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Slide1

Optimisation of the life cycle for

new product development is a challenge for R&D in steel industry. The biggest being to reduce time to market by speeding up development time:Complex production processesLarge number of influencing factorsLimited a priori knowledgeEarly stage experiment design using the definitive screening designs in JMP® 14.0 results in a significant reduction of experiments as the total number of runs is almost four times smaller than that in the FF design.

Enhanced product development through experimental design using JMP 14.0 Bernard Ennis, Patricia Gobernado and Dave HanlonTata Steel Research and Development, IJmuiden, The Netherlands

Abstract

New Product Development cycle

Analysis Methods

Experimental Methods

Continuous AnnealingFinal step in sheet steel production consisting of:Casting, Hot-Rolling, Cold-Rolling and Continuous Annealing:

Design of ExperimentsComparison of full factorial (FF) designs with definitive screening designs (DSD):FF design, two level factors (minimum and maximum) FF design, 3-level factors (min, max and mid-point)DSD, 3-level factors.

Test chucks

Miniature tensile specimen used for DSD experiments

Response variables: Yield strength, YS, MPaUltimate tensile strength, UTS, MPaTotal elongation, TE, %

Mechanical properties

Key issue: capacity and capability to deliver first-time- right and fit-for-purpose

Five temperature factors;

Line speed influences time at each temperature proportionally.

Applied load

Applied load

DoE

Factors

Levels

No. Runs

FF

6

2

64FF63729DSD6317

Design efficiency and orthogonality

DSD

FF

DSD

Main effects are orthogonal

Main effects not confounded with 2-way interactions

Slide2

Enhanced product development through experimental design using JMP

 14.0 Bernard Ennis, Patricia Gobernado and Dave HanlonTata Steel Research and Development, IJmuiden, The Netherlands

ResultsBenefits for NPD

Augmented design

Comparing designs

FF and DSD experiments run for comparison:

FF design, two level factors – multiple random blocks

DSD, 3-level factors – single experimental block

Both designs identify the same significant factors (screening)OAS T, and RTH and SCSGood agreement regarding goodness of fit in the actual vs predicted graphs from both designsDSDFF

Augmenting DSD design

Variation of significant factors ONLY

Blocking as statistically significant factor

Correlation between factors (RTH and OAS)

Noise in the response (YS) related to measurement error

Improvement in DSD confidence intervals

Comparable R

2

values for quarter of runs (DSD)

CI DSD wider at extremities

Benefit:65-75% Reduction in burden on research equipment and material

Very fast turnaround:Traditional NPDBasic understanding with+/-200 experiments

21 months using pilot plant and/or production materialDSD

Small scale sample routeSamples produced and tested within one week

Second iteration within one monthSame factors identified to traditional NPD

Next steps

Introduce DSD as best practice for all new product developmentExplore JMP® statistical platform for:Advanced analytics of production dataReliability and repeatability (measurement error)

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new product development is a challenge for RampD in steel industry The biggest being to reduce time to market by speeding up development time Complex production processes Large number of influencing factors ID: 797712 Download

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