July 9, 2014

1. July 9, 2014 Using Simulation Approach to move from Manual to Real-Time Autonomous Scheduling for a Batch Heat Treatment ProcessSteve ThorntonScientific Fellow - Through Process IntegrationTata Steel Research and Development

2. Agenda • Tata Group • Tata Steel • Tata Steel Speciality • Through Process Integration • Knowledge Engineering • Data Mining • Simulation • TSB AUTOPLAN Project • Deployment into Real Time Operations • Summary and Questions

3. A Part of the Tata Group In 2007 Tata Steel Limited acquired Corus Group plc On 27 September 2010 Corus rebranded to Tata Steel Tata Group is one of the world’s fastest growing and most respected corporations Tata’s businesses span seven major industry sectors: engineering, communications and IT, materials, services, energy, consumer products and chemicals Tata is India’s largest private sector employer and has over 540,000 employees in over 100 countries.

5. Tata Steel • One of the world’s top 500 companies • A top 10 global steel producer and the second most geographically-diverse steel company • Annual crude steel capacity of 28 million tonnes • Tata Steel employs more than 80,000 people across five continents • Manufacturing operations in 26 countries and customers in more than 50 markets worldwide • Turnover in 2012-13 was $22.5 billion • A major part of the Tata Group

6. Key Facts – Deliveries, Turnover and EBITDA Expansion at Jamshedpur + New Steel Works at Kalinganagar in Odisha will add 6Mtpa to Tata Steel India by 2015 [1 Rs. crore = Rs. 10,000,000 = $166,852 - 134,712 Crore = $22.5bn]

7. Through Process Integration - Tools Knowledge Engineering Discrete Event Simulation Data Mining

8. Through Process Integration Example – Manufacturing Process for Rail – Prediction of Final Product Internal Quality and Application to Process Improvement • Time scales – between 2 and 8 weeks • Decoupled Processes • Different Technologies (Manufacturing and IT) • Generally individual processes not well integrated (People and Data)

9. Integration Technologies Knowledge Engineering Discrete Event Simulation Data Mining

10. Knowledge Engineering – Mapping the Landscape • What is Knowledge ? • (Often intangible) is what is applied to go from data and information, to a decision and/or action. • What do we need to achieve (value to the business) ? • What Knowledge do we need ? • What Knowledge do we have ? • Where is it ? • Is the Knowledge Base secure ? • Are we applying our Knowledge effectively ? • Could we do it differently ? • Could we do something else ? • Knowledge about knowledge is the raw material for business improvement

11. Knowledge Guided Data Mining – Workshop Templates • Template to Support Knowledge Capture from different process perspectives • Opportunity to pose cross-process questions and concerns • Identify needed data rather than starting with ‘what have we got’ • Develop collaborative knowledge framework integrating KM with Analytics

12. Integration Technologies – Tools in the Box Knowledge Engineering Discrete Event Simulation Data Mining

13. Data Mining – Is it Cheating to Ask ? Action Knowledge Information Data 75% of an Organisation’s Knowledge is ‘hidden’ in Data and People !

14. Knowledge Guided Data Mining – Workshop Templates

15. Data Mining – Making Data Earn Its Keep • Multi-Process, Multi-Variable, Time shifted • Finding Patterns in Your Data • Which you can Use • To do Business Better • Tools and Techniques for effective combination of business knowledge and data • Hunching not Crunching • Highly collaborative approach requiring knowledge engineering as well as data analysis skills • BIG DATA – Volume, Velocity, Variety • Real-Time Analytics – Pattern Recognition, Rule Induction etc. The “Latest”

16. Data Exploitation Maturity Curves Where Next ? Functionality time Data Capture / Measurement Data Integration / Product Tracking Business / Supply Chain Integration

17. Data Exploitation Maturity Curves Functionality time Data Capture / Measurement Data Integration / Product Tracking Business / Supply Chain Integration

18. Integration Technologies Knowledge Engineering Discrete Event Simulation Data Mining

19. Discrete Event Simulation (DES) - Characteristics • Computer based technique for building models of real-life systems • Which exhibit behaviour approximating that of the real system which can incorporate natural variability • Can deal with complex systems which are difficult or impossible to model rigorously [But cannot recreate “reality”] • Allow possible outcomes of a scenario to be investigated and thus assessment of risk and robustness • Stimulates knowledge capture and ideas generation • Achieve shared insight and good decisions, more quickly • Permit simplification of existing situation to provide opportunity to throw away knowledge legacy Experiment with your business in the safe virtual world of the computer

20. Verification Iteration Interviews and Data Collection Model Building and Validation Experimentation and Recommendations Extension (If required and have time) 25% 25% 50% Simulation Project – Why ? Start with Objectives ! What do we want to achieve ? Why do we need a model ? What will we do with it ?

21. Verification Iteration Interviews and Data Collection Model Building and Validation Experimentation and Recommendations Extension (If required and have time) 25% 25% 50% Simulation Project Process – Questions to Insight

22. How Real Does it Need to Feel ? As real as it needs to be (to gain confidence of target audience)

23. Applications of Simulation Approach • Lean Manufacturing • Explore impact on coupled processes and develop business logic • Restructuring • New Plant design and Business configuration, investment and operations decisions • Optimising Logistics in line with configuration changes • To ensure continued service for example in conjunction with throughput changes • Scheduling Applications • Capturing scheduling knowledge and application for real-time decision support • Product Application Strategy • Alternative product application strategy, move decisions downstream • Supply Chain Optimisation • Changing scheduling and stock policies to reduce supply chain inventory for major supply chains Overall, develop simulation models for experimentation in safe virtual world, and routine application for decision support Achieve Shared Insights about the ‘As-Is’ and ‘Could-Be’

24. TPI – Development Directions Knowledge Engineering > Remove Risk > Improve Processes > Exploit Capability > Knowledge Systems Data Integration > Data Mining > Supply Chain Monitors > Order Fulfilment > Decision Support Tools Simulation > Operational Strategy > Information and technology Needs > Scheduling Support > Supply Chain Development > Real-Time Application

25. Tata Steel in Europe Second largest steel producer in Europe Crude steel capacity of 20 mtpa Approximately 35,000 employees worldwide Major manufacturing sites in the UK, the Netherlands, Germany, France and Belgium Supplier to the most demanding markets: Automotive Construction Packaging Energy & Power Material Handling Consumer Goods Engineering Rail Shipbuilding Aerospace Defence & Security

26. Tata Steel in Europe Second largest steel producer in Europe Crude steel capacity of 20 mtpa Approximately 35,000 employees worldwide Major manufacturing sites in the UK, the Netherlands, Germany, France and Belgium Supplier to the most demanding markets: Automotive Construction Packaging Energy & Power Material Handling Consumer Goods Engineering Rail Shipbuilding Aerospace Defence & Security Tata Speciality Main Sectors Carbon Steels Alloyed Stainless Heat Treated

27. Qualities of Steel Offered by Tata Steel Speciality • Alloy through-hardening, case-hardening and nitriding steels • Carbon and carbon-manganese steels • Micro-alloyed steels • Stainless steels • High quality aerospace grades

28. Manufacturing processes • Melting • Continuous bloom casting • Ingot Casting • Re-melting • Primary rolling • Re-rolling • Finishing • Inspection and Testing http://www.tatasteeleurope.com/en/products_and_services/products/long/speciality_steels_and_bar/manufacturing_processes/

29. Process Route £15M project to commission unit at Stocksbridge in early 2015

30. Continuous Heat Treatment Line No 7 Hardening Furnace “Available” Stock Enhancements Conveyor Extra Temper F10 Quench Temper F8 Downstream Brinell & Saw If Forming Bed 1 Occupied Forming Bed 1 Out1 Temper F9 Cooling time required dependent on bar diameter Temper F10 Forming Bed 2 Out2 19 Pitch Walking Cooling Bank Brinell • Heating to 800°C or 880°C in Continuous Furnace • In-Line Quencher to harden in homogeneous manner • Tempering at sub-critical temperature (450°C – 650°C) to soften/modify • Cool to ambient for Brinell Hardness Testing (approx 5 hours) • Saw to remove ends and cut required test pieces (3 – 5 cuts per bar) Saw Exit

31. Run Furnace Without Gaps and Use F10 to avoid blocking – example Schedule ‘A’ Furnace 10 relatively well utilised for this scenario but some spare capacity evident.

32. Run Furnace Without Gaps and Use F10 to avoid blocking – example Schedule ‘206’ Matching of Hardening Furnace to Original Design with 2 Temper Furnaces is very good when charges are ‘filled’ to max

33. Batch Heat Treatment Process “Supply” No Oil Quenched Charges CB1 F1 F2 CB2 WQ1 CB3 TC1 TC2 CB4 F3 CB5 WQ2 F4 OQ1 CB6 CB7 SL Access F5 F6 Charges delivered (and removed) by Sideloader Batch Furnaces (Note 2 shorter than others) Water Quench Oil Quench Air Cool Transfer Car ‘Temporary store) Gantry Crane Charger No Hot bars; OK ex water Quench • Supply Assumptions • Assume scheduled material all available,i.e. • No Overlength Bars • Dot Matrix stamps replaced by Hard Stamps • All necessary ultrasonic testing done • Correct number of bars CB also used as ‘parking’ places, e.g. waiting for removal or to start treatment, or pause in non-critical timed treatment Pit - Charges can be placed here by crane prior to removal from compound ? Also air cooling here ? Side Loader access, introduction of charges into compound and removal of some (some ?)

34. CHT & BHT - Scheduling • Manual Scheduling approach is the norm • Until recently, process team leaders were responsible for detailed scheduling ‘on the job’ • ‘Hard’ scheduling recently introduced • Scheduler prepares detailed 24-36 hour schedule each morning from ‘charge’ information on Heat Treatment system • Considers • Hard rules and constraints, e.g. next slide • Prioritising ‘late’ or ‘current’ orders – Customer focus • Minimising step changes in temperature between charges – Energy focus • Maximising Utilisation – Throughput focus • ‘Availability’ of material • Commercial interventions and requests • In general, heat treatment is not the final process so some flexibility • 12-24 hours published to the Heat Treatment System so material can be assembled and delivered • Usually, refreshing and/or repairing of schedules only done once per day.

35. BHT - Examples of Rules and Constraints Confirmed/Determined through Trials (Experiments), Experience and Measurements • Some orders must temper within x hours (rare) • Standard practice is to allocate some furnaces to hardening and some to tempering (because of temperature setting differences) – choice driven by job sheets and experience • Aerospace material – tempering restricted to furnaces F1 and F2 or F5 and F6 • For high temperature hardening charges (e.g. 1000+), use ‘suitable’ furnace next to quench station (if possible) • F1 – only furnace which can be controlled to 450°C for low temperature quenching • F2 – Can be controlled accurately at temperatures down to 500°C • F3, F4 & F5 tend to favour for higher hardening temperatures • Can harden in any furnace however • F6 ‘always’ reserved for tempering • Some charges specify quench method, others give option but times are specified. • Time from hardening to quenching should be ‘as quickly as possible’ • If a furnace is empty it is set to ‘minimum fire’. • Furnace temperature losses and ramp rates need to be accounted for. • Example, if a furnace was used to harden at 850°C and then is used for tempering at 650°C, would take 2 to 3 hours to cool sufficiently. Generally consider a cooling rate of R°C per minute applies. • During removal of charges from a furnace, the control is set to manual whilst thermocouples are removed. • Ramp up rate is based on is based on volume of steel in furnace. • See next slide for further consideration of heating and cooling rates.

36. BHT – Calculating Required Ramp Up Time Furnace Temp Y – Ramp Up Time X – Soak Time °C Y mins X mins Minutes • Regression modelling based on • charge weight • Surface area • Volume • Time since last use • Temperature last use • Last temp – This temp • Charge weight most influential, note that the data incorporates a lot of unrecorded actions by operators. • A lot of scatter Best result so far shown right. Up to 10 hours red line is mean of ramp up hours for ranges of batch weight shown. Afterwards no evidence to suggest other than flat 4.25 hours should be possible. Red Line Hours = 0.325*ChargeWeight + 1 From the data however it does seem feasible that this could be accelerated, maybe taking the bottom 25 percentile as the envelope…

37. TSB Research Project led by Preactor International To Develop an Autonomous Systems Development Tool (ASDT) To Assess the effectiveness of Autonomous Scheduling using end users in real applications Project due to be completed in February 2015 Collaborators DeMontfort University C4FF - Centre for Factories of the Future Tata Steel (end user) Plessey Semiconductors (end user) TDK-Lambda (end user) Autoplan – Advanced scheduling algorithms to autonomously produce and update schedules with minimal human intervention http://www.preactor.com/Home.aspx

38. Looking at different ways of production scheduling that:- Produces schedules autonomously without the need for manual input Produces schedules more often and at fixed times (minutes, hours) Uses different scheduling rules and objectives, compares current status to performance measures and selects the best rule for the next scheduling run Using three companies in different industries to develop the tools and assess the benefits and effectiveness of autonomous scheduling Also looking at the capability of Genetic Algorithms (GAs) in scheduling applications (DMU and C4FF) Autoplan – What is it?

39. Case Studies – Proof of Concept Three collaborators/plants have been selected to provide a proof of concept of autonomous scheduling. Tata Steel TDK-Lambda PlesseySemiconductors Sheffield, Steel Bars Ilfracombe – PowerSupplies Plymouth – Wafer Fabrication Scheduling of heattreatmentfurnaces Scheduling of 6” waferfabproduction. Scheduling of PCB componentinsertionlines.

40. Process Route £15M project to commission unit at Stocksbridge in early 2015

41. Autonomous Agent Approach Performance Objectives C Importance E C – Customer E – Energy U - Utilisation U Tolerance • Independent rule sets are associated with each objective • Positioning on grid determines which objectives dominate and when to act • Moving look ahead window evaluates impact of current rule at each ‘event’ and changes rule if tolerances are exceeded • Multiple iterations may be required to test alternative choices and seek better solutions Currently evaluating different strategies for Application

42. Consider CHT and BHT as Combined System Supply as per Heat Treatment System What is throughput potential of the line for variety of typical CHT Schedules ? Utilisations and potential bottlenecks ? Impact on BHT Scheduling ? Sideloader from BHT (Cooled Charges) Charges cool using linear rule, processed as soon as reach end of prescribed cooling time. Insert BHT (cooled) charge in gap of more than 60 minutes available Supply Sideloader Movements Middle Crane Movements End (Magnet) Crane Movements BUFFER Sideloader to Cooling Beds BHT Tempers inserted to F10 if free and forming bed on F8/F9 free F7 ‘Mark and test’ time per bar before transfer Crane removes bars one at a time, between 1.5 and 2.5 mins per cycle Brinell Saw Quench F8 F9 F10 ReBundle F8 and F9 used normally, F10 used if system blocked or formed charge for F8/F9 would have to wait more than (30) minutes Straight Through Option ? (Remove from Process)

43. Run Furnace Without Gaps and Use F10 to avoid blocking – example Schedule ‘206’ Matching of Hardening Furnace to Original Design with 2 Temper Furnaces is very good when charges are ‘filled’ to max

44. Run Furnace Without Gaps and Use F10 to avoid blocking and BHT tempering when free + Downstream processing of BHT charges when gaps available. –Schedule ‘206’

45. Pathway to Intelligent Manufacturing Deployment Real-Time Autonomous Scheduling Interactive Workshops Knowledge Management Data Mining Rules, Relationships & Patterns Process Control

46. Summary • Through Process Integration – a key concept to keep in mind • Knowledge engineering – understand, secure, deploy • Data Mining > Analytics • Discrete Event Simulation • Growth Curves for Data Integration and Exploitation • Store Everything – Cheaply – (on one platform ?) • Enable Access – Analysis of Anything (by anyone ?) • Distill on Demand – Concept of a ‘data ecosystem’ • Establish Frameworks and Tools to Support Collaboration • Attention to Knowledge Management • “From now on we know it” • Visualisation and Visibility key aspects • Deliver Shared Insight and Confidence • Supportive of culture open to automation and process re-engineering • Develop/Identify Options for Real-Time Deployment