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This study examines the effect of elevated printing speeds (100–600 mm/s) on the dimensional accuracy and tensile strength of PLA components fabricated via fused deposition modeling (FDM). To isolate the influence of printing speed, all other parameters were kept constant, and two filament variants—natural (unpigmented) and black PLA—were analyzed. ISO 527-2 type 1A specimens were produced and tested for dimensional deviations and ultimate tensile strength (UTS). The results indicate that printing speed has a marked impact on both geometric precision and mechanical performance. The optimal speed of 300 mm/s provided the best compromise between dimensional accuracy and tensile strength for both filaments. At speeds below 300 mm/s, under-extrusion caused weak layer bonding and air gaps, while speeds above 300 mm/s led to over-extrusion and structural defects due to thermal stress and rapid cooling. Black PLA yielded better dimensional accuracy at higher speeds, with cross-sectional deviations between 2.76% and 5.33%, while natural PLA showed larger deviations of up to 8.63%. However, natural PLA exhibited superior tensile strength, reaching up to 46.59 MPa, with black PLA showing up to 13.16% lower UTS values. The findings emphasize the importance of speed tuning and material selection for achieving high-quality, reliable, and efficient FDM prints.

This research aims to develop ensemble machine-learning methods for forecasting the ultimate tensile strength (UTS) of friction stir welding (FSW). The substance utilized in the experiment was a mixture of aluminum alloys AA5083 and AA5061. An ensemble machine learning model was created to predict the UTS of the friction stir-welded seam, utilizing 11 FSW parameters as input factors and the UTS as a response variable. The proposed approach used the Gaussian process regression (GPR) and the support vector machine (SVM) model of machine learning to build the ensemble machine learning model. In addition, an efficient technique using a differential evolution algorithm to optimize the weight for the decision fusion was incorporated into the proposed model. The effectiveness of the model was evaluated using three datasets. The first and second datasets were divided into two groups, with 80% for the training dataset and 20% for the testing dataset, while the third dataset comprised the test data to validate the model’s accuracy. The computational results indicated that the proposed model provides more accurate forecasts than existing methods, such as random forest, gradient boosting, ADA boosting, and the original SVM and GPR, by 30.67, 49.18, 16.50, 48.87, and 49.33 %, respectively. In terms of prediction accuracy, the suggested technique for decision fusion surpasses unweighted average ensemble learning (UWE) by 10.32%.

Friction stir welding is a material processing technique used to combine dissimilar and similar materials. Ultimate tensile strength (UTS) is one of the most common objectives of welding, especially friction stir welding (FSW). Typically, destructive testing is utilized to measure the UTS of a welded seam. Testing for the UTS of a weld seam typically involves cutting the specimen and utilizing a machine capable of testing for UTS. In this study, an ensemble deep learning model was developed to classify the UTS of the FSW weld seam. Consequently, the model could classify the quality of the weld seam in relation to its UTS using only an image of the weld seam. Five distinct convolutional neural networks (CNNs) were employed to form the heterogeneous ensemble deep learning model in the proposed model. In addition, image segmentation, image augmentation, and an efficient decision fusion approach were implemented in the proposed model. To test the model, 1664 pictures of weld seams were created and tested using the model. The weld seam UTS quality was divided into three categories: below 70% (low quality), 70–85% (moderate quality), and above 85% (high quality) of the base material. AA5083 and AA5061 were the base materials used for this study. The computational results demonstrate that the accuracy of the suggested model is 96.23%, which is 0.35% to 8.91% greater than the accuracy of the literature’s most advanced CNN model.

Automotive monocoque is in need of AA7075 with enhanced strength and hardness properties. Fabrication of Surface Hybrid Composites (SHCs) by Friction Stir Processing is a prominent technique to satisfactorily enhance the aforementioned characteristics. SHCs are formed through different volume proportions of Al 2 O 3 /SiC reinforcements. Heat generation during the processing stage shows a linear trend along the longitudinal axis due to the thermal conductivity of AA7075. Microstructure of composites is observed with fine grain formation and homogeneous distribution of reinforcements. X-ray Diffraction pattern confirms the existence of both reinforcements in matrix alloy. Specimens with identical volume ratio of Al 2 O 3 and SiC particles depict the superior micro-hardness of 265 VHN which is 8.5 and 19.08% higher than the individual contribution of reinforcements. This is attributed to the homogeneous settlement of reinforcements and particle pinning with parent matrix. Addition of Al 2 O 3 improves the impact toughness due to its wettability with base AA7075 as a result of Al-Al bond. Incorporation of SiC particles enhances the Ultimate Tensile Strength of the composites by virtue of its high load-bearing capacity. Fracto-graphic analysis of the specimens with different Al 2 O 3 /SiC weight ratios indicated the fractures along the breakdown of reinforcement particles.

This study investigates the effects of magnesium (Mg) content, silicon carbide (SiC) reinforcement, and aging temperature (AT) on the ultimate tensile strength (UTS) and Brinell hardness number (BHN) of eutectic Al-Si composites using a full factorial experimental approach. The analysis reveals that increasing Mg content from 0 wt% to 1.5 wt% significantly enhances UTS, likely due to solid solution strengthening and improved particle reinforcement. Similarly, a rise in SiC content up to 4 wt% leads to a notable increase in UTS, indicating effective matrix reinforcement. AT is crucial, with the highest UTS achieved at 100 °C; however, overaging at 200 °C results in reduced strength due to precipitate coarsening. Interaction plots demonstrate a synergistic effect between Mg and SiC, where higher levels of both contribute to a more substantial increase in UTS. The results also show that while both Mg and SiC improve UTS, their effects are optimized with appropriate aging conditions, although overaging diminishes these benefits. Analysis of variance (ANOVA) highlights that AT, Mg, and SiC each significantly impact UTS and BHN, with SiC having the greatest effect of 47.92% on hardness and AT having the greatest effect of 36.58% on the UTS. The interaction between SiC particles and AT is particularly influential on BHN. These findings emphasize the importance of carefully optimizing processing conditions to enhance the mechanical properties of eutectic Al-Si composites.

A study of the Friction Stir Welding (FSW) process was carried out in order to evaluate the influence of process parameters on the mechanical properties of aluminum plates (AA5754-H111). The process was monitored during each test by means of infrared cameras in order to correlate temperature information with eventual changes of the mechanical properties of joints. In particular, two process parameters were considered for tests: the welding tool rotation speed and the welding tool traverse speed. The quality of joints was evaluated by means of destructive and non-destructive tests. In this regard, the presence of defects and the ultimate tensile strength (UTS) were investigated for each combination of the process parameters. A statistical analysis was carried out to assess the correlation between the thermal behavior of joints and the process parameters, also proving the capability of Infrared Thermography for on-line monitoring of the quality of joints.

This research work highlights the prediction of hardness behaviour of age-hardened LM4 and its composites fabricated using a two-stage stir casting method with TiB 2 and Si 3 N 4 . MATLAB - Artificial Neural Networks is used to predict the age-hardening behaviour of LM4 and its composites. Experiments (hardness and tensile tests) are conducted to collect data for training an ANN model as well as to investigate the effect of reinforcements and age-hardening treatment on LM4 and its composites. The results show that with an increment in the reinforcement wt%, there is an enhancement in hardness and ultimate tensile strength (UTS) values within the monolithic composites. As-cast hybrid composites display a 37 to 54% improvement in hardness compared to as-cast LM4. Heat-treated samples, specifically those treated with peak aging with MSHT and 100 °C aging, perform better than as-cast samples and other heat-treated samples in terms of UTS and hardness. Compared to as-cast LM4, MSHT, and 100 °C aged samples display an 85 to 202% increment in VHN. Hybrid composites perform better in terms of hardness, while composites with 3 wt% of TiB 2 (L3TB) perform better in terms of UTS, peak aged (MSHT and 100 °C aging) L3TB display 68% increment in UTS when compared to as-cast LM4. ANN model is developed and trained with five inputs (wt% of TiB 2 , wt% of Si 3 N 4 , type of solutionizing, aging temperature, and aging time) and one output (VHN) using different algorithms and a different number of hidden neurons to predict the age hardening behaviour of composites. Among them, Lavenberg-Marquardt (LM) training algorithm with normalized data and 30 hidden neurons performs well and shows a least average error of 1.588364. The confirmation test confirms that the trained ANN model can predict the output with an average %error of 0.14 using unseen data.

Fiber reinforced polymeric (FRP) composite materials are currently used in numerous structural and materials related applications. But, during their in-service period these composites were exposed to different changing environmental conditions. Present investigation is planned to explore the effect of thermal shock exposure on the mechanical properties of nanoTiO2 enhanced glass fiber reinforced polymeric (GFRP) composites. The samples were conditioned at +70°C temperature for 36 h followed by further conditioning at – 60°C temperature for the similar interval of time. In order to estimate the thermal shock influence on the mechanical properties, tensile tests of the conditioned samples were carried out at 1 mm/min loading rate. The polymer phase i.e. epoxy was modified with different nanoTiO2 content (i.e. 0.1, 0.3 and 0.5 wt. %). The tensile strength of 0.1 wt.% nanoTiO2 GFRP filled composites exhibited higher ultimate tensile strength (UTS) among all other composites. The possible reason may be attributed to the good dispersion of nanoparticles in polymer matrix corresponds to proper stress transfer during thermal shock conditioning. In order to access the variations in the viscoelastic behavior and glass transition temperature due to the addition of nanoTiO2 in GFRP composite and also due to the thermal shock conditioning, dynamic mechanical thermal analysis (DMTA) measurements were carried out. Different modes of failures and strengthening morphology in the composites were analyzed under scanning electron microscope (SEM).

Accumulative roll bonding (ARB) technique is used in this paper to produce aluminum/alumina composite sheets. Alumina content was added as 1,3 and 5wt%. The produced Al/Al2O3 composite sheets are piled up and processed by accumulative roll bonding (50% reduction) after preheating at 280 °C with different regimes (2-8 cycles). Statistical design analysis was applied to examine the effects of alumina content and no. of accumulative roll bonding cycles on the ultimate tensile strength for aluminum/alumina composite sheets. Empirical formulas were deduced to recognize key parameters that controlling tensile behavior. XRD detection was carried out to explore dominant planes controlling plasticity Al/Al2O3 composites. In general, addition of alumina and proceeding different cycles increases strength. FE-SEM microstructure showed that alumina plays important roll on the aluminum sheets during ARB process where the metal of aluminum flow among them producing highly sheared matrix.

Simultaneously enhancing strength and ductility of metals and alloys has been a tremendous challenge. Here, we investigate a CoCuFeNiPd high-entropy alloy (HEA), using a combination of Monte Carlo method, molecular dynamic simulation, and density-functional theory calculation. Our results show that this HEA is energetically favorable to undergo short-range ordering (SRO), and the SRO leads to a pseudo-composite microstructure, which surprisingly enhances both the ultimate strength and ductility. The SRO-induced composite microstructure consists of three categories of clusters: face-center-cubic-preferred (FCCP) clusters, indifferent clusters, and body-center-cubic-preferred (BCCP) clusters, with the indifferent clusters playing the role of the matrix, the FCCP clusters serving as hard fillers to enhance the strength, while the BCCP clusters acting as soft fillers to increase the ductility. Our work highlights the importance of SRO in influencing the mechanical properties of HEAs and presents a fascinating route for designing HEAs to achieve superior mechanical properties. The strength-ductility trade-off has been a long-standing problem for alloy development. Here the authors present a route for designing high-entropy alloys to overcome this trade-off via short-range ordering shown by combined Monte Carlo, molecular dynamic, and density-functional theory simulations.