This paper proposes asystem for theautomatic programming of a CNC milling machine by particle swarm optimization (PSO). In the presented research, each individual swarm particle presents a possible NC programme. Voxel representation of machining area was used. Bresenham's algorithm was implemented, for the rasterisation of the cuts. Optimisation with PSO was carried out within avoxelised machining area. The system automatically finds the NC programme for optimal machining. The NC programme guarantees an optimal selection of tools, the shortest possible work and rapid motions, and minimisation of the manufacturing time. Thus,achieving a reduction in machining costs and increased productivity. Testing using test work-pieces and 2.5 D milling confirmed the efficiency of the proposed approach. The proposed intelligent system is easily adaptable for programming other types of CNC machines, by PSO.
COBISS.SI-ID: 16252694
Based on hybrid process modeling, off-line optimization and neural control scheme (NCS), the combined system for off-line optimization and adaptive adjustment of cutting parameters is built. This is an adaptive control system controlling the cutting force by digital adaptation of cutting parameters. In this way, it compensates all disturbances during the cutting process, prevents excessive tool wear, and maintains a high chip removal rate. It is the combination of these methods that yields accurate force control. The basic control principle is based on the NCS consisting of two neural identifiers of the process dynamics and feedback controller. An overall procedure of hybrid modeling of cutting process, used for working out the computer numerical control (CNC) milling simulator has been prepared. CNC simulator is used to evaluate the controller design before conducting experimental tests. Numerous simulations and experiments have been conducted to confirm the efficiency of this control architecture. The experimental results show that not only does the end-milling system with the design controller have high robustness and global stability, but also the machining efficiency of the end milling system with the proposed controller is 27% higher than for traditional CNC milling system.
COBISS.SI-ID: 15798550
An efficient optimization algorithm called teaching-learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners ina class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, epsilon-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.
COBISS.SI-ID: 15904278