An analysis of machine shape defects in British Columbia sawmills and their classification using neural networks
Notice bibliographique
Résumé
Ideally, the edges of lumber are parallel to each other and its ends are rectangular and in line with each other. However, sub-optimal occurrences in the sawing processes cause deviations from this ideal shape. In the sawmill, these deviations are often detected as off-size variations in thickness, and one particular defect shape is not necessarily distinguished from another in the downgrading process. These defects, different from machine defects like torn grain or skip, are referred to as machine shape defects in this thesis. The first part of this thesis implements a survey to analyse machine shape defects in British Columbia sawmills, while the second part employs neural networks as an experimental approach in the classification of these defects. A survey was designed and implemented to determine the industrial significance of machine shape defects in British Columbia sawmills. Completed in 2000, the survey focused on six machine shape defects commonly caused by the sawing process: snipe, flare, wedge, taper, thin snake and fat snake. Responses came from mills located across BC and from both large and small forest companies responsible for 33% of BC softwood lumber production in 2000. Characterising BC sawmills according to machine shape defects and annual production shows that for each category of mill, with one exception, there is over a 20% probability of producing at least five types of machine shape defects. The most common grade cited for all machine shape defects was No. 2 Structural. By ranking the machine shape defects in terms of occurrence and by determining which ones are most serious in terms of final quality, it was established that thin snake, snipe and taper have the most serious impact on the industry. Neural networks were trained to detect and classify snipe in rough green lumber, using more than one hundred trim ends sampled randomly from a mill experiencing difficulty processing frozen wood. A self-contained measuring apparatus was constructed to support measuring equipment and to convey the sample boards through the measuring range of six lasers at a steady rate, using the automatic feedrollers of a shaper table. A statistical model was developed to interpret the physical characteristics of the board's surface, focussing on its shape. This model was used to preprocess the laser data into a set of variables, simplifying the data set for input into the neural networks. It was demonstrated that neural networks can be applied with limited success to detect machine shape defects, in particular snipe, in random samples of rough green lumber. However, it was established that more training data is required to train the neural networks to classify the sample cases with combination snipe.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».