–

Ernst-Udo Sievers, eiffo eG

Brussels, 15 June 2017

MEMAN – Surface finishing cluster

200 industrial SMEs of the German surfaceengineering industry

• Metal plating & other surface finishing

• Technology suppliers

• Chemical suppliers

• Mechanical engineering companies

Research institutions

• Electrochemistry

• Surface engineering

• Process automation

• Production automation

• Resource efficiency

• Process simulation

• Hydrodynamics

• Measurement & testing

eiffo

cluster

STJ

RD&I Roadmap implemented through several dedicated workgroups andnumerous targeted research and innovation projects.

MEMAN Surface Finishing Cluster – Resource efficiency workgroup

Waste water

treatment

Metal recycling

Electroplating

plants

Process optimisation

Electric power

systems

Process & factory

automation & control

systems

Heat recovery

Emission control

From large-size,

single production parts

(mech. engineering)

to mass production parts

(automotive)

MEMAN is essential to the Resource Efficiency targets of our RD&I roadmap

Use case for value chain innovation of the MEMAN Surface Finishing Cluster

Bla

nk

pis

ton

rod

s

Ch

rom

e p

late

d

pis

ton

rod

s

Manufacturing 1 Manufacturing 3

VOEST

ALPINE

(steel

maker) Steel

bars

Global view on the value chain – 4 main stakeholders involved

Piston

rodsLIEBHERR

(Typical OEM)

Value chain of hydraulic piston rods for construction machines

Manufacturing 2

MEMAN Surface Finishing Cluster - Use case for value chain optimisation

OEMProcess / factory automation

& control systems

Detailed view on the metal-mechanic manufacturing part of the hydraulic piston rod

value chain

Electrolyticaldevarnishing

Rejects

Steel bars

Surface hardening

Heat treatment

Oiling,, packaging,transport“

Stretch levelling

Welding GrindingRolling

DegreasingHard chrome

plating“Grinding and

polishingRinsing

Quality control

Oiling,, packaging,transport“

Three dimensions Value Chain Innovation

MEMAN Surface Finishing Cluster - Focus on Value Chain Innovation

Objective: Innovation in the business processes of the

value chain, to optimise critical interfaces

(esp. between different companies)

What is to be achieved?

► Assessment of the costs of disorders in

quality and quantity of materials supplies;

► Reduce and abandon supply disorders

and/or their impact on downstream

manufacturing efficiencies.

Approach:

► Identify critical material parameters and

business parameters to control value chain

manufacturing performance;

► Advanced specifications & materials data

management for optimised quality of base

materials (metal substrates);

► Reduction of quantitative material flow

disorders through optimisation of business

and information processes.

Critical interfaces, often not well defined

Resource balance with poor substrate steel quality (Primary energy balance in MJ of combined materials & energy savings)

Optimal performance

Assessment of one single quality disorder in the framework of a preliminary study

MEMAN Surface Finishing Cluster – Estimated impacts of supply disorders

Estimated annually cumulated impact on Resource Efficiency, based on

controlling data, expert assessments, and interpolation

Shortfall or delay in raw material

supply / other upstream manufacturing 4 35% 20 5

14,6%

Excess supply at Thoma 3 25% 4 10 4,2%

Excess supply at STJ 4 25% 20 0,5 1,0%

Changes in raw materials quality and /

or suppliers5 45% 3 15

8,4%

Changes in auxiliary materials quality 2 15% 10 1 0,6%Unscheduled changes of upstream

manufacturing parameters4

35%50 0,3

2,2%Unspecified properties in process

chemicals and intermediates5 45% 1 3 0,6%

Missing order data 5 45% 50 0,15 1,4%

… … … … … …

≈ 12%

15 - 20 %

Supply

quantity

disorders

Supply

quality

disorders

Duration

(days)Interface disturbances

Impact

factor

Relative loss

of resource

efficiency

Frequency

per year

Annual loss of

resource

efficiency

Slightly higher burdens in steel production yield significant savings in subsequent process steps

Blank

Piston

Rods

finishingpreparation,

rack

mounting

elektrolyticaldegreasing

rinsing activating

chrome

plating

I

etching

chrome

plating

II

rinsingrack

demounting

grinding

(optional)polishing

QM /

packaging

Chrome

Plated

Piston

Rodsrinsin

g

deva

rnis

hin

g

dry

ing

QM reject

QM reject

Rinse water rcycling

Rin

se

wate

rrc

yclin

g

dry

ing

MEMAN Surface Finishing Cluster – Key technical issues

‚Merry-go-round‘ („Oktoberfest“)

due to disorders in base metal

quality

Cr layer

Base metal (steel)

Grain orientations of steel substrates may be critical parameters influencing

downstream manufacturing performance

AFM-

measurements

Grain dependent corrosion

MEMAN Surface Finishing Cluster – First results

Induced corrosion through etching

Before etching After 90 sec etching

Grain boundary

Relative width

Relative width

Relative width

MEMAN Surface Finishing Cluster – First results

Electro-polished Fe Chemically polished Fe

positive grain

boundaries (walls)

(100) – (100)

stepped grain

boundaries

(100) – (101)

grain boundaries

hardly visible

(111) – (111)

negative grain boundaries

(trenches)

(111) – (111)

Stepped grain

boundaries

(101) – (100)

Strong influence of grain orientation

Highest metal removal on (100)- planeScale: 5µm edge length

Scale: 15µm edge length

MEMAN Surface Finishing Cluster – Towards advanced materials data management

Materials data is a critical resource for manufacturing organisations

► The diversity of materials used is ever increasing, also for metals: ultra-strength steels such as PHS (press-

hardened steel), DP (dual phase), QP (quenching and partitioning) and TWIP (twinning induced plasticity) steels as

well as aluminium, magnesium and titanium are increasingly used.

► The properties of materials are more and more individually adapted to the ever broadening range of applications.

► The inter-relation of materials micro-structures with detailed materials models, the use scenario, the

designed durability of the component, the properties of the base material, the manufacturing process, and the

actual resulting component properties, poses a specific challenge due to the complexity involved.

The integration of materials data flows along the manufacturing value chain provides substantial

opportunities and benefits:

► The development of new materials and tailor-made optimisation of existing materials will be faster and cheaper if

component and manufacturing requirements are fully available

► Self-learning, self-optimising manufacturing processes will receive feed-back regarding the effect on component

properties through virtual process chain simulation. This will increase process efficiency and quality rate, reduce

rejects and improve profits.

► The digital representation of production materials along the value chain creates additional value since this digital

representation could itself be the basis for new business models.

Compare also: „Towards a digital infrastructure for engineering materials data”, Tim Austin, European

Commission, Joint Research Centre (JRC, Petten), in Materials Discovery 3 (2016) 1–12

Thank you for your attention