Ruukki Metals Mika Judin

Transcription

1 The Impact of MATLAB, Database Toolbox, and MATLAB Deployment Products in Ruukki Metals' Steelworks to Achieve Better Performance in Processes and Products 1

2 The Impact of MATLAB, Database Toolbox, and MATLAB Deployment Products in Ruukki Metals' Steelworks to Achieve Better Performance in Processes and Products Challenge Flexible and low cost early warning system for processing lines easy to implement and update Solution Process information transfer system (PITS) has been created using MATLAB Builder NE, Database Toolbox, MATLAB Compiler Results Significant savings have been obtained by having less failures, MTBF higher less scrap higher production speed 2

3 University of Oulu 1996 Department of process engineering Mass & heat transfer laboratory Master thesis The model of plastic coating s stoaving MATLAB user since 1995 Some programming in Fortran, C++, development of Level2 models in the mill Finite element modelling: heat transfer, mass transfer, structural mechanics R&D Engineer 3

4 Ruukki Metals produces special steels for use in buildings (facades, roofings), automotive industry, mining industry.. Steel products (cold rolled, galvanized, color coated) mainly in coils This presentation focuses on the works in Hämeenlinna, Finland coils produced annually visit: Process steps hot rolling (raw material producer, incoming hot coils) pickling (cleaning, remove dirt) cold rolling (make thinner) galvanising lines 3pcs (hot dip to melt zinc) batch annealing+temper mill (finishing surface) color coating lines (application of paint & drying) recoiling and slitting 4

5 Foundation - a lot of data available... DATABASES in Ruukki s Production datawarehouse: MSSQL (>200 tables) process planning data material data transversal profiles (thickness,zn mass, Fe%) setup data overall results for coils (classification results) ORACLE (>200 tables) defects test results setup data MySQL (>50 tables) setup data measured data Wonderware Historian >5000 different measurements flatness, thickness temperatures, speeds, forces, strip position,.. 5

6 The Impact of MATLAB, Database Toolbox, and MATLAB Deployment Products in Ruukki Metals' Steelworks to Achieve Better Performance in Processes and Products Challenge Create a flexible and low cost early warning system for processing lines, easy to implement and update Coils with critical properties should be recognized before processing lines: Possible risk of misalignment with flatness charts to avoid: missalignment Risk of thickness defects with thickness charts to avoid: strip break in finish rolling to avoid: recoiling, (scrapping done in line) Risk of thermal treatment defects with inspection data of surface, position in strip indicated to avoid: thermal necking, reduction in area 6

7 Solution 1. stage Collecting Data MATLAB Database toolbox creation of database queries functions timeseries database (Wonderware) flatness & thickness data relational database (Oracle, MSSQL) titledata for the pictures tolerances processing moments (coil start/end) query-functions: inputs / outputs / connection JDBC-connection Querybuilder app was used to create a prototype for further editing selecting column names testing connections 7

8 Challenge 2. stage Visulization of process data Preprocessing of data remove incorrect parts (Nans,nulls), outliers,.. partially missing data missing coils Calculations of characteristics 8

9 Solution 2. stage Visulization of processdata Creating a figure was divided to 2 parts (depending on if new coils occured to list): long route 30 queries Collection of data, handling missing data logical, custom function for fixings Create a main figure with multiple subplots for all incoming coils contourf for flatness data/ zinc mass data/ thickness map vs. length fill used as indicator of progress plot : centerline thickness vs. length Addition of general and defect data to titles short route Earlier data is used to reform pictures 2 queries (position in coil) Move the progress indicator Add current time to coil in line title Output as a bitstream (used in.net) bitstream=figtoimstream('fighandle',gcf,'imageformat','png','outputtype','uint8'); 9

10 Challenge 3. stage : Deployment: Compiler/Builder NE (easiest (MATLAB) part of the solution) Generated with MATLAB Compiler s Deployment Tool (R2012a).NET assembly added main-function as m-file added helper files for database connections created dll sent to.net developer Solution: dll:s output as bitstream Webfigure (wasn t ideal for this purpose).net part (websites) done as outsourced uses created dll:s problems solved with cooperation with Mathworks Tech Support 10

11 RESULTS: PITS: early warning system in galvanizing line 3 lines 9 screens flattness thickness defects (if there is) offgauges coil in production next coil waiting coils 4pcs (also in used in other lines: color coating lines, coiling line, slitting line) 11

12 Results: Flexible and low cost early warning system for processing lines But problems occurred.. bottleneck queries (response time varies seconds) caused too long delays: no change on screens for several hours root cause:.net program called dll:s every 12 seconds, task buffer grows.. if too much trafic in server, number of new queries grows took even hours to unload opening of new PITS website could take seconds 30 opened web-sites : heavy load to IIS server size of dll 11MB need of IIS boot/reset to heal Solution use of standalone exe to create BLOB directly to database for certain purposes:.net application uses stored BLOB without MATLAB sligthly altered dll:s used only in websites where coilnumber is an input 12

13 Why we have chosen MATLAB? Not yet discovered: what is MATLAB not capable of Main highlights: Database queries/updates No limitations discovered Only java heap memory sometimes.. Finite element calculation GUI Tailor-made GUIs for outside FEM-tools (Comsol MP) Very powerful tool for visualization Multiple subplots deploytool Tailor-made tools Easy implementation & installation Only weakness : no neuralnetwork training possibility in deployed versions 13

14 The Impact of MATLAB, Database Toolbox, and MATLAB Deployment Products in Ruukki Metals' Steelworks to Achieve Better Performance in Processes and Products SUMMARY NEED: Flexible and low cost early warning system for processing lines easy to implement and update Solution Process information transfer system (PITS) has been created using MATLAB Builder NE, Database Toolbox, MATLAB Compiler 1: non-continuous monitoring: builder NE (dll in.net part) 2: continuous monitoring: compiler (exe + separate.net) Results Significant saving have been obtained by having less failures, MTBF higher less scrap higher production speed Has been in use since April

15 TIPS Useful MATLAB functions subplot contourf logical nn-functions: newsom,train,sim nchoosek (easy way to create combinations selector as a building part ) listdlg questdlg inputdlg THANK you for your attention! 15