Uros Zuperl

Optimization of cutting conditions during cutting by using neural networks

Abstract Optimum selection of cutting conditions importantly contribute to the increase of productivity and the reduction of costs, therefore utmost attention is paid to this problem in this contribution. In this paper, a neural network-based approach to complex optimization of cutting parameters is proposed. It describes the multi-objective technique of optimization of cutting conditions by means of the neural networks taking into consideration the technological, economic and organizational limitations. To reach higher precision of the predicted results, a neural optimization algorithm is developed and presented to ensure simple, fast and efficient optimization of all important turning parameters. The approach is suitable for fast determination of optimum cutting parameters during machining, where there is not enough time for deep analysis. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.

Modeling and adaptive force control of milling by using artificial techniques

The contribution discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modeling and adaptively controlling the process of ball-end milling. On the basis of the hybrid process modeling, off-line optimization and feed-forward neural control scheme (UNKS) 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 and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. In this way it compensates all disturbances during the cutting process: tool wear, non-homogeneity of the workpiece material, vibrations, chatter, etc. The basic control principle is based on the control scheme (UNKS) consisting of two neural identifiers of the process dynamics and primary regulator. An overall procedure of hybrid modeling of cutting process used for creating the CNC milling simulator has been prepared. The experimental results show that not only does the milling system with the design controller have high robustness, and global stability, but also the machining efficiency of the milling system with the adaptive controller is 27% higher than for traditional CNC milling system.

Dynamic neural network approach for tool cutting force modelling of end milling operations

This paper uses the artificial neural networks (ANNs) approach to evolve an efficient model for estimation of cutting forces, based on a set of input cutting conditions. Neural network (NN) algorithms are developed for use as a direct modelling method, to predict forces for ball-end milling operations. Prediction of cutting forces in ball-end milling is often needed in order to establish automation or optimization of the machining processes. Supervised NNs are used to successfully estimate the cutting forces developed during end milling processes. The training of the networks is preformed with experimental machining data. The predictive capability of using analytical and NN approaches is compared. NN predictions for three cutting force components were predicted with 4% error by comparing with the experimental measurements. Exhaustive experimentation is conduced to develop the model and to validate it. By means of the developed method, it is possible to forecast the development of events that will take place during the milling process without executing the tests. The force model can be used for simulation purposes and for defining threshold values in cutting tool condition monitoring system. It can be used also in the combination for monitoring and optimizing of the machining process—cutting parameters.

Adaptive Force Control in High-Speed Machining by Using a System of Neural Networks

The contribution discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modeling and adaptively controlling the process of ball-end milling. A combination of off-line feedrate optimization and on-line adaptive force control is used to maintain a reference peak cutting force during end milling for safe, accurate, and efficient machining. The basic control principle is based on the neural control scheme (UNKS) consisting of two neural identificators of the process dynamics and primary artificial controller. Design parameters for the adaptive controller are selected using an experimentally validated machining process model. The controller was successfully applied to computer numerical control (CNC) milling machine Heller. Experiments have confirmed efficiency of the adaptive control system, which reflected in improved surface quality and decreased tool wear

Optimization in ball-end milling by using adaptive neural controller

In this paper, a neural controller with optimisation for the ball end milling process is described. An architecture with two different kinds of neural networks is proposed, and is used for the on-line optimal control of the milling process. A BP neural network is used to identify the milling state and to learn the appropriate mappings between the input and output variables of the machining process. The feedrate is selected as the optimised variable, and the milling state is estimated by the measured cutting forces. The goal is also to obtain an improvement of the milling process productivity by the use of an automatic regulation of the cutting force. Numerous simulations are conducted to confirm the efficiency of this architecture.

Neural Network Assisted Particle Swarm Optimization of Machining Process

In this paper, optimization system based on the artificial neural networks (ANN) and particle swarm optimization (PSO) algorithm was developed for the optimization of machining parameters for turning operation. The optimization system integrates the neural network modeling of the objective function and particle swarm optimization of turning parameters. New neural network assisted PSO algorithm is explained in detail. An objective function based on maximum profit, minimum costs and maximum cutting quality in turning operation has been used. This paper also exhibits the efficiency of the proposed optimization over the genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA).

Object Oriented Approach in Computer Aided Process Planning

Process planning is one of the key activities for product design and manufacturing. Impact of process plans on all phases of product design and manufacture requires high level of interaction of different activities and tight integration of them into coherent system. In this paper, an object-oriented knowledge representation approach is presented with module for parts modeling and module for generation of process plan. Description of machining process entities and their relationships with features, machines and tools are provided. The benefits of the proposed representation, which include connection with geometric model, reduced search space and alternative plan generation, are discussed. These new contributions provide for a new generation of computer aided process planning (CAPP) systems that can be adapted for various manufacturing systems and can be integrated with other computer integrated manufacturing (CIM) modules.