Search

The present study aims to investigate the influence of tool profiles such as hybrid square, hybrid triangular and conventional threaded tapered on the microstructure evolution and mechanical properties of the aluminium alloys of grade AA7075-T651. The softening of the thermo mechanical affected zone/heat affected zone is the main issue, and it weakens the weldment, which in turn is detrimental to the mechanical properties. There are limited studies on the influence of tool profiles on the mechanical properties of FSW of AA7075-T651. The weld joints were fabricated with the optimised parameters using the design of experiment ANOVA analysis. The processed weldments with all three tool profiles are characterised on the ground of microstructure evolution using optical microscopy, electron back scattered diffraction and correlated with mechanical properties such as hardness and tensile test. The results conceded that the hybrid square tool profile has a maximum ultimate strength of 450.3 MPa and elongation of 7% with process parameters such as rotational speed of 1325 rpm and 120 mm/min. [ABSTRACT FROM AUTHOR]

Producing defect-free and high-quality welds are still challenging by the friction stir welding process. In this paper, ZrO2 nanoparticles of 20 nm average size were used in the weld area of Aluminium 8011 alloy. Three different types of tool profiles semi-circular, plus tool and trapezium profiles were used for better material mixing and improved joint strength. All of the experiments were performed with rotational speeds of 1800, 1120, and 560 RPM, and a constant welding speed of 25 mm/min. It was observed that there is no significant effect of the ZrO2 addition in the weld area. On the other hand, tool profile plays a great role in increasing tensile strength. The maximum tensile strength of 116.70 MPa was obtained with the semi-circular tool at 1800 RPM and 25 mm/min welding speed because of the improved material mixing and defect-free joint. [ABSTRACT FROM AUTHOR]

In large format additive manufacturing (LFAM), a keener understanding of the relationship between the manufacture method and material temperature dependency is needed for the production of large polymer parts. Statistical analyses supported by material properties and a meso-structural understanding of LFAM are applied to elucidate tensile data trends. The data from LFAM polyethylene terephthalate glycol with 30% carbon fiber (CF) (PETG CF30%) panels (diagonal, horizontal, and vertical in the x-y print plane) and injection-molded specimens tensile tested at six different testing temperatures (room temperature, 40 °C, 50 °C, 60 °C, 70 °C, and 80 °C) were used for statistical analyses. A standard deviation, a coefficient of variation, and a two-way and one-way analyses of variance (ANOVA) were conducted. The manufacturing method (44.2%) and temperature (47.4%) have a strong effect on the ultimate tensile strength, in which temperature (82.6%) dominates Young's modulus. To explain the difference between the ultimate tensile strength of vertical, diagonal, and horizontal specimens at room temperature, a visual inspection of the specimen failure was conducted and the maximum stress at the crack tip was calculated analytically. The decreased strength in the diagonal specimens resulted from the reliance on interlaminar adhesion strength. Future work will consider the effect of the void space variation on tensile strength variance. [ABSTRACT FROM AUTHOR]

AbstractMost published works related to gas metal arc welding processes focus on quality criteria, while energy consumed has not been considered. In this study, key parameters of the gas metal arc welding (GMAW) operation of the AISI 1045 steel, including the current (I), voltage (V), flow rate (F), and nozzle stand-off distance (D) are optimized to minimize energy consumed (EC) and enhance the ultimate tensile strength (TS) as well as elongation (EL). The radial basis function network (RBFN) is employed to propose GMAW responses. The Entropy method, modified grey wolf optimizer (MGWO), and Multi-Attributive Border Approximation Area Comparison (MABAC) were utilized to compute the weights, generate feasible solutions, and determine the best optimality. As a result, the optimal I, V, F, and D were 137 A, 23 V, 12 L/min, and 12 mm, respectively. The EC was reduced by 5.8%, while the TS and EL were improved by 3.3% and 20.7%, respectively, at the optimality. The EC and EL models were primarily affected by the I, V, F, and D, respectively. The TS model was mainly influenced by the I, D, F, and V, respectively. The RBFN-MGWO could be employed to present non-linear data and achieve better optimal results, as compared to the traditional approach. The obtained outcomes could be used to produce high-quality welds with minimizing environmental impacts. [ABSTRACT FROM AUTHOR]

Equal Channel Angular Pressing (ECAP) is one of the severe plastic deformation methods that is applied to metals, alloys and composite materials in order to convert the normal grain structure into ultra-fine grain structure, so as to increase the strength of the metal and its ductility. The uniqueness of ECAP is that the severe plastic deformation apply large strains on the material without affecting the original dimensions of material. ECAP process was chosen among the other severe plastic deformation methods, because of its cost-effectiveness and simplicity. Die angle, corner angle, ECAP routes, number of passes and heat treatments have great impact on the micro-structure and mechanical properties of various materials. The grain refinement enhances the yield strength, tensile strength, hardness, ductility and corrosion resistance on ECAPed materials, which are commonly used for engineering and bio-medical applications. Many researchers have contributed greatly to this method. This article mainly focuses on the effect of ECAP die orientations and on novel ECAP methods that exclusively combines ECAP with heat treatment or hot working on aluminium, magnesium, titanium and other materials. [ABSTRACT FROM AUTHOR]

A machine learning-based approach is presented for predicting the mechanical properties of Cu-Ti alloys utilizing a dataset of various features, including compositional elements and processing parameters. The features encompass chemical composition elements such as Cu, Al, Ce, Cr, Fe, Mg, Ti, and Zr, as well as various thermo-mechanical processing parameters. This dataset, comprising more than 1000 data points, was selected from a larger collection of various Cu-based alloys. The dataset was divided into training, validation, and test sets, with a Random Forest Regressor model being trained and optimized using GridSearchCV. The model's performance was evaluated based on the R2 score. The results demonstrate high predictive accuracy, with R2 scores of 0.9929, 0.9851, and 0.9937 for the training, validation, and testing sets, respectively. The Random Forest model was compared with other machine learning models and showed better results in terms of predictive accuracy. A feature importance analysis of the mechanical characteristics was conducted, further clarifying the influence of each feature. The correlation heatmap further elucidates the relationships among the features, offering insights into the effects of alloy composition and processing on mechanical properties. This study underscores the potential of machine learning in advancing the development and optimization of Cu-Ti alloys, providing a valuable tool for materials scientists and engineers. [ABSTRACT FROM AUTHOR]

The inherent imperfections introduced during the manufacturing processes of casting, welding, and additive manufacturing often result in compromised mechanical properties. The concentrated deformation around these defects during loading significantly impacts tensile ductility and fatigue performance. This study introduces an innovative defect-tolerant design approach for predicting the uniaxial tensile behaviour of ADC12 aluminium alloys. Employing precise micro-drilling techniques, a deliberately created flaw is strategically positioned at the midpoint along the central axis of the rectangular uniaxial tensile test specimen. The proposed methodology accurately forecasts uniaxial tensile elongation, ultimate tensile strength, and the entire stress–strain curve for materials affected by defects. [ABSTRACT FROM AUTHOR]

Copyright of Materials & Technologies / Materiali in Tehnologije is the property of Institute of Metals & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A machine learning-based approach is presented for predicting the mechanical properties of Cu-Ti alloys utilizing a dataset of various features, including compositional elements and processing parameters. The features encompass chemical composition elements such as Cu, Al, Ce, Cr, Fe, Mg, Ti, and Zr, as well as various thermo-mechanical processing parameters. This dataset, comprising more than 1000 data points, was selected from a larger collection of various Cu-based alloys. The dataset was divided into training, validation, and test sets, with a Random Forest Regressor model being trained and optimized using GridSearchCV. The model’s performance was evaluated based on the R2 score. The results demonstrate high predictive accuracy, with R2 scores of 0.9929, 0.9851, and 0.9937 for the training, validation, and testing sets, respectively. The Random Forest model was compared with other machine learning models and showed better results in terms of predictive accuracy. A feature importance analysis of the mechanical characteristics was conducted, further clarifying the influence of each feature. The correlation heatmap further elucidates the relationships among the features, offering insights into the effects of alloy composition and processing on mechanical properties. This study underscores the potential of machine learning in advancing the development and optimization of Cu-Ti alloys, providing a valuable tool for materials scientists and engineers.

In the modern era, the demand for lightweight and high-strength aluminium matrix composites (AMCs) has significantly grown as a result of their structural applications in various sectors. The distinctive characteristics of AMCs are intricately impacted by variables like the type and weight percentage (wt.%) of reinforcement, the selection of processing techniques, and the matrix employed. Fabricating hybrid AMCs poses several challenges, including achieving desired geometrical shapes, homogeneous mixing of reinforcements, addressing porosity, and ensuring strong interfacial bonding of reinforcement-matrix interfaces. Stir-squeeze casting has emerged as a non-traditional casting technique with the potential to address these challenges effectively. This study intends to look into the impacts of the distinct reinforcement particles (Al2O3, SiC, Si3N4, and BN) with varying wt.% on the development of different configurations of hybrid AMCs based on AA2024. The key properties that have been examined include porosity, ultimate tensile strength (UTS), elongation percentage (EL%), hardness, and impact energy of developed hybrid AMCs. The results reveal that UTS (235.6 to 377.8 MPa) and hardness (51.5 to 85.1 HRB) significantly increased with the addition of reinforcement particles, while EL% (11.6 to 7.9%) and impact energy (6.62 to 5.61 J) decreased. AA2024/2%Al2O3/2%SiC/2%Si3N4/2%BN outperformed in terms of UTS and hardness and gave 60.36% and 65.24%, respectively; however, the porosity, EL%, and impact energy have been compromised from the matrix material (AA2024) by 1.61%, 31.89%, and 15.26%, respectively. Fractography analysis of UTS and Charpy impact fractured samples depicts that a dimple fracture has been observed for the AA2024 without any reinforcement. However, with the inclusion of particles for reinforcing (Al2O3, SiC), the fabricated AMC AA2024/2%Al2O3/2%SiC provided the cleavage fracture, while transgranular cleavage fractures have been observed in the fractured surfaces of the third and fourth configurations (AA2024/2%Al2O3/2%SiC/2%Si3N4, AA2024/2%Al2O3/2%SiC/2%BN). Moreover, the brittle fracture has been depicted in the broken sample of AA2024/2%Al2O3/2%SiC/2%Si3N4/2%BN. [ABSTRACT FROM AUTHOR]

The fabrication of welded joints in steel sheets has become a focal point, especially in meeting the demands for interconnections within battery packs for electric vehicles (EVs). This study delves into the impact arising from the initiation arc during the micro-tungsten inert gas (TIG) welding of nickel-plated steel sheets. The investigation involved the manipulation of various current modulations and arc lengths. Notably, optimal results were achieved with a 5 mm arc length paired with a 25 A current modulation. Microstructural analysis, conducted through scanning electron microscopy (SEM), unveiled a higher penetration depth, contributing to a more extensive and shallower fusion zone at the interface between the filler metal and the base material. Tensile testing revealed impressive mechanical properties, with the ultimate tensile strength peaking at 90 N/mm2, a yield strength of 85 N/mm2, and the highest elastic modulus. This underscores the weld's robustness in withstanding applied loads and resisting fracture. Furthermore, the calculation of the lowest K factor at 1.0375 indicated a reduction in resistance across the specimen, resulting in enhanced conductivity. Micro-TIG welding emerges as an efficient method for nickel-plated steel in connecting individual battery cells to form a high-capacity battery pack. These interconnections ensure efficient current flow and maintain the overall integrity and performance of the battery pack. The reliability and quality of these interconnects directly affect the battery's efficiency, safety, and lifespan in EVs application. [ABSTRACT FROM AUTHOR]

Aluminum metal-matrix composites (AMMCs) were prepared by dispersing TiO2 dispersoids of different volume fractions into an AA6063 matrix via stir casting and subjected to process-structure correlation studies. Four different samples based on weight ratio were considered herein: 99Al-1TiO2, 97Al-3TiO2, 95Al-5TiO2, and the as-received AA6063. Their mechanical properties namely, microhardness, tensile strength, and tribological behavior, were determined. In addition, the microstructure of the samples was also analysed. It was observed that the addition of 5% TiO2 particles enabled the AA6063 matrix to accommodate a higher strain energy while providing the required driving force to generate dislocations and substructures. Therefore, considering the plastic deformation, the ultimate tensile strength (sut) increased gradually with the addition of TiO2 (in weight%). The flow curves of the 95Al-5TiO2 sample showed the highest value of sut, whereas the as-received AA6063 matrix exhibited the lowest value. For linear elastic deformation, AA6063 showed the lowest yield strength (sys) as compared to the AMMC samples for all TiO2 weight% values; however, the variation in sys among the AMMC samples was minimal. The microhardness of the samples increased gradually with the addition of TiO2, and the percentage reduction in area at the fracture was largest for 95Al-5TiO2. The Taguchi's L9 array and variance analysis of the process parameters indicated that the material wear was largely affected by the normal load, followed by weight% of TiO2 and sliding speed. Wear surface characteristics, such as microvoids, delamination, microcracks, and wear debris, were qualitatively observed in all the AMMC samples. The overall strength improvement was attributable to the effects of addition of the dispersoids. During melt solidification, the TiO2 particles surpassed/pinned and hindered the grain growth, resulting in grainsize refinement. [ABSTRACT FROM AUTHOR]

AbstractTo correlate µTBS of different resin cements with their film thickness (FT), Vickers hardness (HV), and ultimate tensile strength (UTS). Human molars (N = 30) were divided into six-groups according to 1: Resin cement: Bifix QM, (BF), GrandioSo Heavy Flow (GHF) and VisCalor Bulk (VB), and 2: Adhesive mode (Optibond Universal): Etch-and-rinse (E&R) or self-etching (SE). CAD/CAM blocks (7 × 7 × 4 mm3) were air-abraded using 50 μm Al2O3 and silane was applied (60 s), and air dried (10 s). For E&R, dentin was etched (15 s), rinsed (30 s), and blot-dried. For both modes, adhesive was applied (20 s), air-dried (5 s), and photo-polymerized (10 s). Sticks (1 × 1 mm2) were stored for 24-h or 6-months. Sticks were pulled in tension (1 mm/min). Debonded sticks were evaluated for failure mode analysis. For FT, nine-bonded slabs from each cement was prepared and evaluated. For UTS, eight-hourglass specimens from each cement were prepared and tested in tension. For HV, five-discs from each cement was prepared and evaluated using a HV tester. Data were analyzed using ANOVA/Pearson’s correlation tests (α = 0.05). No correlation between µTBS/HV was revealed (p > 0.05). A positive correlation between µTBS/FT and µTBS/UTS (p < 0.05) was observed. Three-way ANOVA revealed all experimental factors had a significant effect on µTBS (p < 0.05). The VB showed significantly lower µTBS (20.8 ± 9.3 MPa). The E&R mode showed higher µTBS (26.0 ± 10.9 MPa), and 24-h revealed a higher µTBS (27.4 ± 10.0 MPa). Mixed failure was the predominant type (42.51%). The VB showed the highest FT (122.2 ± 11.9 µm), with GHF revealed higher UTS and HV (112.5 ± 19.7 MPa and 91.6 ± 0.4 Kgf, respectively). Photo-polymerized flowable composite can substitute dual-polymerized cement for CAD/CAM composite bonding. [ABSTRACT FROM AUTHOR]

Aluminium-Silicon (Al-Si) based alloys are gaining more prominence among researchers for automotive applications. In this paper, the impact of copper alloying element and artificial ageing treatment on the mechanical response and microstructural evolution of Al-Si based alloys synthesised from aluminium scrap are examined, as there is need to gear up the urge for the usage of metallic scraps for production of alloys for automotive applications. The as-cast Al-Si alloy and its form with copper as alloying element (1.5% and 3% Cu) were produced using liquid metallurgy route. Solution heat treatment was carried out at 500℃ and artificial ageing was carried out at 190℃ for 0.5, 1, 1.5, 2 and 2.5 h. Mechanical properties and microstructural characterisations of the as-cast and artificially aged forms of Al-Si based alloys were performed. The hardness tests were performed according to ASTM A29/A29M–15. Impact strength was determined according to ASTM D256. Tensile tests were performed based on ASTM E8. Microstructures of the synthesised Al-Si alloys were examined using optical microscopy and scanning electron microscopy techniques. In as-cast condition of the Al-5Si-Cu alloy, impact strength increases with increase in copper content. Al5Si-Cu alloy with 3% copper content and artificially aged at 190°C for 2.5 h exhibited the highest impact strength. Hardness number increases with the increase in copper content up to 1.5% and the ageing time. Additional increase in the ageing time yielded decrease in the hardness number. Results revealed that artificial ageing enhances mechanical and microstructural characteristics of the Al-Si alloys. Ageing treated Al-Si alloys exhibited peak hardness, impact strength and ultimate tensile strength (UTS) values of 77.47 kgf/mm2, 17 J/mm2 and 137.80 MPa, respectively. The obtained results are indicative of mechanical properties enhancement when compared with as-cast Al-Si alloys with maximum hardness, impact strength and UTS of 43.71 kgf/mm2, 12.92 J/mm2 and 123.18 MPa, respectively. Significant improvement in mechanical properties was achieved in Al-Si-Cu alloy with copper content and artificial ageing when compared to that without copper inclusion. This may be attributed to the evolution of equiaxed precipitates and the fact that copper-rich Al-Si-Cu alloys promotes formation of precipitation strengthening phases of α-AlFeMnSi. This study shows that the mechanical properties of Al-5Si-Cu alloys produced using aluminium scrap-based materials were enhanced using artificial ageing treatment and the benefit can be utilised for automotive applications. [ABSTRACT FROM AUTHOR]

Aiming at ensuring the quality of the product and reducing the cost of steel manufacturing, an increasing number of studies have been developing nonlinear regression models for the prediction of the mechanical properties of steel rebars using machine learning techniques. Bearing this in mind, we revisit this problem by developing a design methodology that amalgamates two powerful concepts in parsimonious model building: (i) sparsity, in the sense that few support vectors are required for building the predictive model, and ( i i ) locality, in the sense that simpler models can be fitted to smaller data partitions. In this regard, two regression models based on the Least Squares Support Vector Regression (LSSVR) model are developed. The first one is an improved sparse version of the one introduced in a previous work. The second one is a novel local LSSVR-based regression model. The task of interest is the prediction of four output variables (the mechanical properties YS, UTS, UTS/YS, and PE) based on information about its chemical composition (12 variables) and the parameters of the heat treatment rolling (6 variables). The proposed LSSVR-based regression models are evaluated using real-world data collected from steel rebar manufacturing and compared with the global LSSVR model. The local sparse LSSVR approach was able to consistently outperform the standard single regression model approach in the task of interest, achieving improvements in the average  R 2  from previous studies: 5.04% for UTS, 5.19% for YS, 1.96% for UTS/YS, and 3.41% for PE. Furthermore, the sparsification of the dataset and the local modeling approach significantly reduce the number of SV operations on average, utilizing 34.0% of the total SVs available for UTS estimation, 44.0% for YS, 31.3% for UTS/YS, and 32.8% for PE. [ABSTRACT FROM AUTHOR]

Aluminium Metal Matrix Composites (AMMCs) reinforced with a particulate form of reinforcement have emerged as a replacement for monolithic alloys in various engineering industries due to their superior mechanical properties and customizable thermal and electrical characteristics. Silicon Carbide (SiC), is renowned for its high-temperature strength, hardness, wear resistance, excellent oxidation resistance, chemical resistance, low thermal expansion, high thermal conductivity, and compatibility with aluminium alloy find extensive utilization in Shipbuilding industries. The necessity for joining AMMCs arises in numerous engineering applications. Friction Stir Welding (FSW) emerges as one of the most fitting welding processes for joining AMMCs reinforced with particulate forms of ceramics without compromising their superior mechanical properties. This study endeavours to establish regression models predicting the Ultimate Tensile Strength (UTS), Percent Elongation (PE), and Weld Nugget Hardness (WNH) of friction stir welded AA6092 matrix composite reinforced with Silicon Carbide particles (SiC). The models correlate significant parameters such as tool rotational speed (TRS), welding speed (WS), axial force (AF), and percentage of SiC reinforcement in the AA6092 metal matrix. Statistical software Design Expert, along with analysis of variance (ANOVA) and student’s t-test, is employed to validate the developed models. It is observed from the investigation that these factors independently influence the UTS, PE, and WNH of the friction stir welded composite joints. The developed regression models are optimized to maximize the UTS and WNH of friction stir welded AA6092/SiC composite joints.

There is a great interest in replacing synthetics with natural materials to minimize global climate change and environmental pollution. This study presents a simple yet effective method to extract natural fibers such as banana, sisal, and false banana plant fibers and their mechanical properties. An effective mechanical and chemical fiber treatment to improve the mechanical behavior of the fibers is presented. The quality of the fibers and the cross-sectional area of each fiber were examined using FTIR and a Zeta 20 optical 3D microscope. Compared with the untreated fibers, the proposed treatment improved the banana, sisal, and false banana fibers by 34.78%, 22.68%, and 3.08%, respectively. The ultimate tensile strength of the treated false banana fibers was higher than those of sisal and banana fibers by 13.05% and 14.08%, respectively, and the failure strain was higher by 40% and 16.66%, respectively. On the other hand, the linear density and tenacity of false banana were slightly lower than sisal fiber but marginally higher than banana fiber. [ABSTRACT FROM AUTHOR]

This work optimizes the current (I), voltage (V), flow rate (F), and arc gap (G) of the gas tungsten arc welding (GTAW) of the Ti40A titanium alloy to decrease the heat input (HI) and improve the ultimate tensile strength (TS) and micro-hardness (MH). The radial basis function network (RBFN) was utilized to present performance measures, while weighted principal component analysis (WPCA) and an adaptive nondominated sorting genetic algorithm II (ANSGA-II) were applied to estimate the weights and generate optimal points. The evaluation via an area-based method of ranking (EAMR) was employed to determine the best solution. The results indicated that the optimal I, V, F, and G are 89 A, 23 V, 20 L/min, and 1.5 mm, respectively. The improvements in the TS and MH were 1.2 % and 19.8 %, respectively, while the HI was saved by 18.4 %. The RBFN models provided acceptable accuracy for prediction purposes. The ANSGA-II provides better optimality than the conventional NSGA-II. The HI, TS, and MH of the practical GTAW Ti40A could be enhanced using optimality. The optimization method could be utilized to deal with optimization problems for not only other GTAW operations but also other machining processes. [ABSTRACT FROM AUTHOR]

The effect of a temperature change on nonlinear axisymmetric straining of the clamped torispherical shell under internal pressure is investigated. The solution is based on the Kirchhoff–Love hypothesis, taking into account geometric and material nonlinearities. The stress-strain state is examined at cryogenic or elevated temperatures with the temperature-dependent material's characteristics. The analysis has been performed numerically using a combination of linearization and S.K. Godunov's orthogonal sweep methods. Shells made of alloys with various mechanical and thermophysical properties are considered. The effect of the alloy's properties on the nature of the stress-strain state dependence of the torispherical heads on temperature is discussed. [ABSTRACT FROM AUTHOR]

This work focused on improving the weld quality and productivity of dissimilar friction stir welding of AA 7075-T6 and AA 2024-T3 aluminium alloys by controlling the process parameters of tool axial force, welding speed, tool offset, and rotational speed. A newly developed hybrid pentagonal tool is used for the fabrication of welds to enhance productivity. In this study, an RSM based on a central composite method is used for the design of experiments, optimisation, and data analysis. The result found that the ideal range of welding process parameters for 6-mm-thick AA7075-T6 and AA2024-T3 aluminium alloys by friction stir welding is to use a tool spindle rotational speed of 1178.51 rpm, an axial load of 7.78 KN, a traverse speed of 132.74 mm/min, and 0.958 mm tool offset towards the AA2024-T3 material side. Based on the UTS, a joint efficiency of 67.19% is achieved. It is observed that while increasing the value of FSW parameters like tool spindle rotational speed and traverse speed, the tensile strength and elongation values tend to increase drastically after further increasing those parameters, UTS and TE seem to decrease. [ABSTRACT FROM AUTHOR]

Artificial Intelligence algorithms based on the machine learning approach finds application in manufacturing and materials industries for the prediction and optimization of mechanical and microstructure properties. In the present study, six supervised machine learning regression-based algorithms i.e., Decision Trees, XGBoost, Artificial Neural networks, Random Forests, Gradient Boosting, and AdaBoost are used for the prediction of the Ultimate Tensile Strength of the Friction Stir Welded magnesium joints. Magnesium alloy type (AM20, AZ61A, AZ31B, and AZ31), Plunge Depth (mm), Shoulder Diameter (mm), Tool Traverse Speed (mm/min), Pin Diameter (mm), Axial Force (kN), and Tool Rotational Speed (RPM) are the input parameters while the Ultimate Tensile Strength (MPa) of the Friction Stir Welded joints is an output parameter. The results showed that the Magnesium Alloy type has the highest feature importance in comparison to other input parameters. It is also observed that the XGBoost algorithms yield highest coefficient of determination of 0.81. [ABSTRACT FROM AUTHOR]

Polyoxymethylene (POM) fiber is a new type of reinforcement material developed in recent years, which provides a new way and choice for the modification and reinforcement of cement-based materials. In order to improve the application of POM in the construction field, fiber-reinforced cementitious materials were prepared using 6- and 12-mm POM fibers. The effects of at two lengths of POM fibers on fluidity, compressive strength, flexural strength, flexural toughness, and tensile strength of cement mortar were investigated, and the bonding interface between fibers and the cement mortar matrix was observed using scanning electron microscopy (SEM). The results show that the addition of POM fibers reduced the fluidity and 7-day compressive strength of cementitious composites, but had little effect on 28-day compressive strength. With the increase of POM fiber volume fraction and length, better flexural toughness and ultimate tensile strength can be obtained. When the POM fiber length was 12 mm and the volume fraction was 0.90%, the equivalent flexural strength and ultimate tensile strength of the POM fiber–reinforced cement-based composite (POM-FRCC) increased by 46.3% and 13.5%, respectively, compared with the control group. Microscopic analysis revealed that the POM fibers were tightly bonded to the cement mortar matrix, and the fibers had a severe surface scraping in the resistance to damage. [ABSTRACT FROM AUTHOR]

Aluminium matrix composites (AMCs) play a vital role in constructing aircraft, marine and automobile components. The major issue in manufacturing of AMCs through stir casting method is a localized ceramic particles agglomeration. In current research, metal castings have been prepared with 97.5 wt% of aluminium alloy (AA 7075) and 2.5 wt% of nano-alumina (n-Al2O3) particles through a modified matrix encapsulated feeding technique. The tensile strength, microhardness and dry sliding wear behaviour of n-Al2O3 reinforced AA 7075 composites were measured following ASTM standards. The ultimate tensile strength of the developed AMCs varies from 280 to 315 MPa, which is 12.5% higher than unreinforced AA 7075 alloy. Yield strength increases from 140 to 158 MPa, whereas microhardness of the composites increases from 138 to 210HV. Due to homogeneous distribution of n-Al2O3, the overall wear resistance of n-Al2O3 reinforced AA 7075 composites increases up to 90% as compared to unreinforced AA 7075 alloy. Also, the addition of n-Al2O3 particles marginally reduces the network of microcracks and deep furrows in the worn surface. The microstructural and elemental studies are also performed through scanning electron microscopy and X-ray diffraction. [ABSTRACT FROM AUTHOR]

Aluminum metal–matrix composites (AMMCs) were prepared by dispersing TiO2 dispersoids of different volume fractions into an AA6063 matrix via stir casting and subjected to process–structure correlation studies. Four different samples based on weight ratio were considered herein: 99Al-1TiO2, 97Al-3TiO2, 95Al-5TiO2, and the as-received AA6063. Their mechanical properties namely, microhardness, tensile strength, and tribological behavior, were determined. In addition, the microstructure of the samples was also analysed. It was observed that the addition of 5% TiO2 particles enabled the AA6063 matrix to accommodate a higher strain energy while providing the required driving force to generate dislocations and substructures. Therefore, considering the plastic deformation, the ultimate tensile strength σut increased gradually with the addition of TiO2 (in weight%). The flow curves of the 95Al-5TiO2 sample showed the highest value of σut, whereas the as-received AA6063 matrix exhibited the lowest value. For linear elastic deformation, AA6063 showed the lowest yield strength (σys) as compared to the AMMC samples for all TiO2 weight% values; however, the variation in σys among the AMMC samples was minimal. The microhardness of the samples increased gradually with the addition of TiO2, and the percentage reduction in area at the fracture was largest for 95Al-5TiO2. The Taguchi’s L9 array and variance analysis of the process parameters indicated that the material wear was largely affected by the normal load, followed by weight% of TiO2 and sliding speed. Wear surface characteristics, such as microvoids, delamination, microcracks, and wear debris, were qualitatively observed in all the AMMC samples. The overall strength improvement was attributable to the effects of addition of the dispersoids. During melt solidification, the TiO2 particles surpassed/pinned and hindered the grain growth, resulting in grain-size refinement.

In this work, a novel microstructure-informed machine learning (MIML) framework is proposed for the longitudinal ultimate tensile strength (UTS) evaluation and the construction of microstructure-property linkages of titanium matrix composites (TMCs). The main components of the MIML framework include (i) high-fidelity simulation and dataset generation using finite element method (FEM), (ii) the quantitative characterization of microstructures obtained by 2-point cross-correlation analysis and principal component analysis, (iii) the extraction of structure-property linkages based on support vector regression model and (iv) the validation of MIML by experimental data. The fiber breakage observed by advanced microstructure characterization is considered as the dominant failure mode during mechanical tests of TMCs, which is embedded in the FEM simulations. The MIML approach reaches capable accuracy in UTS prediction of TMCs using FEM-generated datasets and trained MIML is validated by experimental data. In addition, the permutation importance-based method is utilized to accurately identify the key feature parameters related to UTS. The first two principal components of microstructural reduced-order representation are recognized as highly relevant features for UTS. The well-trained MIML model is a promising tool in the evaluation of the mechanical property, and further provides a technical chain for the establishment of microstructure-property linkages of multi-phase composites.

In large format additive manufacturing (LFAM), a keener understanding of the relationship between the manufacture method and material temperature dependency is needed for the production of large polymer parts. Statistical analyses supported by material properties and a meso-structural understanding of LFAM are applied to elucidate tensile data trends. The data from LFAM polyethylene terephthalate glycol with 30% carbon fiber (CF) (PETG CF30%) panels (diagonal, horizontal, and vertical in the x-y print plane) and injection-molded specimens tensile tested at six different testing temperatures (room temperature, 40 °C, 50 °C, 60 °C, 70 °C, and 80 °C) were used for statistical analyses. A standard deviation, a coefficient of variation, and a two-way and one-way analyses of variance (ANOVA) were conducted. The manufacturing method (44.2%) and temperature (47.4%) have a strong effect on the ultimate tensile strength, in which temperature (82.6%) dominates Young's modulus. To explain the difference between the ultimate tensile strength of vertical, diagonal, and horizontal specimens at room temperature, a visual inspection of the specimen failure was conducted and the maximum stress at the crack tip was calculated analytically. The decreased strength in the diagonal specimens resulted from the reliance on interlaminar adhesion strength. Future work will consider the effect of the void space variation on tensile strength variance.

The ultimate tensile strength (UTS) of gray cast iron (GCI) can be affected by numerous parameters due to its complex microstructures. To further understand the UTS of GCI, it is necessary to evaluate the impact of various parameters. Herein, a UTS prediction method based on microstructure features and machine learning (ML) algorithms is proposed. The six regression algorithms, namely, Bayesian Ridge, Linear Regression, Elastic Net Regression, Support Vector Regression, Gradient Boosting Regressor (GBR), and Random Forest Regressor are used to develop the prediction models. The predicted results show that the GBR has the best prediction performance for the predicted UTS and the error bands within 5%. The feature importance indicates that matrix hardness has the greatest effect on the UTS in the ML models. [ABSTRACT FROM AUTHOR]

This paper gives the investigation of the mechanical properties of Jutton-Glass hybrid polymer nanocomposite with multi walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) as reinforcements. For preparing the composites, the vacuum bagging technique was used with the composition of hybrid nano-material taken as 0, 0.25, and 0.5 wt. % of Graphene nanoplatelets and MWCNTs and the samples were tested for mechanical properties as per ASTM standards. The samples prepared have been subjected to various testing, and the results are reported. Prior to the preparations of composites, the polymer was mixed with surface-modified nanomaterials and stability tests were conducted to assess uniform dispersion with the aid of UV-Vis spectroscopy, the outcomes showed that the samples were exceptionally uniform over a period. The hardness and tensile properties were evaluated, and it was found that surface-modified nanomaterials could improve the properties better compared to the use of pristine nanomaterials as reinforcements. It was found that 0.25% MWCNT and graphene nanoplatelets (GNPs) show more improved properties than 0.5% MWCNT and Graphene nanoplatelets. However, the composite samples with Graphene nanoplatelets also showcased encouraging mechanical properties like hardness and tensile strength. [ABSTRACT FROM AUTHOR]

Copyright of New Building Materials / Xinxing Jianzhu Cailiao is the property of New Building Materials Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper explores the optimization of Tungsten-Inert-Gas (TIG) welding process parameters for creating a hybrid structure of Aluminium 6061 and Stainless Steel 304 using a copper filler rod (ER-Cu). The Welding of these two materials has industrial relevance owing to its weight reduction capabilities and environmental benefits. However, Aluminium and Stainless-Steel have different melting points and thermal properties. Aluminium has twice coefficient of thermal expansion and six times coefficient of thermal conductance as compared to Stainless-Steel. This difference often results in residual stresses and brittle intermetallic compounds in the weld region. We have chosen the Welding Current, Welding Speed, and Gas Flow Rate as input parameters, and Ultimate Tensile Strength (UTS) and Micro-hardness as response parameters. We have employed the Response Surface Methodology (RSM) using a Box-Behnken design to evaluate the influence of input parameters on UTS and Micro-hardness. Furthermore, an Analysis of Variance (ANOVA) is conducted to determine the input parameters' significance on the response parameters. Our surface plots demonstrate that UTS improves with increased Welding Current and reduced Welding Speed. Simultaneously, Micro-hardness increases with elevated Welding Speed and decreased current, up to a specific limit. The peak value of UTS (79 MPa) was observed with a Current range of 85-90 A, Speed range of 95-100 mm/min, and Gas Flow Rate of 14.5-15 l/min. On the other hand, maximum Micro-hardness (260HV) was obtained with a Current range of 80-85 A, Speed range of 105-110 mm/min, and Gas Flow Rate of 14.5-15 l/min. This research contributes to improving the manufacturing process of hybrid structures, specifically by optimizing the advantages of both Aluminium and Stainless Steel while addressing the challenges that arise during their combination. The study's conclusions have major consequences for sectors looking to take advantage on the mutually beneficial characteristics of different metals in welding applications. [ABSTRACT FROM AUTHOR]

Due to the time-consuming and costly nature of high-cycle fatigue experiments, correlations between fatigue strength and mechanical properties obtained through more simple and fast experiments can be interesting from an economic perspective. This review article aims to provide an overview of such relations established in the open literature from the 1980s to 2023 for conventionally manufactured steel grades. The majority of these models relate fatigue strength at a given fatigue life (often termed "fatigue limit" or "endurance limit") to ultimate tensile strength, yield strength (both static and cyclic), hardness, elongation, reduction in area, and Charpy impact energy. Relations taking flaws such as nonmetallic inclusions into account are also discussed. Additionally, models predicting S–N curves are provided. The various estimations are presented in tables, together with the materials and test conditions for which they were established. [ABSTRACT FROM AUTHOR]

Copper and its alloys are structural materials used in industries and engineering applications due to their excellent thermal and electrical conductivity and chemical stability. Integrating graphene, known for its exceptional electrical conductivity, into the copper matrix is a promising strategy to enhance mechanical properties without sacrificing electrical conductivity. The Accumulative Roll Bonding (ARB) process can effectively and homogeneously introduce graphene into the metal matrix and is adaptable to an industrial scale. This study investigates the impact of varying graphene concentrations and two heat treatment protocols (without a controlled atmosphere) on the mechanical and electrical properties of ARBed copper/graphene composites. Optical microscopy revealed minimal voids and graphene clumps, and the energy dispersive spectroscopy analysis revealed the absence of copper oxide in some samples. The conductivity test showed little influence of the graphene content and stress relief heat treatment temperature on electrical conductivity (~86% of the International Annealed Copper Standard) within a limited number of ARB cycles. The tensile tests did not reveal a significant influence of the graphene content and stress relief heat treatment temperature on the ultimate tensile strength (220–420 MPa) and elongation (~2%). [ABSTRACT FROM AUTHOR]

The present study aims to refine the solidification structure of 2319 Al-alloy Wire Arc Additive Manufacturing (WAAM) deposits by adding SiC powder to the interlayer during deposition. The fabricated deposits are subjected to aging treatment. The influence of SiC particle addition and aging treatment on evolution of microstructure and the corresponding hardness and tensile properties was investigated. To enable systematic understanding of the process, thermal simulation using ANSYS has been done for a single layer deposition and compared with the experimental data. The peak temperature for the deposits made with and without SiC particle addition is around 927.53 °C and 915.43 °C respectively. Three distinct structural zones were clearly seen in as-deposited specimens made without SiC particle addition. However, the deposits with SiC favored the formation of equiaxed grain throughout the specimen. The presence of α-Al and θ-Al2Cu phases in the as deposited specimen (without SiC) is confirmed through X-Ray Diffraction (XRD) and Electron Probe Micron Analyzer (EPMA), while the specimen deposited with addition of SiC particles demonstrates the presence of α-Al, θ-Al2Cu, and SiC phases. Post-Deposition Heat Treatment (PDHT) aids in uniform formation of θ-Al2Cu precipitates in both specimens. Deposits fabricated with SiC (308 ± 5 MPa) recorded higher Ultimate Tensile Strength (UTS) in comparison to without SiC deposits (260 ± 5 MPa). On the other hand, after heat treatment, the as-deposited specimen (without SiC) achieved an UTS of 468 ± 5 MPa, while the deposit fabricated with SiC recorded an UTS of 484 ± 5 MPa. [ABSTRACT FROM AUTHOR]

The fabrication of welded joints in steel sheets has become a focal point, especially in meeting the demands for interconnections within battery packs for electric vehicles (EVs). This study delves into the impact arising from the initiation arc during the micro-tungsten inert gas (TIG) welding of nickel-plated steel sheets. The investigation involved the manipulation of various current modulations and arc lengths. Notably, optimal results were achieved with a 5 mm arc length paired with a 25 A current modulation. Microstructural analysis, conducted through scanning electron microscopy (SEM), unveiled a higher penetration depth, contributing to a more extensive and shallower fusion zone at the interface between the filler metal and the base material. Tensile testing revealed impressive mechanical properties, with the ultimate tensile strength peaking at 90 N/mm2, a yield strength of 85 N/mm2, and the highest elastic modulus. This underscores the weld’s robustness in withstanding applied loads and resisting fracture. Furthermore, the calculation of the lowest K factor at 1.0375 indicated a reduction in resistance across the specimen, resulting in enhanced conductivity. Micro-TIG welding emerges as an efficient method for nickel-plated steel in connecting individual battery cells to form a high-capacity battery pack. These interconnections ensure efficient current flow and maintain the overall integrity and performance of the battery pack. The reliability and quality of these interconnects directly affect the battery’s efficiency, safety, and lifespan in EVs application.

Aiming at ensuring the quality of the product and reducing the cost of steel manufacturing, an increasing number of studies have been developing nonlinear regression models for the prediction of the mechanical properties of steel rebars using machine learning techniques. Bearing this in mind, we revisit this problem by developing a design methodology that amalgamates two powerful concepts in parsimonious model building: (i) sparsity, in the sense that few support vectors are required for building the predictive model, and (ii) locality, in the sense that simpler models can be fitted to smaller data partitions. In this regard, two regression models based on the Least Squares Support Vector Regression (LSSVR) model are developed. The first one is an improved sparse version of the one introduced in a previous work. The second one is a novel local LSSVR-based regression model. The task of interest is the prediction of four output variables (the mechanical properties YS, UTS, UTS/YS, and PE) based on information about its chemical composition (12 variables) and the parameters of the heat treatment rolling (6 variables). The proposed LSSVR-based regression models are evaluated using real-world data collected from steel rebar manufacturing and compared with the global LSSVR model. The local sparse LSSVR approach was able to consistently outperform the standard single regression model approach in the task of interest, achieving improvements in the average R2 from previous studies: 5.04% for UTS, 5.19% for YS, 1.96% for UTS/YS, and 3.41% for PE. Furthermore, the sparsification of the dataset and the local modeling approach significantly reduce the number of SV operations on average, utilizing 34.0% of the total SVs available for UTS estimation, 44.0% for YS, 31.3% for UTS/YS, and 32.8% for PE.

Structural components are generally composed of material discontinuities, including open holes, which are considered stress concentrators in engineering components. In view of this, assessing the influence of open holes on the tensile properties is crucial to determine the sensitivity and tensile strength of a particular material. Nevertheless, investigation of the impact of open holes on the tensile properties of SS400 steel sheets is very limited and yet to be explored. Therefore, this study was performed to optimize the effects of open holes on the tensile properties of SS400 sheet specimens based on a Full Factorial Design (FFD) experiment. Four input parameters that represent various hole configurations, which include the hole diameter, location of the hole, number of holes, and hole shape, were considered in this study to develop the experimental-based prediction models to optimize the output performance, namely yield strength, ultimate tensile strength, and ultimate elongation, commonly denoted as YS, UTS, and UE respectively. A total of 10 additional experimental trials were then utilized to verify the constructed models. In addition, the weight fractions for YS, UTS, and UE were identified using the Criteria Importance Through Inter-Criteria Correlation (CRITIC) method. Subsequently, the Desirability Function Analysis (DFA) is utilized to pinpoint the optimal parameter conditions for maximizing the tensile properties. Based on the results, all four parameters showed significant effects on the response variables, except the number of holes for UTS and hole location for UE. The diameter also recorded the highest contribution toward UTS and UE, followed by the hole shape. Regarding YS, hole diameter takes precedence, with the number of holes as the second most influential factor. Furthermore, the average absolute percent deviation for the prediction responses of 10 experimental cases were 1.06 %, 0.90 %, and 0.85 % for YS, UTS, and UE, respectively, confirming the validity of the constructed models. Meanwhile, the CRITIC method estimated the weight fractions for YS, UTS and UE from the experimental data, which were 0.3825, 0.2559, and 0.3616, respectively. The DFA-derived composite desirability, rated at 0.9820, suggests optimal conditions: a 1 mm hole diameter, centered hole location, three holes, and a hexagonal shape. The minimal deviations between predicted and experimental values affirm the robustness of the models. Overall, this investigation yields important insights for optimizing open holes and elevating the tensile performance of SS400 sheet specimens.