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 of monitoring 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.

中文翻译：

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测试低成本工具和人工智能技术以自动检测小型手动带锯机完成的操作的能力

研究重点：开发了一种低成本的实验系统，以通过传感器收集的数据和实施人工智能（AI）技术的方式，对小型木材加工设施的生产进行监控，从而为最重要的应用提供准确的结果工作作业。背景和目标：小型家族企业生产的木质产品通常受到缺乏监测数据的影响，一方面，监测数据可能会阻碍决策过程，另一方面可能导致监测结果减少可能导致业务失败的技术效率。由于数据收集和分析需要大量资源，因此此类制造设施的长期性能受到限制，从而阻止了为提高竞争力而可能采用的方法。材料和方法：由两个数据记录器（三轴加速度计和声压级计）组成的外部传感器系统与视频摄像机结合使用，以提供输入信号和元文档，以训练和测试人工神经网络（ANN）检查自动分类操作所花费时间的准确性。该研究是基于大约一个样本。以1 Hz的频率收集了90 k个观测值。结果：该方法在训练（约20 k）和测试（约60 k）数据集中均提供了可喜的结果，全局分类精度约为。85％。然而，表征有效锯切的事件（需要电力）甚至被更好地识别，分类精度达到98％。结论：系统需要低成本的设备和免费提供的软件，这些软件可以通过直接连接到本地设备来在本地计算机上进行数据馈送。这样，它可以收集，分析和绘制可用于保持传统技术竞争力的生产数据。