In order to investigate the influence of structural parameters of hydraulic pipeline on vibration characteristics,a finite element model of hydraulic pipeline with different geometric structure parameters was established,and the influence of different structural materials,different bending radius and different bending angles on the vibration characteristics of hydraulic pipeline was studied based on fluid-structure coupling.It was found that under the condition of certain piping structure,the inherent frequency of titanium alloy Ti-3Al-2.5V was the smallest and the deformation of stainless steel 1Cr18Ni9Ti is the smallest. With a fixed total length of hydraulic pipeline,an increase in the bending radius raises intrinsic frequency and decreases deformation,while an increase in the bending internal angle leads to a decrease in intrinsic frequency but decreases deformation. In the case of a fixed length of straight pipe section,an increase in the bending radius leads to a decrease in the intrinsic frequency and results in an increase in the amount of deformation,while an increase in the bending internal angle leads to a decrease in intrinsic frequency but decreases the amount of deformation.

There are many restrictions and unreasonable design schemes in the layout of multi-row workshop facilities.Aiming at the characteristics of multi-row plant layout problem,a shop layout optimization model was proposed,and the genetic algorithm variation strategy was used to improve the particle swarm optimization algorithm,which made the design scheme more reasonable. Firstly,the workshop equipment is coordinate,and the objective function with minimum total logistics cost is established.After comprehensive analysis,quantified the logistics relationship between each process. Then,according to non-logistics factors such as fire access,equipment area and distance,the idea of hierarchical punishment is used to establish various constraints. In addition,by using the initial population of random survival,the particle swarm optimization algorithm is applied to search for the optimal workshop layout. Finally,a case study shows how the model works. Compared with Systematic Layout Planning (SLP) and Genetic Algorithm (GA),this method can get a better solution for multi-row equipment layout problems.

In order to improve customer recognition of the product, aiming at the problem that customer′s perceptual demand preference and difficulty in obtaining customer demand are not fully considered in the traditional conceptual design stage, a method of customer emotional perceptual needs recognition and mapping to design features by online reviews of product data is proposed. Firstly, based on morphological analysis, try to find all design features from products by building Energy Material Signal (EMS) model, and core features that users were concerned about were screened out from all the design features based on online review data. Secondly, adjectives are extracted from the review data to form a perceptual word pair. The perceptual word is used as the head word to obtain the non head word using word vector technology. Based on the emotional dictionary, the perceptual score of the product′s perceptual word pair is calculated using the given method. Thirdly, based on the quantitative theory Ⅰ, the mapping model between perceptual evaluation and design elements is established, which provides a basis for improving product design. Finally, the feasibility and effectiveness of the proposed method are verified by an example.

An improved ant colony path planning algorithm was proposed to solve the problems such as blindness,slow convergence,long path and many break points.Firstly,by improving the mathematical model of heuristic factor,the selection probability of roulette in eight directions is limited,so as to reduce the number of iterations.Then,the adaptive updating influence factor is established to further improve the routing method and the robustness of the algorithm by monitoring the location of target points in real time. Finally,through the path secondary optimization,the shortest path formed by the ant colony algorithm is further eliminated redundant nodes,and the shortest path is further optimized,so as to smooth path,reduce the break point of the path,reduce the path length,and improve the efficiency of the material transport trolley.Through the simulation experiment of different proportions of obstacles in the grid environment map,it is verified that the proposed improved ant colony algorithm has faster iteration rate,stronger searching ability,better robustness and shorter path.

In order to meet the diverse needs of robot work trajectories, a new multi-objective sparrow search algorithm with optimization objectives of time,energy and impact was proposed to find the optimal trajectory of robots.Firstly,the joint space trajectory was constructed by the 7-degree B-spline interpolation method,and the multi-objective synthetic optimal trajectory planning model was established.Secondly,the sparrow search algorithm was improved by constraint violation computing,non-dominated ranking and elitist retention to solve the multi-objective trajectory planning problem.Finally,the data in the random population was deleted by the bag-tree classification algorithm,and a five-layer BP neural network was built to replace and improve the numerical calculation part of the fitness value in the multi-objective sparrow search algorithm,thus improving the efficiency of the algorithm.Through the MATLAB simulation and experiment,the feasibility and validity of the algorithm were demonstrated.

Scheme evaluation is one of the key links of product service system design.The rationality of evaluation results is very important for the development of enterprises.Based on the least sum of squares method,cloud-TOPSIS-grey correlation analysis model is constructed to evaluate the product service system scheme.Cloud model and relative preference relation were introduced to quantify discrete indexes to solve the problems of fuzziness and randomness in expert evaluation.The positive and negative ideal grey association coefficient matrices were determined by TOPSIS grey association analysis method,and the positive and negative ideal grey association coefficient matrices were established by integrating index weights and positive and negative ideal grey association coefficient matrices,taking into account the position proximity and shape similarity of corresponding geometric scheme sequence curves.The least sum of squares method was used to solve the grey correlation coefficient and determine the scheme ranking.Taking the project evaluation of tractor product service system in an agricultural equipment enterprise as an example,the science and feasibility of the proposed model were verified.

Aiming at the dynamic hysteresis nonlinear problem of automotive electronic throttle system,a design method of fuzzy neural network PID controller was proposed.The controller combines the particle swarm optimization algorithm which adjusts the inertia weight dynamically with BP algorithm to optimize the parameters of fuzzy neural network,and corrects the shortcomings of slow convergence and easy to fall into the local minimum in the optimization process of fuzzy neural network.Using the self-learning ability of fuzzy neural network,the PID controller parameters were adjusted.The simulation results show that the optimized fuzzy neural network PID controller has a significant improvement in response time,overshoot and oscillation times compared with the fuzzy PID controller.After the disturbance signal was applied to simulate the airflow disturbance condition,the controller shows good anti-interference performance.In the electronic throttle response experiment,the throttle response curve has a slight overshoot,but the steady state error is small,which indicates that the electronic throttle has good dynamic response characteristics under this control method.

For power split hybrid electric vehicles,firstly,based on the design parameters and power performance requirements of the entire vehicle,parameter matching was performed on the transmission system components such as the motor,power battery pack,engine,main reducer,and planetary gear,and AVL-Cruise software was used to establish the target vehicle model. Secondly,on the premise of meeting the power performance goals,a joint simulation model was built using AVL-Cruise and Isight software,using particle swarm optimization algorithm and multi island genetic algorithm respectively.The planetary gear characteristic parameters and main reducer transmission ratio were used as optimization parameters,and the weighted sum of fuel and electricity consumption per 100 km was used as the optimization objective for optimization research.The calculation results of the two optimization algorithms were compared and analyzed to obtain the optimal structural parameters that balance power and economy,which provide a theoretical basis for studying the power split hybrid electric vehicle transmission system.

In order to optimize the milling cutter grinding process,taking the grinding force in the grinding process of the milling cutter as the object,the grinding solution model of the grinding cutter spiral groove grinding process was established by finding the expression of the contact line between the grinding wheel and the milling cutter,and the influence of the grinding force on the grinding quality of the milling cutter was discussed.Taking the main factors affecting the grinding force,milling cutter feed speed and grinding depth,two optimization schemes were proposed—equal feed speed and variable feed speed. By adjusting the feed speed and grinding depth,the grinding force was reduced under the condition that the grinding efficiency remains unchanged,thereby reducing the shape variable caused by the grinding force in the grinding process of the spiral groove.The 3D model of the milling cutter was established and imported into ANSYS for grinding process simulation to obtain specific shape variables to verify the accuracy of the grinding optimization scheme.

Aiming at the problem of low processing efficiency caused by unreasonable process route compilation of a complex shaft, starting from the existing process route optimization scheme, the processing characteristics were divided in detail with the processing accuracy as the processing element node,and a reasonable processing element coding was constructed. Based on the actual clamping in production,the process constraint relationship was grouped and analyzed to obtain a better constraint matrix expression.Differential evolution algorithm was used to reasonably design differential mutation,crossover,sequence validity check and adjustment algorithm with the minimum replacement of machine tools,cutting tools and clamping as objective function.The comparison shows that the optimization scheme has reduced the number of tool and clamping replacements compared with the pre-optimization scheme and the process constraint non-grouping scheme.

To study the cutting performance of the internal structure asymmetric microtexture on the tool and its influence on the derivative cutting,based on the Johnson-Cook model,the finite element was used to simulate and analyze the cutting 45# steel of Non-woven Tool (NT),Forward asymmetric microtexture Tool (FT),and Reverse asymmetric microtexture Tool (RT),and the corresponding cutting experiments were carried out,and the cutting force,cutting temperature,chip morphology,derivative cutting and tool wear of NT, FT, and RT were compared and analyzed.It is found that the cutting performance of internal structure asymmetric microtexture tools is better than that of non-woven tools,but the existence of texture will also induce derivative cutting phenomenon,which will have a certain negative impact on cutting performance and aggravate tool wear. The average main cutting forces of NT,RT,and FT are 601.9,196.4 and 419.1 N,respectively,and the cutting force of microtexture tools is reduced by about 30.4 %~67.4 % compared with non-woven tools. The results show that the introduction of microtexture is conducive to reducing the shear angle,reducing the cutting force,improving the temperature distribution of the tooltip,reducing the adhesion of chips,and improving the cutting stability. However,the microtexture-induced derivative cutting will aggravate the wear of the cutter surface near the microtexture,which will affect the cutting temperature to a certain extent,increase the cutting force,and the coupling effect of microtexture and derivative cutting makes the cutting performance of RT greater than that of FT.

As a product of Electrical Discharge Machining (EDM),debris particles can not only reflect the state of the discharge gap but also help to understand the mechanism of material removal.Therefore,debris particles have always been a research hotspot in the field of EDM. As a novel,high-efficiency EDM method,Short Electric Arc Milling (SEAM) has problems such as poor chip removal and frequent short cir cuits.Based on the above problems,debris particles were used as the research object. Firstly,the microscopic morphological characteristics of the debris particles were studied,and the effects of different workpiece polarity,electrode materials,voltage,duty cycle and frequency on particle size distribution of the debris particles were discussed from the perspective of discharge energy. The influence of particle size distribution of the debris particles on the processing quality was further summarized. Guidance for revealing the material erosion mechanism,improving the gap chip removal performance and enhancing the stability of the machining process was provided.

Aiming at the problem of insufficient monitoring of the production process in tobacco logistics automation workshop,which led to low productivity and difficult control,a production process-oriented visualization and monitoring method was researched,which built a digital twin workshop functional model with multi-level mapping rules from various aspects such as physical equipment and workshop operation process.On this basis,a twin data mapping logic model was established,a unified architecture of real-time data collection and transmission based on S7 protocol was built,the twin model was driven to run synchronously,and a digital workshop monitoring system was designed and developed.Finally,the system is validated by carrying out examples,and the results show that the system can effectively integrate and fuse the workshop production data,and realize the effective monitoring of the whole process of workshop operation.

Aiming at the current problems that logistics enterprises cannot predict the damage situation of cargo packaging schemes during transportation,a cloud service system for cargo transportation damage analysis has been developed to provide logistics enterprises with reasonable and safe packaging layout schemes. Firstly,combined with the ANSYS APDL command stream,a static analysis program,a modal analysis program,and a random vibration analysis program oriented to the packing scheme are compiled. Then,through Three.js three-dimensional visualization technology,the visualization of packing layout scheme and the visualization of finite element analysis results are realized. Finally,all the functions are integrated into the system,so that the transportation personnel of logistics enterprises only need to upload the corresponding packing layout result data. Through the simulation analysis of the system,they can predict the damage situation of the packing layout. An example is used to demonstrate the system functions,proving that the system can effectively guide the transportation personnel to complete the safe packing layout.

In the process of visual inspection,aiming at the problem of poor fitting accuracy of multi-incomplete and multi-noise circle,a circle fitting algorithm based on RANSAC was proposed.Firstly,the edge of the image is detected,and the Canny algorithm based on Scharr operator can obtain high quality edge distribution map. The Hough gradient method is used to roughly locate the target arc in the edge distribution map,and the point set containing the target arc is extracted. The extracted point set contains a large number of irrelevant edge pixels. RANSAC algorithm is used to eliminate the abnormal data in the point set,and the circle point set to be fitted is composed of effective sample data. Finally,LIN algorithm is used to fit the point set of the circle to be fitted,and the final fitting result is obtained.The proposed algorithm performs well in the anti-jamming experiments of salt and pepper noise and curve noise,and has strong anti-jamming ability. In addition,this method has been successfully applied to the circle fitting of threaded holes,and the accuracy and real-time performance can meet the requirements of actual production.

For the shortcomings of the upper limb exoskeleton interconnection,low intelligence level,large ontology size,and high manufacturing costs,a new desktop three degrees of freedom end traction upper limb exoskeleton robot with simple structure,low price,and mass customization was designed.First of all,according to the patient′s rehabilitation function requirements,the overall structure design was carried out in combination with the size of the upper limbs of the human body. Secondly, the finite element simulation software Ansys Workbench to perform static simulation on key parts,optimize the stiffness,strength and size of the parts,so as to verify the rationality of the material and size of the exoskeleton. Then the Denavit-Hartenberg (D-H) parameter method was used to establish a coordinate system,derives the orthodox motion and solve the work space of the end position,and draws the work space point cloud chart. Finally,the kinematics analysis of the end traction upper limb exoskeleton was carried out using Adams software. The results show that the structure design of the end traction upper limb exoskeleton is reasonable and can meet the rehabilitation training requirements of patients with upper limb motor dysfunction.

To address the problem of low diagnostic accuracy due to the difficulty in extracting fault features of rolling bearings in electric motors,a feature extraction method based on Variational Modal Decomposition (VMD) combined with Wavelet Packet Information Entropy (WPIE) is proposed. A Support Vector Machine (SVM) optimized by Golden Jackal Optimization (GJO) is used for the fault diagnosis of motor bearings. Firstly,the collected signal is decomposed by VMD and the optimal eigenmode component Intrinsic Mode Function (IMF) is filtered based on the local minimal envelope entropy;secondly,the wavelet packet is decomposed again and the information entropy is extracted as the feature vector matrix;finally,the penalty and kernel parameters in the support vector machine are optimally selected by the GJO algorithm,and the GJO-SVM fault diagnosis model is established, the feature vector matrix is input into Golden Jackal Optimizes algorithm the Support Vector Machine (GJO-SVM) for fault diagnosis. The VMD combined with wavelet packet information entropy feature extraction is compared with VMD combined with approximate entropy feature extraction,and the experimental results show that the accuracy of VMD combined with wavelet information entropy feature extraction is improved by 2.5 %,and its feature extraction is more superior. The experimental results show that the average accuracy of GJO-SVM reaches 99.16 %,which is 2.5 % and 3.61 % higher than that of PSO-SVM and FOA-SVM respectively. GJO-SVM can extract and diagnose bearing faults more effectively.

Aiming at the problem of difficulty in detecting local occlusion defects at the side wall of narrow gap GMAW,an online detection method for occlusion defects based on arc acoustic signal feature extraction and processing was proposed. By analyzing the arc morphology and arc acoustic signal characteristics of normal,critical occlusion and occlusion,it was confirmed that the arc morphology change caused by the side wall of the groove was an important factor affecting the change of arc acoustic signal. On this basis,the time-frequency analysis of wavelet packets was used,and the standard deviation between feature classes was introduced as an evaluation index to determine the sensitive features that can effectively identify the three welding states.Sigmoid support vector machine and five-fold cross-validation were used to establish a prediction model,and the experimental results show that the model can better realize the prediction classification of three welding states,and the recognition accuracy reaches 96.0 %.

To realize the real-time energy-saving mining of intelligent electric shovels, a multi-objective optimal mining trajectory planning method was put forward for intelligent electric shovels based on Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ). Firstly, Lagrange's equations were used to establish the dynamic model of a working device for an intelligent electric shovel. Then, the mining trajectory was interpolated by adopting higher-order polynomials. Additionally, the issue of mining trajectory optimization was transformed into a polynomial coefficient optimization problem. Finally, minimizing the mining time and energy consumption per unit volume of material was taken as the optimization objective. By taking the motor performance and geometric conditions during the mining process as constraints, and utilizing the multi-objective optimization platform PlatEMO, NSGA-Ⅱ was adopted as the multi-objective optimization algorithm. The optimal solution set of multi-objective optimization Pareto was acquired by specifying the objective function and constraint function of the problem to be optimized. The weights were set in accordance with decision preference and the optimal solution was obtained by employing the TOPSIS method. Given this, the results of multi-objective optimal mining trajectory planning were acquired. From the results, the optimized mining trajectory was found to be able to satisfy the mining requirements of real-time energy saving.

The wall-following algorithm is a local path planning algorithm,and its global convergence is often better than other traditional methods.In order to promote the research of wall-following algorithm in the field of path planning,the latest progress and research results of mobile robots using wall-following algorithm to plan paths were reviewed.According to the obstacle avoidance strategy and implementation of wall-following algorithm,wall-following algorithm was divided into the wall-following algorithm based on the target direction angle,the wall-following algorithm based on M-Line,the wall-following algorithm based on the optimal obstacle avoidance direction,the wall-following algorithm based on historical information and the wall-following algorithm based on multi algorithm fusion,and the advantages and disadvantages of various algorithms were summarized.However,in the actual environment,due to the idealization of the simulation environment,the rigidity of the behavior cooperation mechanism,and the noise of sensors,there is a big gap between the wall-following algorithm in practical application and the theoretical results.Considering that there are still many shortcomings in the existing algorithms,the future development trend of the wall-following algorithm was prospected from three aspects,namely developing a wall-algorithm suitable for complex real environments,exploring a more efficient and reasonable multi-behavior switching mechanism and reducing the impact of sensor noise in real environments.