ABSTRACT

For addressing the problem that the quality indicators of gear hobbing are complicated and the influencing factors are unknown, a characteristic processing method combining improved multi-objective differential evolution (IMODE) and clustering based on peak density (DPCA) is proposed. This method can extract the characteristic parameters that strongly influence gear hobbing quality for multi-process parameters and multi-quality indicators, and quantify their importance to the comprehensive quality indicators. First, based on correlation analysis of the quality inspection parameters by DPCA, a set of relatively independent gear hobbing quality inspection indicators is obtained, and the dimensions of the quality inspection parameters are reduced for more effectively reflecting the hobbing processing quality. Next, multi-threshold Birch (IBirch) clusters are obtained for different gear hobbing quality inspection data under different process parameters to obtain cluster labels. Finally, Rough Sets theory and IMODE are used to reduce the gear hobbing process parameters and design parameters. Feature parameters that significantly affect the hobbing process quality are extracted from the process parameters and their importance is quantified. The validity and practicability of the method are verified by processing experiments, and the advantages of the proposed method are proved.

摘要

为了解决滚齿质量指标复杂，影响因素未知的问题，提出了一种结合改进的多目标差分进化算法（IMODE）和基于峰值密度聚类算法（DPCA）的特征处理方法。该方法可以为多过程参数和多质量指标提取对齿轮滚齿质量有重要影响的特征参数，并量化它们对综合质量指标的重要性。首先，基于DPCA对质量检验参数的相关性分析，获得了一套相对独立的齿轮滚齿质量检验指标，并缩小了质量检验参数的维数，以更有效地反映滚齿加工的质量。下一个，针对不同的滚齿质量检验数据，在不同的工艺参数下获得了多阈值桦木（IBirch）聚类，得到了聚类标签。最后，使用粗糙集理论和IMODE来减少滚齿工艺参数和设计参数。从工艺参数中提取对滚齿加工质量有重大影响的特征参数，并对它们的重要性进行量化。通过加工实验验证了该方法的有效性和实用性，证明了该方法的优点。从工艺参数中提取对滚齿加工质量有重大影响的特征参数，并对它们的重要性进行量化。通过加工实验验证了该方法的有效性和实用性，证明了该方法的优点。从工艺参数中提取对滚齿加工质量有重大影响的特征参数，并对它们的重要性进行量化。通过加工实验验证了该方法的有效性和实用性，证明了该方法的优点。