Yuan Xiuhua, Wang Chong, Li Mingqing, Sun Qun
School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China.
Materials (Basel). 2022 Nov 4;15(21):7805. doi: 10.3390/ma15217805.
Although ceramic fiber brushes have been widely used for deburring and surface finishing, the associated relationship between process parameters and lapping quality is still unclear. In order to optimize the lapping process of ceramic fiber brushes, this paper proposes a multi-layer neural network based on the Gaussian-restricted Boltzmann machine (GRBM), and verified its prediction effectiveness. Compared with a traditional back-propagation neural network, its prediction error was reduced from 7.6% to 4.5%, and the determination coefficient was increased from 0.96 to 0.98, respectively. The comparison results showed that the proposed model can better grasp the relationship between process parameters and machining quality, which can be used as a decision-making foundation for lapping-process optimization.
尽管陶瓷纤维刷已广泛用于去毛刺和表面精加工，但工艺参数与研磨质量之间的关联关系仍不明确。为了优化陶瓷纤维刷的研磨工艺，本文提出了一种基于高斯受限玻尔兹曼机（GRBM）的多层神经网络，并验证了其预测有效性。与传统的反向传播神经网络相比，其预测误差分别从7.6%降至4.5%，决定系数从0.96提高到0.98。比较结果表明，所提出的模型能够更好地把握工艺参数与加工质量之间的关系，可作为研磨工艺优化的决策依据。
