Mud-Armeen Munlin

Optimization of rotations of a five-axis milling machine near stationary points

Abstract We consider a new algorithm designed for five-axis milling to minimize the kinematics error near the stationary points of the machined surface. Given the tool orientations, the algorithm optimizes the required rotations on the set of the solutions of the corresponding inverse kinematics equations. We solve the problem by means of the shortest path scheme based on minimization of the kinematics error. We present an application of the proposed algorithm to tool-path planning and demonstrate the efficiency of the proposed scheme verified by practical machining.

Radius Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a swarm intelligence based and stochastic algorithm to solve the optimization problem. Nevertheless, the traditional PSO has disadvantage from the premature convergence when finding the global optimization. To prevent from falling into the local optimum, we propose the Radius particle swarm optimization (R-PSO) which extends the Particle Swarm Optimization by regrouping the agent particles within the given radius of the circle. It initializes the group of particles, calculates the fitness function, and finds the best particle in that group. The R-PSO employs the group-swarm to keep the swarm diversity and evolution by sharing information from the agent particles which successfully maintain the balance between the global exploration and the local exploitation. Therefore the agent particle guides the neighbour particles to jump out of the local optimum and achieve the global best. The proposed method is tested against the well-known benchmark dataset. The results show that the R-PSO performs better than the traditional PSO in solving the multimodal complex problems.

Tool path generation, simulation and optimization of a five-axis milling machine

The inverse kinematics of five-axis milling machines produces large errors near stationary points of the required surface. When the tool travels across or around the point the rotation angles may jump considerably leading to unexpected deviations from the prescribed trajectories. We propose two new algorithms to repair the trajectories by adjusting the rotation angles in such a way that the kinematics error is minimized. The angle switching algorithm computes all feasible rotations and identifies an optimized sequence of rotations by the shortest path scheme. Further error reduction is accomplished by the angle insertion algorithm based on the equi distribution principle applied to rotations near the singularity. We have verified the algorithms by two five-axis milling machines, namely, MAHO600E at the CIM Lab of Asian Institute of Technology and HERMLE UWF920H at the CIM Lab of Kasetsart University.

Tool path simulation using a virtual 5-axis milling machine

Presents the algorithms to simulate nonlinear kinematics of a 5-axis milling machine. The simulator is based on 3D representation and employing the inverse kinematics approach to derive the corresponding rotational and translation movement of the mechanism. The simulator makes it possible to analyze the accuracy of a 3D tool-path based on a prescribed set of the cutter location (CL) points as well as a set of the cutter contact (CC) points with tool inclination angle. The resulting trajectory of the tool is not unique and depends on the initial set up of the machine which in turn is problem dependent. Furthermore, the simulator can be used to simulate the milling process, verify the final cut and estimate the errors of the actual tool-path before the real workpiece is actually being tested with the real machine. Thus, reducing the cost of iterative trial and error. Tool path simulation is verified by a series of cutting experiments performed by means of the proposed software and evaluates the accuracy of milling. It has been shown that the proposed graphical 3D software presents an efficient interactive approach to the interactive modification of a tool path based on an appropriate set of transformations as well as to verification of the tool path optimization algorithms.

Hybrid Radius Particle Swarm Optimization

This paper proposes the Hybrid Radius Particle Swarm Optimization (H-RPSO) to solve the RCPSP. The approach extends the Radius Particle Swarm Optimization (RPSO) by incorparating the adaptive mutation and forward-backward improvement to hybridize local search algorithm for constructing the feasible project scheduling with the minimal makespan. The efficiency of the proposed method is tested against the existing methods. The results show that the H-RPSO gives better optimum rate and standard deviation over PSO and RPSO.

Hybrid K-means and Particle Swarm Optimization for symmetric Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is well-known established scheduling problems. We propose a novel method for the TSP using the divide-and-conquer strategy. We employ K-means algorithm to find the city clustering and then solve a sequence of sub-city in a given order by Particle Swarm Optimization (PSO). The PSO is modified by incorporating genetic algorithm operators, namely mutation, so that it can the escape from the local optimum. The performance of proposed method is tested against a number of instances from the TSPLIB. Results demonstrate the effectiveness of the proposed method. Moreover, the novel method gives better results in the standard TSP problem than the exist algorithm.

Fusing Binary Particle Swarm Optimzation with Simulated Annealing for Knapsack Problems

The Knapsack Problems (KPs) is a well-known combinatorial optimization problem. It has a variety of practical applications. We propose the algorithm to solve both 0-1 Knapsack problem (KP) and Multidimensional Knapsack Problem (MKP) by fusing the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) with maximum profit objective. The main contribution is to develop a novel approach by hybridizing BPSO at the local optimum with the simulated annealing to help escape from the local optimum to reach the global optimum. The results indicate that the fusion approach outperforms individual implementation of both binary particle swarm optimization and simulated annealing.