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EN
Hot deformation of metals is a widely used process to produce end products with the desired geometry and required mechanical properties. To properly design the hot forming process, it is necessary to examine how the tested material behaves during hot deformation. Model studies carried out to characterize the behaviour of materials in the hot deformation process can be roughly divided into physical and mathematical simulation techniques. The methodology proposed in this study highlights the possibility of creating rheological models for selected materials using methods of artificial intelligence, such as neuro-fuzzy systems. The main goal of the study is to examine the selected method of artificial intelligence to know how far it is possible to use this method in the development of a predictive model describing the flow of metals in the process of hot deformation. The test material was Inconel 718 alloy, which belongs to the family of austenitic nickel-based superalloys characterized by exceptionally high mechanical properties, physicochemical properties and creep resistance. This alloy is hardly deformable and requires proper understanding of the constitutive behaviour of the material under process conditions to directly enable the optimization of deformability and, indirectly, the development of effective shaping technologies that can guarantee obtaining products with the required microstructure and desired final mechanical properties. To be able to predict the behaviour of the material under non-experimentally tested conditions, a rheological model was developed using the selected method of artificial intelligence, i.e. the Adaptive Neuro-Fuzzy Inference System (ANFIS). The source data used in these studies comes from a material experiment involving compression of the tested alloy on a Gleeble 3800 thermo-mechanical simulator at temperatures of 900, 1000, 1050, 1100, 1150oC with the strain rates of 0.01 - 100 s-1 to a constant true strain value of 0.9. To assess the ability of the developed model to describe the behaviour of the examined alloy during hot deformation, the values of yield stress determined by the developed model (ANFIS) were compared with the results obtained experimentally. The obtained results may also support the numerical modelling of stress-strain curves.
2
Content available remote Determination of the shear speed of soil triaxial testing based on fuzzy logic
EN
To design foundations, embankments and other soil structures, geotechnical engineers require methods of assessing engineering properties of soils. Some of the more complex phenomena that occur in soils have often been difficult to recreate in a laboratory: seismic activity, vibration, unsaturated condition, control of principal stresses etc. are areas which have proven difficult to replicate, despite their importance of being understood. This was partly due to the lack of test systems capable of reproducing these effects and the complexity of test systems that were developed to carry out such work. A number of advanced computer/software controlled systems allow the geotechnical engineer to perform the most complex test regimes via a user-friendly software interface. However, it is difficult to determine firstly parameters needed, e.g. shear speed in soil triaxial testing. In this paper we represent a new approach to determine this shear speed by solving the inverse problem using testing results obtained by the forward procedure. Direct search method, i.e. Adaptive Neuro-Fuzzy Inference System (ANFIS), is developed and applied to soil triaxial shear tests. It allows us to use the advanced sensor and actuator technologies in order to change the traditional triaxial shear apparatus from a mechanical system to a mechatronics system in next work.
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