Yang Duo, Tang Jinyuan, Xia Fujia, Zhou Wei
State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410017, China.
College of Mechanical and Electrical Engineering, Central South University, Changsha 410017, China.
Materials (Basel). 2022 Aug 29;15(17):5971. doi: 10.3390/ma15175971.
Among the 26 roughness parameters described in ISO 25178 standard, the parameters used to characterize surface performance in characterization parameter set () lack scientificity and unity, resulting in application confusion. The current comes from empirical selection or small sample experiments, thus featuring low generality. A new method for constructing in rough surfaces is proposed to solve the above issues. Based on a data mining method, statistical theory, and roughness parameters definitions, the 26 roughness parameters are divided into and redundant parameter sets () with the help of reconstructed surfaces and machining experiments, and the mapping relationships between and are established. The research shows that accounts for 50%, and , of great significance for surface performance, and has the ability to fully cover surface topography information. The birth of provides an accurate parameter set for the subsequent study of different surface performance, and it provides more effective parameters for evaluating the workpiece surface performance from the same batch.
在ISO 25178标准中描述的26个粗糙度参数中，用于在表征参数集（）中表征表面性能的参数缺乏科学性和统一性，导致应用混乱。当前的（参数集）来自经验选择或小样本实验，因此普遍性较低。为解决上述问题，提出了一种在粗糙表面构建（参数集）的新方法。基于数据挖掘方法、统计理论和粗糙度参数定义，借助重构表面和加工实验，将26个粗糙度参数分为（核心参数集）和冗余参数集（），并建立了（核心参数集）与（冗余参数集）之间的映射关系。研究表明，（核心参数集）占50%，对表面性能具有重要意义，并且有能力充分覆盖表面形貌信息。（核心参数集）的诞生为后续不同表面性能的研究提供了准确的参数集，为评估同一批次工件表面性能提供了更有效的参数。
