedule Work rolls: worn? Thermal crown? Not acceptable wedge Profile & flatness Coil box operation Corrective Action (immediate correction, if possible): Adjust, roughing mill pass schedule Improve scheduling role consider intermediate roll change Check mill hysteresis and plan inspection of finishing mill stands (load cells, liners, ) Compensational action at later process units: Inspection of hot coil + decision for rewinding yes/no? Reduce pickling speed in order to avoid mechanical damage Page 8 6/26/2018 J.F. Plaul CE TPKH

9 Rules for TPQC Rule definition with Rules Editor Input signals & data Automatic or Manual Limits RULE EVALUATION Data processing (=calculations) Rating Coil OK Coil NOT confirming Possible root causes Corrective actions (HSM) HSM RULES 1. Slab residence time and charging temperature 2. Furnace discharge temperature 3. Slab temperature homogeneity 4. Primary descaler operation 5. Roughing mill descaling 6. Roughing mill passes 7. Roughing mill exit temperature 8. Coilbox usage 9. Strip profile 10. Strip width 11. Strip thickness 12. Strip flatness 13. Strip camber 14. Strip wedge 15. Finishing mill exit temperature 16. Coiling temperature Compensational actions (next PUs) Corrective Action: Set of actions for resolving the cause of the problem. Compensational Action: Set of actions compensating the nonconformities. Page 9 6/26/2018 J.F. Plaul CE TPKH

10 Example of a Root-Cause Analysis Melt-Shop Example End-Blow-Temperature End of Blow Target Temp 1320 ºC 1620 ºC 1680 ºC 1620 ºC 1585 ºC 1560 ºC Hot Metal DeS DeC LF RH-OB CCM BOF #2/3 Temperature too low Immediate compensational action: Reblow at BOF TPQC identifies possible root-causes reduction of casting speed within limits to avoid a strand-break Corrective Action Check / change material related data of new material in use Input material (scrap, hot metal analysis, temp., weight) Input of additions, cooling, heating agents (weight) as calculated L2 /Offgas Model working / in use New material quality in use Page 10 6/26/2018 J.F. Plaul CE TPKH

11 Root cause Analysis List of possible Root-Causes Most likely root-cause on top Manual root-cause confirmation increased root-cause awareness root-cause documentation Time TPQC keeps track of most frequent root-causes and allows for trend analysis with respect to continual improvement. Information about most likely root-cause to sort possible root-causes by their documented frequency. Page 11 6/26/2018 J.F. Plaul CE TPKH

12 Through-Process Example: Compensational Action Across Processing Units Coil storage, other order EXAMPLE Quality issue at HSM: Incorrect Coiling Temperature POSSIBLE COMPENSATIONS 1.Adapt / slow down pickling speed 2.Adapt annealing cycle on annealing furnace of CAL 3.Adapt annealing cycle on annealing furnace of CGL 4.Downgrade and re-allocate an appropriate production order Page 12 6/26/2018 J.F. Plaul CE TPKH

13 Process Data Inspection Configurable data analysis screens Data inspection over the entire production line from a single location Supports data analysis for product development and customer claims Page 13 6/26/2018 J.F. Plaul CE TPKH

14 Process Data Inspection Surface-Inspection Data Data inspection over the entire production line from a single location Supports data analysis for product development and customer claims Page 14 6/26/2018 J.F. Plaul CE TPKH

15 Key Performance Indicators Overview of production and quality related performance at a glance Monitor effectiveness of QM-measures related to quality and process improvements (typically required by automotive industry and acc. quality standards e.g. ISO9001, TS16949) Reviewing the effectiveness of corrective actions with respect to quality and production efficiency Overview of the achievement of business plan objectives Page 15 6/26/2018 J.F. Plaul CE TPKH

16 KPI CGL Yield not trimmed Definition Coil weight at exit reel versus entry coil weights Target Yield not trimmed > 99.00% Time period 1 month: July 17: Yield 99,62 % Avg. last year 98,5% Formula σ Exit coil weight + σ Sample weight Σ Zinc weight σ Entry coil weight 100 [%] Events / Activities / Recommendations Too much entry and exit scrap or samplers analysis for each grade analysis for each incoming coil source and route Incoming strip defects at head and tai Cost evaluation Yield trimmed: Δ to target: +0,62% 192,6 tons gained revenue this month: 180 k Scrap minimization at entry and exit section Page 16 6/26/2018 J.F. Plaul CE TPKH

17 SPC - Statistical Process Control Page 17 6/26/2018 J.F. Plaul CE TPKH

18 Product Development Task Force for Product Development and Optimization for IF, Dual Phase and AlSi Coating Defined Team of Tangshan and Primetals; regular meetings and reviews at side or live meeting Results: Commercial Production of DP590, DP780, DP980 (CAL, CGL); Optimization of DC04 regarding anisotropy, commercial production of AlSi & ZnMg coated products Define Through- Process Parameters Trial Production Evaluation Review and next steps Define Chemistry and Process Parameters for entire production route Support of parameter setting by simulator Trials and numerical modeling Planning and scheduling of Trial Production Accompany trail production with Experts on site Automatic standard reporting of parameters with TPQC Analysis of trial production with Data Analytic Tools Analyze results of test production Define next steps in development Page 18 6/26/2018 J.F. Plaul CE TPKH

19 Through-Process Know-How training Product Development Training Quality Management Training P-FMEA Training SWOT Audit Plant training Maintenance Audit and Training Page 19 6/26/2018 J.F. Plaul CE TPKH

20 Data Mining and Analysis 1. Problem Definition Why do some grades have a high number of surface defects per strip 2. Data Understanding Are there missing values, constant values, outliers 3. Models Applying Machine Learning methods to isolate Root Causes 4. Derive actions Create tasks and implement new Rules, KPIs and SPC charts Page 20 6/26/2018 J.F. Plaul CE TPKH

21 Example for Product optimization with TPQC and Data Analytics Problem:unstable mechanical properties Target: for DP600 production stabilize tensile strength within pre defined targets by re adjusting process parameters of Process Units Target Galvanized Coil Data Collection: Collection of approx. 200 pre selected critical process parameters (out of 8000 available signals) for 500 produced coils via TPQC => Solution found within shortest time and low manpower (7 h versus weeks) Page 21 6/26/2018 J.F. Plaul CE TPKH

22 Deployment Data Analytics Example for Product optimization with TPQC and Data Analytics Dual Phase Steel Training different models, scoring and visualizing the results Decision Trees Random Forests Process Data Neural Networks Linear Regression, etc. Improved Process Parameters Carbon equivalent > 0.44 Cold reduction > 60 % Strip temperature at slow cooling > 695 ºC Strip temperature snout > 460 ºC Cr > 0.54 % Furnace entry speed CGL > 120 m/min Strip temperature rapid cooling < 437 ºC CCM speed > 1.5 m/min After confirmation of Process Parameters by Trial Tests implement new quality rules in TPQC Page 22 6/26/2018 J.F. Plaul CE TPKH

23 HBIS Tangshan Reference UPSTREAM > DOWNSTREAM > 150 t 150 t De-P Page 23 6/26/2018 J.F. Plaul CE TPKH

24 Tangshan TPQC implementation STATUS Through-Process Quality System implementation was done at TSS & CRM2 in four roll outs Approx. 20 plants and systems have been connected to TPQC TPQC receiving approx signals; 132 KPIs are continuously monitored, 395 rules have been developed Online SPC charts for every control room developed Installation of cube data base for fast evaluation of process data Integration of Big Data Analytics tool Numerous trainings for Tangshan employees have bee conducted FUTURE ACTIVITIES Intensify KPI tracking and benchmarking for further improvement of performance Develop new through-process rules supporting product development and quality Page 24 6/26/2018 J.F. Plaul CE TPKH

25 SUMMARY - Key Benefits of Through-Process Optimization Holistic approach to improve overall performance Quick understanding and remedy of complex technological problems: defects, process problems, product development, rejects Saving expensive resources Data driven approach to improve technological and metallurgical understanding Quick product development Know-How development Clear cost improvement Page 25 6/26/2018 J.F. Plaul CE TPKH

26 THANK YOU primetals.com