The research removes common constraints regarding the design of layout of flexible manufacturing system, and the subsequent search for a good solution is left solely to artificial intelligence. The proposed system is composed of a creative subsystem and a subsystem for evaluating layouts. Evaluation of solution quality is made using intelligent search of the shortest travel paths within the layout. This system has proved to be innovative since it proposes very good solutions which are oriented to the main task of the system and are not simplified because of human limitations.
COBISS.SI-ID: 13896470
In this paper we present how with Artifical Neural Network (ANN) the prediction of milling tool-path strategy could be made, according to set technological aim. In our case the best possible surface quality of machined surface was taken as the primary technological aim. This paper shows how feature extraction from 3D CAD model and classification with self-organization neural network are done. The experimental results presented in this paper suggest that the prediction of milling strategy with self organization neural network (SOM) is effective.
COBISS.SI-ID: 13941782
This paper discusses the application of neural adaptive control strategy to the problem of cutting force control in high speed end milling operations. The purpose of the paper is to present a reliable, robust neural controller aimed at adaptively adjusting feed-rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate.
COBISS.SI-ID: 14702614
Based on proposed control system which consists of neural network dynamics model of the process and fuzzy feedback control module. The developed control system can reduce the machining time, protect the cutting tool, and increase the cutting efficiency. The main advantage of this approach is that the use of an adaptive learning control of milling processes does not require a priori knowledge about the servo-loops and the milling process dynamics. It has the capability of parallel processing, the utilization of a large amount of sensory information, on-line learning, etc
COBISS.SI-ID: 14723350
The purpose of this paper is to establish a general method for achievable speed and accuracy evaluation of additive manufacturing (AM) machines and an objective comparison among them. First, a general schematic is defined that enables description of all currently available AM machines. This schematic is used to define two influential factors describing certain parts' properties regarding the machines' yield during manufacturing. A test part is defined, that will enable testing the influence of these factors on the speed and accuracy of manufacturing.
COBISS.SI-ID: 14723094