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
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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
Slide2Enhanced 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)