Optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm

Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.

Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms

In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.

A cluster positioning architecture and relative positioning algorithm based on pigeon fock bionics

Unmanned clusters can realize collaborative work,fexible confguration,and efcient operation,which has become an important development trend of unmanned platforms.Cluster positioning is important for ensuring the normal operation of unmanned clusters.The existing solutions have some problems such as requiring external system assistance,high system complexity,poor architecture scalability,and accumulation of positioning errors over time.Without the aid of the information outside the cluster,we plan to construct the relative position relationship with north alignment to adopt formation control and achieve robust cluster relative positioning.Based on the idea of bionics,this paper proposes a cluster robust hierarchical positioning architecture by analyzing the autonomous behavior of pigeon focks.We divide the clusters into follower clusters,core clusters,and leader nodes,which can realize fexible networking and cluster expansion.Aiming at the core cluster that is the most critical to relative positioning in the architecture,we propose a cluster relative positioning algorithm based on spatiotemporal correlation information.With the design idea of low cost and large-scale application,the algorithm uses intra-cluster ranging and the inertial navigation motion vector to construct the positioning equation and solves it through the Multidimensional Scaling(MDS)and Multiple Objective Particle Swarm Optimization(MOPSO)algorithms.The cluster formation is abstracted as a mixed direction-distance graph and the graph rigidity theory is used to analyze localizability conditions of the algorithm.We designed the cluster positioning simulation software and conducted localizability tests and positioning accuracy tests in diferent scenarios.Compared with the relative positioning algorithm based on Extended Kalman Filter(EKF),the algorithm proposed in this paper has more relaxed positioning conditions and can adapt to a variety of scenarios.It also has higher relative positioning accuracy,and the error does not accumulate over time.