In order to address the limitations of limited flexibility, poor mechanical durability, and insufficient biocompatibility of traditional wearable pneumatic wrists, this paper introduces a novel structural material of phosphorus conjugated microporous polymer as a pneumatic flexible actuator. Firstly, this paper prepares phosphorus containing conjugated microporous compounds using the Sonogashira method, and then uses the prepared phosphorus containing conjugated microporous compounds as materials for pneumatic flexible actuators to obtain actual actuators. On this basis, this paper combines commonly used materials to develop a wearable conjugate material pneumatic wrist. Finally, this paper conducts a control analysis on the deformation and mechanical properties of the pneumatic wrist. The research results indicate that when the wrist joint movement angle is 90 degrees, the output torque of the wearable conjugate material pneumatic wrist under a pressure of 0.02 MPa is 0.039. This indicates that the wearable conjugate material pneumatic wrist has good safety and adaptability.
This paper uses finite element analysis and fibre optic sensing technology to dynamically monitor and analyse the stress and strain changes of materials during the removal of reinforced concrete internal support beams, revealing the impact of different removal processes on the mechanical properties of materials. First, a three-dimensional finite element model is established using ANSYS, and the material parameters of the steel bars and concrete are input. Second, fibre optic Bragg grating (FBG) sensors are placed at key locations on the support beams to collect stress and strain data, which are then processed using Python. Finally, BIM technology is used to optimise the demolition sequence, and the impact of dynamic loads on the structure is evaluated based on finite element analysis. The experimental data revealed the effects of different demolition methods on the mechanical properties of reinforced concrete support beams.
Traditional carbon fibre composite manufacturing processes, such as hand lay-up and moulding, often encounter challenges including complex geometry, low manufacturing precision, and a disconnect between design and manufacturing. Especially when dealing with complex structural designs, it is challenging to precisely control the fibre arrangement and internal structure, resulting in low production efficiency and difficulty in meeting high-performance requirements. This paper applies a 3D printing manufacturing process to provide a more efficient and precise design and manufacturing solution. The design and modelling of carbon fibre composite materials are carried out through computer-aided design (CAD) software, considering factors such as fibre arrangement, stacking order, geometry, and material thickness. The material properties are adjusted according to the requirements, and the model is converted into the STL (stereolithography) format required for 3D printing.
There is an inherent 'collaborative failure' in product recycling and remanufacturing in CSCs, which stems from the conflict between individual members pursuing personal interests maximisation and optimising overall system efficiency. To address this conflict, this paper proposes a blockchain driven collaborative innovation mechanism centred around cost-effective shared smart contracts. This paper constructs a Stackelberg game model to characterise non cooperative benchmarks and a cooperative game model to maximise system profits, and then uses a hybrid genetic simulated annealing algorithm to solve the optimal coordination strategy. The simulation results show that this mechanism increases the total profit of the supply chain from 6.37 million yuan to 7.98 million yuan, and the product recovery rate from 28.0% to 55.0%. Research has shown that this mechanism can balance individual rationality and system optimisation, solve the dilemma of 'collaborative failure', and provide executable decision support for value co creation in closed-loop supply chains.
This paper proposes a nanostructure control algorithm based on the photothermal conversion effect. First, a copper/iron-based metal-organic framework (MOF) material is prepared by solution impregnation and loaded with gold nanoparticles to enhance light absorption. The localised surface plasmon resonance (LSPR) effect is then exploited to improve photothermal conversion efficiency. Then, a temperature/light dual-sensing mechanism is established. The drift of the reflectance spectrum of thermochromic molecules and the change of the absorption coefficient of photochromic units are used to separate the coupled signals through a linear decoupling model to reduce cross-interference. Finally, a three-layer fully connected artificial neural network (ANN) is combined with the normalised temperature/light signals as input. The weights are optimised through the back-propagation algorithm, and the Lab colour space parameters are output to drive the light-emitting diode (LED) and the colour-changing element to meet the needs of high-end decorative lighting for dynamic environment adaptability.
Given the increasing demand for lightweight materials, polyetheretherketone (PEEK) is gaining attention as a potential alternative to metals. To evaluate its mechanical suitability for engineering applications, it is necessary to predict the elastic, plastic, and fracture behaviour of PEEK under common loading conditions. The tensile, compressive, and three-point bending experiments were conducted on the PEEK specimens in this study. The yield and failure strains of PEEK were 2.27% and 4.69% in tension and 3.88% and 5.79% in compression. Based on the continuum damage mechanics, the back-calculation analyses were performed to predict the damage variable expression. Subsequently, a simulation model for PEEK under different loading conditions was developed. The back-calculation results identified optimal damage variable yield index (Dy) values of 0.295 for tension and 0.465 for compression. These findings validated the feasibility and applicability of the proposed model, providing a foundation for further investigations into the mechanical properties of PEEK material.
The parameters affecting the drawing process of 410 stainless steel tube were determined such as die angle, friction coefficient and drawing force. Microstructural changes using optical microscopy included elongated grains and distribution of chromium carbide precipitates in the matrix. Using uniaxial tensile testing and the Johnson-Cook equation, parameters related to material deformation were obtained. Evaluation of XRD patterns after drawing showed the formation of preferential orientations from {110} to {200}. The highest texture coefficient was related to the {200}. In addition, ring tests showed that the best lubricants for drawing were oxalate and soap, which provided lowest friction. Also, based on the simulation results, the optimal half-die angles of drawing were attained as 16 degrees, which resulted in the lowest drawing force.
Given the high toxicity of microcystin-RR (MC-RR) and its persistence in drinking water systems, this study aims to provide a viable and efficient solution for its removal using zinc ferrite nanoparticles. Various experimental parameters influencing adsorption efficiency were examined, including initial MC-RR concentration, pH, adsorption time, and temperature. The surface charge properties of nano-ZnFe2O4 and MC-RR were investigated, and the electrostatic interactions during the adsorption process were analysed through the examination of various ion types and a comparison of Fourier-transform infrared (FTIR) spectra before and after adsorption. The adsorption performance of nano-ZnFe2O4 revealed that the adsorption kinetics and isotherms could be effectively described by the pseudo-second-order model and the Langmuir model, respectively. The empirical findings demonstrated that nano-ZnFe2O4 functioned as an effective adsorbent and achieved a maximum removal capacity of 1.28 mg/L for MC-RR at 273 K. The negative values of free energy (Delta G) and enthalpy change (Delta H) also confirmed that the process is both spontaneous and exothermic, which is favourable for adsorption.
This article aims to simulate geomaterial alterations in hydraulic concrete constructions through experimentation. Three types of unreinforced concrete were identified (I, II and III), two of which (II and III) were alkali-reactive and doped with Na2Oeq, and were then compared with the control concrete (I). Then 27 cores were obtained from 9 beams following a three-point mechanical bending test. The conservation period of 28 weeks was in a heated and saturated environment 100% RH; 12-50 degrees C. Additional treatment is only considered for the cores to investigate the internal concrete matrix and determine very short-term water absorption (24 to 48 hours), divided into three states: normal, dry, and saturated, an ultrasonic test NDT is planned during the treatment. The monitoring results demonstrate the following: 0.6% AAR expansion, 2 mm (CO) width crack opening; a 60% decrease in velocity. The statistical method (PCA) is used to elucidate the correlations between the experiment duration and pathological symptoms.
In packaging material research, insufficient attention has been paid to biodegradable and traceable materials. This paper aims to explore the environmental friendliness and application potential of traceable biodegradable packaging materials. This paper modified polylactic acid (PLA) with talc and calcium carbonate (particle size less than 10 microns) at a 20% addition ratio. A traceability system based on Hyperledger Fabric was constructed to enhance transparency, and a life cycle assessment was conducted using SimaPro software. Experimental results showed that the modified PLA exhibited good degradation properties in soil and compost environments, good mechanical and thermal properties, and a total energy consumption of 7.95 MJ and total greenhouse gas emissions of 6.34 kg CO2e over its entire life cycle. These results demonstrate the environmental friendliness of the modified PLA in practical applications and provide new perspectives for future packaging material design, which will contribute to the industry's environmentally friendly development.
In order to effectively improve the accuracy of non-destructive testing of vehicle body weld defects, a non-destructive testing method of vehicle body weld defects based on yolov5 algorithm is proposed. The image information was collected to extract the weld area, and the spatial enhancement method and median filtering method were combined to denoise the extracted weld image. After the weld defect target is detected by the combination of yolov5 algorithm and support vector machine, the improved support vector machine completes the classification and recognition of the defect category, and realises the non-destructive detection of vehicle body weld defects. The results show that the uniformity of the proposed method is maintained above 0.96, and the peak signal-to-noise ratio of the image is above 40 dB, The Pratt quality factor is always stable above 0.93, and the maximum error rate is less than 1%, which shows that the proposed method has strong detection performance.
The purpose of this study is to evaluate the earthquake resilience of reinforced concrete shear walls in high-rise structures by employing different methods of horizontal joint integration and concrete pouring. Five distinct specimen models (SJ-1 to SJ-5) were developed and subjected to Northridge seismic wave simulations on a shaking table. The results indicated that the SJ-1 model exhibited superior seismic resistance, attributed to its high cracking threshold and compression strength. In contrast, the seismic performance of models SJ-2 to SJ-5 was significantly affected by the joint assembly technique, resulting in suboptimal outcomes. Notably, the SJ-3, SJ-4, and SJ-5 models showed a considerable decrease in load-bearing capacity due to joint malfunction during advanced testing stages. Additionally, the rate of stiffness degradation in these models accelerated once displacements exceeded 10 mm, indicating poor ductility and energy absorption capabilities.
In order to overcome the problems of low defect location detection rate, low defect type detection accuracy, and long detection time in traditional defect detection methods, an ultrasonic testing method of internal defects in welding seam of steel pipe pile in port terminal bent structure is proposed. Using an ultrasonic flaw detector to collect ultrasonic signals from steel pipe pile welds, the CEEMD algorithm is used to denoise the ultrasonic signals and extract signal features. The extracted features are input into a support vector machine, which introduces Karush-Kuhn-Tucker (KKT) conditions in optimisation theory and Lagrange multipliers to find the hyperplane with the maximum spacing. This hyperplane is used to achieve ultrasonic detection of internal defects in the weld. Experimental results show that the maximum defect location detection rate of the proposed method is 98.76%, the maximum defect type detection accuracy is 98.69%, and the detection time varies between 0.21 s and 0.39 s.
This study explores the optimisation of nanocomposite materials in architectural landscape design using a genetic algorithm (GA). Nanocomposites, known for high strength, hardness, and thermal conductivity, offer great potential but require precise design tuning. The research establishes a GA-based model by defining design objectives and parameter space, then develops a fitness function combining user satisfaction and material performance. Through selection, crossover, and mutation, GA generates and refines design solutions. Comparative experiments with artificial neural networks (ANN), ant colony optimisation (ACO), and particle swarm optimisation (PSO) demonstrate GA's superiority; it achieves a tensile strength of 486.5 MPa, compressive strength of 756.3 MPa, and user satisfaction of 8.2 points - outperforming other methods. Results indicate that GA effectively optimises nanocomposite properties to meet diverse landscape design requirements. This work provides a robust framework for intelligent material design, supporting future advancements in sustainable and high-performance architectural environments.
In order to reduce the fluctuation of the motor stator current waveform and the torque fluctuation coefficient of the motor, this paper proposes a torque ripple suppression method of crop harvesters based on unscented Kalman filter (UKF). Firstly, this article analyses the working principle of the motor in depth by constructing mathematical models of voltage, torque, and motion equations. Secondly, the key step is to use the unscented Kalman filter technique to process the signals of the nonlinear dynamic model. Unscented Kalman filter approximates the probability distribution of nonlinear functions by selecting a set of Sigma points, effectively estimating the system state without introducing linearisation errors. The experimental results show that this method not only makes the stator winding current present a standard sine waveform with a fluctuation range controlled within [-15 A, 15 A], but also reduces the maximum torque fluctuation coefficient to 0.0141.
In order to overcome the problems of low smoothness, long time, and low success rate of traditional industrial robot grasping path planning methods, a rapid planning method of industrial robot grasping path in unstructured environment is proposed. Unstructured scene data is collected through LiDAR and voxel filtering is performed on the collected point cloud data. Based on the processed point cloud data and RBPF-SLAM algorithm, an environmental map is constructed, and the industrial robot grasping path is rapid planned in the environmental map through the deep deterministic policy gradient algorithm. The experimental results show that the maximum smoothness of the grasping path of the industrial robot proposed by the method is 0.98, the minimum path planning time is 0.57 s, the grasping success rate is between 96.2% and 97.9%, and the grasping path planning effect is good.
The study addresses poor plasticity and toughness in steel plate forming by proposing a microstructure analysis and mould life prediction algorithm using machine vision. A distance sensor determines the forging edge, while a displacement sensor and industrial camera enhance measurement accuracy. The system utilises OpenCV to improve image processing speed and precision. A wear simulation for U-shaped hot forming moulds is developed using numerical methods to predict the effect of process parameters on wear depth and distribution. Results show that wear is most significant in the arc area, especially at the upper fillet, and the wear location shifts with increased forming cycles. The optimal superclosure value of 0.6 delta yields a 1.16% error between simulation and experimental results. The study concludes with an optimised wear model for predicting mould life, based on pin disk wear tests and finite element analysis.
This article integrates 3D visualisation and AI technologies, specifically the YOLO object detection model, into architectural and structural design to enhance project quality and efficiency. Using the BIM visualisation platform, it presents a data-driven, standardised, and shared design information model. The platform extracts 3D structural features from spatial data, offering functions like data sharing, information exchange, and linkage processing. Tests show that the predicted construction cycle matches the actual value, with a cycle ratio < 0.9 and shear weight ratio > 1.60%, meeting specifications. YOLO demonstrates the highest accuracy and lowest error margin among tested algorithms. It aids in predicting on-site construction progress, optimising structural solutions, and enhancing intelligent visualisation effects for architectural and structural design.
We present an in-depth study on the synthesis of nanowires using sol-gel methods and their characterisation with advanced analytical tools, including X-ray diffraction (XRD), UV-VIS spectroscopy, and scanning electron microscopy (SEM). ZnO nanowires were fabricated with exceptional crystallinity and consistent morphology through precise optimisation of synthesis parameters. XRD analysis confirmed the hexagonal wurtzite crystal structure of the samples. UV-VIS Spectroscopy revealed a prominent absorption peak at 320 nm and an estimated energy band gap of 3.50 eV. SEM imaging demonstrated the nanowire morphology with diameters ranging from 100 to 180 nm and lengths extending to several micrometres.
This article collected the scale and growth rate of China's NE material market from 2016 to 2022. SC cost optimisation and delivery time were used as the criteria for evaluating the optimisation model. The optimisation algorithm was applied to a company mainly composed of nanomaterials. From the cost optimisation and delivery time, it was found that the 20 factories under the company have improved their costs in all aspects after optimising the SC model. After optimisation, the delivery time of the route was maintained at 2-4 days. This article analysed the SC optimisation model and algorithm for the NE material industry under blockchain technology. The research results can help promote the sustainable development of the NE material industry and improve the overall efficiency of the industry.
