Automated inspection of steel surface defects is an important task in intelligent manufacturing systems to ensure product quality and reduce manual inspection efforts. This study proposes a hybrid deep learning framework for accurate classification of metal surface defects by integrating convolutional feature extraction, residual shrinkage learning, transformer-based attention modelling, and recurrent sequence analysis. Initially, greyscale defect images from the NEU metal surface defect dataset are processed using a convolutional neural network (CNN) combined with Batch Normalisation and Improved Residual Shrinkage Network (IRSN) blocks to extract noise-resilient and discriminative local features. The IRSN mechanism performs adaptive soft thresholding to suppress noise and enhance relevant defect patterns. Transformer encoder with multi-head attention learns long-range relationships among extracted features. In parallel, a Gated Recurrent Unit (GRU) branch models sequential dependencies in the feature representation. The outputs from both branches are fused and passed through fully connected layers for multi-class defect classification. A Dual-Condition Cost-Sensitive (DCCS) optimisation strategy is applied to automatically tune hyperparameters. The proposed model attains an accuracy of 93.06% and 98.40% on the NEU metal surface defect dataset and Severstal Steel Defect Detection dataset, respectively. The proposed model underscores an improvement of around 0.06% and 1.2% over the recent benchmark model.
The power requirement of a centrifugal pump for distributing service and potable water increases with pipe size due to higher friction losses and flow resistance. Larger pipes reduce friction but require more power for pumping, but less than the smaller pipe size, while smaller pipes increase friction, necessitating higher power to maintain flow rates. The research includes service water for ship usage and toilet flushing, and potable water for domestic use in the jetty and Coastal Regulation Zone (CRZ) areas. Service and potable water are supplied by the administration to a 300 KL underground tank, with HDPE tanks on building terraces for storage. The key issue is how varying pipe sizes influence power requirements for centrifugal pumps. The challenge is to balance power consumption with effective water distribution, given the coastal location and the specific use cases for service and potable water. The objective is to compare power requirements for different pipe sizes and configurations, aiming to optimise the selection process for centrifugal pumps to ensure efficient water distribution. The analysis involves comparing actual pump configurations with alternate pipe sizes and assessing power consumption and pressure drops for various scenarios.
This study investigates the influence of tool-electrode material in electrical discharge machining on the surface of biomedical Ti6Al4V. Graphite, electrolytic copper, and Ti6Al4V electrodes were compared under identical conditions. The Ti6Al4V electrode significantly improved surface quality by reducing material transfer and crack density. Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy analyses revealed smoother, more homogeneous surfaces with minimal chemical contamination. Corrosion tests in Ringer's solution showed enhanced corrosion resistance for all samples versus the non-machined reference, with the Ti6Al4V electrode reducing corrosion rate by 87%. Antibacterial tests against Staphylococcus aureus demonstrated an effective bactericidal effect for the copper-machined surface, attributed to copper oxide incorporation in the recast layer. These multifunctional surface properties reflect the critical impact of tool material on electrochemical stability and biological behaviour. The findings confirm that electrical discharge machining can be employed as a surface treatment method to tailor and optimise the functional performance of biomedical Ti6Al4V.
To analyse the structural characteristics and evaluate fatigue life of spiral bevel gear transmission with crack faults, a comprehensive and in-depth analysis is conducted based on the optimisation analysis flow from the aspects of static contact, transient dynamics, modal and fatigue life. The related investigations are carried out, and some findings are revealed as well. First, a parametric spiral bevel gear FEA model with different crack parameters including crack depth and penetration length is established. Second, the bending stress and natural frequency under the parameters of gear crack depth and penetration length are analysed based on three different aspects including static analysis, transient dynamics and modal analysis. The influences caused by different crack parameters are obtained, and the variation trend is analysed as well. Finally, a fatigue life prediction model for cracked gear based on fracture mechanics and fatigue cumulative damage theory is established, and the fatigue life curve under different crack parameters is obtained and analysed. Key factors such as crack growth rate, stress concentration factor and material fatigue properties are considered in this fatigue model. This research not only provides a scientific basis for fault diagnosis and maintenance of helicopter intermediate gearboxes, but also serves as a valuable reference for the structural characteristic analysis and fatigue life assessment of rotating machinery.
Erosion wear caused by the impact of solid particles in centrifugal slurry pumps presents a significant engineering challenge, affecting pump performance. This study investigates the effects of particle shape, size, and mass flow rate on erosion wear and pump efficiency using ANSYS CFX simulations with the Finnie erosion model. The results demonstrate that increasing the shape factor from 0.2 to 0.8, while keeping the particle mass flow rate at 0.5 kg/s and particle size at 500 & micro;m, reduces erosion wear rate density from 5.23 & times; 10-5 to 3.26 & times; 10-5 kg/m2, improving pump performance from 56.87% to 67.35%. Conversely, when the particle size increases from 500 & micro;m to 1500 & micro;m, with a fixed mass flow rate of 0.5 kg/s and shape factor of 0.2, the erosion wear rate density rises from 5.23 & times; 10-5 to 9.75 & times; 10-5 kg/m & sup2;, resulting in a performance drop from 65.85% to 54.45%. Furthermore, increasing the particle mass flow rate from 0.5 kg/s to 1.5 kg/s, with a particle size of 500 & micro;m and shape factor of 0.2, elevates the erosion wear rate density from 5.23 & times; 10-5 to 7.87 & times; 10-5 kg/m2, causing pump efficiency to decline from 56.87% to 50.28%. The study concludes that more spherical particles and smaller particle sizes lead to lower erosion rates and better pump performance.
This study investigates the effectiveness of microwave-assisted debinding as a sustainable and efficient post-processing technique for 316 L stainless steel components fabricated via material extrusion additive manufacturing (MEAM). Traditional debinding methods, such as solvent and thermal debinding, are often energy-intensive, time-consuming, and prone to causing part defects due to incomplete binder removal and dimensional distortion. Microwave-assisted debinding offers uniform volumetric heating, which can enhance the binder removal process and reduce processing time. Eight different microwave debinding parameter sets were tested, varying in power level, heating duration, and heating mode, applied to green parts produced using BASF Ultrafuse 316 L filament. The performance of each condition was evaluated based on binder removal rate, dimensional stability, relative density, microhardness, phase composition, and microstructural integrity. Among the tested conditions, specimen S_026 (parameter 8) showed optimal performance, achieving a binder removal rate of 5.14%, a dimensional expansion of below 2%, and a microhardness of 279.08 HV. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses confirmed the presence of strong phase formation with minimal porosity in S_026. In contrast, suboptimal parameters led to increased porosity, microcracks, and compromised dimensional stability. The study also revealed that debinding conditions significantly influence the mechanical properties of the sintered parts. Overall, the findings demonstrate that optimised microwave-assisted debinding is a viable alternative to conventional methods, offering improved efficiency and quality in metal AM processing. This method holds significant promise for industrial-scale MEAM applications by enabling the production of dense, mechanically robust components with reduced processing time and energy consumption.
Projectile impact damages could chang the mass distribution and stiffness characteristics of the tail drive shaft. However, the effect mechanisms of its effect on mass distribution and stiffness characteristics are unclear. Hence, this paper conducts research on the projectile impact damage of the helicopter tail drive shaft and its effect on mass distribution and stiffness characteristics. The finite element simulation model of the projectile impact damage is established, and the stiffness simulation model is further proposed. The residual velocity, projectile impact duration, and projectile impact damage morphology have been analysed in detail. The effect of projectile impact damage on mass loss, centre of mass displacement, stiffness reduction, stiffness asymmetry, and cross stiffness is evaluated. The projectile impact experiment bench is established. The maximum error between the experimental incidence velocity and the ideal incidence velocity is 3.4%. The projectile impact damage morphology obtained from the experiment is larger than the simulated damage, with a maximum error of 26.5%, because the armour and lead sheath expand the damage area. Experimental results effectively validated the accuracy of the finite element simulation model of the projectile impact damage. This paper provides important theoretical guidance and technical support for the helicopters' survival ability.
This paper studies the free vibration analysis of a nanocomposite conical shell with an adhesive lap joint. As the adherents, two polymer-based nanocomposite conical shells enriched with graphene nanoplatelets (GNPs) are considered, which are connected with an elastic adhesive. The distribution patterns and mass fractions of the GNPs in two adherents are not necessarily the same. The set of coupled partial differential equations is solved analytically by considering appropriate trigonometric functions in the circumferential direction, and approximately by applying the differential quadrature method (DQM) in the meridional direction. Numerical results demonstrate that the natural frequencies increase as the lap joint length increases. However, the variation of each natural frequency versus the variation in the thickness of the adhesive depends on the vibrational mode. It is demonstrated that the natural frequencies reach their maximum values when the GNPs are distributed as far away as possible from the middle surfaces of the outer and inner parts of the shell. The presented study is the first theoretical work regarding to the free vibrational analysis of a nanocomposite conical shell with an adhesive lap joint.
Ensuring high-quality production of glass products is essential in modern manufacturing, where even minor defects such as scratches, cracks, or surface irregularities can significantly affect safety, performance, and usability. Traditional visual inspection methods are labour-intensive, time-consuming, and prone to human error, creating a strong need for automated and reliable defect detection systems. The main objective of this research is to develop an efficient Deep Learning (DL) based framework for accurate binary classification of defective and non-defective glass products while addressing limitations such as subtle defect visibility, variation in lighting conditions, and dataset imbalance. Furthermore, the study aims to evaluate model performance under different hyperparameter settings using an intelligent optimisation technique. The proposed work employs the Residual Network 50 Version 2 (ResNet50V2) Convolutional Neural Network (CNN) for defect classification, supported by a comprehensive preprocessing pipeline consisting of resizing, normalisation, greyscale conversion, and data augmentation. Bayesian Optimisation (BO) is integrated to fine-tune key hyperparameters, ensuring optimal learning efficiency and improved generalisation. The framework is trained using a curated glass defect dataset and evaluated using standard performance metrics to demonstrate its robustness and suitability for real-world industrial inspection scenarios. Experimental results show that the optimised ResNet50V2-BO model achieves 98.36% accuracy, along with a precision of 98%, recall of 93%, and F1-score of 95% for the defective class. The confusion matrix and PR curves further confirm the model's ability to reliably distinguish subtle surface defects. These findings highlight the potential of the proposed approach as a fast, reliable, and scalable solution for automated quality control in glass manufacturing industries.
To address wear degradation in 40Cr alloy steel, this study employs laser additive manufacturing to fabricate TiC-reinforced coatings on 40Cr surfaces. Using 12CrNi2 alloy steel powder as the metal matrix, both gradient-reinforced and uniformly reinforced coatings are produced and systematically compared. The influence of TiC on wear resistance is evaluated through detailed analyses of phase composition and microstructure using X-ray diffraction, optical microscopy, and scanning electron microscopy. Microhardness and wear performance under varying loads are assessed using a micro-Vickers hardness tester and friction-and-wear tester, respectively. Results indicate a strong metallurgical bond between coating and substrate, with TiC and ferrite as the dominant phases. TiC nanoparticles are uniformly distributed within the coatings, showing enhanced concentration in gradient-reinforced coating, which exhibits dendritic and equiaxed crystal structures. The average microhardness of the gradient-reinforced coating reaches 861 HV, exceeding the 803 HV of the uniformly reinforced coating. Wear tests under 50 N and 100 N loads demonstrate that the gradient-reinforced coating experiences significantly lower material loss compared to uniformly reinforced coating and 40Cr substrate. Overall, the gradient-reinforced coating displays superior wear resistance, roughly double that of the bare 40Cr alloy steel, highlighting the effectiveness of TiC reinforcement and gradient distribution in enhancing tribological performance.
To overcome the problems of low iteration efficiency and a propensity to drift towards local optimal solutions when choosing optimal parameters in the standard CNC machine tool fault data training model, an intelligent optimisation technique based on PSO-BFA is proposed. By simulating the bacteria's local foraging activity, the BFA algorithm is introduced, giving the particle swarm the qualities of local convergence, replicability, and migration. This enhancement improves the model's fitness value, facilitates precise local parameter optimisation, and accelerates convergence towards the optimal solution. The fault data training model determines the average fitness value of the particles in the swarm, selects a deep confidence network model with customisable scale, and uses the PSO method to get the ideal value in the global range. When the training process rapidly converges to the optimal solution, the ability to identify problematic spots and diagnose problems is improved. Simulation results show that the PSO-BFA intelligent optimisation method outperforms the traditional swarm intelligence strategy in multi-fault diagnosis and classification, achieving the peak fitting value in less iterations.
Based on the theory of polymer material rheology, non-Newtonian fluid mechanics, vibration mechanics, diesel engine dynamics and the viscoelastic mechanics model of silicone oil, combining with the practical working characteristics of the silicone-oil vibration absorber, the vibration reduction mechanism is analysed, and the dynamic equilibrium matching calculation method of the silicone-oil vibration dampers for the diesel engine based on non-Newtonian fluid is established, improving the accuracy and reliability of the matching calculation of the vibration dampers. The calculated results have a good correspondence with the measured values, which promotes in-depth research on the vibration damping mechanism of the silicone-oil dampers.
This paper details a hybrid operational and computational study for improving the thermodynamic performance of a Gas-Steam Combined Cycle Power Plant (CCGT) through a novel hybrid optimisation framework Adaptive Annealed NSGA (AANSGA. This framework integrates Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Simulated Annealing (SA). The study applies second-law (exergy) analysis and optimised heat integration approaches such as the pinch point method and the approach temperature difference method to reduce exergy destruction and improve thermal efficiency. Operational results were obtained from a laboratory-based CCGT study, where the gas turbine inlet temperature was 1400 K, the Heat Recovery Steam Generator (HRSG) outlet steam pressure was 20 bar, and the steam mass flow rate was 25 kg/s. The results show an increase in thermal efficiency from 46.96% to 54.12%, and an increase in the exergy efficiency from 84.95% to 85.55%. Similarly, fuel consumption improved from 2.425 to 2.405 kg/s, and CO2 emissions stabilised at 352 kg/MWh. The HRSG pinch point temperature difference was improved between 9.35 degrees C - 9.47 degrees C. The hybrid AANSGA approach achieved a reduction in total exergy destruction per cycle and improved operational stability. Overall, AANSGA provides a useful decision-support tool for the development of sustainable, high-performance power systems under dynamic conditions.
The nuclear industry is exploring applications of machine learning, including autonomous control and management of reactors and nuclear power plant and their components. The accurate diagnosis and classification of motor bearing faults under diverse operating conditions remain a significant challenge due to the complex nature of signal patterns, overlapping features, and dynamic environments. This study presents a comprehensive comparative analysis of multiple machine learning algorithms, including Random Forest, Gradient Boosting Machine, Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN), applied to the HUST bearing dataset. This work also underscores the importance of load-dependent analysis, as fault signatures in bearings vary significantly with operating conditions. Experimental results indicate that ensemble models, particularly Random Forest, deliver superior performance across both binary and multi-class classification tasks, achieving up to 99.70% accuracy for 7-class cases and 99.37% for 21-class scenarios. Performance metrics further highlight the Random Forest model's robustness, achieving 99.37% precision, recall, and F1-score with 15 features, confirming its suitability for real-time predictive maintenance applications. This study emphasises the importance of appropriate segmentation strategies and model selection, offering a reliable and scalable framework for industrial fault diagnostics and condition monitoring systems.
Casting is a fundamental manufacturing process for producing components with complex geometries; however, surface and subsurface defects continue to compromise product reliability andproduction efficiency. To support automated and consistent quality inspection, this paper presents a hybrid deep learning framework termed SCNNBN - TBiG for intelligent casting defect classification. The proposed approach integrates stacked convolutional neural networks with batch normalisation to extract stable and discriminative spatial features, followed by a Transformer encoder that captures long-range contextual relationships through multi-head self-attention. The resulting representations are compressed using global average pooling and subsequently analysed by stacked bidirectional gated recurrent unit layers to model sequential dependencies within the learned feature space. The framework is evaluated on a publicly available industrial casting image dataset comprising 7,348 samples under both defective and non-defective categories. Experimental results demonstrate that the proposed model achieves a testing accuracy of 99.44%, outperforming several existing deep learning and hybrid architectures. The findings confirm that the synergistic integration of spatial, global, and sequential feature learning provides a robust and efficient solution for high-precision industrial quality inspection.
Gears are essential components in power transmission systems, where their ability to withstand stress and maintain structural integrity determines performance and service life. This study investigates the comparative behaviour of non-phased and phased spur gears through a combination of finite element simulations and physical testing. Three-dimensional models were created in Unigraphics NX-8 and evaluated in ANSYS Workbench 16.0 to determine stress patterns, deformation levels, contact pressures, and safety margins. The phased gear incorporated a calculated phase angle aimed at improving load distribution and reducing distortion. Under dynamic conditions, the phased gear exhibited a marked improvement, with deformation levels reduced by more than 85% compared to the non-phased configuration. Experimental vibration velocity measurements closely matched the simulation outputs, with differences kept within 10%, validating the reliability of the numerical model. The study also compared theoretical and simulated factor of safety values, which were in strong agreement for both designs. Based on these findings, practical recommendations are provided to guide the selection of gear configurations depending on cost, precision, and load requirements. The combined simulation - testing approach demonstrated here offers a structured path for optimising gear performance in demanding industrial environments.
Sustainability in machining has emerged as important in modern manufacturing, aiming to reduce the environmental impact, maintain economic viability, and social responsibility. This investigation focuses on the machinability of Ti-6Al-4 V alloy, a high-strength material used in aerospace, biomedical, and automotive applications, using a wiper geometry tool. The research aims to minimise machining power by employing advanced machine learning models and a meta-heuristic optimisation algorithm. Machining power is one of the critical parameters of environmental impact. The study fills a gap in the existing literature by integrating machine learning-based techniques for power modelling in machining. To predict machining power, two machine learning-driven regression approaches are implemented, relying on primary machining parameters including cutting speed, feed rate, and depth of cut. The predictive performance is evaluated for practical applications, and the predictive model is combined with a meta-heuristic optimisation algorithm to identify optimal machining conditions in minimum quantity lubrication (MQL) that minimise power consumption while maintaining process performance. The study offers valuable insights into sustainable machining strategies for Ti-6Al-4 V alloy, providing a robust framework for industrial applications and contributing to sustainable manufacturing and energy-efficient production.
Due to the high compressibility and other nonlinear characteristics of the pneumatic positioning system, it has the disadvantages of poor positioning accuracy, response lag and so on. Therefore, a pneumatic precision positioning mechanism based on micro-displacement compensation is studied. Firstly, using piezoelectric ceramic actuator as electro-mechanical converter, combined with hydraulic amplification principle, a membrane hydraulic amplification mechanism driven by piezoelectric actuator is developed. On this basis, a pneumatic precision positioning mechanism with hydraulic amplification mechanism for micro displacement compensation is proposed and its prototype is developed. Then, the dynamic mathematical model of the pneumatic precision positioning mechanism is deduced. The co-simulation model is built based on AMESim and Matlab/Simulink software. Finally, the hardware and software platform of its measurement and control system is built, its control experiment is carried out with segmented PID. By comparing the simulation results with the experimental results, the accuracy of the simulation model is proved. Experimental results also show that the positioning accuracy of the positioning mechanism prototype can reach +/- 0.1 mu m within 80 mm. The large-stroke precise positioning of the pneumatic precise positioning mechanism is realised by micro displacement compensation, and its application range is effectively expanded.
Faults such as rotor unbalance and shaft cracks pose serious risks to the integrity of high-speed rotating machinery, often leading to catastrophic failures if not identified early. While conventional bearings have long been the standard, air foil bearings are rapidly gaining traction due to their superior reliability, durability, and minimal maintenance requirements. In this paper, a machine learning-based diagnostic framework has been presented for fault classification in a Jeffcott rotor system supported by air foil bearings. The rotor model features a centrally mounted rigid disc and is analysed under three fault scenarios: unbalance, crack, and a combination of both. Using Newton's second law and incorporating the equivalent stiffness and damping characteristics of the shaft and foil bearings, the system's equations of motion are derived. Displacement responses at the disc location are simulated via a MATLAB Simulink model, capturing dynamic behaviour under varying fault conditions. These time-domain signals are then processed using the HistGradientBoostingClassifier, a robust and efficient machine learning algorithm to accurately classify fault types. Comparative analysis with Support Vector Machine, Deep Neural Network, and Random Forest models demonstrates superior performance of the proposed approach in both accuracy and computational efficiency. It is revealed that Random Forest and HistGradientBoosting consistently achieved the most generalisable and robust performance. The proposed machine learning approach not only enhances predictive maintenance strategies but also contributes to the reliability and safety of advanced rotating machinery systems.
