Online performance optimization for complex robotic assembly processes

Abstract

The variations and uncertainties in complex assembly processes could cause conventional industrial robots failure to perform assemblies. Therefore, intelligent robots must be developed which can adapt to these variations and uncertainties; moreover, the assembly process parameters must be optimized to satisfy the performance requirements such as First Time Throughput (FTT) rate and cycle time. However, it is challenging to optimize the performance of complex robotic assembly systems. The existing solutions are not efficient and human knowledge is needed to perform experiments and explore optimal parameters. These requirements greatly limit the applications of robotic assembly in manufacturing automation. In this paper, a robotic assembly process optimization method is developed for complex assembly processes. We propose a modeling method based on Gaussian Process Regression (GPR) to construct a model to build the relationship between the process parameters (input) and system performance (output). The GPR surrogated Bayesian Optimization Algorithm (GPRBOA) is improved to iteratively optimize the robotic assembly performance. Two industrial assembly processes (a tight-tolerance peg-in-hole assembly process and a torque converter assembly process) are used to verify the proposed method. The experiments were performed many times and the results demonstrate that the proposed GPRBOA method can optimize the complex assembly process parameters effectively and efficiently. The proposed complex assembly process optimization method opens a door for online manufacturing process optimization and will greatly reduce the manufacturing cost.