Statistical Analysis of the Influence of Imperfect Texture Shape and Dimensional Uncertainty on Surface Texture Performance

This paper investigates the effects of imperfect texture shape and dimensional uncertainty on the surface texture performance (load-carrying capacity and coefficient of friction) by adopting numerical experiments, statistical models, and artificial neural network. The imperfect texture shape is regarded as a polygon, and the uncertain geometrical dimensions include the dimple diameter, the area density, and the dimple depth. Results reveal that the most critical geometric parameters that influence the friction force are manufacturing errors associated with the texture's area density. With respect to the load-carrying capacity and the coefficient of friction, manufacturing errors associated with the dimple diameter are more influential than those of the dimple depth and the area density. It is shown that insofar as the optimization of surface texture performance is concerned, the imperfect texture shape and the dimensional uncertainty associated with the laser texturing with three-sigma performance level are harmless, but manufacturing errors with the one-sigma level can dramatically reduce the load-carrying capacity and increase the coefficient of friction. Specifically, when the dimensions of the area density, the dimple depth, and the dimple diameter are set as 30%, 5.5 μm, and 100 μm, respectively, the imperfect texture shape at the three-sigma level can achieve higher performance than lower levels of control of machining precision.