The Role of Fibre Characteristics for Online Process Adaptation in the Manufacturing of MDF

SWST June 23-27 2014 in Zvolen, Slovakia

Martin Riegler

Martin Weigl

Ulrich Müller

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industrial production of wood-based panels

complex manufacturing process

variability of raw material

challenges in wood-based panels industry:

• decrease variability of final board properties (lower safety margin)

• minimize costs of production

• flexible production due to alternative raw materials or new products

motivation

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soft sensor data

consistently recorded machining parameters

well understood

final board properties

consistently characterized

well-established standards over the last centuries

raw material properties

hard to determine

high influence on final board properties can be assumed

data acquisition

… through statistics!

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fiber morphology

challenges:

• separation of fiber bundles

• analysis of crooked fibers

• high number of fibers to investigate (~2*106 per g) representative sampling!

• measuring three dimensions

• width (e.g. sieving)

• length (e.g. optical analysis)

• thickness ?

optical solutions (Camsizer, FIBERCAM, FibreShape, QIC-PIC, QualScan, Benthien et.al.)

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optical method

Aim: develop an easy to apply method to determine fiber morphology

• 115 batches of industrially manufactured resinated MDF fibers

• 7mg were evenly spread onto a black paper using a sieve (mesh size 0.5mm)

• fibers were fixed using a self-adhesive transparency film

• scanning by a flat bed scanner at 1000dpi

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image analysis

• images were modified in Adobe Photoshop (remove artefacts at edges and enhance contrast)

• threshold (mean grey scale distribution)

• image analysis with macros in ImageJ skeletonisation algorithm

scan threshold outlines

“largest

shortest path”

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analyzed parameters

• fiber length („largest shortest path“)

• circularity (shape descriptor)

slender particles would have circularities towards 0 and circular particles towards 1

• average fiber width (dividing the area of a particle by its length)

• branching factor (number and length of branches per fiber)

statistics, plots and PLSR modelling were realized in MATLAB

2ncecircumfere

area4*π*ycircularit

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results – fiber length

preliminary tests revealed good validation of skeletonization algorithm

mean = 0.43 mm

lwl = 1.72 mm

to promote underrepresented but technologically important longer particles

arithmetic mean

length weighted length (lwl)

],[

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mml

llwl

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results – fiber area

85 % smaller than typical spruce fiber (length: ~4 mm, width: ~0.04 mm area: 0.16 mm²)

high proportion of fiber fracture can be assumed

similar findings

for width: • slender fibers: 0.08 mm

• circular fibers: 0.17 mm

and circularity:

majority rather circular

typical spruce fiber

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results – circularity

30 % of particles rather circular (0.9 - 1) indication of particle fractures that occur in defibration process

5 % of particles rather slender (0 – 0.1)

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results – PLSR modelling

• fiber length selected as third most important parameter for predicting the internal bond strength (IB) of MDF (after board density and digester steam consumption)

• longer slender fibers (circularity 0.1-0.2) increased the IB of boards (Z-scaled regression coefficient: 0.21)

• resulting in a mean normalised root mean squared error of prediction (MNRMSEP) of 4.8 %

• error was increased to 7.3 % if no fiber parameters were used for modelling

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conclusion & outlook

• laboratory approach for determining fiber morphology is easy to implement

• low investment costs

• accurate determination of fiber length due to skeletonization algorithm

• automatic analysis by macros and scripts

• inclusion of fiber morphology characteristics improved PLSR modelling by 50 %

underlines the significant importance of fiber morphology

outlook:

to increase number of fibers analyzed per time, automatic sample preparation is needed

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Thank you for

your attention!

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Literature

Benthien JT, Bähnisch C, Heldner S, Ohlmeyer M (2014) Effect of fiber size distribution on medium-density fiberboard properties caused by varied steaming time and temperature of defibration process. Wood and Fiber Science 46 (2):1-11

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Wang HBE (2007) Fiber Property Characterization by Image Processing. Texas Tech University, Lubbock

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