Multivariate product adapted grading of Scots Pine sawn timber for an industrial customer, part 2: Robustness to disturbances

Olofsson, Linus; Broman, Olof; Skog, Johan; Fredriksson, Magnus; Sandberg, Dick

doi:10.1080/17480272.2019.1612944

Cited by 5 publications

(13 citation statements)

References 2 publications

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“…As long as this requirement is met, the smaller number of pieces of sawn timber delivered by a strict PLS-based approach in the CT Log is not a problem. Since the PLS models use a classification threshold, the balance of strict sorting vs high volume deliveries is a simple balancing problem controlled by a single parameter (see Olofsson et al 2019a for a more detailed discussion on this balancing act).…”

Section: Discussionmentioning

confidence: 99%

“…Both models were then used to predict the productgrade Y p of an independent test set. For a detailed description of a similar implementation of PLS for product-specific grading in the dry-sorting station, see Olofsson et al (2019a).…”

Section: The Machine Learning Methods -Plsmentioning

confidence: 99%

“…The CTbased variables X were used for PLS modelling (D), and the measurements made by the Boardmaster were used to create a few rules to remove outliers (I). The full X variable set, consisting of approximately 1800 variables, is similar in structure and scope to the set detailed in Olofsson et al (2019a) but was based on the CT Log measurements described below. The variables are similar enough that they do not require a re-definition here.…”

Section: Variable Set Created Using the Ct Logmentioning

confidence: 99%

“…In particular, it is difficult for the customer to describe their subjective quality criteria of the entire face of the sawn timber in a way that can be described by a set of rules governing individual features. Lycken and Oja (2006), Berglund et al (2015), and Olofsson et al (2019a) showed that a partial least squares regression model [or projection to latent structures, PLS (Geladi and Kowalski 1986;Wold et al 2001)] could beneficially replace the rule-based grading for a product-specific grading, requiring only the final product-grade as input from the customer. The benefits of PLS-based grading over rule-based grading suggested by Lycken and Oja (2006) and Olofsson et al (2019a) can be summarised by saying that the PLS grading model captures the customer's subjective and holistic grading criteria, which are difficult to describe using a rule-based approach.…”

Section: Introductionmentioning

confidence: 99%

“…Relatable to the studies Lycken andOja (2006), Berglund et al (2015), Olofsson et al (2019a), andBreinig et al (2015b) performed cluster analysis and classification of floor boards according to their subjective visual characteristics, showing the possibility to use a set of knot descriptive variables to group floor boards into homogeneous clusters with significant differences. Furthermore, in a continuation study, Breinig et al (2015a) used simulated sawing of CT-scanned logs to optimise the rotational angle of the logs to yield the most number of floor boards of cluster-grades defined as in the previous study.…”

Section: Introductionmentioning

confidence: 99%

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Product-adapted grading of Scots pine sawn timber by an industrial CT-scanner using a visually-trained machine-learning method

Olofsson

Broman

Oja

et al. 2021

Wood Material Science & Engineering

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Computed tomography (CT) scanning of logs makes appearance-grading virtual sawn timber possible before the log is sawn. A CT-scanner can measure the knot structure inside a scanned log, inferring how to saw the log. The knot structure of virtual sawn timber was graded as being suitable or not for a specific product by the existing rule-based approach and used to create a set of descriptive statistical variables used by two machine learning models. The PLS models were trained on two quality references; the quality grade of the finished product or the image-grade based on images of the sawn timber, extracted from the dry-sorting station's automatic grading system and graded by two experienced researchers. The results show that the two PLS models perform equally well when sorting sawn timber to the customer, indicating that the quality references are equally useful for training a PLS model. The PLS models both delivered 93% of the dried sawn timber to the customer, leaving very little sawn timber with customer-specific properties at the sawmill, of which 89% and 90% of the delivered sawn timber passed the intended product's quality demands. The rule-based approach delivered 85% dried sawn timber with a 73% pass rate.

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Section: Discussionmentioning

confidence: 99%

Section: The Machine Learning Methods -Plsmentioning

confidence: 99%

Section: Variable Set Created Using the Ct Logmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Product-adapted grading of Scots pine sawn timber by an industrial CT-scanner using a visually-trained machine-learning method

Olofsson

Broman

Oja

et al. 2021

Wood Material Science & Engineering

Self Cite

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Process Control and Grading in Primary Wood Processing

Sandberg

Fink

Hasener³

et al. 2023

Springer Handbook of Wood Science and Technology

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Sustainability in Wood Products: A New Perspective for Handling Natural Diversity

2022

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Wood is a renewable resource with excellent qualities and the potential to become a key element of a future bioeconomy. The increasing environmental awareness and drive to achieve sustainability is leading to a resurgence of research on wood materials. Nevertheless, the global climate changes and associated consequences will soon challenge the wood-value chains in several regions (e.g., central Europe). To cope with these challenges, it is necessary to rethink the current practice of wood sourcing and transformation. The goal of this review is to address the intrinsic natural diversity of wood, from its origin to its technological consequences for the present and future manufacturing of wood products. So far, industrial processes have been optimized to repress the variability of wood properties, enabling more efficient processing and production of reliable products. However, the need to preserve biodiversity and the impact of climate change on forests call for new wood processing techniques and green chemistry protocols for wood modification as enabling factors necessary for managing a more diverse wood provision in the future. This article discusses the past developments that have resulted in the current wood value chains and provides a perspective about how natural variability could be turned into an asset for making truly sustainable wood products. After briefly introducing the chemical and structural complexity of wood, the methods conventionally adopted for industrial homogenization and modification of wood are discussed in relation to their evolution toward increased sustainability. Finally, a perspective is given on technological potentials of machine learning techniques and of novel functional wood materials. Here the main message is that through a combination of sustainable forestry, adherence to green chemistry principles and adapted processes based on machine learning, the wood industry could not only overcome current challenges but also thrive in the near future despite the awaiting challenges.

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Multivariate product adapted grading of Scots Pine sawn timber for an industrial customer, part 2: Robustness to disturbances

Cited by 5 publications

References 2 publications

Product-adapted grading of Scots pine sawn timber by an industrial CT-scanner using a visually-trained machine-learning method

Product-adapted grading of Scots pine sawn timber by an industrial CT-scanner using a visually-trained machine-learning method

Process Control and Grading in Primary Wood Processing

Sustainability in Wood Products: A New Perspective for Handling Natural Diversity

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