Ebook: Material Strength and Applied Mechanics

Since its inception in 2018, the International Conference on Material Strength and Applied Mechanics (MSAM) has become an important annual event providing a platform for discussion between researchers, scientists, young scholars, and engineers where they can deepen their understanding of the mechanical behaviors of materials under different conditions, It also provides an excellent opportunity for participants to present and discuss their latest analytical, experimental and computational results in the field of applied mechanics.

Organized by Széchenyi István University, the 7th International Conference on Material Strength and Applied Mechanics (MSAM 2024) was held from 29 July to 1 August 2024 in Győr, Hungary. The conference was attended by over a hundred professors, researchers and young scholars from 23 countries worldwide. Internationally renowned experts were invited to deliver keynote and invited speeches, sharing their latest research findings in the fields of material strength and applied mechanics. These distinguished keynote speakers included Prof. Paul H. Mayrhofer (TU Wien, Austria), Prof. Klaus Holschemacher (Leipzig University of Applied Sciences, Germany), and Prof. Giuseppe Carlo Marano (Politecnico di Torino, Italy). The conference consisted of five parallel oral sessions and one poster session, all of which yielded fruitful results in the field of material strength and applied mechanics.

The proceedings of MSAM 2024 contains the 70 accepted papers presented at the conference, all of which were thoroughly reviewed by members of the technical program committee and a panel of invited international reviewers. The selection criteria were based on factors such as relevance to the scope of the conference, applications, research merit, experimental techniques, grammar, etc. This volume is divided into four parts, according to respective topics: strength of materials, applied mechanics, structural mechanics, and biomechanics. The results presented here will benefit the academic community in material science, and in mechanical, automobile, aerospace, civil, and bio engineering, as well as other cross-disciplinary fields, fostering a deeper perception, and addressing the challenges encountered within these disciplines to enable the adoption of appropriate solutions, while also driving innovation and practical applications in technology and industry.

We would like to express our gratitude to the committee members and reviewers from various disciplines who actively participated in the review process, and, on behalf of the conference participants, we thank the conference organizers, local committee chairs, and committee members for their dedication in organizing a productive technical program and an enjoyable social event. Special thanks are also due to Kelly Feng, Conference Secretary, for her valuable facilitating contribution.

Guest Editors

Dr. Alexander Khotsianovsky, Associate Editor of Strength of Materials,

Pisarenko Institute of Problems of Strength, National Academy of Sciences of Ukraine, Kyiv, Ukraine

Dr. Yuan Chen, Editor-in-Chief of Advanced Manufacturing: Polymer and Composites Science, Southern University of Science and Technology, China

Creep rupture life prediction of superalloys is very important to ensure the safety of aerospace and power stations. However, the prediction is challenging due to high cost of time, materials and creep test. Here, we propose a multi-station compressive creep test method to predict and evaluate the long-term creep life efficiently and cheaply. Experiments were demonstrated on Co-based superalloy Co-Ni-Cr-W-Fe-Mn. Multi-station compressive creep tests were performed at 650 °C, 750 °C and 850 °C, tensile creep tests were also carried out under the same conditions. P parameter method is proposed to quantitatively establish the relationship between tensile minimum creep rate and compressive minimum creep rate, and compressive minimum creep rates from multi-station compressive creep tests were used to predict creep rupture life. Results show that predicted creep rupture life is well consistent with test data, and this method shows great potential for the evaluation and assessment of creep properties on high-temperature structural materials faster and cheaper.

The dynamic failure behavior of additive manufacturing (AM) materials is an important factor in their engineering applications. In this article, 316L stainless steel (SS316L) hat-shaped specimens along the material building direction were manufactured by a selective laser melting (SLM) machine. By carrying out split Hopkinson compression bar tests with impact bar speeds of 20m/s and 50m/s respectively, morphological characteristics of the adiabatic shear band (ASB) of the hat-shaped specimen were investigated. Optical microscopy (OM) and scanning electron microscopy (SEM) results show that a plastic deformation band with a width of approximately 750μm is formed in the shear area of the hat-shaped specimen at an impact speed of 20m/s, and no ASB is formed. At an impact speed of 50m/s, an ASB with a width of approximately 20∼80μm was observed, and the ASB was located between a plastic deformation region and the molten pool rows. The Electron Backscatter Diffraction (EBSD) results show that there are nanoscale grains in the ASB, and a strip-like preferred orientation of grains appears in the shear region, which coincides with the molten pool rows. This may be related to the remelting mechanism. Finally, the evolution mechanism of the ASB in the SS316L hat-shaped specimen was also explained. The research provides insights into the revelation of the adiabatic shear dynamic failure mechanism of AM materials, the prediction of dynamic instability behavior of AM materials, and even the failure modes of customized AM materials.

This study investigated the possibility of fatigue life improvement by reducing the weld residual stress with local heating for the steel bridge member. A crossing welded part of longitudinal and transverse stiffeners of a steel plate deck was chosen as the target of this study because it is one of the parts prone to fatigue damage in steel bridge structures. To reduce weld residual stresses and improve the fatigue life of this part, the authors applied a local heating method using a high-frequency induction heating (i.e., IH) device. The welded part was heated up to 250 °C for 9 seconds and 550 °C for 30 seconds. This quick and local heating provided the newly generated compressive stress to the welding tensile stress originally existed. Eventually, the tensile residual stress around 200 N/mm² near the weld toe was reduced to less than –30 N/mm². The reduction effect of weld residual stress by local heating on the fatigue life was examined through the experimental approach. The locally heated group had higher durability than that of the non-locally heated group in certain stress ranges.

This study investigates the impact of uncertain load positions through an innovative optimization technique. It seamlessly integrates reliability-based design into the optimization of the structural topology, with a specific focus on the web portion of steel I-beams and including geometrical imperfection analysis. The study operates under the premise that the applied load is randomly positioned, engaging in a comprehensive probabilistic analysis. This methodology extends its considerations to additional factors, such as material properties, volume fraction, and geometric imperfections. The assumption of a normal distribution for each of these parameters aids in quantifying uncertainties. Moreover, the proposed work leverages the notion of plastic ultimate load multipliers to illustrate how the algorithm can improve the performance of steel beams. To evaluate the algorithm, the results of a benchmark problem were meticulously analyzed. While exploring the probabilistic nature of externally applied force positions, a numerical example was conducted involving a steel I-beam within the context of reliability-based imperfect geometry topology optimization. The outcomes of the proposed method underscore that the integration of probabilistic design significantly influences the topology optimization process.

In the case of embedded rail structures, the rails are fixed with a flexible embedding material that runs through a specially designed steel or reinforced concrete channel. The majority of the rail cross-section is embedded in these channels, eliminating the possibility of horizontal buckling as a failure mode. This paper investigates vertical stability, aiming to determine the stability resistance of a rail loaded with an initial failure in the vertical plane while considering elastic resistance. Vertical plane buckling is assumed to be a non-hazard for conventional rails because the wide rail footing can significantly inhibit vertical displacement, even without adhesion between the rail surface and the embedding material. However, the adhesion of the embedding material to the channel is adequate. Some rail sections lack adhesion between the rail and the embedding material due to the narrow width of the rail foot, making the phenomenon of “form-locking” uncertain, or the rail is partially embedded. In this article, the authors present a theoretical calculation method to investigate this problem, to transfer the results to everyday practice as simply as possible, and to present a solution suitable for manual calculation. Measuring the vertical spring constant in the method’s input parameters under laboratory conditions is a difficult task, but by utilizing the possibilities provided by finite element modeling, the design can be significantly simplified. The introduced method has the significant advantage of quantifying the effect of vertical plane misalignments in the track compared to simpler solutions in the literature.

Modification of asphalt binder with crumb rubber or SBS type polymer can further enhance the viscoelastic properties of asphalt mixtures in terms of reduced permanent deformation and increased operational temperature range. In this research, the effect of different variations of crumb rubber (CR) and styrene-butadiene-styrene (SBS) consisting of Base asphalt, 4% SBS, 7% SBS, 7% CR, 15% CR and 20% have been analyzed in terms of rutting accumulation. This research is significant in terms of performance characteristics of CR and SBS modified mixtures, where based on availability and price of these modifiers, agencies can perform selection of either variations of these modifiers based on their requirement and standards in order to optimize the performance of asphalt pavements. Finite element analysis has been performed using ABAQUS, where a dual wheel having an axle load of 100 kN has been simulated with a total of 50,000 passes on a 2D model. Validated creep parameters using the Burger’s model have been utilized for simulation of material decay under creep loading for each variation. Visco step loading has been used to measure rutting progression. Results show increased rutting accumulation of base asphalt among other scenarios. Furthermore, CR-20 outperforms other variations in terms of rutting accumulation. Both CR-20 and SBS-7 yield the minimum rutting magnitude of 3.2 m and 3.3 mm respectively. SBS-7 leads to 39% less rutting magnitude when compared to that of base asphalt.

Sheet metal blanking is a manufacturing process widely used in many industries. It consists in shearing thin sheets metal using two sharp tools called punch and die. During a blanking operation, these cutting tools are subjected to extreme stress, which leads to their progressive wear. Wear of blanking tools is an inevitable phenomenon during the blanking process. It leads to significant press shutdowns and can have a significant impact on the quality of the blanked parts, particularly the quality of the cut edge. Additionally, punch wear noticeably affects punch force, making the punch force/penetration curve a good wear indicator that can be combined with cut edge quality to quantify wear. In this context, this work focuses on the study of the effect of punch wear on the cutting force curve and on the quality of the cut edge with the aim of establishing a correlation between these two indicators and the degree of punch wear. To achieve that, realistic wear profiles of the punch based on wear profile measurements were implemented in a finite element model to predict the force on the punch during the entire punching process (i.e. cutting phase, phase of punch penetration into the die and stripping phase). As high-fidelity predictions are required, particular attention is paid to the sheet metal constitutive model. In this work the sheet metal behavior is described using a J2 plasticity model combined with Modified Mohr-Coulomb (MMC) fracture criterion. The procedure thus developed was used to link the state of the punch wear to the cutting force curve and the shape of the cut edge. These results are intended to enrich a database of physical test measurements for machine learning training purposes.

Discrete Element Modelling (DEM), employing the replacement method, has been extensively utilized to investigate the micro and macroscopic behavior of soil with particle breakage. Despite numerous breakage criteria proposed in the literature, an agreement on the most appropriate criterion remains unclear. In this study, three-dimensional DEM analyses were conducted using Particle Flow Code (PFC3D) to assess stress distribution and identify potential locations of particle crushing during direct shear tests for coarse sand subjected to different high normal stresses. The investigation focused on employing a breakage criterion featuring Weibull distribution of particle strengths and considers the effect of particle size on average strength to predict the occurrence of fractures. Various breakage criteria, including major principal stress, mean stress, octahedral shear stress within a particle, and stress calculated from the maximum contact force on a particle, were each examined. The findings indicate that potential crushable particles were predominantly situated near diagonal shear band. Notably, results demonstrate that criteria based on octahedral shear stress and maximum contact force prove more effective in accurately reproducing the concentration of crushed particles near the shear band.

The harsh climate and environmental hazards contribute to the structural damage of steel bridges. Substantial dynamic loading from heavy trucks can worsen existing cracks. This paper investigates the dynamic behavior of a steel box girder bridge, the Szapáry bridge, with a fractured girder subjected to moving truck loads. Initially, a finite element model simulates the seven-span continuous bridge behavior during static load testing. The model also accurately simulated the dynamic load tests performed. A series of hypothetical damage (fractured girder) and dynamic loading scenarios reveal the effects of truck positions on the damaged bridge’s dynamic response. Dynamic displacement induced due to traffic loading helps evaluate a bridge’s structural health. Results of the parametric analysis highlight that several factors, including truck velocity and position, bridge span length, and truck lateral spacing, significantly affect the dynamic vibration of the fractured bridge. The results offer insight into the effectiveness of dynamic response analysis for conditioned-based maintenance and damage detection.

Acid (usually by phosphoric acid) activation of aluminosilicate precursors provides a new group of inorganic materials with promising chemical and thermal stability. The three mixtures in system metakaolin – phosphoric acid were prepared; they differed in Al/P molar ratio (1, 1.5, 2). Compressive and bending strength were determined in the age of 1 week; the 1 day 60 °C curing was applied. The highest compressive strength reached sample with Al/P ratio 1.5, what falls within the range found in literature. The porosity of the samples increased with the increasing Al/P ratio, thus there was not any simple relationship between strength and porosity. It implies that Al/P ratio influenced not only porosity and pore size distribution, but also the nature of activated cementing product.

Corrugated paperboard boxes are widely used as packaging in logistics processes. Corrugated boxes must have adequate strength to ensure proper protection of the product. In many cases, the product will require cutouts in the side walls of the boxes. These significantly affect the strength behavior of the box which can be determined by measurements and various models. The aim of this study is to determine the compressive strength of corrugated cardboard boxes of different sizes and different rectangle shaped cutouts formed on them. Five different box dimensions and five different cutout rates were investigated. Box compression tests were carried out to obtain the compression force result of the boxes. The results of these measurements were compared with the box compression force estimation formulas. Two types of McKee formula were used for the comparison that are commonly used in industry. The complete and the simplified McKee formulas are the used models in this study. The constant parameters for both McKee formulas was calculated for each cutout rates. For higher cutout ratios the accuracy of both the simplified and the complete McKee formula increases. In three groups at 0%, 4% and 16% cutouts the fitted complete and simplified McKee formulas predict the BCT results with significant differences. It can be observed that neither the complete nor the simplified McKee formula is able to account for changes in compression force due to variations in box size for any cutout category.

The dimension of particles can significantly influence the load response and the performance of uniformly graded ballast layers in railway track structures in real-world conditions. Yet, the micromechanical behavior of the unbound aggregate ballast layer assembly, particularly affected by particle size, remains largely unexplored. In this study, the distinct element model of a direct shear test was initially simulated using friction coefficients of 0.8, 0.9, and 1, and particle size distribution No.4A. The dimensions of the shear test box were 300 mm in width, 300 mm in length, and 180 mm in height. Additionally, a constant normal force of 333 kPa was applied to the sample during the simulation. Subsequently, this model was compared with experimental results, revealing a close correspondence between simulated and experimental shear stress-displacement curves, particularly for the friction coefficient of 1. Following this verification, the validated model was employed to investigate three other particle size distributions: No.4, No.5, and No.57. The results demonstrated a reduction in shear stress for particle size distributions No.4, No.5, and No.57 compared to No.4A, with quantified decreases of 11.9%, 38.2%, and 56.7%, respectively.

The objective of this work is to improve the punching strength and control the plastic deformation of two-way reinforced concrete (RC) slabs using carbon fiber reinforced polymer (CFRP) bars. The efficacy of this reinforcement technique was evaluated by constructing four reinforced concrete flat slabs. One specimen was utilized as a reference slab, while the other three specimens were reinforced using the Near Surface Mounted (NSM) CFRP bars approach. The slabs, which had identical dimensions and steel reinforcement, were exposed to patch load, and tested until they reached the point of failure. For evaluating the strength of two-way reinforced concrete (RC) slabs, the Concrete Plastic Damage (CDP) constitutive model was developed and implemented. CFRP bars are inserted into the slab at a depth from the tension face to enhance their strength. The investigation commences with the calibration of a numerical model utilizing data obtained from laboratory experiments. This will be achieved by establishing an advanced analytical method that incorporates the plasticity of concrete damage and the use of CFRP bars, along with a multiplier to determine the plastic limit load. Numerical simulations are employed to investigate shear dynamics by including diverse elements. The results showed that an increase in the ratio of strengthening had a significant effect on shear strength.

In the context of structural analysis and design, natural frequencies play a vital role, and their prediction is essential in machine and vehicle design processes. The simulations related to the modal parameters are computationally intensive for systems with large complexity. This paper demonstrates on an illustrative academic example that natural frequencies can be successfully predicted using ML models. This paper aims to develop a model based on machine learning (ML) to predict a simple cantilever’s natural frequencies based on the physical parameters of the beam. The independent variables X are the geometric parameters including width, length, and thickness, while the dependent variable Y is the natural frequency. The study is framed using a systematic methodology that covers the stages of data collection, ML model selection, model training and validation. The validation process proves the effectiveness of ML as a computationally cheap replacement for traditional methods of prediction. The current research contributes to the investigation of the usage of commercially available ML tools in structural engineering. We report that the ODYSSEE A-Eye software is capable of natural frequency prediction with a varying geometry structure with less than 4% error for an 80-member training set of cantilever beam with various dimensions. Further developments will include considerations of noise, vibration, and harshness (NVH) to enhance system performance and improve user comfort.

When control algorithms of robots are constructed, the joint coordinates and the coordinates describing the dynamics might be different and the transformation between them might be necessary in both directions. The back-and-forth transformations are related to the inverse function theorem, which is well understood for single and multivariable continuous functions: the conditions are described under which the inverse function exist, furthermore the method is provided to calculate the Jacobian of the inverse function. A generalization of the theorem is necessary, when there are fewer dependent variables than independent ones, and furthermore there are constraint equations for the independent variables. It is exactly the case for model-based inverse dynamics control of multibody systems, when the dynamic model is given in terms of a redundant coordinate set, but the controller is formulated for minimum set coordinates. The widely used so-called natural coordinates are a typical redundant set. Minimum-coordinates come in the picture when the control is formulated for the joint coordinates. Clearly, when the natural coordinates are transformed to joint coordinates, there is information loss. The inverse transformation is however still possible, since there are constraint equations for the redundant set. This paper demonstrates a method for the transformation from minimum to redundant coordinates and vice versa with the help of the generalized inverse of the non-square constraint Jacobian and the projection matrices related to the constrained and admissible subspace of the redundant set. An illustrative numerical example and a robotic application demonstrate the theory. The results are relevant in the model-based control of complex-structure parallel kinematic chain robots.

I-beams with corrugated webs have higher torsional stiffness than that of flat web beams. Furthermore, the geometrical dimensions of the beam and the web corrugation heavily influence the precision of the currently used traditional pen-and-paper methods for determining the elastic lateral-torsional buckling moment. This study aims to suggest several machine learning models with the intention of predicting the elastic lateral-torsional buckling moment of corrugated web beams. Multiple machine learning models, including Random Forests, Gradient Boosting, Categorical Boosting, and Deep Neural Networks, were deployed to develop and train models to predict the elastic critical lateral-torsional buckling moments of I-beams with corrugated web. The database used for training the different models was compiled through linear bifurcation analyses conducted on shell finite element models. The study evaluates the precision of the various machine learning models by examining their performance against statistical parameters derived from both predicted and test data. The findings from the parametric evaluation highlight the surprisingly high performance and accuracy of the machine learning models.

Thermomechanical fatigue is one of the most common cause of the failure in microelectronic technology in the solder joints. The lifetime prediction for microelectronic components is a very important area in nowadays automotive industry, because the lifetime estimation fatigue models in the literature differ in their results by orders of magnitude. However, developing an accurate lifetime estimation methodology for microelectronic components is not straightforward, because the failure mechanism of the solder joints under cyclic thermomechanical load is not fully understood. In addition, there are numerous tolerances and uncertainties during the designing and manufacturing processes, such as component size, copper pad area, solder material volume or the formed standoff height of the component from the copper pad. These parameters can hugely affect the lifetime of the solder joint. In this paper a benchmark analysis based on finite element method were carried out with four plastic strain-based fatigue models to understand the impact of the standoff height to the estimated lifetimes. Three CAD models were created with identical parameters, except the standoff height of the components. Creating the solder geometries for the 3D models, Surface Evolver software were used. The result shows that the fatigue models give the same tendencies varying the standoff height values. However, changing the standoff height increases the differences between models, even if they are tuned so that the estimated lifetime matches for a certain standoff height.

This study explores the relation between corrosion behavior and fatigue performance of Aluminum alloy as a biomaterial for intertrochanteric femoral fracture fixation using Schanz screws. Finite element analysis via ANSYS Workbench software revealed a fatigue life of approximately 6 months for the screws, with a safety factor of 1.66. The dynamic fatigue test results indicated favorable fatigue performance of the aluminum alloy. Despite this, biocompatibility and corrosion concerns remain regarding aluminum alloys. Hence, it is suggested to adopt a biomaterial with superior fatigue performance for Schanz screws to ensure fixator safety during patient mobilization. Additionally, proposing a real-time monitoring Digital Twin Model for aluminum alloy-based Schanz screws is recommended. Equipped with sensors, this model enables continuous data collection on corrosion, stress, and temperature, aiding healthcare professionals in early issue detection and predictive maintenance, thus enhancing long-term reliability and safety of orthopedic implant systems.

This article presents experimental tests and numerical modeling of steel-composite connections. The study considers the interaction and friction coefficient between the steel structure and concrete, as well as between the steel studs and concrete. The numerical model underwent validation through an iterative process, considering various friction coefficients. The friction coefficient between the structure and the concrete exerted the most significant impact on the load capacity. A new finite element model has been developed, with various friction coefficients between steel structure and concrete, and in this model, the concrete was also subjected to different vertical compression loads. A total of 25 numerical tests have been conducted, using various configurations of vertical load and friction coefficient parameters. It was observed that higher friction coefficients increase the impact of vertical forces on the horizontal load capacity. The friction coefficient can be increased through technological interventions, such as surface roughening techniques or the introduction of intermediate materials designed to elevate it.

Cracks are a common phenomenon in large-volume concrete and are an important issue of concern in the engineering field. To address the so-called problem of “no dam without cracks,” engineering technicians have adopted a series of technical measures, such as mix proportion optimization, high fly ash content, and expansive agents, which have played a certain role in controlling the generation and propagation of cracks. This paper introduces the proposed layered water-locking technology and a new type of excellent anti-cracking material developed using this technology, which maximizes the reduction or even prevention of cracks in large-volume concrete, enhancing the concrete’s own crack resistance.

In this work, results of a numerical parametric analysis are presented in order to compare them with experimentally observed concrete cone failure features. Such a kind of failure is typical for cast-in steel anchors embedded in concrete substrates. Thus, numerical simulations via finite element method have been performed in order to investigate basic fracture parameters for specimens with a short fatigue crack and stress distribution for specimens without any crack. Several parameters were varied and their effect on the stress intensity factors or distribution of stress tensor components around the anchor corner were studied and discussed. Slight differences between the average angle typical for concrete cone failure observed experimentally and numerical results were found out and thus, more complex numerical simulations shall be recommended.

Unlike traditional material structures, the design of composite structure requires consideration of more parameters, and involves a more complex design space. By utilizing superior intelligent optimization algorithms, it is possible for us to get a better design effectively. In this paper, a Suboptimal individuals Retaining Differential Evolution algorithm (SNDE for short) is proposed according to the characteristics of optimization design of composite laminated structure. In SNDE, Good-point set technique is introduced to initialize the individuals which aims to improve distribution of the initial population; and in order to overcome the problem of individuals concentrating and diversity deteriorating, a new niche mechanism for enhancing the diversity of population is proposed. An optimization design example of composite drive shaft was conducted to check the performance of different algorithms and it was seen that the SNDE achieved the optimal result.