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Ruukki Metals | Mika Judin

The Impact of MATLAB, Database Toolbox, and MATLAB

Deployment Products in Ruukki Metals' Steelworks to

Achieve Better Performance in Processes and Products

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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

Ruukki Metals | Mika JudinThe Impact of MATLAB, Database Toolbox, and MATLAB Deployment Products in Ruukki

Metals' Steelworks to Achieve Better Performance in Processes and Products

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Ruukki Metals | Mika Judin

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

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Ruukki Metals | Mika Judin

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

70 000 coils produced annually

visit: www.ruukki.com

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

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Ruukki Metals | Mika Judin

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, ..

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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

Ruukki Metals | Mika JudinThe Impact of MATLAB, Database Toolbox, and MATLAB Deployment Products in Ruukki

Metals' Steelworks to Achieve Better Performance in Processes and Products

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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

Ruukki Metals | Mika Judin

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Challenge 2. stage

Visulization of process data

Preprocessing of data

remove incorrect parts (Nans,nulls), outliers,..

partially missing data

missing coils

Calculations of characteristics

Ruukki Metals | Mika Judin

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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');

Ruukki Metals | Mika Judin

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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

Ruukki Metals | Mika Judin

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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)

Ruukki Metals | Mika Judin

RESULTS:

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Ruukki Metals | Mika Judin

Results: Flexible and low cost early warning system for processing lines

But problems occurred..

bottleneck queries (response time varies 0.5-30seconds)

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 10-30 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

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Ruukki Metals | Mika Judin

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

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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 2013

Ruukki Metals | Mika JudinThe Impact of MATLAB, Database Toolbox, and MATLAB Deployment Products in Ruukki

Metals' Steelworks to Achieve Better Performance in Processes and Products

15

Ruukki Metals | Mika Judin

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 !