Research And Implementation Of Screw Tightening Algorithm Based On Industrial Cloud Platform And NSGA2

Screw joint is one of the most important assembly methods in industrial assembly.Improving the efficiency and quality of screw tightening is of great significance to the realization of intelligent assembly.In the current screw assembly process,the complex tightening parameter setting mainly relies on the technician's empirical debugging or parameter setting in a single working condition,which requires a lot of time and manpower.In this paper,aiming at the low efficiency of obtaining high-quality tightening parameters at present,an algorithm that can automatically generate high-quality tightening parameters iteratively according to different working conditions is studied based on NSGA2 and industrial cloud platform.The screw tightening theory and automatic screw tightening technology are studied,the advantages and disadvantages of three main screw tightening control methods and the significance of screw tightening parameters are discussed,and the whole screw tightening process is analyzed in sections according to the parameters.On this basis,tightening time and tightening precision are taken as two indexes to evaluate the advantages and disadvantages of parameters,and parameter optimization of torque control method is selected as the research direction of the algorithm.The optimization of tightening parameters is abstracted into a multi-objective optimization problem.The multi-objective optimization problem and the classical multi-objective optimization algorithm are studied.According to the practical requirements of tightening parameter optimization problem and the superiority of NSGA2 in solving the low-dimensional multi-objective optimization problem,NSGA2 was selected as the basis of algorithm research.The mathematical model of screw tightening parameter optimization is established,and two optimization objectives,seven parameters to be optimized and their constraints are defined.Then,on the basis of NSGA2 algorithm,aiming at the limitation of NSGA2 to deal with constrained multi-objective optimization problem and its inapplicable elite retention mechanism,the improved NSGA2 algorithm with constraints is implemented in these two aspects.The algorithm proposes a constrained partial order to sort the advantages and disadvantages of the feasible and non-feasible solutions.The algorithm proposes an adaptive elite retention strategy,which can not only improve the distribution of the solution,ensure the search of the global optimal solution,but also improve the convergence speed of the algorithm and ensure the efficiency of the algorithm.The powerful data integration capability of the cloud platform enables the algorithm to efficiently obtain a large amount of data and improve the efficiency of parameter iteration.Based on the enterprise's industrial cloud platform and electric tightening equipment,a set of software and hardware experimental platform is built.Through the improved NSGA2 algorithm deployed on the cloud platform,a complete tightening parameter optimization experiment is conducted.The parameters generated by the algorithm have a significant effecton tightening efficiency and tightening quality.Experience for the same conditions,using artificial method of adjustable parameter,the contrast of manual and tight as a result,the algorithm of joining tighten accuracy from 5.6% to 2.7%,to verify the efficiency and quality of screw algorithm to obtain parameters than artificial experience method parameter,algorithm with high efficiency and reliability,to improve the quality and efficiency of screw assembly has important significance.