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innovative machine vision pty ltd [email protected]

Blast Fragmentation Measurement based on 3D Imaging

in sublevel caving drawpoints and underground excavator buckets

at LKAB Kiruna M.J.Thurley, M.Wimmer, A.Nordqvist

Matthew Thurley Innovative Machine Vision Pty Ltd, Melbourne, Australia

Luleå University of Technology, Luleå, Sweden

mailto:[email protected]


Outline Vision for the future Why 3D data for fragmentation measurement? Automated PSD algorithms proven in other applications This paper : Expectations vs Outcome 3D data in context Sizing results for Drawpoints and LHD buckets This paper : Expectations vs Outcome Discussion



Vision for the Future

Automated, online measurements systems providing...

Consistent automated measurement Repeatable automated analysis Big data to make robust decisions

(statistically significant) Automatic control of mineral processes Integrated control strategies become

possible



Why 3D imaging for fragmentation assessment?

Automated high resolution 3D imaging and advanced algorithms Overcomes limitations of 2D imaging Avoids particle delineation errors due to color variation in the rocks Directly measure 3D dimension of the material therefore no scaling objects or

perspecive distortion errors Detect overlapped particles preventing error from mis-sizing as smaller particles Detect areas-of-fines preventing error from mis-sizing as large boulders These are opposing errors As a result can provides a stable measurement suitable for closed loop automatic

control

4



Automated PSD algorithms proven in real applications Commercial system for online measurement system on conveyor. 3D from laser

triangulation. Latest pub IMPC 2014 Prototype system for measurement of moving LHD buckets. 3D from industrial laser

scanners. Publised Fragblast 2009 Offline measurement of muckpiles in an open pit based on a Maptek laser scanners,

6 Blasts, 95 3D images during excavation, Published 2015 Transactions of the IMMM, Section A Mining Technology



Proven in real applications Online measurement system on conveyor. 3D

from laser triangulation. Swedish partner MBV-Systems is the product

owner and integrator As of this year; ABB is the global distributer

for the mining industry


innovative machine vision pty ltd [email protected] 7

Boliden Tara Mine: between primary crusher and grinding mill (2011 ongoing)


innovative machine vision pty ltd [email protected] 8

Finland: Close loop automatic control of agglomeration (2014 ongoing)



Proven in real applications Automated sizing in LHD buckets

• 3D data from cheap industrial 3D laser scanners

• Fully automated analysis • Estimated the sieve size of the

visible fragments • Published at Fragblast 2009



Automated sizing in LHD buckets

Image courtesy of LKAB



This Paper : Expectation Evaluation of fragmentation measurement in drawpoints and LHD

buckets using; 3D imaging from ShapeMetriX3D stereo photogrammetry and automated PSD analysis algorithms by Thurley

Expectation Sharp and accurate the 3D data and lots of it Detect small particles down to 20mm or less Detection of areas-of-fines (subject to some errors) Detection of overlapped/non-overlapped (subject to some errors) Relative size distribution comparison of measurements



3D DATA EXAMPLES



Conveyor belt Laser triangulation



Muckpile Maptek laser scanner



Mining Truck Stereo 3D from AdamTech Stereo 3D data has missing data in areas of highly uniform color such as the truck body, and edges of rocks Images courtesy of AdamTech



Drawpoint 03 ”Densified” Stereo 3D data from ShapeMetrix 3GSM



Bucket m5 ”Densified”Stereo 3D data from ShapeMetrix 3GSM



CAVE FRONTS



01 purple



02 blue



04 pink



03 green



03



03 non-overlapped



03 areas-of-fines



BUCKETS



01 purple



02 blue



04 pink



03 green



This Paper : Expectation Expectation Sharp and accurate the 3D data and lots of it Detect small particles down to 20mm or less Detection of areas-of-fines (subject to some errors) Detection of overlapped/non-overlapped (subject to some errors) Relative size distribution comparison of measurements



This Paper : Outcome 3D data is highly smoothed and does not show sharp rock edges Small particles down to approx 60mm detected 3GSM applied a custom up-sampling to the raw data to produce the

desired high density 3D data set. But this had the side effect of smoothing the data, removing small particles, degrading edges and negatively affecting all of the analysis algorithms

LHD data most affected as it was also smoothed from motion blur Can detect areas-of-fines, and overlapped, non-overlapped rocks but

more errors



Comments The size curves attempt to capture the reality of the visible rocks on the pile surface,

and are not be representative of the sieving result. Size curves trend in the right direction given the observed size of the material Size curves are very different between the cave front, the LHD bucket, and the

sieving results, which is expected as the measured material is different in all three cases

The stereo 3D data was overly smoothed which had significant negative effects on fragmentation measurement algorithms

Stereo 3D data is not ideal for measuring a fast moving scene (such as LHD buckets in motion) as the data becomes more smoothed and less sharp

Lessons learned for successful collaboration with remote partners

36



Future Build an automated online roof mounted system based on the 2009 prototype

(cheap industrial laser scanners) to measure LHD bucket loads, all day, every day. These data sets contain approx 50,000 points from an LMS400 scanner, compared to

the ShapeMetrix3D data sets with 2 million points

37

LMS400 LMS400



Future Build an automated online roof mounted system based on the 2009 prototype

(cheap industrial laser scanners) to measure LHD bucket loads, all day, every day. These data sets contain approx 50,000 points from an LMS400 scanner, compared to

the ShapeMetrix3D data sets with 2 million points

38

LMS400 ShapeMetrix3D



Final Comments on Stereo 3D Stereo 3D depends heavily on the reconstructions algorithms reconstruction time : seconds or minutes, accuracy vs speed trade-off

Good for volume measurement Good for measuring high walls and relatively flat surfaces Not as good for fragmentation measurement 3D can be overly smooth Has areas of missing data both at rock edges and on rock surfaces

complicating delineation Not necessarily suitable for fast moving scenes



Thank you


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