This paper addresses the detection of thickness reduction defects in thermal barrier coating （TBC） systems. It compares eddy current testing responses obtained by pulse-modulation waves at different frequencies with those from corresponding sinusoidal waves via linear regression analysis， demonstrating the advantages of pulse-modulation eddy current testing. A numerical simulation model for coating thinning defects in TBC systems is established. Based on the simulation results， three regression models are developed to fit the experimental data， and these models effectively characterize the relationship between the measured signals and defect size. Probability of detection （POD） analysis is performed on the regression results. The detection threshold is determined using the receiver operating characteristic curve， and the POD curve is constructed. The POD analysis results indicate that a 90% POD corresponds to a defect size of 50.62 μm， with a 95% lower-confidence bound.

Taking the Φ215.9 mm wellbore of a horizontal section at Lianggaoshan in Nanchong as the research subject， a particle swarm-slime mold optimization algorithm for force balance design was employed， specifically addressing the characteristics of highly abrasive formations. The final design solution adopted a configuration featuring with six blades， double-row staggered track cutters and hybrid cutter layouts.Two PDC bits designed in accordance with the scheme were subsequently applied in the Lianggaoshan Formation. Compared with other bits， they exhibite excellent performance in footage and rate of penetration（ROP）. The total footage achieved by the first bit is approximately equivalent to the cumulative footage of the previous three bits， setting a new record for the longest single-trip footage in this formation. The second bit was pulled out prematurely after drilling 96.29 meters due to the loss of signals from downhole tools. Compared with bits that achieved a similar footage， the ROP of this bit is increased by at least 25.9%. Additionally， the drilling time is shortened， the weight on bit（WOB） is lower， rock-breaking efficiency is enhanced， and the bit's conditions remain over 90% new after tripping out. Field tests confirm that the personalized PDC bit design for the Lianggaoshan Formation significantly improves rock-breaking efficiency and service life.

To investigate the statictodynamic friction transition characteristics during instantaneous highstress startup， a test apparatus was constructed to simulate friction behavior under operating conditions. Experiments and simulations were conducted on a GCr15/GCr15SiMn friction pair， and the effects of contact stress and acceleration on the friction transition were examined. The results show that increasing contact stress causes significant plastic deformation of asperities， weakens the interlocking effect between them， and reduces both static and dynamic friction coefficients. Increasing acceleration enhances the asperity interlocking effect and raises the static friction coefficient， whereas the kinetic friction coefficient remains relatively unchanged.

Nickel-based superalloys were widely used in aerospace and other fields. However， significant challenges were in machining， including severe tool wear， poor surface integrity， and low processing efficiency， which could not be effectively mitigated by conventional lubrication techniques. To address these issues， C60 nanofluid cutting fluid was introduced to enhance lubrication at the tool-workpiece interfaces， thereby reducing friction and suppressing heat accumulation. A novel cutting force modeling approach was developed， incorporating the tribological properties and cooling effects of nanofluids. The model integrated oblique cutting theory， mirror heat source method， and Johnson-Cook constitutive equation to calculate cutting forces. Experimental results demonstrate that the proposed model accurately quantifies the synergistic effects of friction reduction and cooling enhancement under nanofluid lubrication， achieving an average prediction error of 6.73% for cutting forces. Notably， the cutting force peak is reduced by 19.6% under nanofluid lubrication compared to conventional cutting fluids. Furthermore， a multi-objective optimization strategy was proposed based on Pareto optimality and PSO. A comprehensive evaluation system was established considering machining efficiency and cutting forces. Optimization results show that the cutting force decreases by 5.7%， while machining efficiency increases by 36.33% after parameter optimization.

The stress and strain distributions of radial bearings for deep drilling tools under the synergistic effects of temperature and load are studied via finite element simulations using ANSYS Workbench. The simulation results show that the maximum equivalent stress and strain occur near the cemented carbide-solder interface （i.e.， the welding interface） and the cemented carbide-cemented carbide interface （i.e.， the sliding interface）. Temperature significantly increases the stress and strain levels； the maximum equivalent stress and strain at 300 ℃ are approximately 2.5 times higher than those at 100 ℃. The frictional stress at both ends of the bearings is higher than that in the middle region， making the end regions more susceptible to adhesive wear. The simulation results are in good agreement with the damage distribution characteristics observed after field application， indicating that these findings can provide a theoretical reference for the optimal design of radial bearings.

This study investigates the effects of reverse flow， axial leakage， and circumferential leakage on the volumetric efficiency of a micro two-dimensional （2D） piston pump through theoretical analysis， numerical simulation， and experimental testing. The results indicate that reverse flow is the primary cause of volumetric efficiency loss. Reducing the trapped volume can significantly decrease reverse flow and enhance volumetric efficiency； specifically， the efficiency increases by 13.31% at a working pressure of 35 MPa.

Increasing milling parameters improves machining efficiency but tends to cause chatter. To address this issue， this paper proposes a stability identification method that considers the static and dynamic characteristics of the milling process as well as the influence of timevarying tool wear. First， a threedimensional stability lobe diagram， incorporating radial cutting depth and tool diameter， is established based on the milling force model to determine the basic machining parameters. Second， frequencydomain signal processing is adopted to eliminate interference from spindle rotational frequency and to identify the actual stability state. Meanwhile， tool wear is monitored based on variations in cutting edge coefficients， and the wear factor is integrated into the identification system. Online identification test results verify that the proposed method can increase the material removal rate while ensuring process stability.

To improve pressure control accuracy in managed-pressure-drilling for deep and ultra-deep wells， this paper proposes a linear design method for multiple-type throttle valves based on variable-diameter profile optimization. A dynamic mapping relationship between valve core stroke and throttling area was established， incorporating the viscous-fluid total-flow Bernoulli equation， Leibniz theorem， and kinetic energy and area correction mechanisms. Using this approach， the profiles of cylindrical and wedge-shaped valve cores were optimized. Experimental results demonstrate that the linear correlation coefficients of pressure drop for the cylindrical valve and wedge-shaped valve reach 0.9932 and 0.9919， respectively， within the 0-100% effective stroke range. These findings confirm that the proposed method enables fullstroke linear pressure control of throttle valves.

To effectively distinguish the lubrication states of the cylinder liner–piston ring system， equivalent friction tests were conducted on a YTRC2110D diesel engine. The variation of the friction coefficient along different positions of the cylinder liner was analyzed. The critical positions corresponding to different lubrication regimes were identified， and zonal division was performed accordingly. The effect of zonal composite lubrication structures was then investigated. The results indicate that the regions near the top dead center and bottom dead center are in boundary lubrication， where the sin-etextured composite structure exhibits the best friction-reducing performance. The middle region is characterized by hydrodynamic lubrication， where a multi-stage structure yields the lowest friction coefficient. The transition region operates under mixed lubrication， and the maximum friction coefficient reduction rate of 53.66% is achieved by the composite structure combining sine-texture filling and circular texture.

To achieve stable grasping of high-temperature forgings， this paper designs a clamp-type end effector driven by dual pneumatic cylinders. Key structural parameters are determined through kinematic， dynamic， and finite element analyses. A center-closed pneumatic system and a PLC-based control system are developed， and grasping performance experiments are conducted. The two cylinders of the end effector have maximum strokes of 33.1 mm and 32.3 mm， with corresponding maximum driving forces of 1178.1 N and 752.9 N， respectively. The system shows the best dynamic response at a speed control valve opening of 60%. The end effector exhibits good adaptability in grasping forgings at various positions and meets the requirements for automatic grasping and repositioning of hammer-forged workpieces.

To tackle the problems of complex morphology and inconspicuous features in surface defects of magnesium alloy laser welding seams， which often lead to high miss-detection and false-positive rates， this paper presents an improved reconstruction network-based defect recognition approach. The network incorporates ASPP， CBAM， SSPCAB， and a multiscale feature fusion module （MSFFM） to enhance feature extraction and anomalous target localization， thereby improving reconstruction accuracy at defect sites and feature information fusion. Experiments conducted on both self-built and public datasets validate the proposed method. Results demonstrate that the approach exhibits strong generalization and can effectively identify， accurately segment， and localize defects characterized by small sample sizes and complex surface morphologies.

To improve the surface quality and mechanical properties of TC17 titanium alloy for aero-engine blades， this paper proposes a synergistic polishing modification method using ultrasonic cavitation and micro-abrasive particles. A mathematical model for bubble evolution and heterogeneous nucleation rate prediction is established， incorporating the perturbation effects of micro-abrasives. A high-speed photography and polishing experimental platform is developed to investigate the effects of ultrasonic frequency on cavitation behavior and modification performance. The experimental results show that at an ultrasonic frequency of 20 kHz， the cavitation cloud exhibits the strongest aggregation and directionality， with a more pronounced collapse impact. Compared with that at 40 kHz， the effective modification energy at 20 kHz is increased by 38.5%. In comparison with the original sample， surface roughness is reduced by 24.9%， microhardness is increased by 16.0%， residual compressive stress is increased by 134.1%， and the width of surface microstructural features is refined by 28.3%. This study reveals the underlying mechanism of ultrasonic cavitation and micro-abrasive synergistic polishing modification.

To address the limitations in accuracy and efficiency of existing techniques for identifying milling vibration states under variable operating conditions， this paper proposes an unsupervised identification method based on hybrid entropy. The proposed method first denoised the milling force signals using bias-compensated sub-band adaptive filtering. A hybrid entropy metric was then derived through the weighted summation of fuzzy entropy and power spectrum entropy to characterize the vibration states. Subsequently， a collaborative clustering model was employed for unsupervised learning and classification of the hybrid entropy， ultimately outputting the identification results over the signal time history. Experimental results demonstrate that the proposed method requires only 16.55 s for model training and eliminates the need for labeled sample sets， demonstrating significant advantages in computational efficiency and practical engineering applications.

To address the lack of efficient nondestructive testing solutions for buried defects in high-temperature equipment， this paper presents an experimental investigation and engineering application of electromagnetic ultrasonic SV-wave oblique-incidence imaging technology. A defect localization method was derived based on the operating principle of the electromagnetic ultrasonic SV-wave. Verification of defect localization and quantification was carried out using fabricated grooved and butt-weld blocks. The proposed method was then applied to the practical inspection of high-temperature heat exchangers. Results from both laboratory tests and field applications demonstrate that the method enables accurate localization and quantitative detection of typical internal defects in high-temperature equipment.

To achieve improved aerodynamic performance of the half-rotating fan-wing （HRFW）， a novel active impeller fan-wing， this paper investigates the effects of bi-wing layout optimization through numerical analysis. The aerodynamic variation patterns among different airfoil combinations are identified， and the influences of wing spacing and height difference on the aerodynamic characteristics of the front and rear wings are examined. The results indicate that the aerodynamic coupling effect formed by different airfoil combinations affects the formation and stability of the eccentric vortex behind the rear wing， and consequently alters the lift-thrust distribution of the bi-wing system. Variations in wing spacing and height difference show negligible influence on lift but significantly enhance thrust. Compared with the sum of the aerodynamic forces of the J-type and C-type single wings， the J-C bi-wing configuration yields a 7.8% reduction in lift and a 20.6% increase in thrust.

To investigate the lumbar spine biomechanical responses and chest injury risk of reclined occupants in a 50% overlap MPDB crash， three sets of simulations are conducted using the THUMS finite element human body model in accordance with the 2024 version of the C-NCAP protocol. The results indicate that foot-interior footrest contact significantly influences the probability of submarining. When a “belt-on-neck” phenomenon occurs for the reclined occupant during the crash， maximum chest compression alone may not adequately reflect the chest injury risk. The axial compressive force on the lumbar vertebrae increases progressively with increasing seatback recline angle. Under different postures， a correspondence exists between the lumbar bending center of rotation and the vertebra sustaining the maximum bending moment. The overall lumbar spine of the reclined occupant exhibits a pronounced S-shaped lateral bending motion and is subjected to combined compression， bending， and lateral bending loads.

This paper addresses the distributed assembly job-shop scheduling problem （DAJSP） by establishing a mixed-integer linear programming （MILP） model to minimize the makespan， and proposes a hybrid genetic-tabu search algorithm with a greedy strategy. The DAJSP is decomposed into two subproblems： job processing and product assembly. For the processing stage， a heuristic algorithm is applied； for the assembly stage， an efficient greedy algorithm is designed to provide a fast and effective scheduling solution. Experimental results on 40 instances show that， compared with a monolithic optimization method， the proposed decomposition strategy reduces computational time and improves solution quality， thereby validating its effectiveness and superiority.

A multi-objective batch scheduling model integrating carbon emissions， makespan， and manufacturing cost is established to accommodate the flexible production mode commonly adopted by small and medium-sized manufacturing enterprises. An improved non-dominated sorting genetic algorithm （NSGA-Ⅱ） was proposed to solve the model. To enhance scheduling flexibility and solution efficiency， a four-layer chromosome encoding scheme was designed. A maximum batch number search method was developed to determine a reasonable batching range. Furthermore， an improved precedence operation crossover （POX） operator was introduced to prevent illegal solutions arising from operation overlap， while an adaptive mutation operator was employed to dynamically adjust the mutation probability and strengthen global search capability. The effectiveness and applicability of the proposed algorithm were validated through both benchmark instances and real-world industrial case studies.

This paper addresses the integrated scheduling problem of heterogeneous transportation resources—comprising an overhead crane and automated guided vehicles—in flexible job shops. A reinforcement learning-based multi-objective evolutionary algorithm is proposed to minimize both makespan and total energy consumption. Three hybrid initialization strategies are designed to enhance population quality and diversity. Subsequently， a reinforcement learning mechanism is introduced for adaptive control of genetic operator parameters， and a critical-path-based hybrid neighborhood structure is constructed to simultaneously optimize makespan and energy consumption， thereby guiding efficient exploration of high-quality solutions. Finally， ablation and comparative experiments validate the effectiveness and stability of the proposed algorithm in solving the heterogeneous-transportation-resource flexible job shop scheduling problem.

This paper proposes a two-stage adaptive competitive reconfiguration algorithm （TACRA） for the dynamic flexible job shop scheduling problem with transportation resources. In the initial phase， TACRA operates in a static environment； once a machine breakdown occurs， it switches to a rescheduling phase. Deletion and reconstruction operators are designed to enhance the algorithm's exploration and exploitation capabilities， and an adaptive selection mechanism is introduced based on the historical performance of these operators. Experimental results on 15 test instances show that TACRA achieves the optimal inverted generational distance in eleven cases， the optimal hypervolume in fifteen cases， and the optimal fitness metric in fifteen cases.

To improve the compensation performance of feedback-feedforward control systems for machine tools， this paper proposes an orthogonal feedback-feedforward composite control system based on generalized cross-product， in which the closed-loop transfer function is Φ₁ and the feedforward controller is 1/Φ₁. Unit step response tests demonstrate that the proposed system exhibits significantly higher robustness than the optimal second-order system. Monte Carlo simulations and machining experiments further reveal that the machining errors of the composite system are substantially lower than those of the optimal second-order system. These results indicate that orthogonal feedback enhances closed-loop robustness， while feedforward control improves steady-state performance. The combination of the two control strategies effectively enhances the machining accuracy of machine tools.

This paper proposes a knowledge-driven iterative greedy （KDIG） algorithm for the distributed assembly hybrid flow shop scheduling problem with dual resource constraints. The algorithm adopts a knowledge-based NEH （Nawaz-Enscore-Ham） initialization strategy to generate the initial solution. Based on the problem characteristics， four local search operators are designed. Combined with a Q-learning mechanism， these operators enable individuals to dynamically select the optimal local search operator during iterative updates， thereby significantly improving search efficiency. Experimental results on 81 large-scale instances， comparing the KDIG algorithm with five other mainstream algorithms， demonstrate that the proposed KDIG algorithm outperforms all benchmark algorithms.

To address uncertain completion times and low customer satisfaction in discrete manufacturing with single-piece， small-batch， and highly customized production， this paper establishes a fuzzy flexible job-shop scheduling model that minimizes makespan， maximizes average customer satisfaction， and maximizes the minimum customer satisfaction. An improved multi-objective evolutionary algorithm is proposed， which incorporates an adaptive crossover-mutation strategy based on population distribution to balance global and local search， and a knowledge-driven neighborhood search strategy that exploits the structure of customer satisfaction. By adjusting critical and non-critical blocks on the critical path， the algorithm reduces makespan and enhances customer satisfaction. Comparative results on multiple benchmark instances confirm the effectiveness of the proposed model and algorithm.

To address the adaptive modification of mechanical products， this paper proposes a method for identifying influential parts under multi-source adaptive changes. Based on change propagation relationships among parts， a product change propagation network is constructed to identify coupled nodes and paths in multi-source change propagation. Vector cosine analysis is applied to calculate the relevance of attribute changes in parts affected by different change sources， and the direct propagation strength of coupled paths from front-end to back-end nodes is computed. Considering the coupling effects among propagation paths， the influence of nodes is quantified by change propagation intensity. The feasibility and validity of the proposed method are demonstrated through a case study on a truck hydraulic tailgate device.

This paper addresses the order acceptance and scheduling problem under a combinatorial auction mechanism on a manufacturing platform. Taking the set of subtask bidding schemes submitted by manufacturing resources as input， and considering both the process correlation constraints among subtasks and the scheduling constraints within each resource， a mixed-integer linear programming model is formulated to maximize platform revenue and user satisfaction. An adaptive large neighborhood search algorithm is developed， featuring a three-layer chromosome encoding structure， a neighborhood correlation removal operator， a repair operator based on the Cartesian product search strategy， and a repair strategy for resource scheduling feasibility. The effectiveness of the proposed model and algorithm is validated through artificial instances and a real-world case of automotive fuel tank manufacturing. The results show that， compared with two rule-based methods currently used by the platform， the proposed method increases platform revenue by 9.83% and 61.06%， and improves user satisfaction by 29.23% and 61.54%， respectively.

To mitigate instability and out-of-roundness in ring rolling processes， this paper presents an adaptive control approach for roll motion trajectory using ring offset and roundness error as feedback. A finite element model was established to compute these two parameters. Through secondary development of existing software， separate control strategies for guide roll， mandrel， and axial roll were designed， together with an integrated control scheme combining all three rolls. Experimental results demonstrate that the integrated method overcomes the limitations of individual strategies， substantially reduces both offset and roundness error， and effectively improves rolling stability.

To clarify the deformation mechanism of aluminum honeycomb sandwich panels during thermal curing， the effects of structural stiffness， interfacial friction， and foaming adhesive are investigated through theoretical analysis， finite element simulation， and experimental testing. Based on the structural characteristics and manufacturing process， a geometric model and a curing theoretical model are established. Combined with Fourier's heat conduction law and the energy balance principle， the thermal and chemical reactions of the adhesive film are described， and a path-dependent model is adopted to analyze curing deformation. The simulation results agree well with the experimental data， verifying the accuracy of the proposed model. Results indicate that structural stiffness has a significant negative correlation with deformation magnitude； reducing interfacial friction decreases the deformation； and the local maximum deformation around embedded parts increases with the rise of the expansion coefficient of the foaming adhesive， while a lower expansion coefficient helps maintain surface flatness.

To address the challenges of severe wheel-rail wear in high-speed trains and the substantial discrepancies among existing wear coefficients for evaluating wear characteristics， this paper proposes a method for determining the wheel-rail wear coefficient that accounts for wheel tread slope. Based on Archard's wear model and incorporating wheel-rail material properties， the proposed method is developed through bench tests and numerical simulations. Experimental measurements and simulation verification demonstrate that， under identical conditions of load， friction coefficient， and relative sliding velocity， the consideration of tread slope leads to higher contact stress and maximum wear rate， smaller contact patch area and wear coefficient， and a nonlinearly increasing reduction rate of the contact patch area.

This paper addresses the problem of excessive pressure surge and speed oscillation during the start-up phase of a hydraulic excavator， caused by lag in pump-valve matching. A hybrid speed regulation method is proposed based on dynamic parameter calibration and pump-valve dynamic matching. A Lagrangian dynamic model with a continuously differentiable friction term is constructed. The inertia parameters and friction model coefficients are identified via excitation trajectory optimization and the leasts-quares method. A neural-network-based pressure feedback controller is designed to achieve dynamic matching between the main pump displacement and the unloading valve opening. Experimental results show that the proposed method reduces the start-up pressure surge by 19.12% to 35.43% without compromising speed response， and significantly enhances start-up stability and maneuverability.

To mitigate the efficiency degradation of thermal power units under deep peak regulation， this paper investigates a variable through-flow turbine technology with multi-source steam admission. The steam flow path was reconstructed to enable dynamic adjustment of the through-flow area， establishing a multi-design-point through-flow configuration that maintains high-efficiency operation across the full load range. The high-pressure cylinder incorporates a three-stage variable enthalpy-drop through-flow structure， allowing flexible switching between series and parallel-series modes to sustain elevated steam parameters under peak regulation conditions. Thermodynamic system modeling and economic analysis demonstrate that the proposed technology is advanced， feasible， and promising for engineering applications.

Based on coupled 1D and 3D simulations， a multi-node heat transfer model for the vehicle cabin was established. Thermophysical properties of cabin materials were measured， and thermal comfort tests were conducted in an environmental chamber. The simulation accuracy was then validated. The results show that the multi-node model predicts occupant head temperature with higher accuracy （error within 5%） than the traditional single-node model， and meets engineering requirements. Evaluation of air conditioning outlet designs for an MPV model indicates that the proposed method can effectively guide the development and optimization of cabin thermal comfort performance.