Abstract
This paper presents a novel approach to enhance chipboard milling process monitoring in the furniture manufacturing sector using Convolutional Neural Networks (CNNs) with pretrained architectures like VGG16, VGG19, and RESNET34. The study leverages spectrogram representations of time-series data obtained during the milling process, providing a unique perspective on tool condition monitoring. The efficiency of the CNN models in accurately classifying tool conditions into distinct states (`Green', `Yellow', and `Red') based on wear levels is thoroughly evaluated. Experimental results demonstrate that VGG16 and VGG19 achieve high accuracy, however with longer training times, while RESNET34 offers faster training at the cost of reduced precision. This research not only highlights the potential of pretrained CNNs in industrial applications but also opens new avenues for predictive maintenance and quality control in manufacturing, underscoring the broader applicability of AI in industrial automation and monitoring systems.
Publisher
Warsaw University of Life Sciences - SGGW Press
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Reference21 articles.
1. M. Gao, D. Qi, H. Mu, and J. Chen. A transfer residual neural network based on ResNet-34 for detection of wood knot defects. Forests, 12(2), 2021. https://doi.org/10.3390/f12020212.
2. P. Iakubovskii (qubvel). Classification models Zoo - Keras (and TensorFlow Keras). https://github.com/qubvel/classification_models, 2023. [Accessed: 2023-12-01].
3. P. Iskra and R. E. Hernández. Toward a process monitoring and control of a cnc wood router: Development of an adaptive control system for routing white birch. Wood and Fiber Science, 42(4):523-535, 2010. https://wfs.swst.org/index.php/wfs/article/view/567.
4. A. Jegorowa, J. Górski, J. Kurek, and M. Kruk. Initial study on the use of support vector machine (SVM) in tool condition monitoring in chipboard drilling. European Journal of Wood and Wood Products, 77(5):957–959, 2019. https://doi.org/10.1007/s00107-019-01428-5.
5. A. Jegorowa, J. Górski, J. Kurek, and M. Kruk. Use of nearest neighbors (K-NN) algorithm in tool condition identification in the case of drilling in melamine faced particleboard. Maderas: Ciencia y Tecnologia, 22(2):189–96, 2020. https://doi.org/10.4067/S0718-221X2020005000205.