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An Energy Analysis of Impact Strength Tests Using Pendulum Hammers

Godzimirski Jan, Komorek Zenon, Komorek Andrzej

Advances in Science and Technology. Research Journal

2019

Vol. 13, no 4

214--222

An attempt to estimate the value of the deformation energy of a metal specimen and the research system during impact strength tests on pendulum hammers was made. In the experimental research it was found that the rectangular metal specimens of the same cross-sectional area exhibit different impact strength, depending on the direction of the load (bending stiffness) whereas the destruction work of such samples exposed to static bending is comparable. The article presents the results of the experimental research, completed with numerical calculations carried out to assess the value of the deformation energy during the impact tests. By performing numerical calculations, the authors estimated the deformation energies of specimens characterised by elastoplastic properties with reinforcement (bilinear) under destructive loads. The energy of the elastic deformation of the hammer arm was estimated analytically. On the basis of the research, it was found that in the impact strength test, a large part of the recorded energy is connected with the deformation of the test device, in particular, the pendulum which undergoes bending. Moreover, the recorded impact strength of the material is not proportional to its actual impact strength.

Possibilities of neural networks using in the design, simulation and verification of the controlled gas nitriding process

Michalski J., Mruk R., Wach P., Burdyński K.

Inżynieria Materiałowa

2017

Vol. 38, nr 4

177--182

The article presents the possibility of neural networks application to design and simulate the growth kinetics of class 1 nitrided layers in steel 32CDV13 and 42CrMo4 (40HM), using data obtained from analytical models. The study analyses unidirectional multilayer neural networks with one hidden layer, with approximation properties. The algorithm developed takes into account the average thickness of the layer of iron nitride. This parameter is most frequently used for the classification of nitrided layers, especially for anticorrosion layers. As a result of research and discussion stated: the neural networks with approximating properties used allowed to build models, well-fitted to the data obtained using analytical models, taught structures of neural networks can be used in systems estimating the results of the nitriding process. The duration of the first stage of the process and the value of the potential in the second degree determine the thickness of the iron nitride layer obtained after the nitriding process. The value of the potential in the second stage also determines the intensity of limiting the thickness of the iron nitride layer. Nitriding decreases the impact strength of steel regardless of the thickness of the subsurface iron nitride layer. The iron nitride layer on the steel increases its resistance to frictional wear. Its resistance to friction wear increases with the increase of the thickness of this layer.

Celem pracy była ocena możliwości wykorzystania sieci neuronowych do projektowania i symulacji kinetyki wzrostu warstw azotowanych na stalli 32CDV13 i 42CrMo4 (40HM), wykorzystując dane uzyskane z modeli analitycznych. W części weryfikacji eksperymentalnej, określenie udarności i odporności na zużycie przez tarcie tych stali po procesie regulowanego azotowania gazowego.