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Johansson, Erik
Publications (9 of 9) Show all publications
Johansson, E., Berglund, A. & Skog, J. (2016). Comparing predictability of board strength between computed tomography, discrete x-ray, and 3D scanning of Norway spruce logs (ed.). Wood Material Science & Engineering, 11(2), 116-125
Open this publication in new window or tab >>Comparing predictability of board strength between computed tomography, discrete x-ray, and 3D scanning of Norway spruce logs
2016 (English)In: Wood Material Science & Engineering, ISSN 1748-0272, E-ISSN 1748-0280, Vol. 11, no 2, p. 116-125Article in journal (Refereed) Published
Abstract [en]

Strength graded boards of Norway spruce (Picea abies (L.) Karst.) are important products for many Scandinavian sawmills. If the bending strength of the produced boards can be predicted before sawing the logs, the raw material can be used more efficiently. In previous studies it is shown that the bending strength can be predicted to some extent using discrete X-ray scanning of logs. In this study, we have evaluated if it is possible to predict bending strength of Norway spruce boards with higher accuracy using computed tomography (CT) scanning of logs compared to a combination of discrete X-ray and 3D scanning. The method was to construct multivariate models of bending strength for three different board dimensions. Our results showed that CT scanning of logs produces better models of bending strength compared to a combination of discrete X-ray and 3D scanning. The main reason for this difference was the benefit of knowing the position of where the boards were cut from the logs and therefore detailed knot information could be used in the prediction models. Due to the small number of observations in this study, care should be taken when comparing the resulting prediction models to results from other studies

National Category
Other Mechanical Engineering
Research subject
Wood Science and Engineering
Identifiers
urn:nbn:se:ltu:diva-11954 (URN)10.1080/17480272.2015.1022875 (DOI)000370663900005 ()2-s2.0-84925425630 (Scopus ID)b00e80f2-0258-4caf-81d1-58b938afafb0 (Local ID)b00e80f2-0258-4caf-81d1-58b938afafb0 (Archive number)b00e80f2-0258-4caf-81d1-58b938afafb0 (OAI)
Note

Validerad; 2016; Nivå 2; 20140926 (erikjo)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Johansson, E. (2015). Computed Tomography and Fingerprint Traceability in the Wood Industry (ed.). (Doctoral dissertation). Paper presented at . : Luleå tekniska universitet
Open this publication in new window or tab >>Computed Tomography and Fingerprint Traceability in the Wood Industry
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The purpose of the work described in this thesis was to develop techniques based on non-invasive measurements of logs and sawn timber that would increase the profitability of the wood industry in general and sawmills in particular. The work has two main focus areas: computed tomography (CT) scanning of logs and traceability of wood products.The first focus was on detecting knots in CT images of logs and to find ways to use the knot information efficiently. The result is an automated algorithm that can successfully detect knots in CT images of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). Knots have a negative impact on the bending strength of sawn timber and, since knots can be detected in CT images of logs, it is possible to adapt the sawing process to take into consideration where the knots are located. This thesis includes an investigation of the profitability gain for a sawmill producing strength-graded sawn timber of Norway spruce when detailed knot information from a CT scanner is used. The strategy was to optimize the log breakdown by rotating logs to an optimum position with respect to the sales value of the sawn timber. The investigation was carried out using computer simulations. The work also includes an investigation into how accurately the bending strength of sawn timber can be predicted from information in CT images of the logs. Features and defects in the CT images were measured and key parameters were inserted into multivariate PLS regression models. The models were calibrated with data from destructive bending tests and, although the results were unclear, there were indications that the bending strength could be predicted with a higher accuracy using CT scanning than by using log scanning techniques that are currently common in the industry. The second focus area was fingerprint traceability of individual wood products, which is valuable for sawmills since it enables detailed process control. Diagnostics and process surveillance could be based on statistics for each individual piece of sawn timber instead of on statistics at a batch level. Without an automatic recognition system for sawn timber, such studies would involve labor-intensive and possibly process-disruptive manual tests. The work includes the development of two wood surface recognition systems based on different techniques. One of them uses information about how knots are positioned in relation to each other to construct scale- and rotationally-invariant descriptors. The performance and robustness of this recognition system were tested on 212 edge-glued panel images of Scots pine with different noise levels. The other recognition system was specialized on sawn timber. This particular method considerably reduces the resolution of the board images and matches them using template matching. Tests were performed by matching three sets of 88 board images to a database of 886 Scots pine boards. The recognition systems have different strengths and weaknesses due to their design, but both of them were fast and had high recognition rates in the tests carried out. Overall, the work led to several computerized methods that enable increases in profitability of sawmills. The proposed knot detection in CT images of logs enables detailed control of the log breakdown process, and the proposed fingerprint traceability methods permit process control based on individual pieces of sawn timber. Results from this thesis also give sawmill managers valuable information on how an industrial CT scanner would affect the profitability of their sawmills.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2015. p. 156
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Other Mechanical Engineering
Research subject
Wood Technology
Identifiers
urn:nbn:se:ltu:diva-26529 (URN)eb63fc35-7b2c-4449-a250-f59d1cfbb8d5 (Local ID)978-91-7583-309-5 (ISBN)978-91-7583-310-1 (ISBN)eb63fc35-7b2c-4449-a250-f59d1cfbb8d5 (Archive number)eb63fc35-7b2c-4449-a250-f59d1cfbb8d5 (OAI)
Note
Godkänd; 2015; 20150418 (erikjo)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-24Bibliographically approved
Johansson, E., Pahlberg, T. & Hagman, O. (2015). Fast visual recognition of Scots pine boards using template matching (ed.). Computers and Electronics in Agriculture, 118, 85-91
Open this publication in new window or tab >>Fast visual recognition of Scots pine boards using template matching
2015 (English)In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 118, p. 85-91Article in journal (Refereed) Published
Abstract [en]

This paper describes how the image processing technique known as template matching performs when used to recognize boards of Scots pine (Pinus sylvestris L.). Recognition of boards enables tracking of individual boards through an industrial process, which is vital for process optimization.A dataset of 886 Scots pine board images were used as a database to match against. The proposed board recognition method was evaluated by rescanning 44 of the boards and matching these to the larger dataset. Three different template matching algorithms have been investigated while reducing the pixel densities of the board images (downsampling the images). Furthermore, the effect of variations in board length has been tested and the computational speed of the recognition with respect to the database size has been measured. Tests were conducted using the open source software package OpenCV due to its highly optimized code which is essential for applications with high production speed.The conducted tests resulted in recognition rates above 99% for board lengths down to 1 m and pixel densities down to 0.06 pixels/mm. This study concluded that template matching is a good choice for recognition of wooden board surfaces.

National Category
Other Mechanical Engineering
Research subject
Wood Science and Engineering
Identifiers
urn:nbn:se:ltu:diva-4185 (URN)10.1016/j.compag.2015.08.026 (DOI)000364603500010 ()2-s2.0-84940976908 (Scopus ID)2166b466-813b-40a2-b89a-f4ea58e190d1 (Local ID)2166b466-813b-40a2-b89a-f4ea58e190d1 (Archive number)2166b466-813b-40a2-b89a-f4ea58e190d1 (OAI)
Note

Validerad; 2015; Nivå 2; 20150226 (erikjo)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Johansson, E., Pahlberg, T. & Hagman, O. (2015). Recognition of Sawn Timber using Template Matching (ed.). Paper presented at International Wood Machining Seminar : 14/06/2015 - 19/06/2015. Paper presented at International Wood Machining Seminar : 14/06/2015 - 19/06/2015.
Open this publication in new window or tab >>Recognition of Sawn Timber using Template Matching
2015 (English)Conference paper, Oral presentation only (Refereed)
National Category
Other Mechanical Engineering
Research subject
Wood Technology; Wood Products Engineering
Identifiers
urn:nbn:se:ltu:diva-29615 (URN)325e0444-dcf2-4ec2-9eab-fc25408a57ea (Local ID)325e0444-dcf2-4ec2-9eab-fc25408a57ea (Archive number)325e0444-dcf2-4ec2-9eab-fc25408a57ea (OAI)
Conference
International Wood Machining Seminar : 14/06/2015 - 19/06/2015
Note
Godkänd; 2015; 20141001 (erikjo)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Pahlberg, T., Johansson, E., Hagman, O. & Thurley, M. (2015). Wood fingerprint recognition using knot neighborhood K-plet descriptors (ed.). Wood Science and Technology, 49(1), 7-20
Open this publication in new window or tab >>Wood fingerprint recognition using knot neighborhood K-plet descriptors
2015 (English)In: Wood Science and Technology, ISSN 0043-7719, E-ISSN 1432-5225, Vol. 49, no 1, p. 7-20Article in journal (Refereed) Published
Abstract [en]

In the wood industry, there is a wish to recognize and track wood products through production chains. Traceability would facilitate improved process control and extraction of quality measures of various production steps. In this paper, a novel wood surface recognition system that uses scale and rotationally invariant feature descriptors called K-plets is described and evaluated. The idea behind these descriptors is to use information of how knots are positioned in relation to each other. The performance and robustness of the proposed system were tested on 212 wood panel images with varying levels of normally distributed errors applied to the knot positions. The results showed that the proposed method is able to successfully identify 99–100 % of all panel images with knot positional error levels that can be expected in practical applications

National Category
Other Mechanical Engineering Signal Processing
Research subject
Signal Processing; Wood Science and Engineering
Identifiers
urn:nbn:se:ltu:diva-7733 (URN)10.1007/s00226-014-0679-3 (DOI)000347166900002 ()2-s2.0-84938410671 (Scopus ID)6266a684-7c00-49d2-ac80-b65cd350c889 (Local ID)6266a684-7c00-49d2-ac80-b65cd350c889 (Archive number)6266a684-7c00-49d2-ac80-b65cd350c889 (OAI)
Note

Validerad; 2015; Nivå 2; 20140429 (erikjo)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Fredriksson, M., Johansson, E. & Berglund, A. (2014). Rotating Pinus sylvestris sawlogs by projecting knots from computed tomography images onto a plane (ed.). Paper presented at . BioResources, 9(1), 816-827
Open this publication in new window or tab >>Rotating Pinus sylvestris sawlogs by projecting knots from computed tomography images onto a plane
2014 (English)In: BioResources, ISSN 1930-2126, E-ISSN 1930-2126, Vol. 9, no 1, p. 816-827Article in journal (Refereed) Published
National Category
Other Mechanical Engineering
Research subject
Wood Technology
Identifiers
urn:nbn:se:ltu:diva-7552 (URN)5f05e085-ba5d-42a7-939b-88d871233acb (Local ID)5f05e085-ba5d-42a7-939b-88d871233acb (Archive number)5f05e085-ba5d-42a7-939b-88d871233acb (OAI)
Projects
CT-Pro
Note
Validerad; 2014; 20130410 (magfre)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Berglund, A., Johansson, E. & Skog, J. (2014). Value optimized log rotation for strength graded boards using computed tomography (ed.). Paper presented at . European Journal of Wood and Wood Products, 72(5), 635-642
Open this publication in new window or tab >>Value optimized log rotation for strength graded boards using computed tomography
2014 (English)In: European Journal of Wood and Wood Products, ISSN 0018-3768, E-ISSN 1436-736X, Vol. 72, no 5, p. 635-642Article in journal (Refereed) Published
Abstract [en]

A possible application for an industrial computed tomography scanner in a sawmill is finding an optimal rotational position of logs with respect to knots and outer shape. Since a computed tomography scanner is a great investment, it is important to investigate potential profitability of such an investment for different production strategies. The objective of this study was to investigate the potential value increase of the sawn timber of Norway spruce (Picea abies (L.) Karst.) by rotating logs to their optimum position prior to sawing compared with sawing all logs in horns down position. The production strategy evaluated by log breakdown simulation in this case study was to produce strength graded timber of the center boards, while the side boards were appearance graded. This case study showed an average value increase with respect to the value of center boards, side boards and chips of 11 %.

National Category
Other Mechanical Engineering
Research subject
Wood Technology
Identifiers
urn:nbn:se:ltu:diva-2494 (URN)10.1007/s00107-014-0822-8 (DOI)000340497800009 ()2-s2.0-84906502712 (Scopus ID)01cce25a-4f3c-49c1-b299-c2cb110e4514 (Local ID)01cce25a-4f3c-49c1-b299-c2cb110e4514 (Archive number)01cce25a-4f3c-49c1-b299-c2cb110e4514 (OAI)
Note
Validerad; 2014; 20130730 (bendar)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Johansson, E., Johansson, D., Skog, J. & Fredriksson, M. (2013). Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces (ed.). Paper presented at . Computers and Electronics in Agriculture, 96, 238-245
Open this publication in new window or tab >>Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces
2013 (English)In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 96, p. 238-245Article in journal (Refereed) Published
Abstract [en]

High speed industrial computed tomography (CT) scanning of sawlogs is new to the sawmill industry and therefore there are no properly evaluated algorithms for detecting knots in such images. This article presents an algorithm that detects knots in CT images of logs by segmenting the knots with variable thresholds on cylindrical shells of the CT images. The knots are fitted to ellipses and matched between several cylindrical shells. Parameterized knots are constructed using regression models from the matched knot ellipses. The algorithm was tested on a variety of Scandinavian Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) with a knot detection rate of 88–94% and generating about 1% falsely detected knots.

National Category
Other Mechanical Engineering
Research subject
Wood Technology
Identifiers
urn:nbn:se:ltu:diva-8418 (URN)10.1016/j.compag.2013.06.003 (DOI)000323795700022 ()2-s2.0-84880384633 (Scopus ID)6ee1639c-6afa-4b91-8c86-8608b761e1eb (Local ID)6ee1639c-6afa-4b91-8c86-8608b761e1eb (Archive number)6ee1639c-6afa-4b91-8c86-8608b761e1eb (OAI)
Note
Validerad; 2013; 20130702 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Johansson, E. (2013). Computed tomography of sawlogs: knot detection and sawing optimization (ed.). (Licentiate dissertation). Paper presented at . Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Computed tomography of sawlogs: knot detection and sawing optimization
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Branches on trees introduce defects on sawn timber called knots. By scanning sawlogs using computed tomography, knots can be detected and accounted for so that the sawing process can be optimized with respect to outgoing product value. How the optimization should be done differs depending on available sawing equipment and the production strategy of the sawmill. It is important to investigate interesting production strategies with computer simulations to obtain an approximation of the profitability for a sawmill if investing in a computed tomography scanner. Another important step in the optimization process is to automatically segment knots so that they can be used by a computer when optimizing. This thesis presents an algorithm that automatically segments knots from computed tomography images of logs. The algorithm uses variable thresholds to segment knots on cylindrical shells of the computed tomography images. The knots are fitted to ellipses and matched between several cylindrical shells. The algorithm was tested on a variety of Scandinavian Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) with a knot detection rate of 88-94 % and generating about 1 % falsely detected knots.Knots are defects with high impact on boards that are graded with respect to their bending strength. Some sawmills specialize in the production of such boards and this thesis includes a simulation study of sawing Norway spruce (Picea abies (L.) Karst.) logs to optimize the outgoing board value for such a sawmill. The production strategy investigated in this thesis was scanning of sawlogs with computed tomography and optimizing the rotational positioning of the logs in the sawing process. This study showed a possible mean value increase of the sawn timber by 11 %.There are additional degrees of freedom in log breakdown than rotational positioning, such as log spatial position, skew and which sawing pattern to use. If every possible combination of sawing parameters would be simulated, enormous computational resources would be required. A study made in this thesis investigates the feasibility to use only parts of the knot information when optimizing log rotational position. This is done by projecting all knots to a plane perpendicular to the log lengthwise direction and filter out the least significant knots. The study showed a great challenge in this approach and the presented algorithm was insufficient in its present form to compete with alternatives that use full information of the knots.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2013. p. 116
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Other Mechanical Engineering
Research subject
Wood Technology
Identifiers
urn:nbn:se:ltu:diva-25899 (URN)ba532986-29fb-45db-9afd-4d953e35f03d (Local ID)978-91-7439-716-1 (ISBN)978-91-7439-717-8 (ISBN)ba532986-29fb-45db-9afd-4d953e35f03d (Archive number)ba532986-29fb-45db-9afd-4d953e35f03d (OAI)
Note
Godkänd; 2013; 20130827 (erikjo); Tillkännagivande licentiatseminarium 2013-09-24 Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Erik Johansson Ämne: Träteknik/Wood Technology Uppsats: Computed Tomography of Sawlogs – Knot Detection and Sawing Optimization Examinator: Professor Anders Grönlund, Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet Diskutant: Professor Johan Carlson, Institutionen för system- och rymdteknik, Luleå tekniska universitet Tid: Fredag den 18 oktober 2013 kl 10.00 Plats: Hörsal A, Luleå tekniska universitet, campus SkellefteåAvailable from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-24Bibliographically approved
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