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This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

This study aimed to analyze the free vibration of a radially graded Ni-Al2O3-based functionally graded (FG) disk with uniform thickness.

Using the energy method, natural frequencies of rotating and non-rotating disks were determined at the limit elastic angular speed. Material properties were estimated using a modified rule of mixture. Both even and uneven porosity variation effects were considered in the material modeling. Finite element analysis validated the analytical approach.

The study explored limit angular speeds and natural frequencies across various grading indices, investigating the impact of porosity types and grading indices on these parameters.

Insights from this research are valuable for researchers and design engineers involved in modeling and fabricating porous FG disks, aiding in more effective design and manufacturing processes.

This study contributes to the field by providing a comprehensive analysis of free vibration behavior in radially graded Ni-Al2O3-based FG disks. The incorporation of material modeling considering both even and uneven porosity variation adds originality to the research. Additionally, the validation through finite element analysis enhances the credibility of the findings.

Additive manufacturing (AM) is growing economically because of its cost-effective design flexibility. However, it faces challenges such as interlaminar weaknesses and reduced strength because of product anisotropy. Therefore, the purpose of this study is to develop a methodology that integrates design for additive manufacturing (AM) principles with fused filament fabrication (FFF) to address these challenges, thereby enhancing product reliability and strength.

Developed through case analysis and literature review, this methodology focuses on design methodology for AM (DFAM) principles applied to FFF for high mechanical performance applications. A DFAM database is constructed to identify common requirements and establish design rules, validated through a case study.

Existing DFAM approaches often lack failure theory integration, especially in FFF, emphasizing mechanical characterizations over predictive failure analysis in functional parts. This methodology addresses this gap by enhancing product reliability through failure prediction in high-performance FFF applications.

While some DFAM methods exist for high-performance FFF, they are often specific cases. Existing DFAM methodologies typically apply broadly across AM processes without a specific focus on failure theories in functional parts. This methodology integrates FFF with a failure theory approach to strengthen product reliability in high-performance applications.

Finite element (FE) analysis can be used for both design and verification of components. In the case of 3D-printed materials, a proper characterization of properties, accounting for anisotropy and raster angles, can help develop efficient material models. This study aims to use compression tests to characterize short carbon-reinforced PA12 made by fused filament fabrication (FFF) and to model its behaviour by the FE method.

In this work, the authors focus on compression tests, using post-processed specimens to overcome external defects introduced by the FFF process. The material’s elastoplastic mechanical behaviour is modelled by an elastic stiffness matrix, Hill’s anisotropic yield criterion and Voce’s isotropic hardening law, considering the stacking sequence of raster angles. A FE analysis is conducted to reproduce the material’s compressive behaviour through the LS-DYNA software.

The proposed model can capture stress values at different deformation levels and peculiar aspects of deformed shapes until the onset of damage mechanisms. Deformation and damage mechanisms are strictly correlated to orientation and raster angle.

The paper aims to contribute to the understanding of 3D-printed material’s behaviour through compression tests on bulk 3D-printed material. The methodology proposed, enriched with an anisotropic damage criterion, could be effectively used for design and verification purposes in the field of 3D-printed components through FE analysis.

Presently in multicolor fused filament-based three-dimensional (3-D) printing, significant amounts of waste material are produced through nozzle priming and purging each time a change from one color to another occurs. G-code generating slicing software typically changes the material on each layer resulting in wipe towers with greater mass than the target object. The purpose of this study is to provide an alternative fabrication approach based on interlayer tool clustering (ITC) for the first time, which reduces the number of tool changes and is compatible with any commercial 3-D printer without the need for hardware modifications.

The authors have developed an open-source PrusaSlicer upgrade, compatible with Slic3r-based software, which uses the described algorithm to generate g-code toolpath and print experimental objects. The theoretical time, material and energy savings are calculated and validated to evaluate the proposed fabrication method qualitatively and quantitatively.

The experimental results show the novel ITC method can significantly increase the efficiency of multimaterial printing, with an average 1.7-fold reduction in material use, and an average 1.4-fold reduction in both time and 3-D printing energy use. In addition, this approach reduces the likelihood of technical failures in the manufacturing of the entire part by reducing the number of tool changes, or material transitions, on average by 2.4 times.

The obtained results support distributed recycling and additive manufacturing, which has both environmental and economic benefits and increasing the number of colors in a 3-D print increases manufacturing savings.

This paper aims to investigate the influence of four motivational elements (i.e. utilitarian, hedonic, social and escapism motivations) on the propensity of customers to utilize a metaverse hotel, as well as whether age, gender and mobility disability play substantial moderating roles in these relationships.

Data was gathered from 843 US residents who had experienced a hotel stay within the past two years. We tested the hypotheses using structural equation modeling and multigroup analysis.

The findings indicated that, in both age and gender groups, hedonic, social and escapism motivations had significant effects on intentions to use a metaverse hotel, whereas utilitarian motivation did not. The influence of escapism motivation on customers’ usage intentions was significantly more pronounced for males than females, suggesting the moderating role of gender in this relationship. Hedonic and social motivations exerted significant effects on usage intentions in both mobility disability and non-disability groups. The relationship between escapism motivation and intentions to use was significant for the non-disability group only, suggesting the moderating role of disability in this association.

This research provides recommendations for hotel managers and technology providers aiming to enhance the adoption of metaverse hotels by customers and to augment the worth of this technology.

This research fills the voids in the current literature by formulating and empirically evaluating a research framework to gain deeper insights into the motivations that drive the acceptance of a metaverse hotel.

The fast fashion industry is one of the most polluting industries. For this reason, the industry should look into new circular business models in order to reduce its material footprint as well as the amount of waste produced. This article focuses on the question of how the sharing economy, as one possible circular business model, can contribute to achieving Sustainable Development Goal 12 (SDG 12) “Ensuring Sustainable Consumption and Production.” After a brief introduction to SDG 12, a short outline of the current development of the sharing economy in the fast fashion sector is given. To develop consumer buying behavior toward environmental sustainability, it is important to understand their motives. Utilitarian and hedonic motives are examined in order to determine to what extent they can positively influence buying intention and thus the acceptance of fashion sharing platforms. The database gathered through a master thesis is used to investigate the specific influence these motives have on buying intention. To increase the acceptance and thus the use of fashion sharing platforms, recommendations for action are developed in the final step of this chapter throughout the five steps of the buying cycle model. Circular business models will play a key role in the context of sustainable transformation in the future. Therefore, it is particularly important to derive concrete recommendations for action based on research in order to get the ecological footprint of environmentally harmful industries – such as the fast fashion industry – under control.

Powder bed density is a key parameter in powder bed additive manufacturing (AM) processes but is not easily monitored. This research evaluates the possibility of non-invasively estimating the density of an AM powder bed via its thermal properties measured using flash thermography (FT).

The thermal diffusivity and conductivity of the samples were found by fitting an analytical model to the measured surface temperature after flash of the powder on a polymer substrate, enabling the estimation of the powder bed density.

FT estimated powder bed was within 8% of weight-based density measurements and the inferred thermal properties are consistent with literature findings. However, multiple flashes were necessary to ensure precise measurements due to noise in the experimental data and the similarity of thermal properties between the powder and substrate.

This paper emphasizes the capability of Flash Thermography (FT) for non-contact measurement of SS 316 L powder bed density, offering a pathway to in-situ monitoring for powder bed AM methods including binder jetting (BJ) and powder bed fusion. Despite the limitations of the current approach, the density knowledge and thermal properties measurements have the potential to enhance process development and thermal modeling powder bed AM processes, aiding in understanding the powder packing and thermal behavior.

This study aims to explore entropy evaluation in the bi-directional flow of Casson hybrid nanofluids within a stagnated domain, a topic of significant importance for optimizing thermal systems. The aim is to investigate the behavior of unsteady, magnetized and laminar flow using a parametric model based on the thermo-physical properties of alumina and copper nanoparticles.

The research uses boundary layer approximations and the Keller-box method to solve the derived ordinary differential equations, ensuring numerical accuracy through convergence and stability analysis. A comparison benchmark has been used to authenticate the accuracy of the numerical outcomes.

Results indicate that increasing the Casson fluid parameter (ranging from 0.1 to 1.0) reduces velocity, the Bejan number decreases with higher bidirectional flow parameter (ranging from 0.1 to 0.9) and the Nusselt number increases with higher nanoparticle concentrations (ranging from 1% to 4%).

This study has limitations, including the assumption of laminar flow and the neglect of possible turbulent effects, which could be significant in practical applications.

The findings offer insights for optimizing thermal management systems, particularly in industries where precise control of heat transfer is crucial. The Keller-box simulation method proves to be effective in accurately predicting the behavior of such complex systems, and the entropy evaluation aids in assessing thermodynamic irreversibilities, which can enhance the efficiency of engineering designs.

These findings provide valuable insights into the thermal management of hybrid nanofluid systems, marking a novel contribution to the field.

Nowadays, residential buildings have become increasingly important due to the growing communities. The purpose of this study is to investigate the behavior of a steel structural framing system that incorporates lightweight load-bearing walls and slabs, and to compare the weight of materials used in cold-formed and hot-finished steel structural systems for affordable housing.

Four types of models consisting of 243 members were simulated. Model 1 is a cold-formed steel structural framing system, while Model 2 is a hot-finished steel structural framing system. Both Models 1 and 2 use lightweight wall panels and lightweight composite slabs. Models 3 and 4 are made with brick walls and precast reinforced concrete systems, respectively. These structures use different wall and slab materials, namely, brick walls and precast reinforced concrete. The analysis includes bending behavior, buckling resistance, shear resistance and torsional rotation analysis.

This study found that using thinner steel sections can increase the deflection value. Meanwhile, increasing member length and the ratio of slenderness will decrease buckling resistance. As the applied load increases, buckling deformation also increases. Furthermore, decreasing shear area causes a reduction in shear resistance. Thicker sections and the use of lightweight materials can decrease the torsional rotation value.

The weight comparison of the steel structures shows that Model 1, which is a cold-formed steel structure with lightweight wall panels and lightweight composite slabs, is the most suitable model due to its lightweight and affordability for housing. This model can also be used as a reference for the optimal design of modular structural framing using cold-formed steel materials in the field of civil engineering and as a promotional tool.