ABSTRACT

The paper describes a study that identifies the influence of the machining parameter on the temperature and the surface roughness for the end milling of AA6082T6 under dry cutting conditions. The experiments are based on Taguchi L9 DOE and ANFIS (Adaptive Neuro-fuzzy information system) is applied to determine optimal parameters. The consequences of period boundaries on execution have been explored with the aid of an effective plot. It was obtained that speed is the dominant aspect of the TEMP influencing the boundary for shifting (parametric commitment is 91.336%), while if surface roughness exists, the speed limit is the most contributing boundary (parametric commitment is 50.174%). Besides, to understand and set up the data yield relationship, the ANFIS-based show was carried out. The experimental results, ANFIS, and the anticipated results of artificial neural network (ANN) were analyzed and finally, it was found that the anticipated results of ANFIS are correct for anticipating the reactions during the AA6082T6 milling operation.

摘要

本文介绍了一项研究，该研究确定了加工参数对干切削条件下 AA6082T6 端铣的温度和表面粗糙度的影响。实验基于田口 L9 DOE 和 ANFIS（自适应神经模糊信息系统）来确定最佳参数。在有效情节的帮助下，已经探索了时期边界对执行的影响。得出速度是影响换挡边界的 TEMP 的主要方面（参数承诺为 91.336%），而如果存在表面粗糙度，则速度限制是影响最大的边界（参数承诺为 50.174%）。此外，为了了解和建立数据产出关系，进行了基于ANFIS的展示。实验结果，ANFIS，