Testing the capability of low-cost tools and artificial intelligence techniques to automatically detect operations done by a small-sized manually driven bandsaw

被引：0
作者：
Cheţa M. [1 ]
Marcu M.V. [1 ]
Iordache E. [1 ]
Borz S.A. [1 ]
机构：
[1] Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Şirul Beethoven 1, Brasov
来源：
Forests | 2020年 / 11卷 / 11期
关键词：
Artificial intelligence; Competitiveness; External sensor system; Improvement; Manually driven bandsaws; Production monitoring; Wood processing;
D O I：
10.3390/F11070739
中图分类号：
学科分类号：
摘要：
Research Highlights: A low-cost experimental system was developed to enable the production monitoring of small-scale wood processing facilities by the means of sensor-collected data and the implementation of artificial intelligence (AI) techniques, which provided accurate results for the most important work operations. Background and Objectives: The manufacturing of wood-based products by small-scale family-held business is commonly affected by a lack ofmonitoring data that, on the one hand, may prevent the decision-making process and, on the other hand, may lead to less technical efficiency that could result in business failure. Long-term performance of such manufacturing facilities is limited because data collection and analysis require significant resources, thus preventing the approaches that could be pursued for competitivity improvement. Materials and Methods: An external sensor system composed of two dataloggers-a triaxial accelerometer and a sound pressure level meter-was used in combination with a video camera to provide the input signals and meta-documentation for the training and testing of an artificial neural network (ANN) to check the accuracy of automatic classification of the time spent in operations. The study was based on a sample of ca. 90 k observations collected at a frequency of 1 Hz. Results: The approach provided promising results in both the training (ca. 20 k) and testing (ca. 60 k) datasets, with global classification accuracies of ca. 85%. However, the events characterizing the effective sawing, which requires electrical power, were even better recognized, reaching a classification accuracy of 98%. Conclusions: The system requires low-cost devices and freely available software that could enable data feeding on local computers by their direct connection to the devices. As such, it could collect, analyze and plot production data that could be used for maintaining the competitiveness of traditional technologies. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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