Perspectives on Digital Manufacturing

Pinakin Chaubal

General Manager, ArcelorMittal Global R&D

Future Steel Forum, June 14-15, 2017 Warsaw, Poland

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?

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The Steel Plant is a Complex set of units

within an Enterprise

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The Center for International Manufacturing (Cambridge University) has come up with what they

believe will make up the “Smart” factory of the future with more a Commercial / Supply Chain focus.

Smart Manufacturing is not just the Smart Factory

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4

What is driving us?

The 4 P’s

P…..

P……

P……..

P………

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What is driving us?

PEOPLE

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What is driving us?

PRODUCTS

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What is driving us?

PROCESS

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What is driving us?

PROFIT

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The Revolution?

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The Industrial World is moving!

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The path forward is not always clear!

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Process Control has evolved taking

advantage of numerous developments

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These technologies can be used individually or in combination to support the development of new

applications, especially in making our manufacturing operations and integrated supply chains “Smarter”.

Future Steel Forum 2017 FocusIndustry 4.0 – Smart Factory Building Blocks

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The Gartner Hype Chart

20/06/2017 Confidential 15

Advance Automation:

KOBM Auto Tap at ArcelorMittal Dofasco

• Automatically tap Steel from KOBM Vessel, includes

– Vessel tilling

– Slag carry over control

– Ladle movement (before, during and after tapping)

– Flux/alloy addition

– Ar stirring

– Slag coating

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Global Platforms:

ArcelorMittal BF Network

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Global Platforms:

ArcelorMittal Global Product Quality System

Global Platforms:

ArcelorMittal Line Scheduling with Artificial Intelligence.

Many simultaneous changes:

thickness, width, steel grades,

thermal cycles, zinc coating,

skin pass elongation,

GA layer, passivation / oiling, …

Earth lifetime

(seconds):119000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000

00

143173440000000000

# of possible sequences

with 70 coils:

Parallel

Computing+ =Flexible &

Optimal

Production

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New Limits: Deep Learning for radical quality check

(1) Accuracy of current ASIS (Automatic

Surface Inspection Systems) is not too high

(2) Post-ASIS filtering + human intervention

improves accuracy by 25%

Human analysis is a very costly process (20

potential slivers to find a real one) and almost

impossible to roll out.

• Collaboration with start up company

• New Deep Learning techniques were applied

on top of current ASIS, creating a model from

a 100.000 images dataset

• Accuracy achieved is similar to (2)

• Radical reduction of human inspection and

model can be rolled out

Traditional ASIS + rule-filtering New Deep Learning techniques

• Slivers are still a production defect to be worked out by all steel manufacturers

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An European initiative – coils that

“communicate”

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So where do we go?

ZERO PROCESS UPSETS

ZERO DEFECTS

ZERO EQUIPMENT FAILURES

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And What are our challenges?

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And What are our challenges

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To succeed – a holistic approach

Prof. Volker Stich, RWTH Aachen

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And Steps to Success

1. Alignment : Actions => drivers that support goals of the enterprise

2. Systematic Gap assessment to determine the roadmap

3. Proof-of-Concepts that demonstrate value : Rapid transition from cost to value

4. The culture of the organization will be impacted on this journey -> prepare the change

management accordingly.

5. The alignment of the Operations Technology (OT) and the Information Technology (IT)

6. Sharing successes / challenges / lessons learned across the company’s enterprise facilities

7. Do not go to it alone

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But InnoTech =

To guarantee a

sustainable future

And in conclusion