arcelormittal co-op presentation - david pearson

Co-op Experience – Spring/Summer 2015

David Pearson

18 September 2015

2

About Me

• School: Drexel University - Materials Science and Engineering

• Position: Metallurgical Engineering Co-op, Quality Department

• Future plans: United States Army Reserve, Grad. School

Projects

3Gray Red

• Load bank quality audit• Handheld scanners implementation• Scale color analysis• Cut-to-length flatness analysis (light-gauge carbon

coils)• Thermal image data collection

Load Bank Quality Audit

Problem:•Nonconforming material being sent to load bank (LB) and shipped to customers

– Losing customers for poor quality (Trinity)– Change of company culture and mindset over time– Quality issues on LB: cut corners, test knotches, improper

skidding, out-of-flatness, grease/rust/debris, fins, camber, surface defects

Goals:• Restore company culture: zero tolerance policy for shipping

defective material•Remove all defective material from LB•Ensure that all material coming to load bank is of good quality

4

Load Bank Quality Audit

Early June, 2015 – scanned LB1 and LB2 for quality violations• Identified over 60 transfers with plates having cut corners, test

knotches, improper skidding, out-of-flatness, grease/rust/debris, fins, camber, surface defects

•Placed each affected piece on ‘hold’•Communicated with supervisors to have pieces transferred from LB and corrected

September 10, 2015All nonconforming material previously on LB:1. Removed from load bank and sent to correction operation2. Already corrected & returned to load bank (released)

5

Handheld Scanners Implementation

• Problem: no locations assigned to transfers going to the load bank – time spent looking for transfers increased delinquency– Material lost– Plates rejected

• Goals:– Continue where previous intern left off– Train load bank personnel how to use the handheld scanners– Track progress of amount of scanned material going to the

load bank– Ensure that all load bank personnel are using the scanners

regularly before the inventory audit

6


• For a month, trained material movers and crane operators how to use handheld scanners to assign locations to transfers going to the load bank

• Current status: all shipping personnel trained and regularly using handheld scanners– No way of knowing the workers who do not scan material,

only the workers who do scan material

• Future: tie handheld location updates / ‘scanned’ or ‘not scanned’ / ‘scanned by’ into Access to determine who is and is not scanning transfers going to LB

7


8


•Future:

– handheld location updates– ‘scanned’ or ‘not scanned’ – ‘scanned by’reported into Access to determine who is and is not scanning

transfers going to LB

9

10

Scale Color Analysis

Problems

1. Plate-to-plate color variation not appealing to customer

2. Red scale harder to remove by customer than grey scale (Meritor complaint)

3. Some plates have both red and grey scale traces• Customer more willing to accept material

with uniform surface appearance

11


Red vs. Grey Scale

12


Goals

1. Continue analyzing scale color data where Jen (previous Intern) left off – quantify color

2. Research instrumentation to determine color rather than assigning a ‘Yes’ for Red and ‘No’ for Grey

Scale Color Measurement

13

PCE Instruments: NR20XE Spectrophotometer

14

Scale Color Analysis Results

• Analyzed over 25 variables

• Correlation b/w redness and Si content / finish surface temperature at Steckel mill relatively strong

• As %Si and surface temperature increase, amount of red scale increases

15


Proposed mechanism (research-supported)

• Si-containing steels form Fayalite (Fe2SiO4) upon heating

• Fayalite penetrates surface to form a mechanical key = difficult to remove

• Fayalite oxidizes and turns red• Result: difficult-to-remove red scale

“Mechanism of Red Scale Defect Formation in Si-Added Hot-rolled Steel Sheets” – Tomoki Fukagawa, Hikaru Okada, Yasuhiro Maehara - 1994

CTL Flatness Improvements (Light Gauge Carbon)

– Problem:• Flatness issues at cut-to-length (CTL)

line– Money wasted sending carbon

grades to be levelled (high NSEP)– High claim rate from customers

due to flatness (Fisher Tank)

– Goals:• Monitor flatness of carbon coils for

different gauges and MPS– Focus on 0.1875” and 0.25”

• Determine which variables have greatest effect on flatness

16

Corrective Actions

• Side shear the light gauge carbon coils – Mill started to provide wider coils to be sheared at CTL

• Work on improved settings at the CTL – More aggressive sheet leveler settings

• Monitor coil quality from the mill (‘displacement’ measurement)

Copyright © ArcelorMittal 05/03/23 17

CTL Flatness Data Analysis

18

Before 5/4/2015Qty Qty

ME CE Total ME CE

Level Good Level Good Level Good Level Good

0.1875 1041 493 142 498 2174 0.1875 67.9% 32.1% 22.2% 77.8%

6 mil 25 69 94 6 mil 26.6% 73.4%

0.25 861 2635 125 1399 5020 0.25 24.6% 75.4% 8.2% 91.8%

7 mil 134 270 404 7 mil 33.2% 66.8%

0.312 145 1285 1 254 1685 0.312 10.1% 89.9% 0.4% 99.6%

9 mil 58 58 9 mil 0.0% 100.0%

0.375 57 263 320 0.375 0.0% 100.0% 0.0% 100.0%

Total 2206 4867 268 2414 9755 Total 31.2% 68.8% 10.0% 90.0%

CTL Flatness Data Analysis

19

After

Qty Qty

ME CE Total ME CE

Level Good Level Good Level Good Level Good 0.1875 346 626 210 974 2156 0.1875 35.60% 64.40% 17.74% 82.26% 0.25 166 923 48 1373 2510 0.25 15.24% 84.76% 3.38% 96.62% 7 mil 2 198 200 7 mil 1.00% 99.00%

0.312 42 493 164 699 0.312 7.85% 92.15% 0.00% 100.00% 0.375 1 457 26 484 0.375 0.22% 99.78% 0.00% 100.00% Total 557 2697 258 2537 6049 Total 17.12% 82.88% 9.23% 90.77%

5/4/2015

Effect of Sideshearing – 0.1875”

• For 0.1875” gauge: 32% to 64%

20

Coil Quality Measurement

• Analyzed over 300 carbon coils

21

Effect of changing CTL Settings– 0.1875” (least to most aggressive settings)

22*Note: date 7/6/15 to 7/18/15 2 weeks of more aggressive settings at CTL

0.1875"

121 + 91-120 0-90

256 15 2033% 15% 54%526 83 1767% 85% 46%

Sht. Lev. Entry Settings

Out-of-flat

Flat

Effect of changing CTL Settings– 0.1875”

23*Note: date 7/6/15 to 7/18/15 2 weeks of more aggressive settings at CTL

• For 0.1875” gauge – 65% to 85%

Flatness Data Analysis (Minitab)

Y variable:

–Flatness (Y or N)• Y – flat• N – out-of-flat

24

X variables:

–Entry/exit settings of• Straightener• Plate Leveler• Sheet Leveler

–Backup rolls–Wrap displacement (coil quality)

Thermal Image Data Collection

Problem•Mill rejections for cold ends, mixed-gauge, rolled-in scale

Goals•Run trials to determine if an IR camera can be a useful tool to have permanently at the mill

– Ability to detect non-uniformities in temperature and scale on the surface of plates being milled

25


Description:

•Took images and videos with FLIR T640 IR camera of:1. Slabs coming out of the furnace and going into Roughing Mill

• Quantified amount of scale remaining on slabs after descale

2. Plates coming out of Roughing Mill and going into Steckel Mill• Characterized non-uniformities in temperature on plate surface• Cold streaks, cold ends, water on plates

•Stood on catwalk and on platform near heat deflectors

26


27

FLIR T640 IR Camera


• Observed over 100 slabs from furnace to Roughing Mill for amount of scale remaining after descale box

– Found that nearly 70% of slabs still had scale heading into RM

28


• Observed over 200 plates from Roughing Mill to Steckel Mill• Characterized plates by non-uniformities in temperature

– Cold ends, cold streaks

29

Thermal Image Data Collection (Video)

30

Ideas for Future Work

• Use data captured with IR camera and correlate:

– Scale going into the mill with rolled-in scale rejections– Non-uniformities in plate temperature with rejections for cold

ends or mixed gauge• Consider if it would be beneficial to have camera mounted

permanently at mill – operators could stop if they notice cold ends, etc.

31

Questions?

32