Search Results (43)

This study addresses the challenge of accurately modeling the dynamic behavior of industrial robots for precision manufacturing applications. Using a comprehensive experimental approach with modal impulse hammer testing and triaxial acceleration measurements, 360 frequency response functions were recorded along orthogonal measurement paths for a KUKA KR10 robot. Two dynamic models with different parameter dimensions (12-parameter and 24-parameter) were developed in Matlab/Simscape, and their parameters were identified using genetic algorithm optimization. The KUKA KR10 features Harmonic Drives at each joint, whose high transmission ratio and zero backlash characteristics significantly influence rotational dynamics and allow for meaningful static structural measurements. Objective functions based on the Frequency Response Assurance Criterion (FRAC) and Root Mean Square Error (RMSE) metrics were employed, utilizing a frequency-dependent weighting function. The performance of the models was evaluated across different robot configurations and frequency ranges. The 24-parameter model demonstrated significantly superior performance, achieving 70% overall average Global FRAC in the limited frequency range (≤200 Hz) compared to 41% for the 12-parameter model when optimized using a representative subset of 9 measurement points. Both models showed substantially better performance in the limited frequency range than in the full spectrum. This research provides a validated methodology for dynamic characterization of industrial robots and demonstrates that higher-dimensional models, incorporating transverse joint compliance, can accurately represent robot dynamics up to approximately 200 Hz. Future work will investigate nonlinear effects such as torsional stiffness hysteresis, particularly relevant for Harmonic Drive systems. Full article

This study presents an integrated stiffness modeling and evaluation framework for an orthopedic surgical robot, aiming to enhance cutting accuracy and operational stability. A comprehensive stiffness model is developed, incorporating the stiffness of the end-effector, cutting tool, and force sensor. End-effector stiffness is computed using the virtual joint method based on the Jacobian matrix, enabling accurate analysis of stiffness distribution within the robot’s workspace. Joint stiffness is experimentally identified through laser tracker-based displacement measurements under controlled loads and calculated using a least-squares method. The results show displacement errors below 0.3 mm and joint stiffness estimation errors under 1.5%, with values more consistent and stable than those reported for typical surgical robots. Simulation studies reveal spatial variations in operational stiffness, identifying zones of low stiffness and excessive stiffness. Compared to prior studies where stiffness varied over 50%, the proposed model exhibits superior uniformity. Experimental validation confirms model fidelity, with prediction errors generally below 5%. Cutting experiments on porcine femurs demonstrate real-world applicability, achieving average stiffness prediction errors below 3%, and under 1% in key directions. The model supports stiffness-aware trajectory planning and control, reducing cutting deviation by up to 10% and improving workspace stiffness stability by 30%. This research offers a validated, high-accuracy approach to stiffness modeling for surgical robots, bridging the gap between simulation and clinical application, and providing a foundation for safer, more precise robotic orthopedic procedures. Full article

This study proposes an improved stiffness ellipsoid method to enhance the stiffness and precision of robotic arms in friction stir welding (FSW) operations. The method involves establishing a joint stiffness model through static identification experiments and developing a novel stiffness index derived from the improved stiffness ellipsoid method. This index provides a refined metric for evaluating the robot’s performance under variable loads during FSW. Simulation experiments demonstrate significant improvements in welding trajectory precision and computational efficiency. The findings highlight the potential of this method to elevate FW quality and consistency. Full article

The accuracy of a space manipulator’s end trajectory and stability is significantly affected by joint clearance. Aiming to improve the prediction accuracy of vibration caused by clearance, a dynamic clearance modeling method is developed based on parameter identification in this study. First, a dynamic model framework for manipulator arms is established based on the Hamilton principle and hypothetical mode method with time-variation damping. Then, a multi-resolution identification is performed for identifying the instantaneous frequency and damping ratio to estimate stiffness and damping by the sensors. The quantum genetic algorithm (QGA) is used to optimize the scale factor, which determines the identification accuracy and calculation efficiency. Finally, a case study is conducted to verify the presented model. In comparison with the initial dynamic model based on constant damping, the modal assurance criterion (MAC) of the proposed improved model based on time-variation damping is improved by 43.97%, the mean relative error (MRE) of the frequency response function (FRF) is reduced by 32.6%, and the root mean square error (RMSE) is reduced by 18.19%. The comparison results indicate the advantages of the proposed model. This modeling method could be used for vibration prediction in control systems for space manipulators to improve control accuracy. Full article

Bolted joints, prevalent in industrial applications for component fastening, are susceptible to self-loosening—a critical issue resulting in a gradual reduction in clamping force. Gaining insight into the underlying mechanisms of self-loosening is crucial. While prior research has largely focused on evaluating component stiffness, limited attention has been given to its impact on the self-loosening behavior of bolted joints under transverse cyclic loading. This study investigates how component stiffness influences self-loosening in bolted joints by varying the material and thickness of clamped members. An experimental setup replicating real-world conditions is devised to simulate loosening caused by cyclic lateral displacement. Tests are conducted using steel and high-density polyethylene (HDPE) clamped members of different grip lengths to explore the relationship between stiffness and self-loosening. Key parameters measured include bolt axial load, transverse force on clamped members, relative displacement, and rotation between the bolt and nut. The findings provide valuable insights into the effects of stiffness across various clamped member materials and grip length combinations, which can enhance the understanding of conditions that promote loosening resistance. Moreover, by highlighting stage-II or rotational loosening, with each test resulting in complete preload loss, the study provides a comparative analysis of the influencing factors. This enables the identification of distinct loosening patterns and supports the development of improved bolted joint designs to reduce loosening. Full article

Single-layer steel reticulated shell structures have a long history and are widely distributed. As their service life increases, corrosion in these structures becomes an increasingly prominent problem. However, currently, there is nearly no research available on the static behavior of corroded steel reticulated shell structures. Therefore, taking the single-layer steel reticulated shell structure as the research object, an accelerated corrosion test was first carried out on Q345, a commonly used steel for this structure, and an equivalent conversion calculation method was proposed. Then, a Python program (version 3.5.0) for single-layer reticulated shells considering component dimensions, joint stiffness, and random corrosion was written, and numerical simulation analysis was carried out on the static behavior of single-layer steel reticulated shell structures after corrosion. Finally, based on parametric analysis, the random corrosion influence coefficient and joint stiffness influence coefficient were derived, and a calculation method for the elastic–plastic ultimate bearing capacity of the structure was established, which could enhance the current design methods for steel reticulated shells by taking corrosion effects into account. The research results will provide specific and programmed references for the damage identification and safety assessment of single-layer steel reticulated shell structures during their service life in strongly corrosive environments along the coast and offshore, holding important engineering significance and scientific value. Full article

In situ dynamic testing is conducted to study the dynamic characteristics of the wooden structure of the North House main hall. The velocity response signals on the measurement points are obtained and analyzed using the self-interaction spectral method and stochastic subspace method, yielding natural frequencies, mode shapes, and damping ratios. This study reveals that the natural frequencies and damping ratios are highly consistent between the two methods. Therefore, to eliminate errors, the average of the results from both modal identification methods is taken as the final measured modal parameters of the structure. The natural frequencies of the first and second order in the X direction were 2.097 Hz and 3.845 Hz and in the Y direction were 3.955 Hz and 5.701 Hz. The modal frequency in the Y direction of the structure exceeds that in the X direction. Concurrently, a three-dimensional finite element model was established using ANSYS 2021R1, considering the semi-rigid properties of mortise–tenon connections, and validated based on in situ dynamic testing. The sensitivity analysis indicates adjustments to parameters such as beam–column elastic modulus, tenon–mortise joint stiffness, and roof mass for finite element model refinement. Modal parameter calculations from the corrected finite element model closely approximate the measured modal results, with maximum errors of 9.41% for the first two frequencies, both within 10% of the measured resonant frequencies. The adjusted finite element model closely matches the experimental results, serving as a benchmark model for the wooden structure of North House main hall. The validation confirms the rationality of the benchmark finite element model, providing valuable insights into ancient timber structures along transportation routes. Full article

Aluminum–steel joints are increasingly used in the automotive industry to meet the requirements for energy saving and emission reduction. Among various joining technologies, self-pierce riveting (SPR) and resistance spot welding (RSW) are two well-established technologies for fabricating dissimilar joints with stable and high mechanical performance. However, corrosion will occur in these joints inevitably due to different electrochemical properties, which can degrade the surface quality and the mechanical performance, such as strength. This paper presents a method of understanding the corrosion mechanisms in joining aluminum and steel. For this understanding, a hybrid method combining experimental observations, mechanical properties identification, and analytical approaches was used to assess the evolution of the impact of corrosion on the joining performance, such as traction separation curves. The study was conducted on common combinations used in the vehicles, e.g., a 1.2 mm thickness aluminum alloy (AA 6022) and 2.0 mm thickness hot deep galvanized steel (HDG HSLA 340) joined by SPR and RSW. After the fabrication of these joints, accelerated cyclic corrosion tests of up to 104 cycles were performed, which reproduced the environmental conditions to which a vehicle was exposed. By investigating the microstructural evolution within the joints, the corrosion mechanisms of SPR and RSW joints were revealed, including the initiation and propagation. Moreover, the intrinsic impact of the corrosion on the mechanical performance, including the strength, axial stiffness, and crashworthiness, was analyzed by performing a lap-shear test. It showed that as corrosion proceeds, the fracture modes and mechanical performance are affected significantly. Full article

Fibromyalgia is a syndrome characterized by chronic widespread musculoskeletal pain, which may or may not be associated with muscle or joint stiffness, accompanied by other symptoms such as fatigue, sleep disturbances, anxiety, and depression. It is a highly prevalent condition globally, being considered the third most common musculoskeletal disorder, following lower back pain and osteoarthritis. It is more prevalent in women than in men, and although it can occur at any age, it is more common between the ages of thirty and thirty-five. Although the pathophysiology and etiopathogenesis remain largely unknown, three underlying processes in fibromyalgia have been investigated. These include central sensitization, associated with an increase in the release of both excitatory and inhibitory neurotransmitters; peripheral sensitization, involving alterations in peripheral nociceptor signaling; and inflammatory and immune mechanisms that develop concurrently with the aforementioned processes. Furthermore, it has been determined that genetic, endocrine, psychological, and sleep disorders may influence the development of this pathology. The accurate diagnosis of fibromyalgia remains challenging as it lacks specific diagnostic biomarkers, which are still under investigation. Nonetheless, diagnostic approaches to the condition have evolved based on the use of scales and questionnaires for pain identification. The complexity associated with this pathology makes it difficult to establish a single effective treatment. Therefore, treatment is multidisciplinary, involving both pharmacological and non-pharmacological interventions aimed at alleviating symptoms. The non-pharmacological treatments outlined in this review are primarily related to physiotherapy interventions. The effectiveness of physical exercise, both on land and in water, as well as the application of electrotherapy combined with transcranial therapy and manual therapy has been highlighted. All of these interventions aim to improve the quality of life of patients highly affected by fibromyalgia. Full article

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model’s outputs and the testbed’s outputs, thus considering the joint position and acceleration of the link’s tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application, wherein the servomotor applies the input torque, and the outputs are the joint angle and the link’s tip acceleration. Then, an optimization problem minimizes the difference between the numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws. Full article

This paper presents the results of an experimental study that focused on the gradual modification of the modal parameters of reinforced concrete beam–column frames subjected to progressive damage under cyclic loading. As is commonly found in structures of the 1970s, the specimen was characterized by the absence of specific shear reinforcement in the nodal panel. The frame modal parameters were investigated using the ambient vibrations test (AVT) as a modal identification technique. In particular, quasi-static cyclic tests with increasing amplitudes were performed on the reinforced concrete frame specimen and the modal parameters were assessed at various stages of frame degradation. By establishing a correlation between the changes in the modal parameters and the mechanical indicators of the structural damage in the frame, this study aimed to determine whether the ambient vibration tests could offer meaningful insights for evaluating the structural health of this type of structural component. As a result of the damage that occurred in the tested RC frame, the residual experimental value of the first natural frequency of the specimen was found to reduce at 52.7% of the original reference value (undamaged stage). Similarly, the residual value of the frame stiffness was found to be as low as 43.82% of the initial one. Both these results confirmed that changes when monitoring the modal frequencies may provide quantitative indexes to describe the structural health of RC frames. In combination with static tests for a direct measure of the structural stiffness variations, the AVT technique was shown to have interesting potential in detecting the type, level, and distribution of the progressive damage in civil structures. In particular, exponential and polynomial regression curves were defined to describe the decay of the first natural frequency as the structural damage increased in various parts of the frame, and it was shown that the variation in the first natural frequency was determined more by the damage on the beam than by the damage on the joint. Full article

Bolted joints structures are critical fastening components widely used in mechanical equipment. Under long-term loading conditions, the bolted joints interface generates strong nonlinearities within the system. The nonlinear stiffness inside the bolt leads to changes in the stiffness of the whole system. This affects the dynamic characteristics of the whole system. It brings challenges and difficulties to the performance prediction and reliability assessment of the equipment. A cross-scale theoretical model study based on the microscopic contact mechanism can provide a more comprehensive understanding and cognition of the degradation behavior of bolted joints interfaces. The current development status and deformation process of asperity models are summarized. The research progress of statistical summation model and contact fractal model based on microscopic contact mechanism is analyzed. The experimental methods for parameter identification of connection interfaces are reviewed. The study of numerical modelling of bolted joints structures from the surface contact mechanism is briefly described. Future research directions for cross-scale modelling of bolted joints structures are outlined. Full article

The implementation of a progressive rehabilitation training model to promote patients’ motivation efforts can greatly restore damaged central nervous system function in patients. Patients’ active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb’s movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient’s expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient’s healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient’s ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human–robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient’s movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient’s healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient’s pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements. Full article

Identifying the parameters of multispan rigid frames is challenging because of their complex structures and large computational workloads. This paper presents a stiffness separation method for the static response parameter identification of multispan rigid frames. The stiffness separation method segments the global stiffness matrix of the overall structure into the stiffness matrices of its substructures, which are to be computed, thereby reducing the computational workload and improving the efficiency of parameter identification. Loads can be applied individually to each separate substructure, thereby guaranteeing obvious local static responses. The veracity and efficacy of the proposed methodology are substantiated by applying it to three- and eight-span continuous rigid frame structures. The findings indicate that the proposed approach significantly enhances the efficiency of parameter identification for multispan rigid frames. Full article

Up to 25% of patients undergoing knee arthroplasty report chronic pain postoperatively. Early identification of high-risk individuals can enhance pain management strategies. This retrospective analysis investigates the incidence of severe postoperative pain and its associated risk factors among 740 patients who underwent total knee arthroplasty. Utilizing a digital application, patients provided comprehensive data encompassing pre- and postoperative pain levels, analgesic usage, and completed a chronic pain risk assessment. Participants were categorized into two distinct groups based on their pain status at three months post-op: Group D+ (14%), characterized by pain scores exceeding 40/100 and/or the utilization of level 2 or 3 analgesics, and Group D− (86%), who did not meet these criteria. An analysis of pain trajectories within these groups revealed a non-linear progression, with specific patterns emerging amongst those predisposed to chronic pain. Notably, patients with a trajectory towards chronic pain exhibited a plateau in pain intensity approximately three weeks post-surgery. Significant preoperative risk factors were identified, including elevated initial pain levels, the presence of comorbidities, pain in other body areas, heightened joint sensitivity and stiffness. This study highlights the utility of digital platforms in enhancing patient care, particularly through the continuous monitoring of pain. Such an approach facilitates the early identification of potential complications and enables timely interventions. Full article