Zsolt János Viharos

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Modeling of manufacturing operations is an important tool for production planning, optimization and control. Artificial neural networks (ANNs) can handle strong non-linearity, large number of parameters, missing information. Based on their inherent learning capabilities ANNs can adapt themselves to changes of the production environment and can be used also(More)
The paper describes a novel approach for learning and applying artificial neural network (ANN) models based on incomplete data. A basic novelty in this approach is not to replace the missing part of incomplete data but to train and apply ANN-based models in a way that they should be able to handle such situations. The root of the idea is inherited form the(More)
Reliable process models are extremely important in different fields of computer integrated manufacturing. They are required e.g. for selecting optimal parameters during process planning, for designing and implementing adaptive control systems or model based monitoring algorithms. Because of their model free estimation, uncertainty handling and learning(More)
The paper presents a novel approach for generating multipurpose models of machining operations combining machine learning and search techniques. These models are intended to be applicable at different engineering and management assignments. Simulated annealing search is used for finding the unknown parameters of the models in given situations. It is(More)
The application of pattern recognition (PR) techniques, artificial neural networks (ANNs), and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The fundamental aim of the paper is to outline the importance of soft computing and hybrid AI techniques in(More)
With the extensive and worldwide increase of the market share of wind energy, the optimal operation of wind farms gains an ever growing significance. The planning and scheduling of maintenance operations is both decisive for turbine availability and a key component of the operational costs. This paper introduces a formal model of wind farm maintenance, and(More)
In the paper different architectures with partly self-developed simulation packages are described illustrating the benefits of combining simulation and machine learning (ML) techniques in manufacturingintelligence (AI) and ML side, artificial neural networks, heuristic search, simulated annealing, and agent-based techniques are put into action. The(More)
. In the paper different architectures with partly self-developed simulation packages are described illustrating the benefits of combining simulation and machine learning (ML) techniques in manufacturing. From the artificial intelligence (AI) and ML side, artificial neural networks, heuristic search, simulated annealing, and agent-based techniques are put(More)
In addition to developing novel ways of part formation (e.g. for the purpose of rapid prototyping), cutting remained one the most important manufacturing technologies [2],[11]. Though being the traditional way of manufacturing, the exact description of cutting processes is unknown, i.e. there are no comprehensive analytical models available for them [11].(More)