Design and Metrological Analysis of a Backlit Vision System for Surface Roughness Measurements of Turned Parts

The focus of this study is to design a backlit vision instrument capable of measuring surface roughness and to discuss its metrological performance compared to traditional measurement instruments. The instrument is a non-contact high-magnification imaging system characterized by short inspection time which opens the perspective of in-line implementation. We combined the use of the modulation transfer function to evaluate the imaging conditions of an electrically tunable lens to obtain an optimally focused image. We prepared a set of turned steel samples with different roughness in the range R-a 2.4 mu m to 15.1 mu m. The layout of the instrument is presented, including a discussion on how optimal imaging conditions were obtained. The paper describes the comparison performed on measurements collected with the vision system designed in this work and state-of-the-art instruments. A comparison of the results of the backlit system depends on the values of surface roughness considered; while at larger values of roughness the offset increases, the results are compatible with the ones of the stylus at lower values of roughness. In fact, the error bands are superimposed by at least 58% based on the cases analyzed.

Automated summary

The study focuses on designing a backlit vision instrument for measuring surface roughness and comparing its metrological performance to traditional instruments. It is characterized by a non-contact high-magnification imaging system with short inspection time, allowing for in-line implementation. Using the modulation transfer function, an optimally focused image was obtained by evaluating the imaging conditions of an electrically tunable lens. The comparison between the vision system designed in this study and state-of-the-art instruments showed that the results of the backlit system are compatible with stylus measurements at lower values of roughness, with an overlap of at least 58% based on the analyzed cases.