Friction stir welding (FSW) and friction stir processing (FSP) are closely related solid-state technologies that employ frictional heating, severe plastic deformation, material flow, and dynamic recrystallization, but their different processing configurations can produce distinct microstructural and mechanical responses. The present study systematically compares the effects of FSW and FSP on AA7075 aluminum alloy using identical tool geometry and processing parameters. Both processes produced substantial grain refinement and predominantly equiaxed microstructures; however, FSP resulted in a finer mean grain size of 5.2 microns, compared with 6.1 microns for FSW. SEM observations revealed redistribution, fragmentation, dissolution, and possible reprecipitation of second-phase constituents during the thermo-mechanical cycles. XRD confirmed α-Al as the dominant phase along with MgZn2 and Al2CuMg secondary phases, while differences in peak intensities indicated variations in crystallographic orientation and microstructural state. FSP exhibited higher surface hardness (121 HV) than FSW (117 HV) and maintained a higher hardness over most of the investigated depth. Furthermore, FSP generated substantially higher near-surface compressive residual stress, reaching approximately −310 MPa, whereas FSW exhibited comparatively lower compression but higher tensile stress at greater depths. The results demonstrate that FSP provides greater grain refinement, higher hardness, and stronger near-surface compressive residual stress than FSW under comparable processing conditions.
This study analyses properties of Al6061-B4C composite, highlighting the stir casting process in view of the increasing need for developing progressive engineering materials. Utilizing scanning electron microscopy SEM investigated uniform dispersion of boron carbide (B4C) particles in 2
Zirconia-toughened alumina (ZTA) is an advanced ceramic bioinert biomaterial which consists of reinforcement of zirconia particles in an alumina matrix. In a conventional composition of ZTA, the primary phase is constituted by alumina, while the balance reinforcing secondary phase constitutes zirconia. In the field of dental research, finite element analysis evaluates the fracture mechanics, restorative devices and dental implants. In this analytical evaluation, the 3D model of a molar tooth dental implant was designed and subjected to analysis under varying loads of 100 N, 200 N, 300 N, and 400 N acted upon by static load conditions. The total deformation, maximum principle elastic strain and maximum principle stress analysis was carried out and was found to be within the safe operating limits for ZTA (45
A combined experimental and computational investigation was performed to evaluate the machinability and thermal performance of oil-hardened AISI 4340 steel (52–55 HRC) during precision hard turning under CuO nanoparticle-enhanced, soybean oil-based minimum quantity lubrication (MQL). A Taguchi L27 (33) orthogonal array comprising 27 machining trials was used to examine the effects of cutting speed, feed rate, and nanofluid concentration on tangential cutting force, surface roughness, flank wear, and tool–chip interface temperature, with depth of cut held constant at 0.3 mm. Cutting-zone temperature was independently characterised through 12 in-situ infrared measurements, a MATLAB artificial neural network (ANN), Minitab quadratic response-surface regression, and Deform-3D finite element simulation. Analysis of variance (ANOVA) confirmed feed rate as the statistically dominant factor for force and roughness (p < 0.001), while cutting speed and feed jointly governed flank wear, with nanoparticle concentration a significant secondary factor in all three responses. SEM–EDS analysis of six representative post-trial inserts identified adhesive, abrasive, diffusion-controlled, and oxidative wear mechanisms, with EDS evidence of oxygen- and carbon-enriched tribofilm formation under CuO-MQL conditions. Multi-objective SLSQP optimisation, validated through three replicate confirmation trials (deviation ≤ 5.3
In design and manufacturing, determining the mechanical properties of engineering materials is of prime importance; however, conventional methods for this task are expensive and time-consuming. This issue is important for small laboratories and industries with limited access to equipment. This study presents a machine learning framework for predicting ultimate tensile strength (UTS), Vickers hardness (HV10), and elongation at break (εf). The model is built using two experimentally obtained inputs: specimen density measured by Archimedes’ principle and a Density-Weighted Proof Stress Index (DPSI) extracted from the 0.2
Polyether ether ketone (PEEK)-based bearings offer a promising alternative to conventional metallic bearings for marine applications owing to their corrosion resistance, low weight, and self-lubricating characteristics. In this research work, a novel concept for designing a four-pad deep-groove ball bearing made of PEEK and using Al6061 and Al2O3 rolling elements has been presented. FEA analysis and experimental testing have been performed to study the tribological and mechanical properties of the bearings under dry and water lubrication with three radial loads. FEA has been used to assess the effects of deformation and stress on the structure, whereas experimental studies have been conducted to test wear, temperature, and power transmission efficiency. From the results, it is concluded that the Al2O3 rolling elements perform much better than the Al6061 rolling elements due to their higher load-carrying capacity and tribological behavior. Lubricating with water reduces wear and bearing temperature. The results clearly indicate that the proposed bearing is appropriate for use in a marine environment. Moreover, the four-pad design provides more uniform load distribution to the structure without causing damage to the PEEK materials under the tested loadings.
Ore dilution is a critical issue in underground metal mining using the ringhole blasting method, as it directly affects ore recovery and operational efficiency. It is important to predict ore dilution before conducting actual blasts. This will help in modifying the blast design parameters to obtain less ore dilution. Accordingly, this study has been carried out to predict ore dilution in ringhole blasting using trial blasts and numerical simulation technique. Trial blasts were carried out at one of the underground metalliferous mines located in Rajasthan state, India. The post blast profile of ringhole was obtained using the Cavity Monitoring System to determine the excavated volume and calculate ore dilution. In addition, numerical models were developed for different experimental scenarios considering variations in burden and the number of blastholes. The simulation outputs were used to estimate the excavated volumes of ore and waste rock to compute the corresponding percentage of ore dilution. The results show that the ore dilution obtained from the experimental blasts ranged from 1.9
This study presents a data-driven framework for optimizing Friction Stir Welding (FSW) parameters to maximize the Ultimate Tensile Strength (UTS) of AA2014 aluminum alloy by integrating Bayesian Optimization with Gaussian Process Regression (Bayesian–GPR). An experimental dataset comprising 300 samples was used, with TRS, TTS, PTG, and TA as input variables. The developed Bayesian-GPR optimization framework achieved an optimal value of UTS at 313.73 MPa in just 15 iterations, which is 5.3
The effects of dispersed-phase particles and simulation temperatures on grain evolution dynamics are examined in this study using the Monte Carlo Potts Model. The transient conditions were simulated by varying the thermal energy in steps to observe its effects on grain growth behavior. The results show that increasing the particle fraction decreases the average grain size, with the greatest influence occurring at lower simulation temperatures. The grain growth kinetics peak at a critical temperature (KTs = 0.4) before declining due to increased disorder in the system. Additionally, the effects of simulation temperature on grain boundary migration, activation energy, and overall grain structure evolution are evaluated.
Open-pit and underground mine environments generate dense mineral dust during drilling, blasting, loading, and haulage operations, severely degrading the visibility of surveillance and machine-vision imagery. Conventional defogging algorithms based on the dark channel prior (DCP) perform poorly in these settings because they assume a bright, spectrally neutral atmospheric veil, whereas mine dust produces spatially non-uniform, polychromatic scattering with a characteristic colour shift. This paper presents an enhanced three-stream dehazing algorithm, designated v2, that addresses these limitations within a physics-based haze formation model. The method introduces a dual-source atmospheric light estimator with conservative per-channel fusion that prevents over-estimation from specular mine surfaces, and replaces the conventional fixed dehazing constant with an adaptive per-pixel weight modulated by local haze density. Transmittance is refined through a guided filter that removes block halo artefacts arising from patch-wise dark-channel erosion while preserving machinery silhouettes, and a spatially varying soft transmittance floor, inversely coupled to haze density, suppresses saturation artefacts. Finally, haze-density-scaled contrast-limited adaptive histogram equalisation is applied to the luminance channel of the L*a*b* colour space so that contrast enhancement is concentrated in occluded regions and leaves already-clear areas essentially undisturbed. Across seven randomly chosen snapshots from real dusty open-pit mine scenes—including haulage truck convoys, rope shovel loading, bulldozer activities, drill-rig blast events, blast plume events, and mixed-equipment scenes—the proposed v2 algorithm consistently outperforms six established comparison algorithms, namely DCP, the Colour Attenuation Prior, Multi-Scale Retinex, the Bright Channel Prior, global CLAHE, and Fattal independent-channel dehazing, with a mean edge restoration rate of 6.84, a mean contrast quality index of 4.76, and a mean saturation ratio of 0.0557. Extended ablation experiments verify the advantages of spatially adaptive, density-conditioned recovery over fixed-parameter methods in dusty industrial environments.
Stringent government and environmental regulations on sand quarrying pose scarcity challenges to meet 70
Ultrasonic welding is one of the popular solid state welding techniques to join light weight dissimilar metals. The present research provides the significance of knurl depth on ultrasonic spot weld joint. Magnesium and copper are frequently employed in several lightweight metal fields. Pure copper and AZ31B Mg alloy were joined using an ultrasonic spot-welding technique. Sonotrode with four tips T1, T2, T3 and T4 having 0.5 mm, 0.6 mm, 0.75 mm and 0.9 mm knurl depth respectively have been used to achieve ultrasonic weld. The peak load achieved with tip T1 and T2 are 2230 N and 1100 N with welding time 0.8 s, welding pressure 1.5 bar and 90
Fiber-reinforced polymer matrix (FRP) composites have emerged as a significant class of structural materials with significant promise as metal substitutes in industries such as architecture, automotive, marine, and aerospace. To determine the optimal fabrication technique, the research began with a comprehensive literature review. Specimens were then made and subjected to wear, compact tension, and axial compression tests. While the tubular glass fiber reinforced polymer (GFRP) composite tubes were constructed with woven glass fabric plies using a manual wrapping method, the flat test specimens were created using conventional hand layup techniques. The compact tension test was used to evaluate the specimens’ intra-laminar fracture strength. Pin-on-disc wear testing was used to determine the coefficient of friction, and the axial compression test was used to examine the energy absorbed by each GFRP composite at various strain rates. A comparative analysis was ultimately conducted.
The present study deals with the experimental and machine learning assisted investigations on flexural behaviour hybrid jute/hemp fibre reinforced epoxy composite developed through hand lay-up technique. To understand the notch sensitivity, Single-edge notch bending (SENB) test was conducted and differences in the obtained results were reported. Flexural testing was carried out according to ASTM D790 (3-point bending) and ASTM D5045 (Single Edge Notch Bending—SENB). Flexural strength of the composite was between 112 and 114 MPa and Young’s moduli of the composite was between 5.6 and 7.2 GPa for both regular and SENB samples respectively. The fractured surface was investigated through Scanning Electron Microscopy (SEM) in order to confirm good fibre dispersion and interfacial bonding, and also, Energy Dispersive X-ray Spectroscopy (EDS) and elemental mapping confirm the organic-dominant nature of the fibre with uniform distribution of carbon and oxygen, and the presence of small amounts of minerals that are inherent in natural fibres. In addition to the experimental investigations, machine learning (ML)-based predictive framework was established to model the flexural behaviour of the developed hybrid composites. The trained models include random forest (RF), XG-Boost, and support vector regression (SVR) models using the experimental load–deformation data so as to achieve better predictive capability. The Random Forest model had the lowest prediction error and shows stable generalisation behaviour with the highest R2 value (0.809) and cross-validation (CV) R2 value (0.828 ± 0.014) among all the models. The ensemble and kernel-based machine learning frameworks are shown to be effective in accurately predicting mechanical performance of hybrid jute/hemp composite, which can be applied in the sustainable semi-structural applications.
Functionally graded materials (FGMs) offer a practical route for integrating the low density and deformability of aluminum with the hardness and dimensional stability of alumina. However, in Al-Al2O3 FGMs, densification is often limited by particle-scale packing incompatibility, disrupted metallic continuity, and route-sensitive pore retention, particularly in transition layers. This study compares the metallurgical response of a five-layer Al-Al2O3 functionally graded pellet fabricated by sequential powder stacking and consolidated by vacuum sintering and spark plasma sintering (SPS). The pellet architecture consisted of 100/0, 75/25, 50/50, 25/75, and 0/100 Al/Al2O3 layers. Powder size distribution, X-ray diffraction, SEM, porosity, density, microhardness, and compression behavior were used to establish a processing-structure–property relationship. Both routes preserved the designed five-layer architecture and retained the dominant FCC Al and α-Al2O3 phases without evidence of major reaction-driven phase transformation. Vacuum sintering produced a non-linear porosity profile of 9.12, 15.26, 10.26, 16.23, and 15.36
Accurate prediction of tribological behavior in aluminum matrix composites (AMCs) remains a significant challenge due to complex nonlinear interactions between operational parameters and material properties. This study develops and validates a machine learning (ML)-based predictive framework for dry sliding wear rate estimation across multiple AMC systems. An experimental tribological database comprising 156 pin-on-disc wear tests was compiled, covering diverse aluminum alloy matrices reinforced with ceramic particles such as SiC, B₄C, Al₂O₃, and TiC, subjected to varying normal loads, sliding speeds, and sliding distances. Seven ML algorithms Gradient Boosting (GB), Random Forest (RF), AdaBoost, Gaussian Process Regression (GPR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Linear Regression (LR) were systematically trained, validated, and compared using R2, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as performance indicators. Gradient Boosting emerged as the superior model, achieving R2 test values of 0.965 and 0.986 alongside MAE values of 0.082 and 0.072 and RMSE values of 0.115 and 0.098 for SiC- and B₄C-reinforced composites, respectively, confirming its strong predictive capability through robust statistical evidence. Microstructural examination of worn surfaces revealed wear mechanism transitions from adhesive and oxidative modes at moderate speeds to abrasive-dominated behavior at elevated loads, consistent with model predictions. The findings demonstrate that ensemble learning methods, particularly Gradient Boosting, offer an effective and computationally efficient approach for accelerating AMC design by reducing experimental characterization effort.
This study comparatively investigates the microstructure, hardness, and abrasive wear behaviour of Fe–Cr–C hardfacing layers deposited on AISI 1020 steel using Shielded Metal Arc Welding (SMAW) and Metal Inert Gas (MIG) welding under varying welding current conditions (130–200 A). The novelty of the work lies in the use of identical Fe–Cr–C consumables and a common substrate for both welding processes, enabling direct process–property comparison under controlled experimental conditions. Microstructural characterization was performed using SEM/EDS and XRD analysis, while Vickers microhardness testing and ASTM G65 abrasive wear testing with statistical validation (ANOVA) were conducted to evaluate mechanical and tribological performance. The results showed that MIG hardfaced specimens developed comparatively finer and more uniformly distributed chromium carbide phases (Cr7C3 and Cr23C6) with lower dilution and reduced microsegregation than SMAW deposits. MIG specimens also exhibited improved hardness uniformity (570 ± 14 to 615 ± 12 HV) and approximately 16.37
Methane is an explosive and dangerous gas that requires quick and reliable safety detection in industries and homes. This research work proposes a Design of Experiment (DOE)–Grey Relational analysis (GRA)-based statistical optimization framework for microstructural optimization of Co-doped ZnO thin films for enhanced methane-sensing performance. A sol–gel spin-coating method was used to prepare the Co-doped ZnO films, and the influence of cobalt concentration, molarity of the precursors, and annealing temperature was investigated in a systematic manner with a Taguchi-based design of experiments. FESEM, XRD, EDAX, and AFM were used to identify microstructural features and the key performance indicators that were identified, such as the grain size and the surface roughness. GRA was employed to establish correlations between fabrication parameters and microstructural responses, enabling identification of the optimum processing conditions. The optimized films have fine grains, homogeneous porosity, and moderate roughness. Methane sensing shows that it was sensitive, quick to respond, and quick to recover. Additionally, the films exhibited excellent repeatability and maintained a stable baseline. The results showed a strong relationship between grey relational grade and methane sensing performance, making it an effective method for statistical microstructural optimization in the design of advanced gas sensors.
