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CertiWood Forum 2007

New and Emerging

Technologies for Log

Grading and Sorting

Peter Lister

Manager, Lumber Manufacturing Technology

Forintek Division

We have a new name….


Creating forest sector solutions


Altered product quality

Product uniformity

Blue- stained wood

Product performance

Product durability

Customers ’ perceptions

Pulp & Paper

Pulping characterizations

Processing modification

New product attributes

The Forest Resource

Wood quality affected by site


Environmental impact

Solid Wood Manufacturing

Wood characterizations

Log Delivery

Processing modification

New product attributes

Chip quality

Forest Sector

Value Chain

Stand Inventory

MPB attacked stands

Longer shelf life stands

Stand susceptibility

Road building

Soft footprint hauling

Optimized truck size

Mill storage

Harvest Planning

Use available knowledge

Equipment & system choice


Harvest scheduling




Soil & stream


Soft footprint


Log sorting


Fibre recovery

Post Harvest

Site preparation

Species selection

Fire risk management

Presentation Outline

• Why grade and sort logs?

• Sensor technologies and their ability to detect

different log and wood attributes

• New and emerging sensors and scanners

– Machine vision systems

– Vibration resonance sensors

– X-ray log scanning systems

• Conclusions

• Questions

Why do we want to grade and sort logs?

• Trees and logs have high levels of natural variability

– Different tree species

– Different log sizes and shapes

– Different quality levels: knots, rot, stain, checks, etc.

– Different wood properties: density, strength, stiffness, etc.

Logs are expensive!

– Maximizing value from log investments requires that we process

every log to extract the highest value end-products

• Processing the wrong logs costs us money

– Low-grade logs can result in low-value products and can create

processing problems, etc.

– We want to ensure that we process only those logs that provide a

positive contribution to the bottom line

Example: Mountain Pine Beetle

• MPB attack results in dry, dead

logs with varying degrees of


• It’s important to process only logs

that result in positive margins

• We need an efficient and cost

effective way to grade and sort

MPB logs based on defect level

and highest value end-use

North American approach

Log grading and sorting in North American is typically

done manually using visual grading rules

Logs are graded based on merchantable volume (Govt. grading rules) or

on end-use (company specific rules)

– Visual grading is subjective and considers only the external indicators

that can be seen by the grader

– Important log and wood quality attributes (E.g. knottiness, wood density,

grain spacing, etc.) can not be easily evaluated

European approach

Logs are automatically scanned, merchandized and

sorted by log grade, diameter class and end-use

Log / wood attributes and sensor technologies

Desired Log Attribute





Log size and shape Laser TS scanners Available

Log surface features

(E.g. bark, checks, knots)

Metal in logs

Camera machine vision



Emerging / Available


Wood density, strength

Grain Angle

Moisture Content

Log “knottiness”,

heartwood/sapwood boundary, rot,

under-bark diameter

Stress wave,

Vibration resonance

Laser trachiod effect,




X-ray radiographic

Emerging / Available

Emerging / Available,




Emerging / Available

3D log models with internal defects X-ray CT Experimental

Laser “True shape” Log Scanners

• New generation of scanners offer better

measurement accuracy and higher scan frequency

• More accurate log measurements help to detect

defects like cat faces, large checks, etc.

Scanner image courtesy of LMI

Machine Vision Systems

• Many new laser scanners now offer camera output for

machine vision applications

Images courtesy of LMI, Hermary Opto

Machine Vision Systems

• Cameras provide images of the log


– Images can be used to identify checks, knots

and other features visible on the log surface

– Software can be developed to automatically

analyze the images and identify features of

interest…but it’s not easy

Experimental Forintek system

Vibrational Resonance Sensors

• Estimate wood stiffness (MOE) by measuring the speed

of longitudinal vibrations

– Sensor measures vibration frequency (velocity) from hammer blow

– Stiffness = density x (velocity) 2

– Stiffness estimate assumes that green density is relatively constant

stiffness ≈ density x velocity



velocity = 2 x length / tim e


Fibre-Gen HM200 hand-held system

Images courtesy of Fibre-gen

On-line Systems

MicroTec ViScan

Fibre-Gen LG640

Images courtesy of Fibre-gen

New Fibre-Gen Processor Head System

X-ray and CT scanners

Forintek-UNBC CT Imaging Centre

X-ray Computed Tomography (X-CT)

• The CT method

requires collecting x-

ray attenuation data

at many rotational


• A computer

reconstructs a 2-D

image using a

technique called

“back projection”

• Large data sets result

is long scan times

X-ray Computed Tomography (CT)

• Cross sectional images can be analyzed and combined

to create accurate 3-D log models

• High cost and low speed have limited CT scanning to

research applications

X-ray Radiographic Scanning

Logs are scanned lineally as they pass

between the x-ray source and detectors

Scanners can have single or multiple


• Resulting radiographs are “shadow”

images showing changes in density within

the log




Scanner image courtesy of MicroTec

Commercial X-ray Log Scanners

Bintec x-ray log scanner

UPM Sawmill, Finland

Detecting checks with x-ray scanners

• Checks are visible only

when the x-ray beam

is reasonably well

aligned with the check

X-ray Radiographs

of same log

rotated 120 o




Not Visible

CT X-Section

Checks can be difficult to detect

These logs both have checks that are the same depth




Multi-sensor scanning systems

New Microtec “Log-Eye” scanner


• Sorting logs and grading logs into end-use categories

can help us to maximize margins

• Common visual grading and sorting rules are subjective

in nature and cannot help us assess internal log and

wood characteristics (E.g. knottiness, wood strength)

• New scanning technologies are beginning to emerge

that can help us automate log merchandizing, grading

and sorting processes

• Different sensor technologies detect different log and

wood attributes

• Multi-sensor scanners provide more log and wood

attribute information and help improve automatic

processing decisions



Log Yard at a European Sawmill

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