Abstract
In the intelligent manufacturing of furniture, the power signal has the characteristics of low cost and high accuracy and is often used as a tool wear condition monitoring signal. However, the power signal is not very sensitive to tool wear conditions. The present work addresses this issue by proposing a novel woodworking tool wear condition monitoring method that employs a limiting arithmetic average filtering method and particle swarm optimization (PSO)-back propagation (BP) neural network algorithm. The limiting arithmetic average filtering method was used to process the power signal and extracted the features of the woodworking tool wear conditions. The spindle speed, depths of milling, features and tool wear conditions were used as sample vectors. The PSO-BP neural network algorithm was used to establish the monitoring model of the woodworking tool wear condition. Experiments show that the proposed limiting arithmetic average filtering method and PSO-BP neural network algorithm can accurately monitor the woodworking tool wear conditions under different milling parameters.
Highlights
Woodworking tools are one of the most important tools in the intelligent manufacturing of furniture [1,2]
The spindle speed, depths of milling, features and tool wear conditions were used as sample vectors, and subsequently, the particle swarm optimization (PSO)-back propagation (BP) neural network algorithm was used to establish a woodworking tool wear monitoring model through the sample to achieve high-precision monitoring of woodworking tool wear conditions under different milling parameters
In order to further verify the accuracy and reliability of the monitoring model established by the PSO-BP neural network algorithm, the genetic algorithm (GA)-optimized BP
Summary
Woodworking tools are one of the most important tools in the intelligent manufacturing of furniture [1,2]. The cutting edge of the tool and the material to be processed interact to produce a force that causes the tool to wear [3]. Severe tool wear will cause defects such as tearing, digging and cutting of the edge surface of the wooden furniture during cutting [4], which affects the surface roughness of the edge processing of the wooden furniture and the overall appearance of the wooden furniture [5,6,7]. Research on the monitoring technology of woodworking tool wear conditions is of great significance to improve the cutting performance of woodworking tools and promote the development of intelligent furniture manufacturing technology. WPC has extremely high corrosion resistance and a low manufacturing cost, and it is a recyclable environmentally friendly green material.
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