Semi-supervised learning for quality control of high-value wood products

Abstract

The quality control of wood products is often only checked at the end of the production process so that countermeasures can only be taken with a time delay in the event of fluctuations in product quality. This often leads to unnecessary and cost-intensive rejects. Furthermore, since quality control often requires additional procedural steps to be performed by a skilled worker, testing is time-consuming and costly. While traditional machine learning (ML) methods based on supervised learning have been used in the field with some success, the limited availability of labeled data is the major hurdle for further improving model performance. In the present study, the potential of enhancing the performance of the ML methods random forest (RF) and support vector machines (SVM) for quality classification by using semi-supervised learning (SSL) was investigated. Labeled and unlabeled data were provided by Swiss Wood Solutions AG, which produces densified wood for high-value wood products such as musical instruments. The developed approach includes labeling of the unlabeled data using SSL, training and 10k cross-validation of the ML algorithms RF and SVM, and determining the generalization ability using the hold-out test set. Based on the evaluation indices such as accuracy, F1-score, recall, false-positive-rate and confusion matrices, it was shown that SSL could enhance the prediction performance of the quality classification of ML models compared to the conventional supervised learning method. Despite having a small dataset, the work paves the way for future applications of SSL for wood quality assessment.

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“…193 SSL methods are more applicable to real-world applications where unlabeled data is readily available and easy to obtain, while labeled instances are often difficult, expensive, and timeconsuming to collect, which is often the case in wood product manufacturing. For instance, Schubert et al 270 used a particularly small data set to determine the effect of the SSL method label spreading 271 on the performance of algorithms based on random forests (RF) and support vector machines (SVM), two common ML techniques for classification. The work clearly showed that SSL can be used to augment small data sets to improve the generalization ability of ML algorithms such as RF for quality classification of real wood product data.…”