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The regression prediction between ultrasonic signal and ultimate tensile strength of stainless-steel pipe weld is studied in this paper. First, ultrasonic signals were collected from the weld of AISI304 austenitic stainless steel welded pipe sample, and its ultimate tensile strength was tested. The data sets of ultrasonic signal and ultimate tensile strength of weld were constructed. The random forest algorithm is used to extract the characteristic value set of the ultrasonic signal set of the welding seam and reduce the dimension of the characteristic value set. Then, the set of eigenvalues after dimensionality reduction is input into the Bayesian BLS network for training. The results of the training were compared with those of other regression models. The results show that the model established by Bayes BLS algorithm is feasible and reliable to predict the ultimate tensile strength of stainless-steel pipe weld. The results also show that the model established by the Bayesian BLS algorithm has high accuracy and stability in predicting the ultimate tensile strength of stainless-steel pipe weld. This method can be used to predict the ultimate tensile strength of welds in different types of materials. [ABSTRACT FROM AUTHOR]

Background –: Suture anchor failures can lead to revision surgeries which are costly and burdensome for patients. The durability of musculoskeletal reconstructions is therefore partly affected by the design of the suture anchors., Purpose –: The purpose of the study was to quantify the strength of different suture anchors whose sizes are suitable for attaching artificial Achilles and tibialis cranialis tendons in a rabbit model, as well as determine the effect of cyclic loading on the anchoring strength., Method –: Four anchors (two with embedded eyelet and two with raised eyelet, n=5 per group) were tested with cyclical loading (1000 cycles and 4.5 mm/sec) and without cycling, to inform the failure loads and mode of failure of the suture anchors. An eyebolt screw with smooth eyelet was used as a control for the test groups., Results –: All samples in all groups completed 1000 cycles and failed via suture breakage in both test conditions. All anchors had failure loads exceeding the peak Achilles tendon force in rabbits during hopping gait. The data analysis showed an effect of anchor type on the maximum tensile force at failure ( F ) in all suture categories but not an effect of loading condition. Also, the Anika anchor had a significantly less adverse effect on suture strength compared to Arthrex anchor (p=0.015), IMEX anchor (p=0.004) and Jorvet anchor (p<0.001). We observed a greater percentage of failure at the mid-section for the anchors with the raised eyelets compared to the anchors with embedded eyelets, which all failed at the knot. max ) in all suture categories but not an effect of loading condition. Also, the Anika anchor had a significantly less adverse effect on suture strength compared to Arthrex anchor (p=0.015), IMEX anchor (p=0.004) and Jorvet anchor (p<0.001). We observed a greater percentage of failure at the mid-section for the anchors with the raised eyelets compared to the anchors with embedded eyelets, which all failed at the knot., Conclusion –: Anchors with embedded eyelets had clinically preferred mode of failure with less adverse effects on suture and, may be more reliable than anchors with raised eyelets for attaching artificial Achilles and tibialis cranialis tendons in rabbits., Competing Interests: Conflict of Interest – None to disclose.

To achieve the desired microstructural properties, the ongoing development and innovation in new structural steels require novel thermal processing. This study aims to improve the mechanical properties of a commercial spring carbon–silicon steel by tailoring its microstructure through a process involving quenching and partitioning (Q&P) followed by bainitic transformation. A two‐stage Q&P process is proposed to generate a nanoscale dispersion of stable retained austenite and carbides within the tempered martensite and bainite microstructure. The resulting tensile properties demonstrate a yield strength of 1280 MPa, an ultimate tensile strength of 1875 MPa, and a total elongation of 8.03%. These values surpass those of conventional spring 9254 steel, highlighting the effectiveness of the thermal treatment design. Microstructure analysis reveals the presence of tempered martensite, bainite sheaves, nanoscale carbides, and aggregates of retained austenite. Moreover, the resulting body‐centered cubic matrix exhibits minimal lattice tetragonality of ≈1.0051, coupled with stable retained austenite featuring a carbon concentration of ≈3.42 ± 0.5 wt%, resulting in outstanding strength–ductility properties. These findings indicate that the proposed two‐stage Q&P process, followed by bainitic transformation, significantly enhances the mechanical properties of carbon–silicon steels, making it a promising candidate for high‐performance spring applications. [ABSTRACT FROM AUTHOR]

Parts made through additive manufacturing (AM) often exhibit mechanical anisotropy due to the time-based deposition of material and processing parameters. In polymer material extrusion (MEX), printed parts have weak points at layer interfaces, perpendicular to the direction of deposition. Poly(lactic acid) with chopped carbon fiber was printed on a large-format pellet printer at various extrusion rates with the same tool pathing to measure the fiber alignment with deposition via two methods and relate it to the ultimate tensile strength (UTS). Within a singular printed bead, an X-ray microscopy (XRM) scan was conducted to produce a reconstruction of the internal microstructure and 3D object data on the length and orientation of fibers. From the scan, discrete images were used in an image analysis technique to determine the fiber alignment to deposition without 3D object data on each fiber's size. Both the object method and the discrete image method showed a negative relationship between the extrusion rate and fiber alignment, with −34.64% and −53.43% alignment per extrusion multiplier, respectively, as the slopes of the linear regression. Tensile testing was conducted to determine the correlation between the fiber alignment and UTS. For all extrusion rates tested, as the extrusion multiplier increased, the percent difference in the UTS decreased, to a minimum of 8.12 ± 14.40%. The use of image analysis for the determination of the fiber alignment provides a possible method for relating the microstructure to the meso-property of AM parts, and the relationship between the microstructure and the properties establishes process–structure–property relationships for large-format AM. [ABSTRACT FROM AUTHOR]

Parts made through additive manufacturing (AM) often exhibit mechanical anisotropy due to the time-based deposition of material and processing parameters. In polymer material extrusion (MEX), printed parts have weak points at layer interfaces, perpendicular to the direction of deposition. Poly(lactic acid) with chopped carbon fiber was printed on a large-format pellet printer at various extrusion rates with the same tool pathing to measure the fiber alignment with deposition via two methods and relate it to the ultimate tensile strength (UTS). Within a singular printed bead, an X-ray microscopy (XRM) scan was conducted to produce a reconstruction of the internal microstructure and 3D object data on the length and orientation of fibers. From the scan, discrete images were used in an image analysis technique to determine the fiber alignment to deposition without 3D object data on each fiber’s size. Both the object method and the discrete image method showed a negative relationship between the extrusion rate and fiber alignment, with −34.64% and −53.43% alignment per extrusion multiplier, respectively, as the slopes of the linear regression. Tensile testing was conducted to determine the correlation between the fiber alignment and UTS. For all extrusion rates tested, as the extrusion multiplier increased, the percent difference in the UTS decreased, to a minimum of 8.12 ± 14.40%. The use of image analysis for the determination of the fiber alignment provides a possible method for relating the microstructure to the meso-property of AM parts, and the relationship between the microstructure and the properties establishes process–structure–property relationships for large-format AM.

This research introduces a comprehensive classification and analysis system tailored for accurately determining the ultimate tensile strength (UTS) of weld seams. Traditional UTS assessment methods typically depend on destructive testing, which tends to be both lengthy and expensive, limiting their continuous application in quality control of welding procedures. This study leverages deep learning techniques, segmenting the dataset into subsets for training and validation in both multi-class and binary classification scenarios. The model devised in this study incorporates cutting-edge methodologies including geometric enhancement, U-Net based image segmentation, an image augmentation of diverse deep learning models, and decision fusion tactics. A significant aspect of this research was the success of Experiment 33, which skillfully combined various methodologies, resulting in outstanding performance. This experiment demonstrated exceptional accuracy in multiclass classification, alongside impressive outcomes in binary classification, achieving a high accuracy rate of 97.4% and an F1 score of 96.5%. This level of accuracy is indicative of the average performance across all models that incorporated the He-UWA for decision fusion strategy. It encompasses the efficacy of all models using He-UWA, with or without image segmentation. These findings underscore the effectiveness of our proposed model in accurately classifying UTS in friction stir welding. This represents a crucial advancement in assessing the quality of welding processes and provides a solid foundation for future investigations in this area. [ABSTRACT FROM AUTHOR]

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.

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.

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.

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.

Steel rebars experience deformation when subjected to bending and re-bending operations. Subsequently, their microstructures are impaired thus affecting the mechanical properties and leading to failure. This study establishes the effects of bending and re-bending the steel rebars on the mechanical properties. Experiments were conducted to determine the effects of bending and re-bending of the steel rebars. Testing was conducted on Ten virgin and Thirty specimens which were bent and re-bent at 45º, 90º, and 180º, and followed by tensile tests. Yield strength, ultimate tensile strength, elongation, and ultimate tensile strength to yield strength ratio (Rm/Re), were recorded. Results showed the yield strength, ultimate tensile strength, elongation, and Rm/Re of virgin steel rebars varied from 553.62 MPa to 618.49 MPa, 634.39 MPa to 745.68 MPa, 17.6 % to 21.67 %, and 1.14 to 1.19. The yield strength, ultimate tensile strength, elongation, and Rm/Re of bent and re-bent steel rebars at angles of 45º, 90º, and 180º varied from 603.67 MPa to 677.38 MPa, 692.82 MPa to 751.76 MPa, 6.88 % to 19 % and 1.09 to 1.21. It was established that bending and re-bending increase strength and reduce the ductility of the rebars proportionally. [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 (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.

Aerospace industries have remained dependent on aluminium alloys for airframe structural components manufacturing due to their superior strength, fracture toughness, and ability to resist corrosion. Especially, AA2024 and AA7075 have been the most prominent and timely tested robust aluminium alloys in these manufacturing sectors. However, joining these aluminium alloys through conventional fusion welding is difficult. The present investigation focused on the mechanical and metallurgical properties of these high-strength dissimilar aluminium alloys 2024-T351 and 7075-T651 using a Friction Stir Welding (FSW) process. The effects of factors such as rotational speed RS (800–1200 rpm), welding speed WS (20–60 mm/min), and tool plunge depth (0.2–0.4 mm) on the ultimate tensile strength (UTS) and yield strength (YS) have been evaluated. The experimental procedure employed is based on RSM. The fractured surface morphology was investigated using SEM. The investigation result showed higher tensile strength (147 MPa) at the combination of welding parameters (1200 rpm, 60 mm/min, and 0.4 mm). The fabrication industries became the great beneficiaries of this emerging technology of the FSW.

Direct Metal Deposition (DMD) is a metal Additive Manufacturing (AM) process. It is used for producing sustainability Functionally Graded Materials (FGM) and repairing of sophisticated parts. In this present research, a commercially available DMD machine deposited three partial FGM blocks of size 26 mm wide × 34 mm thick × 32 mm heights. The commonly influence parameters on Ultimate Tensile Strength (UTS) are scan velocity and laser power. The powders used for deposition were Stainless Steel 316L (SS316L), Inconel 625 (IN625), and their three different compositions. ASTM E8 tensile samples were cut from those blocks by wire cut-EDM. Micro-tensile tests were carried out on ASTM E8 samples using a SHIMADZU micro-tensile machine. The results revealed that partial FGM sample-2 had high sustainability UTS of 532 MPa as compared to remaining two samples. It is illustrated that for joining two dissimilar materials to obtain high UTS thick layered (i.e., thickness more than 1 mm) gradient path method should be selected at the medium laser power available on the DMD machine. However, the sample-3 has higher hardness at high laser power.

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.

The fused filament fabrication 3D printing technique yields low mechanical strength components as a result of the layer-by-layer building process. The choice of appropriate pre-processing and post-processing procedures resulted in a significant improvement in the mechanical attributes. In this work, the effects of process parameters namely infill density, pattern, extruder temperature, layer thickness, and print speed on the ultimate tensile strength of polylactic acid and carbon fiber polylactic acid components were analyzed. For both the filaments, the maximum mechanical strength was found at 90% infill volume for the gyroid infill with a layer height of 0.1 mm at a print speed of 30 mm/s. The tensile properties were higher in carbon fiber reinforced tensile samples than in standard polylactic acid. The influence of thermal annealing on the mechanical strength of tensile samples (ASTM D638 Type IV) was also examined. Annealing polylactic acid samples at 95 °C for 60 min demonstrated a maximum tensile strength of 38.27 MPa, representing a 23.02% increase. Similarly, annealing reinforced thermoplastic samples at 95 °C for 120 min resulted in ultimate tensile strength of 42.49 MPa, representing a 14.01% increase. Heat treatment settings that are optimized can considerably improve the mechanical characteristics of the fused filament fabricated end-use products thereby enhancing the potential of the 3D printing sector. [ABSTRACT FROM AUTHOR]

Friction stir welding (FSW) and ultrasonic vibration enhanced FSW (UVeFSW) experiments were conducted by using 6061-T6 Al alloy and AZ31B-H24 Mg alloy sheets of thickness 2 mm. The suitable process parameters windows were obtained for the butt joining of Al/Mg sheets. The effect of ultrasonic vibration on the macrostructure and mechanical properties of the dissimilar joints was studied. The results showed that the width of the weld nugget zone (WNZ) was enlarged to some extent and the hardness distribution in WNZ was more uniform in UVeFSW. In addition, the application of ultrasonic vibration effectively promoted the interpenetration degree of dissimilar materials in the WNZ so that the mechanical interlocking on the bonding interface of dissimilar Al/Mg materials was enhanced. The facture positions were changed from the bonding interface in FSW to the boundary between WNZ and the thermo-mechanical affected zone, and the ductile fracture zone was expanded. The highest ultimate tensile strength was 205 MPa at the process parameters set of 1200 rpm–50 mm/min in UVeFSW in this experiment. The average ultimate tensile strength of FSW/UVeFSW joints was 172.3 MPa and 184.4 MPa, respectively, and the average ultimate tensile strength was increased by 7.02% with the introduction of ultrasonic vibration. [ABSTRACT FROM AUTHOR]

This study explores the mechanical properties of Glass Fiber-Reinforced Polymer (GFRP), a high-performance composite material, focusing on how varying diameters affect its tensile strength, modulus, and elongation. Experimental data obtained from three sets of tensile tests on 10, 12, and 25 mm bars helped establish a stress–strain relationship for GFRP reinforcements, considering diameter changes, and a formula for calculating the ultimate tensile strength based on diameter. Utilizing the weakest chain theory and the Weibull distribution, the research found that GFRP's tensile strength diminished with increased diameter, while the elastic modulus behaves oppositely. The analysis, grounded in the weakest chain theory, identifies the specimen's effective volume as a critical factor in the size effect of GFRP bars. Moreover, the study proves a significant size effect on GFRP's tensile properties, validating the theory's application in predicting the strength of GFRP bars of varying sizes and recommending a specimen length range of 30–40 times its diameter for standardization purposes. [ABSTRACT FROM AUTHOR]

Purpose: This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries. Design/methodology/approach: Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers. A reference dataset contains comprehensive information regarding mechanical behavior of the lignocellulosic fibers was compiled from previous experimental investigations on mechanical properties for eight different fiber materials. Data encompass three key factors: Density of 0.9–1.6 g/cm3, Diameter of 5.9–1,000 µm, and Microfibrillar angle of 2–49 deg were utilized. Initially, fuzzy clustering technique was utilized for the data. For validating proposed model, ultimate tensile strength and elongation at break were predicted and then examined against unseen new data that had not been used during model development. Findings: The output results demonstrated remarkably accurate and highly acceptable predictions results. The error analysis for the proposed method was discussed by using statistical criteria. The stacked model proved to be effective in significantly reducing level of uncertainty in predicting the mechanical properties, thereby enhancing model's reliability and precision. The study demonstrates the robustness and efficacy of the stacked method in accurately estimating mechanical properties of lignocellulosic fibers, making it a valuable tool for material scientists and engineers in various applications. Originality/value: Cellulosic fibers are essential for biomaterials to enhance developing green sustainable bio-products. However, such fibers have diverse characteristics according to their types, chemical composition and structure causing inconsistent mechanical performance. This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries. Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers. [ABSTRACT FROM AUTHOR]