Johan Oja

Automatic grading of sawlogs: A comparison between X-ray scanning, optical three-dimensional scanning and combinations of both methods

As sawmills become increasingly efficient, the importance of focusing on value recovery becomes obvious. To maximize value recovery, sawmills require the ability to sort logs according to quality. This study compares four different combinations of three-dimensional (3D) and X-ray scanning that can be used to grade logs automatically. The study was based on 135 Scots pine (Pinus sylvestris L.) logs that had been scanned with both a 3D scanner and an X-ray scanner with two X-ray sources. The percentage of boards with correct grade sawn from automatically graded logs varied from 57% when using only 3D scanning to 66% when using a combination of 3D scanning and X-ray scanning in two directions. The highest possible result, with ideal log grading, was 81%. The result also shows that the combination of a 3D scanner and one X-ray direction results in higher accuracy than a scanner based on two X-ray directions.

Measuring the outer shape of Pinus sylvestris saw logs with an x-ray LogScanner

The accuracy of measuring the outer shape of Scots pine (Pinus sylvestris L.) saw logs with an X‐ray LogScanner has been compared with the accuracy of using a 2‐axis optical scanner, a 3‐axis optical scanner and an ideal 3‐D optical scanner. The different scanners were simulated using computed tomography (CT) data from the Swedish Stem Bank. The outer shape of 60 saw logs was measured every third centimeter. The error attributable to bark when using optical scanners was simulated separately. The results from the simulations showed that when measuring the outer shape on bark, the X‐ray LogScanner facilitated measurement of the minimum shadow diameter with the same accuracy as with a 3‐D optical scanner. The results also showed that the potential of combining the X‐ray LogScanner with a 3‐D optical scanner should be investigated.

Automatic grading of Scots pine (Pinus sylvestris L.) sawlogs using an industrial X-ray log scanner

Abstract The successful running of a sawmill is dependent on its ability to achieve the highest possible value recovery from the sawlogs, i.e. to optimize the use of the raw material. Such optimization requires information about the properties of every log. One method of measuring these properties is to use an X-ray log scanner. The objective of the present study was to determine the accuracy when grading Scots pine ( Pinus sylvestris L.) sawlogs using an industrial scanner known as the X-ray LogScanner. The study was based on 150 Scots pine sawlogs from a sawmill in northern Sweden. All logs were scanned in the LogScanner at a speed of 125 m/min. The X-ray images were analyzed on-line with measures of different properties as a result (e.g. density and density variations). The logs were then sawn with a normal sawing pattern (50×125 mm) and the logs were graded depending on the result from the manual grading of the center boards. Finally, partial least squares (PLS) regression was used to calibrate statistical models that predict the log grade based on the properties measured by the X-ray LogScanner. The study showed that 77–83% of the logs were correctly sorted when using the scanner to sort logs into three groups according to the predicted grade of the center boards. After sawing the sorted logs, 67% of the boards had the correct grade. When scanning the same logs repeatedly, the relative standard deviation of the predicted grade was 12–20%. The study also showed that it is possible to sort out 10 and 16%, respectively, of the material into two groups with high quality logs, without changing the grade distribution of the rest of the material to any great extent.

The appearance of resin pockets in CT-images of Norway spruce (Picea abies (L.) Karst.)

Picea abies (L.) Karst.) was studied. The study also includes a comparison between measured and calculated CT-numbers of Norway spruce resin and wood. It was found that it should be possible to create algorithms that automatically detect large resin pockets in CT-images of Norway spruce. Compared to resin pockets in heartwood, resin pockets in green sapwood are more difficult to detect due to the high density of the surrounding wood. The study also showed that the correlation between measured and calculated CT-numbers was high and that it is possible to use the same function for conversion between CT-number and density for both green wood and resin.

Predicting board strength by X-ray scanning of logs: The impact of different measurement concepts

Abstract The objective of this study was to compare the individual board strength predictions from an X-ray log scanner by using either two or four X-ray directions. The benefit of applying traceability between log and board was also studied. In total, 119 Norway spruce [Picea abies (L.) Karst.] sawlogs were scanned by an X-ray log scanner at the log sorting station of a sawmill and sawn into two centre pieces per log. Individual board traceability was enabled by following the rotational position of the log in the scanner and at the succeeding sawing. All boards were graded by a commercial strength grading machine before destructive testing was done. The resulting data were used to derive variables for building multivariate partial least squares strength prediction models. In the modelling a hierarchical modelling approach was used, where annual ring width, dry density and elasticity were also modelled. For all concepts studied the models’ fit was similar. Only minor benefits could be found when using four directions and traceability compared with two directions and no traceability. One conclusion is that the result for traceability, from four directions in particular, is more sensitive for the interior knot reconstruction result. The strength prediction was on the same R 2 level as for the strength grading machine.

Predicting moisture content and density of Scots pine by microwave scanning of sawn timber

Non-destructive testing of wood for prediction of strength is significantly influenced by wood density and moisture content. A sensor capable of measuring both density and moisture content would be a good tool to aid in predicting the strength of sawn timber. This study was carried out to investigate the possibility of calibrating a prediction model for the moisture content and density of Scots pine (Pinus sylvestris) using microwave sensors. The material was initially at green moisture content, and thereafter dried in several steps to zero moisture content. At each step all the samples were weighted, scanned with a microwave camera (Satimo 9.4 GHz) and CT scanned with a medical CT scanner (Siemens Somatom AR.T.). The output variables from the microwave camera were used as predictors, and CT images correlated with known moisture content were used as response variables. Multivariate models to predict moisture content and density were calibrated using partial least squares (PLS) regression. The result shows that it is possible to predict both moisture content and density with very high accuracy using microwave sensors.

A comparison between three different methods of measuring knot parameters in picea abies

Both foresters and sawmillers are interested in the knot structure of trees; in particular, position and number of knots, knot diameter, knot length and dead knot border. For research purposes, it is possible today to carry out non‐destructive measurements using computer tomography (CT) and image analysis. The aim of this study was to measure knot parameters on Norway spruce (Picea abies (L.) Karst.) using a non‐destructive method developed for Scots pine (Pinus sylvestris L.), and to compare the results of this method with the results of two different destructive methods. In order to do this, two Norway spruce stems were scanned by CT. Then five logs from one stem were cut into flitches 20 mm thick and the defects on the sawn surfaces were scanned manually. The other stem was cut just above every whorl and then each knot was split through its centre and the knot parameters were measured manually. The study showed that the CT method compares well with the destructive methods. It is a reasonably fast, non‐...

Predicting spiral grain by computed tomography of Norway spruce

Spiral grain is a feature of wood that affects the shape of the sawn timber. Boards sawn from logs with a large spiral grain have a tendency to twist when the moisture content changes. The aim of this study was to investigate the possibility of predicting spiral grain based on variables that should be measurable with an X-ray LogScanner. The study was based on 49 Norway spruce (Picea abies) logs from three stands in Sweden. The logs were scanned with a computed tomography (CT) scanner every 10mm along the log. Concentric surfaces at various distances from the pith were then reconstructed from the stack of CT images. The spiral grain angle was measured in these concentric surface images, and a statistical model for predicting spiral grain was calibrated using partial least squares (PLS) regression. The PLS model predicts the spiral grain of a log at a distance 50mm from the pith based on different variables that should be measurable with an industrial X-ray LogScanner. The result was a PLS model withR2=0.52 for the training set andR2=0.37 for the test set. We concluded that it should be possible to predict the spiral grain of a log based on variables measured by an industrial X-ray LogScanner. The most important variables for predicting spiral grain were measures of sapwood content, variation in the ratio between the heartwood and log areas, and the standard deviation for the mean log density in 10mm thick cross slices along the log. The accuracy when sorting the logs into two groups with spiral grain of ≥2.0° and of <2.0°, respectively, was 84% of the correctly sorted logs.

Prediction of Board Values in Pinus sylvestris Sawlogs Using X-ray Scanning and Optical Three-dimensional Scanning of Stems

As the sawmill industry strives towards customer orientation, the need for sorting of logs according to quality has been recognized, and automatic sorting based on measurements by three-dimensional (3D) optical log scanners has been implemented at sawmills. There is even a small number of sawmills using the X-ray log scanner for automatic log-sorting. At the log-sorting stage, the potential of the raw material to fulfil the needs has already been reduced by the decisions taken when the trees were bucked (cross-cut) into logs. Thus, the application of predictions of the boards’ properties at the bucking stage is desirable. This study investigates the possibility of predicting board values from logs based on 3D scanning alone and 3D scanning in combination with X-ray scanning of stems. This study is based on 628 logs scanned by computed tomography that make up the Swedish Pine Stem Bank. Simulated sawing of the logs gave product values for each log. Prediction models on product value were adapted using partial least squares regression and x-variables derived from the properties of the logs and their original stems, measurable with a 3D log scanner and the X-ray LogScanner. The results were promising. Using a 3D scanner alone, R 2 was 0.68, and using a 3D scanner in combination with an X-ray LogScanner, R 2 was 0.72.