Case study: Performance analysis and development of robotized screwing application with integrated vision sensing system for automotive industry

Abstract

Industrial cameras starting to play a significant role in current industrial environment and they represent a strong tool for robotics mainly in cases when they are combined with high-speed robots. However, there are still some difficulties in vision system integration. The capability of such system (e.g. assembly or technological system) depends on several factors, for instance, the camera position, lightning conditions, pattern recognition algorithms, precise setup, as well as well-trained programmer and engineer. The skills of the engineering staff and the precise analysis of conditions and process requirements seem to be crucial for successful solution, what was proved also in our experimental test. The main aim of the article is development and complex performance analysis of robotized screwing application with integrated vision system, concretely the case study of automated assembly system in automotive industry—bolting tightening robotized station as a part of car seat assembly process. The main key elements of the designed workplace are industrial robot FANUC M-20 iA/20M with integrated iRVision system containing the industrial camera Sony XC-56. The real influence of inaccuracies during the design process, gradual “step-by-step” refinement, and final influence of all changings on the final quality and efficiency of designed system was demonstrated. At the end, we reached the point where the total number of screwing operations with NOK results (not OK / negative results) is about six single negative results per day, which represents less than 0.1% of all recognitions (overall reliability is higher than 99.9%).