Understanding the volume fractions of microstructure constituents such as ferrite, pearlite, bainite, and martensite in low-alloy steels is critical for tailoring mechanical properties to specific engineering applications. To address the complexity of these relationships, this study explores the use of artificial neural networks (ANNs) as a robust tool for predicting these microstructure constituents based on alloy composition, specific Jominy distance, and heat treatment parameters. Unlike previous ANN-based predictions that rely on the hardness after quenching as an input parameter, this study excludes hardness. The developed model relies on readily available input parameters, enabling accurate estimation of microstructure composition prior to heat treatment, which significantly improves its practicality for process planning, optimization, and reducing trial-and-error on industrial applications. Three different input configurations were tested to evaluate the predictive capabilities of ANNs, with results showing that the use of specific Jominy distance as an input variable enhances model performance. Furthermore, the findings suggest that specific Jominy distance could serve as a practical alternative to detailed chemical composition data in industrial applications. The predictions for ferrite, pearlite, and martensite were more accurate than those for bainite, which can be attributed to the complex nature of bainite formation.

The maximal vertical distance (MVD) recursive algorithm, a novel approach for the optimal discretization of stress–strain material curves, is proposed. The algorithm simplifies the process of defining multilinear curves from material stress–strain curves when conducting a finite element analysis (FEA) of components. By directly selecting points on the material curve, the MVD algorithm eliminates the requirement for initial discretization, thereby minimizing information loss. As the measure of goodness of fit of the simplified polyline to the original curve, the percentage of stress deviation (SD) is proposed. The algorithm can generate multiple multilinear curves while keeping the stress deviation of each curve within a predefined limit. This feature is particularly beneficial during the finite element analysis of components exhibiting complex and position-dependent material properties, such as surface-hardened components, ensuring consistent modelling accuracy of material properties across the components’ geometry. Consistent accuracy also proves advantageous when exploring multiple differing material states of quenched and tempered steel, ensuring fair and reliable comparisons. The MVD algorithm was compared with existing algorithms from the literature, consistently maintaining the accuracy of the multilinear curves within predetermined limits using the fewest possible points.

A number of conventional and machine learning-based methods for the estimation of strain-life fatigue parameters from monotonic properties have been proposed in the literature and new ones are continuously being developed. With the development of new steels having improved properties such as higher ultimate strength, selecting appropriate estimation method becomes both increasingly difficult and important. In this study, according to the proposed detailed evaluation methodology, 10 conventional estimation methods have been evaluated regarding their accuracy and applicability for estimation of low- and high-cycle fatigue lives of unalloyed, low-alloy and high-alloy steels which were further divided into low- and high-strength subgroups. For this purpose, detailed, fully populated material dataset was assembled, including monotonic properties, cyclic stress - strain, and strain-life fatigue parameters of 75 unalloyed steels, 104 low-alloy steels and 44 high-alloy steels. With separate analyses, averaging of evaluation results was avoided and more detailed and precise ratings of individual methods were obtained. Complete material data and full results of the evaluations are made available in the paper which may further facilitate development and objective evaluations of new estimation methods and other predictive models.

A number of engineering components, such as gears, shafts, and bearings, are frequently subjected to high and very localized, static and dynamic stresses and strains. In order to increase the load-carrying capacity and durability of such components, various types of treatments may be applied, primarily including heat treatments. In particular, surface heat treatments are used to selectively enhance the load-bearing capacity of the most heavily stressed regions of the component. As a consequence, the resulting material exhibits a surface layer that is considerably harder and stronger than the material at the core. Such materials, possessing gradually varying material properties, are known as functionally graded materials (FGMs) and with them, the aim is to improve the structural integrity of components in an optimal, targeted manner. In this study, a finite element analysis of the stress-strain response of unnotched and notched specimens made from homogeneous and functionally graded low-alloy steel 42CrMo4 subjected to static loading was performed. Results of the mechanical response of specimens with homogeneous and functionally graded material properties are presented in this study, highlighting significant differences.

This paper introduces a novel method for estimating the cyclic stress–strain curves of steels based on their monotonic properties and plastic strain amplitudes, utilizing artificial neural networks (ANNs). ANNs were trained on a substantial number of experimental data for steels, collected from relevant literature, and divided into subgroups according to alloying elements content (unalloyed, low-alloy, and high-alloy steels). Only monotonic properties that were proven to be relevant for the estimation of points on the stress–strain curve were used. The performance of the developed ANNs was assessed using an independent set of data, and the results were compared to experimental values, values obtained by existing empirical estimation methods, and by previously developed ANNs. The results showed that the new approach which combines relevant monotonic properties and plastic strain amplitudes as inputs to ANNs for cyclic stress–strain curve estimation is better than the previously used approach where ANNs estimate the parameters of the Ramberg–Osgood material model separately. This shows that a more favorable approach to the estimation of cyclic stress–strain behavior would be to directly estimate corresponding material curves using monotonic properties. Additionally, this may also reduce inaccuracies resulting from simplified representations of the actual material behavior inherent in the material model.

This paper presents DIC-based experimental verification of topology optimization of the 3D printed cantilever plate load-bearing element. Test samples were 3D printed from ABS and PET-G materials using FDM technology. A 3D geometrical parametric model was created and FEA and topology optimization were performed using computer program Autodesk Inventor. In order to successfully define the optimization problem and obtain reliable results, it is extremely important to properly define the analysis input parameters, such as material parameters and boundary conditions. Young's moduli of ABS and PET-G were determined experimentally on samples which were made using 3D printing technology with identical settings as cantilever plate samples. Stresses and strains in optimized samples were determined using FEA. To verify the FEA and topology optimization results, an experimental setup for holding and loading of 3D printed samples was prepared and displacements were measured using digital image correlation system ARAMIS. Results obtained experimentally were compared with the results obtained by finite element analysis of optimized cantilever plate sample and show very good agreement for both ABS and PET-G samples, with deviation of around 4 % and 11 % respectively.

Determination of the expected fatigue life of components operating under rolling-sliding contact loading (gears, bearings, wheels) is quite challenging due to the complex stresses and strains in the material which are constantly changing during the loading cycles. Additional challenge is a lack of the exact properties of the material that components are made of, which is especially pronounced in cases of heat-treated components in which values of the material properties can vary significantly within the component. Based on the results of fatigue testing of involute spur gears made of differently heat-treated steel 42CrMo4 steel (normalized, quenched&tempered) reported in literature, fatigue life analyses of gear teeth flanks were performed using the previously proposed multiaxial fatigue life calculation model based on Fatemi-Socie critical plane based crack initiation criterion. Since the actual cyclic and fatigue material parameters were unavailable, they were estimated using the estimation method developed specifically for 42CrMo4 steel using monotonic properties of the materials which were reported in corresponding studies. Comparisons of experimental and calculated fatigue lives i.e. load carrying capacities show very good agreement thus confirming the applicability of estimation of the advanced material parameters in fatigue and failure analyses of the actual components subjected to rolling-sliding contact loading.

Evaluations of methods for estimation of fatigue parameters from materials’ monotonic properties and comparisons of estimated and experimental fatigue lives are regularly performed for complete range of fatigue lives and materials with wide range of monotonic properties. In some cases this causes in significant averaging of results of evaluations and possibly erroneous conclusions on estimation methods accuracy. A comprehensive evaluation of three selected, estimation methods was performed using established methodology on an extensive number of detailed fatigue datasets on unalloyed, low-alloy and high alloy steels. Results provide detailed insight on the accuracy of the selected estimation methods and their applicability for the estimation of fatigue parameters of various materials divided to low and high strength subgroups and for low- and high-cycle fatigue regimes.

Successful prediction of the relevant mechanical properties of steels is of great importance to materials engineering. The aim of this research is to investigate the possibility of reducing the complexity of artificial neural networks-based prediction of total hardness of hypoeutectoid, low-alloy steels based on chemical composition, by introducing the specific Jominy distance as a new input variable. For prediction of total hardness after continuous cooling of steel (output variable), ANNs were developed for different combinations of inputs. Input variables for the first configuration of ANNs were the main alloying elements (C, Si, Mn, Cr, Mo, Ni), the austenitizing temperature, the austenitizing time, and the cooling time to 500 °C, while in the second configuration alloying elements were substituted by the specific Jominy distance. Comparing the results of total hardness prediction, it can be seen that the ANN using the specific Jominy distance as input variable (runseen = 0.873, RMSEunseen = 67, MAPE = 14.8%) is almost as successful as ANN using main alloying elements (runseen = 0.940, RMSEunseen = 46, MAPE = 10.7%). The research results indicate that the prediction of total hardness of steel can be successfully performed only based on four input variables: the austenitizing temperature, the austenitizing time, the cooling time to 500 °C, and the specific Jominy distance.

Composite materials are in use in the shipbuilding industry for a long period of time. Composites appear in vast number of fibre – matrix combinations and can be produced with several different production processes. Due to the specific nature of the composite material structure, the selection of the production process and the limitations in the quality control procedures, composite materials will always be subject to defects and imperfections which may, under certain circumstances, lead to the appearance and propagation of cracks. The size and the shape of the crack, the load type and the stress field in the material surrounding the crack will be crucial for crack growth and crack propagation. This paper reviews the composite material damage processes especially relevant for shipbuilding. The basic principles of composite material fracture mechanics are briefly explained, and finally, mechanisms responsible for the development of damage and fracture of composite materials are presented. This paper has emerged from the need to summarize information about composite material fracture and failure mechanisms and modes relevant for the shipbuilding industry.

Important components such as gears, rollers, or bearings operate in rolling-sliding contact loading conditions. Determination of their fatigue lives remains a challenging task due to complex states of stress and strain in the contact region, as well as complex contact conditions such as variable loading amplitude and complex geometry of contact. A mathematical model of rolling-sliding line contact combined with a multiaxial fatigue life calculation model based on the Fatemi-Socie critical plane crack initiation criterion is proposed. The developed model was applied to gears' teeth in mesh and compared with fatigue lives of gears reported in the literature. Good agreement was determined confirming the validity of the proposed model. A further advantage is obtaining locations of initiated cracks and the orientation of critical plane(s), which can subsequently be used for the estimation of crack shapes in initial phases of their growth and the damage type that they can be expected to develop into.

Number of important engineering components and elements such as gears, rollers, bearings operate in conditions of rolling-sliding contact loading. Determination of fatigue lives of such components and elements is very important for engineering practice but remains quite chalenging task due to complex states of stress and strain in the material in the vicinity of contact (multiaxiality, non-proportionality, rotation of principal axes, mean compressive stress) as well as complex contact conditions such as loading amplitude, complex geometry of bodies in contact, type of lubrication, value of coefficient of friction, etc. Proposed fatigue life calculation model for cases of rolling-sliding contact is based on critical plane approach in the form of Fatemi-Socie crack initiation criterion. Developed model was implemented in the case of gears teeth flanks in mesh and compared with results and fatigue lives of gears reported in literature. Good agreement was determined confirming validity of developed model. Further advantage of presented approach and developed model is obtained information on critical location(s) and critical plane(s) orientation which can subsequently be used for estimation of crack shapes in initial phases of their growth and later damage type into which they can be expected to develop.

Most existing methods for estimation of cyclic yield stress and cyclic Ramberg-Osgood stress-strain parameters of steels from their monotonic properties were developed on relatively modest number of material datasets and without considerations of the particularities of different steel subgroups formed according to their chemical composition (unalloyed, low-alloy, and high-alloy steels) or delivery, i.e., testing condition. Furthermore, some methods were evaluated using the same datasets that were used for their development. In this paper, a comprehensive statistical analysis and evaluation of existing estimation methods were performed using an independent set of experimental material data compriseding 116 steels. Results of performed statistical analyses reveal that statistically significant differences exist among unalloyed, low-alloy, and high-alloy steels regarding their cyclic yield stress and cyclic Ramberg-Osgood stress-strain parameters. Therefore, estimation methods were evaluated separately for mentioned steel subgroups in order to more precisely determine their applicability for the estimation of cyclic behavior of steels belonging to individual subgroups. Evaluations revealed that considering all steels as a single group results in averaging and that subgroups should be treated independently. Based on results of performed statistical analysis, guidelines are provided for identification and selection of suitable methods to be applied for the estimation of cyclic stress-strain parameters of steels.

Presented work is focused on experiment-based characterization and modelling of normalized and quenched and tempered low-alloy steel 42CrMo4 subjected to monotonic and cyclic loading and possibility to determine parameters of more complex models from simple ones. The main characteristics of the cyclic Ramberg-Osgood and rate-dependent as well as rate-independent Chaboche's material models, both of which are applicable for this material, have been presented. Similarities in the modelling and simulation of stabilized material behaviour confirmed possibility of such approximation for Ramberg-Osgood and rate-independent Chaboche model. However, due to inadequacy of Ramberg-Osgood for modelling of evolutionary material behaviour throughout loading cycles, separate study of the influence of saturation rate of isotropic hardening of rate-dependent Chaboche's model on modelling and simulation of material behaviour has been performed. The study showed that deviations of simulated material behaviour from the experimentally obtained behaviour are low, and in majority of the materials life negligible and recommendations on the range of values of saturation rate parameters differently heat treated 42CrMo4 steel are provided. In order to make possible further comparisons of analysed models and evaluate applicability of proposed approximations on other materials, further analyses on additional materials would need to be performed.

Engineering design process consists of three main parts: material selection, components dimensioning and the choice of production technology. The material selection relies on the knowledge of material behavior in different loading conditions. Due to their wide application, majority of research still deals with characterization of metallic materials. Innovative materials hold potential so research of characterization and modeling of their behavior is increasingly getting into focus. Therefore, it is important to resolve main objectives required for characterization of these materials. This paper discusses development of procedures required for effective soft tissues characterization, based on previously developed ones for metallic materials characterization.

Most of existing methods for estimation of cyclic stress-strain parameters have been developed for steels in general with no regard to the peculiarities of individual steel subgroups. Also, proposed models were commonly developed and evaluated without systematically determining if, and to what extent, individual monotonic properties contribute to their accuracy. In this work, a thorough statistical analysis of experimental datasets of 116 different steels obtained from literature was performed in order to determine which monotonic properties might be relevant for the estimation of cyclic yield stress and cyclic Ramberg-Osgood parameters of unalloyed, low-alloy and high-alloy steels. Only certain monotonic properties used in existing methods were found to be suitable for estimation purposes, while for a number of monotonic properties used in those references no such conclusion can be given. Furthermore, obtained results indicate that steels should not be treated as a single group since different sets of monotonic properties proved to be relevant for unalloyed, low- and high-alloy steel subgroups. Provided list of specific monotonic properties relevant for estimation of individual cyclic parameters of particular steel subgroups can be used for improving the accuracy of existing or development of new estimation methods.

It is becoming increasingly important to make possible constitutive modelling and simulation of material behaviour for the prediction of possible failures in material. This can allow to the optimization of design of highly loaded engineering components. In order to achieve that goal, material parameters should be accurately determined for the chosen material model. The major step in material parameters identification is material behaviour simulation. The procedure of material behaviour simulation is based on the results of the fatigue testing on the materials' samples. The paper presents the procedures required for the material behaviour simulation of 42CrMo4 steel, starting from the fatigue testing, through numerical procedures related to complex material model, which results in material parameters identification, to the validation of described procedures by comparison of the simulated and real materials response in cyclic loading conditions.