Szwajka Krzysztof, Zielińska-Szwajka Joanna, Trzepieciński Tomasz
Department of Integrated Design and Tribology Systems, Faculty of Mechanics and Technology, Rzeszow University of Technology, ul. Kwiatkowskiego 4, 37-450 Stalowa Wola, Poland.
Department of Component Manufacturing and Production Organization, Faculty of Mechanics and Technology, Rzeszow University of Technology, ul. Kwiatkowskiego 4, 37-450 Stalowa Wola, Poland.
Materials (Basel). 2023 Jul 27;16(15):5292. doi: 10.3390/ma16155292.
Wood-based composites are increasingly used in the industry not only because of the shortage of solid wood, but above all because of the better properties, such as high strength and aesthetic appearance compared to wood. Medium-density fiberboard (MDF) is a wood-based composite that is widely used in the furniture industry. In this work, an attempt was made to predict the surface roughness of the machined MDF in the milling process based on acceleration signals from an industrial piezoelectric sensor installed in the cutting zone. The surface roughness parameter Sq was adopted for the evaluation and measurement of surface roughness. The surface roughness prediction was performed using a radial basis function (RBF) artificial neural network (ANN) and a Takagi-Sugeno--Kang (TSK) fuzzy model with subtractive clustering. In the research, as inputs to the ANNs and fuzzy model, the kinematic parameters of the cutting process and selected measures of the acceleration signal were adopted. At the output, the values of the surface roughness parameter Sq were obtained. The results of the experiments show that the surface roughness is influenced not only by the kinematic parameters of the cutting, but also by the vibrations generated during the milling process. Therefore, by combining information on the cutting kinematics parameters and vibration, the accuracy of the surface roughness prediction in the milling process of MDF can be improved. The use of TSK fuzzy modelling based on the subtractive clustering method for integrating the information from many acceleration signal measurements in the examined range of cutting conditions meant the surface roughness was predicted with high accuracy and high reliability. With the help of two tested artificial intelligence tools, it is possible to estimate the surface roughness of the workpiece with only a small error. When using a radial neural network, the root mean square error for estimating the value of the Sq parameter was 0.379 μm, while the estimation error based on fuzzy logic was 0.198 μm. The surface of the sample made with the cutting parameters v = 76 m/min and v = 1200 mm/min is characterized by a less concentrated distribution of ordinate densities, compared to the surface of the sample cut with lower feed rates but at the same cutting speed. The most concentrated distribution of ordinate density (for the cutting speed v = 76 m/min) is characterized by the surface, where the feed rate value was v = 200 mm/min, with 90% of the material concentrated in the profile height of 28.2 μm. When using an RBF neural network, the RMSE of estimating the value of the Sq parameter was 0.379 μm, while the estimation error based on fuzzy logic was 0.198 μm.
木质复合材料在工业中越来越多地被使用，这不仅是因为实木短缺，更重要的是其具有比木材更好的性能，如高强度和美观的外观。中密度纤维板（MDF）是一种广泛应用于家具行业的木质复合材料。在这项工作中，尝试基于安装在切削区域的工业压电传感器的加速度信号来预测铣削过程中加工的MDF的表面粗糙度。采用表面粗糙度参数Sq来评估和测量表面粗糙度。使用径向基函数（RBF）人工神经网络（ANN）和具有减法聚类的高木-菅野（TSK）模糊模型进行表面粗糙度预测。在研究中，将切削过程的运动学参数和加速度信号的选定测量值作为ANN和模糊模型的输入。在输出端，获得表面粗糙度参数Sq的值。实验结果表明，表面粗糙度不仅受切削运动学参数的影响，还受铣削过程中产生的振动的影响。因此，通过结合切削运动学参数和振动的信息，可以提高MDF铣削过程中表面粗糙度预测的准确性。基于减法聚类方法的TSK模糊建模用于整合在所研究的切削条件范围内多次加速度信号测量的信息，这意味着可以高精度、高可靠性地预测表面粗糙度。借助两种经过测试的人工智能工具，仅以很小的误差就可以估计工件的表面粗糙度。使用径向神经网络时，估计Sq参数值的均方根误差为0.379μm，而基于模糊逻辑的估计误差为0.198μm。与以较低进给速度但相同切削速度切割的样品表面相比，以切削参数v = 76 m/min和v = 1200 mm/min制成的样品表面的纵坐标密度分布不太集中。纵坐标密度最集中的分布（切削速度v = 76 m/min）的特征是表面，其进给速度值为v = 200 mm/min，90%的材料集中在28.2μm的轮廓高度内。使用RBF神经网络时，估计Sq参数值的均方根误差为0.379μm，而基于模糊逻辑的估计误差为0.198μm。
