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This study aims to investigate the impact of different printing parameters on the free vibration characteristics of 3D printed cantilever beams. Through a comprehensive analysis of material extrusion (ME) variables such as extrusion rate, printing pattern and layer thickness, the study seeks to enhance the understanding of how these parameters influence the vibrational properties, particularly the natural frequency, of printed components.

The experimental design involves conducting a series of experiments using a central composite design approach to gather data on the vibrational response of ABS cantilever beams under diverse ME parameters. These parameters are systematically varied across different levels, facilitating a thorough exploration of their effects on the vibrational behavior of the printed specimens. The collected data are then used to develop a predictive model leveraging a hybrid artificial neural network (ANN)/ particle swarm optimization (PSO) approach, which combines the strengths of ANN in modeling complex relationships and PSO in optimizing model parameters.

The developed ANN/PSO hybrid model demonstrates high accuracy in predicting the natural frequency of 3D printed cantilever beams, with a correlation ratio (R) of 0.9846 when tested against experimental data. Through iterative fine-tuning with PSO, the model achieves a low mean square error (MSE) of 1.1353e-5, underscoring its precision in estimating the vibrational characteristics of printed specimens. Furthermore, the model’s transformation into a regression model enables the derivation of surface response characteristics governing the vibration properties of 3D printed objects in response to input parameters, facilitating the identification of optimal parameter configurations for maximizing vibration characteristics in 3D printed products.

This study introduces a novel predictive model that combines ANNs with PSO to analyze the vibrational behavior of 3D printed ABS cantilever beams produced under various ME parameters. By integrating these advanced methodologies, the research offers a pioneering approach to precisely estimating the natural frequency of 3D printed objects, contributing to the advancement of predictive modeling in additive manufacturing.

This study aims to comprehensively investigate the mixed-mode fracture behavior and mechanical properties of selective laser sintering (SLS) polyamide 12 (PA12) components, considering different build orientations and layer thicknesses. The primary objectives include the following. Conducting mixed-mode fracture and mechanical analyses on SLS PA12 parts. Investigating the influence of build orientation and layer thickness on the mechanical properties of SLS-printed components. Examining the fracture mechanisms of SLS-produced Arcan fracture and tensile specimens through experimental methods and finite element analyses.

The research used a combination of experimental techniques and numerical analyses. Tensile and Arcan fracture specimens were fabricated using the SLS process with varying build orientations (X, X–Y, Z) and layer thicknesses (0.1 mm, 0.2 mm). Mechanical properties, including tensile strength, modulus of elasticity and critical stress intensity factor, were quantified through experimental testing. Mixed-mode fracture tests were conducted using a specialized fixture, and finite element analyses using the J-integral method were performed to calculate fracture toughness. Scanning electron microscopy (SEM) was used for detailed morphological analysis of fractured surfaces.

The investigation revealed that the highest tensile properties were achieved in samples fabricated horizontally in the X orientation with a layer thickness of 0.1 mm. Additionally, parts manufactured with a layer thickness of 0.2 mm exhibited favorable mixed-mode fracture behavior. The results emphasize the significance of build orientation and layer thickness in influencing mechanical properties and fracture behavior. SEM analysis provided valuable insights into the failure mechanisms of SLS-produced PA12 components.

This study contributes to the field of additive manufacturing by providing a comprehensive analysis of the mixed-mode fracture behavior and mechanical properties of SLS-produced PA12 components. The investigation offers novel insights into the influence of build orientation and layer thickness on the performance of such components. The combination of experimental testing, numerical analyses and SEM morphological observations enhances the understanding of fracture behavior in additive manufacturing processes. The findings contribute to optimizing the design and manufacturing of high-quality PA12 components using SLS technology.

The purpose of this study was to investigate the effects of CaCO₃ nanoparticles on the mechanical properties, and mixed-mode fracture behavior of acrylonitrile butadiene styrene 3D printed samples with different internal architectures.

The nanocomposite filaments have been fabricated by a melt-blending technique. The standard tensile, compact tension and special fracture test samples, named Arcan specimens, have been printed at constant extrusion parameters and at four different internal patterns. A special fixture was used to carry out the mixed-mode fracture tests of Arcan samples. Finite element analyses using the J-integral method were performed to calculate the fracture toughness of such samples. The fractographic observations were used to evaluate the mechanism of fracture at different concentrations of nanoparticles.

The addition of CaCO₃ nanoparticles has resulted in a significant increase in the fracture loading of the samples, although this increase was not consistent for all the filling patterns, being more significant for samples with linear and triangular structures. According to the fractographic observations, the creation of uniformly distributed microvoids due to the blunting effect of nanoparticles and 3D stress state at the crack tip in the samples with linear and triangular structures justify the enhancement in the fracture loading by the addition of CaCO₃ nanoparticles in the matrix.

There is a significant gap in the knowledge of the effects of different nanoparticles in the polymer samples produced by the fused filament fabrication process. One of such nanoparticles is an inorganic CaCO₃ nanoparticle that has been frequently used as nanofillers to improve the thermomechanical properties of thermoplastic polymers. Here, experimental and numerical studies have been conducted to investigate the effects of such nanoadditives on the mechanical and fracture behavior of 3D printed samples.

Acrylonitrile butadiene styrene (ABS), as a light and high strength thermoplastic polymer, has found extensive applications in different industries. Fused filament fabrication, known as three-dimensional (3D) printing technique is considered a rapid prototyping technique that is frequently applied for production of samples of ABS material. Therefore, the purpose of this study is to investigate the mechanical and fracture behavior of such materials and the techniques to improve such properties.

Experimental and numerical analyses have been conducted to investigate the effects of internal architecture and chopped carbon fiber (CF) fillers on the mechanical properties and mixed mode fracture behavior of the ABS samples made by 3D printing technique. Four different filling types at 70% filling ratios have been used to produce tensile and special fracture test samples with pure and CF filled ABS filaments (CF-ABS) using 3D process. A special fixture has been developed to apply mixed mode loading on fracture samples, and finite element analyses have been conducted to determine the geometric function of such samples at different loading angles.

It has been determined that the printing pattern has a significant effect on the mechanical properties of the sample. The addition of 15% CF to pure ABS resulted in a significant increase in tensile strength of 46.02% for line filling type and 15.04% for hexagon filling type. It has been determined that as the loading angle increases from 0° to 90°, the KIC value decreases. The addition of 15% CF increased the K_IC values for hexagonal and line filling type by 64.14% and 12.5%, respectively.

The damage that will occur in ABS samples produced in 3D printers depends on the type, amount, filling speed, filling type, filling ratio, filling direction and mechanical properties of the additives. All these features are clearly dependent on the production method. Even if the same additive is used, the production method difference shows different microstructural parameters, especially different mechanical properties.

Microwave curing (MC) can facilitate rapid concrete repair in cold climates without using conventional accelerated curing technologies which are environmentally unsustainable. Accelerated curing of concrete under MC can contribute to the decarbonisation of the environment and provide economies in construction in several ways such as reducing construction time, energy efficiency, lower cement content, lower carbonation risk and reducing emissions from equipment.

The paper investigates moisture loss and pore properties of six cement-based proprietary concrete repair materials subjected to MC. The impact of MC on these properties is critically important for its successful implementation in practice and current literature lacks this information. Specimens were microwave cured for 40–45 min to surface temperatures between 39.9 and 44.1 °C. The fast-setting repair material was microwave cured for 15 min to 40.7 °C. MC causes a higher water loss which shows the importance of preventing drying during MC and the following 24 h.

Portland cement-based normal density repair mortars, including materials incorporating pfa and polymer latex, benefit from the thermal effect of MC on hydration, resulting in up to 24% reduction in porosity relative to normal curing. Low density and flowing repair materials suffer an increase in porosity up to 16% due to MC. The moisture loss at the end of MC and after 24h is related to the mix water content and porosity, respectively.

The research on the application of MC for rapid repair of concrete is original. The research was funded by the European commission following a very rigorous and competitive review process which ensured its originality. Original data on the parameters of porosity and moisture loss under MC are provided for different generic cementitious repair materials which have not been studied before. Application of MC to concrete construction especially in cold climates will provide environmental, economic and energy benefits.

The corrosion of reinforcing steel bars reduces significantly the life and durability of concrete structures. This critical concern causes great losses to the economy and industry. The purpose of this paper is to estimate the effects of corrosion on the tensile mechanical properties of embedded steel bars B500c in concrete.

The concept is based on the curve fitting modelling, as well the mathematical correlation of the tensile mechanical properties between corroded bare and corroded embedded steel bars. In order to achieve this, extensive experiments were carried out on both bare (Ø8, 10, 12, 16 and 18 mm) and embedded (Ø8 mm) steel bars B500c, which were subjected to artificially accelerated corrosive conditions in a chloride‐rich atmosphere for several exposure times.

The research results show that the estimation method is available and effective in simulating the tensile mechanical behaviour of corroded reinforcing steel bars B500c.

As far as is known, this is the first time that an advanced data processing technique has been employed to try to find the mathematical correlation of the existing corrosion damage on the residual tensile properties between bare and embedded steel bars. It is argued that these models can be developed in order to reduce the need for expensive experimental investigation in materials.

The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms.

In this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model.

In this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model.

Based on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.