Robotic force control assembly parameter optimization for adaptive production

This paper presents a study on the Design Of Experiments (DOE)-based parameter optimization technique to adapt to the manufacturing environment changes in robotic force control assembly. Based on a real-world transmission torque converter assembly production process, investigation and analysis are performed in production. An on-pendant robotic assembly parameter optimization tool is introduced. When manufacturing environment changes such as the changes of geometrical dimension of part and tool (the location of feature on part, the size of the feature, the dimension of the tool, etc.), the changes of position and orientation of part, fixture or robot; the changes of properties of part (weight, spring constant, etc.), the performance metrics such as mean of the cycle time, mean plus 3 sigma of the cycle time, first time through (FTT) rate are degraded. The on-pendant optimization tool applies full factorial experiments on the most influential parameters. Then the results are subjected to statistical analysis to find the optimal parameter set. Finally verifying the optimized parameter set through running a number of experiments and checking on performance of the force control assembly to adapt the changes. The efficiency of proposed method is proved in the Ford Powertrain assembly production. The program continues running in production and adjusting the process parameters to adapt the manufacturing variations. The real factory acceptance testing results are presented and analyzed. Finally, conclusions are drawn and discussion and further investigation is proposed.