Focusing on the service life of the fracturing pump head body,a key component of oil and gas exploitation,and laser shock strengthening technology was used to treat the surface of the pump head material 30CrNi2MoVA,and the influence of different spot overlap rates (30 %,50 % and 70 %)on the residual stress distribution and mechanical properties of the material surface was studied by combining numerical simulation and experimental verification. The stress distribution of the inner cavity of the pump head body before and after laser shock strengthening was analyzed by simulation method,and the fatigue life was calculated by combining the S-N curve of the material. The results show that the residual compressive stress values on the surface measured by X-Ray Diffraction (XRD) test at 30 %,50 % and 70 % spot overlap rates were -690,-666 and -567 MPa,respectively,and the residual compressive stress and the depth of the affected layer decrease with the increase of the spot overlap rate.Under the condition of 30 % spot overlap rate,the depth of the residual compressive stress affected layer was 1.580 mm,which was significantly higher than that under other conditions,and the error between the simulation results and the test was 6.5 %,6.2 % and 8.8 %,respectively,indicating that the numerical simulation results have high reliability. At the same time,the mechanical properties of the material were most significantly improved by the 50 % spot overlap rate,and the elongation strength and tensile strength of the material were increased by 5.3 % and 3.6 %,respectively. The maximum equivalent stress value of the inner cavity of the pump head body was reduced from 599 MPa before laser shock strengthening to 505 MPa after treatment,and the fatigue life increased from 123 651 times before treatment to 398 107 times. This study provides an important theoretical basis and process reference for the application of laser shock strengthening technology in the field of fracturing equipment,and has positive significance for improving the service life of the pump head body.

In the actual industrial production process,due to the limited resources of Automate Guided Vehicles (AGVs), the integrated problem FJSP-AGV comsidering the constraint of alimited number of AGVs in the Flexible Job-shop Scheduling Problem (FJSP) has significant research value. Traditional evolutionary algorithms are easy to fall into local optimum and are not suitable for solving this scheduling problem with high complexity.In light of the aforementioned challenges, it initially established a mathematical model for FJSP-AGV and subsequently proposed an improved genetic algorithm guided by heuristic rules. The algorithm utilized various crossover and mutation methods to evolve the population for different coding segments.Simultaneously,it adjusted parameters adaptively during the evolutionary process and guided mutations through heuristic rules for local search,thereby enhancing the algorithm′s capability to escape local optima and consequently minimize the maximum completion time of the system. Comparison and analysis with other advanced algorithms on two small and medium-sized datasets demonstrated that the algorithm proposed yielded the most comprehensive solving effect.

To replace the traditional Computer-Aided Design (CAD)production mode which mainly relies on two-dimensional engineering drawings and supplemented by three-dimensional models, a Model-Based Definition (MBD) technology was proposed to improve the production efficiency of cabin products. By analyzing the characteristics of cabin products and the application of CAD technology in production,a digital definition framework based on MBD for cabin products was developed. In which,a SolidWorks 3D design software platform and typical cabin products were combined as examples,while the MBD dataset representation standards for cabin models were expressed on parts,subassemblies,and final assemblies. Finally,proposed cabin products MBD-based digital definition and management system was applied in real industry,which developed on Application Programming Interface (API) call of SolidWorks by VB.NET.

Addressing the search matching and service composition issues between manufacturing capabilities and task requirements in the context of intelligent manufacturing resource service sharing in cloud computing environments,this study presents a cloud manufacturing capability demand model and matching strategy based on semantic similarity and an improved Particle Swarm Optimization (PSO) algorithm is presented. First,building upon the proposed cloud manufacturing capability demand model,the concept of domain ontology tree is introduced to calculate concept similarity,sentence similarity,and numerical similarity,thereby achieving intelligent service search for cloud manufacturing capability demands based on semantic similarity. Subsequently,addressing the issue of service composition for cloud manufacturing capabilities,based on analyzing the Quality of Service (QoS) attributes of manufacturing capabilities, a service composition method based on an improved PSO algorithm is proposed using the Analytic Hierarchy Process (AHP) to normalize and sum various attributes. Finally,experimental comparisons demonstrate the superiority of the proposed approach over existing methods,leading to the realization of an intelligent matching prototype system for cloud manufacturing capability demands.

Aiming at the problems of low material distribution efficiency, confusing coding and identification, and low degree of visualisation in special transformer manufacturing workshop, on the basis of analysing the transformer manufacturing process and logistics mode, a workshop material identification and coding system based on identification resolution is established, and the collection and modelling method of material information in the transformer workshop is researched. A framework of cooperative distribution system for workshop materials based on digital twin is proposed, and a digital twin scheduling model for material distribution with the goal of cost minimisation is constructed. Finally, a collaborative intelligent material distribution digital twin prototype system for special transformer manufacturing workshop is developed in combination with the actual production process, which realises the intelligent distribution and management of materials, and verifies the reasonableness and effectiveness of the method.

To address the issues of long optimal paths and slow convergence speed in traditional ant colony algorithm for path planning applications,a mobile robot path planning method based on the sparrow search-ant colony algorithm is proposed.In the sparrow search-ant colony algorithm,the sparrow search algorithm is first used to generate a suboptimal path which is then used to establish the initial pheromone distribution for the ant colony algorithm. Next, in the calculation of state transition probability, the heuristic function of the A^* algorithm,a bending constraint factor,and a distance weight coefficient are introduced to make the state transition probability selected by the ant colony algorithm optimal at each node,thereby shortening the optimal path. Finally,in the pheromone update strategy,an adaptive pheromone update strategy based on iteration,angle factors,and a reward-punishment mechanism is employed to accelerate the convergence speed of the ant colony algorithm. According to the simulation results,the suggested method could produce high-quality optimal solutions while simultaneously decreasing the number of turns and increasing convergence speed when compared to alternative approaches. The suggested method has further confirmed its stability and practicability in complex contexts by demonstrating notable improvements in path smoothness and robustness to adapt to complex maps.

In order to improve the efficiency of welding robotic arm in different obstacle environments,a multi-strategy improved particle swarm algorithm for obstacle avoidance trajectory planning was proposed. The first three joints of the robotic arm were interpolated using a 6th degree polynomial function to obtain the motion trajectory. The inertia weights and learning factors of the particle swarm optimization algorithm were dynamically adjusted to balance the global and local search ability of the algorithm;the dynamic lens imaging reverse learning strategy was introduced,and the restart strategy and greedy algorithm were integrated to enhance the ability of the algorithm to jump out of the local optimum.The IRT120 robotic arm was taken as the research object and simulated by MATLAB software. The simulation results show that the improved particle swarm algorithm has significant improvements in convergence speed and optimization accuracy,and the motion trajectory is smooth without sudden changes.

Aiming at the problem of redundancy resolution and fault-tolerant trajectory control of robotic manipulators with joint rotational speed constraints,a Zeroing Neural Network (ZNN) control architecture based on Quadratic Programming (QP) embedded with performance constraints was proposed. Firstly,the resolution model of robotic manipulator redundancy with constraints (time-varying underdetermined linear system) was constructed in the velocity layer. Furthermore,a nonlinear reversible mapping was introduced to transform the constrained system state variables into unconstrained variables,and at the same time,a system error form including the joint velocity term and the end position deviation term was constructed,and the prescribed performance constraints (the upper and lower bounds of the end tracking error of robotic manipulator) were embedded into the system error through nonlinear transformation,and then a QP problem model for redundant resolution of robotic manipulator was constructed,together with a ZNN-based QP problem solving architecture proposed. Then,the global stability and convergence of the proposed control architecture were analyzed by combining convex optimization theory and Lyapunov stability theory. Finally,to solve the problem of fault-tolerant trajectory control of KUKA LBR IIWA 14 R820 robotic manipulator,the performance of the proposed control architecture was verified by simulation analysis and physical experiments. The simulation results show that the proposed QP-ZNN solution architecture can drive the trajectory tracking error of the robotic manipulator to converge to 10^-5 m for different forms of expected trajectories,even if there is initial position deviation of the end-effector,and the embedded performance constraints can greatly improve the performance of the proposed control architecture. Compared with classical and varying parameter ZNN in the existing literature,the control accuracy of the proposed QP-ZNN control architecture with embedded performance constraints can be significantly improved. The results of physical experiments further show that the proposed QP-ZNN control architecture with embedded performance constraints can still drive the trajectory tracking error of the end-effector of the manipulator to converge to 10^-4 m for different types of expected trajectories,even if there are multi-joint faults in the robotic manipulator.

In order to realise laser cladding repair of mining scrapers,for the problem of maintaining high efficiency and stability of laser cladding robots during operation,the trajectory planning method in the joint space was studied according to the kinematic characteristics of the robots,and a hybrid genetic particle swarm optimization algorithm was proposed. Based on the particle swarm optimization algorithm,the method introduced the crossover and mutation behaviours in the genetic algorithm by constructing adaptive inertia weights and dynamic learning factors,and fitted the trajectory into the joint space of the robot using 3-5-3 polynomial interpolation. The hybrid genetic particle swarm optimization algorithm,chaotic particle swarm optimization algorithm and standard particle swarm optimization algorithm were compared,and after obtaining the optimal interpolation time,simulation was carried out in MATLAB software,and the change process of position,velocity and acceleration of each joint over time was kept in the ideal continuity interval,which realised the time-optimal motion planning in the joint space,and the optimal time was reduced from 5.058 0 s of the standard particle swarm optimization algorithm to 4.633 0 s,and the robotic arm trajectory planning time was shortened by 8.4 %,which verified the feasibility of the proposed algorithm in the trajectory planning of the laser cladding robot for repairing the mining scraper.

In order to achieve the precise optimization of the front floor composite layup of the body-in-white as well as the level of structural lightweighting,a multilevel combination optimization and design method based on the body-in-white is proposed,which adopts a partitioned carbon fiber composite flooring strategy to individually design the layup of the front floor of the carbon fiber composite material. A finite element model of an electric vehicle body-in-white is established to verify the correctness of the model. Through the mechanical property test of carbon fiber composite materials,the mechanical parameters of the T700/WP-R2300 resin-based fiber-reinforced materials are determined. The super-layer free size optimisation,ply block cutting and ply block size optimisation methods are used to determine the ply thickness,ply block shape and ply layer number of the carbon fiber composite front floor,and the ply lay-up sequence is adjusted through ply sequence optimisation,and is placed in the body-in-white to carry out simulation verification. The results show that the static bending stiffness and static torsional stiffness of the carbon fiber composite front floor after paving design are 198.69 % and 153.59 % higher than those of the original steel floor,and the effect of lightweighting is remarkable. The optimized carbon fiber composite front floor reduces weight by 45.33 %. At the same time,the static bending and torsional stiffness of the body-in-white are increased by 1.82 % and 1.96 % respectively,and the first-order bending and torsional frequencies are increased by 3.05 % and 3.72 % respectively,and the static stiffness and low-order modal performance are significantly enhanced.

Aiming at the complex problem that the running stability of passenger trailer combination is affected by multiple parameters of trailer and the parameters are interrelated,a comprehensive parameter called mass distribution coefficient is analyzed. Firstly, a passenger trailer combination dynamics model is established. Combined with the impact center theory and parallel axis theorem,the mass distribution coefficient integrating trailer mass,moment of inertia, center of mass position and axle position are derived. Then a passenger trailer combination simulation model is established in the TruckSim software, and the validity of the simulation model is verified through real vehicle tests. Finally, by adjusting tongue weight and moment of inertia simulation and model car test, the relationship between the mass distribution coefficient and driving stability are investigated. The simulation and test results show that the mass distribution coefficient varies from 1.1 to 3.4 and the rear amplification coefficient varies from 0.9 to 1.5. The smaller the mass distribution coefficient is,the smaller the rear amplification coefficient of the passenger car train will be. Therefore,the mass distribution coefficient has a strong correlation with the running stability. The smaller the mass distribution coefficient,the better the running stability of the passenger trailer combination.

Based on the complex surface structure of turbine blades and the difficulty of comprehensive and uniform machining,a horizontal vibration finishing method for turbine blades was proposed. Using Discrete Element Method (DEM) the influence of container size,media filling amount,and amplitude on wear depth and particle velocity vector of blade surface was analyzed,and the process parameters were clarified. Using experiment and characterization,the changes in surface defects,surface morphology,and surface residual stress of blades before and after finishing were compared and analyzed. The results show that the blade surface is more uniform after finishing,when the container size was 80 mm×310 mm×200 mm,the media filling amount was 70 %,and the amplitude was 2 mm. The horizontal vibration finishing can effectively improve the surface roughness of blades,remove surface oxide layers of blades,and improve surface residual stress of blades.

Gait event detection is an important part of exoskeleton robot motion control and performance evaluation.In order to improve the efficiency and accuracy of gait event detection of exoskeleton robots,a wearable gait time detection system based on infrared distance sensor was proposed. Firstly,an intelligent wearable device with infrared distance sensor was developed to obtain stable distance signal. The height of the rear heel and toe gap above the ground was used to detect all six gait events in a gait cycle and convert them into effective foot posture information.Secondly,an online detection algorithm using local search window and fixed threshold was proposed.By combining this algorithm with infrared distance sensor,the minimum time delay and reduced computational load can be achieved. Finally,the proposed method was integrated into the exoskeleton robot system for experiments to detect gait events of human-robot collaborative walking. The experimental results show that when the proposed method is applied to the lower limb exoskeleton robot,all gait events of human-robot synchronous walking can be successfully detected,with an average detection error of less than 34 ms and an average detection accuracy of 99.62 %,which has good real-time performance and high detection accuracy.

Non-ferromagnetic metal materials are widely used in aerospace,shipbuilding,chemical industry and other fields due to their strong corrosion resistance and high strength. A magnetic detection technique was proposed to realize the non-destructive testing of crack defects in non-ferromagnetic metal materials. The self-developed magnetic testing equipment was used to carry out testing and research on aluminum alloy specimens and superalloy specimens,and the magnetic induction intensity signal was collected and analyzed. The results showed that the detection signal of the magnetic detection technology would show abnormal signal characteristics when detecting surface opening defects and closure defects of aluminum alloys and superalloys,and the change amount of abnormal signal was correlated with the size and buried depth of the defect at the same time,and the depth or burial depth of the defect could be quantitatively analyzed by linear fitting. The proposed magnetic detection technology could be used for the rapid detection of cracks in non-ferromagnetic metal materials,and had high reliability and effectiveness,which provided a new idea for the quantitative detection of non-ferromagnetic metal materials.

In order to improve the efficiency and accuracy of intelligent automatic spraying of soles,the trajectory curve of automatic spraying of soles was planned and obtained based on point cloud processing technology. In the pretreatment of sole point clouds,straight-through filtering and Euclidean clustering were used to obtain sole body point clouds. An adaptive voxel down-sampling method for curvature feature division was proposed to analyze the curvature characteristics of shoe sole point clouds,introducing proportional error and Softsign function to adaptively obtain the voxel size,and remove redundant points while retaining features.In order to improve the accuracy of the glue spraying curve and solve the problem of sudden change in the contour curve of edge points obtained by scanning latitude and longitude lines,the contour of the highest point of the sole was reconstructed and optimized based on principal component analysis,Non-Uniform Rational B-Spline curve (NURBS) fitting was used to obtain the sole glue spraying trajectory curve. The test results show that the optimized and reconstructed sole gule spraying trajectory curve is smoother and more in line with the physical characteristics of the high edge of the sole,meeting the need for uniform glue spraying operation.

The existing bag hanging mechanism is difficult to adapt to the high-speed production line packaging demand due to the lack of synchronization performance.A high-speed bag hanging mechanism that could realize efficient long distance synchronization bag hanging processes was proposed. Firstly,the bag hanging action was analyzed,and a high-speed bag hanging mechanism based on the cam linkage composite mechanism was designed. Subsequently,a kinematic model of the mechanism was constructed,and the key factors affecting its synchronization as well as the fixed cam parameter model required to realize the bag hanging trajectory were obtained. On this basis,the synchronization of the vacuum upper and lower suction blocks was established as the optimization objective. Combining with specific production examples,using the MATLAB optimization toolbox for high-speed bag hanging mechanism parameter optimization design,the best mechanism parameters and convex contour line were determined. Subsequently,the use of ADAMS simulation was employed to obtain the motion curve and comparison and analysis with the theoretical results calculated by MATLAB were constructed to verify the optimization design and the accuracy of the kinematics model. Finally,the actual bag-hanging test further verified the significant advantages of the designed high-speed bag hanging mechanism in improving the efficiency and qualification rate of the packaging of sanitary products,which providing effective technical support for the high-speed production line.

Discrete manufacturing commonly faces bottleneck issues that affect production efficiency. Traditional static bottleneck identification methods cannot effectively resolve the dynamic bottleneck shifts in complex remanufacturing environments. To address this, it proposes a dynamic bottleneck prediction model termed LMD-QPSO-LSTM, which integrates Local Mean Decomposition (LMD) method with Long Short-Term Memory (LSTM) networks and utilizes an enhanced Quantum Particle Swarm Optimization (QPSO) algorithm to fine-tune LSTM. Initially, machine energy consumption attributes are used to define the dynamic bottleneck index, and LMD method is employed to decompose bottleneck sequences, thereby reducing data volatility. Subsequently, an Attention Mechanism (AM) is integrated to improve the LSTM network′s learning capability, alongside the use of an enhanced QPSO algorithm to optimize LSTM parameters for optimal selection. Finally, the components of the bottleneck index are predicted, and the prediction results are reconstructed. Simulation experiments demonstrate that the dynamic bottleneck prediction method based on LMD-QPSO-LSTM effectively enhances prediction accuracy and accurately tracks changes in bottleneck positions. Compared to other models, this approach reduces the Mean Absolute Error (MAE) by at least 52.63 %, the Mean Absolute Percentage Error (MAPE) by 25.14 %, and the Root Mean Square Error (RMSE) by 45.78 %.