Comprehensive angular scattering distribution analysis for resource-efficient manufacturing

Abstract

Most measuring principles for surface topography measurement cannot be broadly used within machine tools due to the rough environment featuring vibrations, dust, cooling liquid, or temperature gradients. Having to measure outside of the machine tool results in large quality control loops. The angular-resolved scattering light sensor is an established measuring principle which can be used directly in the manufacturing environment and thus allows small quality control loops and a resource-efficient manufacturing since deviations in the manufacturing process can be detected almost immediately. For the evaluation of the surface characteristics, the angular distribution of the surface within a lightspot is typically characterized by its center of gravity (M-parameter) and its variance (Aq-parameter) as described in the guideline VDA 2009. These two parameters provide only little information about the angular distribution and the workpiece quality and for certain, function-oriented applications and expanded data analysis can be beneficial to allow the monitoring in more manufacturing systems with high benefits for resource-efficient manufacturing. We suggest using additional parameters of the angular distribution and perform a correlation analysis with the machining parameters of an exemplified manufacturing process to find parameters that feature a more pronounced correlation with the process parameters than Aq and thus allow an improved monitoring of the manufacturing process in this paper. The example process chosen is a grinding process with different tool grit sizes, tool surface speeds, feed rates, and coolant air flow power. With this case study it can be demonstrated how a more comprehensive analysis of the scattering of surface angles can lead to an improved process monitoring since the correlation between the process parameters and the parameters of the angular distribution shows how sensitively different deviations in the process can be detected, e.g. when the grinding belt must be replaced. For an efficient implementation of quality control loops, simple and traceable monitoring parameters are essential. A smart evaluation of the measurement data allows an optimization of the manufacturing process and the associated quality control loops.