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Optimal sensor placement (OSP) is one of the essential factors affecting the accuracy of health management, particularly in health monitoring driven by mode information. A novel OSP method based on active learning is proposed to effectively capture modal shapes for Structural Health Monitoring (SHM). First, the optimal Latin Hypercube Sampling is carried out to generate initial sensor positions, and the corresponding amplitudes of modal shapes at these positions are obtained by a frequency response function. Subsequently, data-driven models are built to be treated as virtual sensors to reconstruct the integrated modal shapes of the structure, and the accuracies of the results are calculated. Then, considering the distribution of the input sensor position, an improved reliability-based expectation improvement function (IREIF2) is applied to find the optimal sensor positions by optimizing the parameters of the probability density function in IREIF2. Finally, the position and response of the optimal sensor are used to update the data-driven models for more accurate modal shape reconstruction, and the accuracies are calculated to determine whether the OSP process continues. Once the accuracies meet the desired criteria, the optimal sensor positions are also obtained. The superiority of the proposed method is verified by the comparisons with other OSP methods, and different case studies are also used to prove the proposed method can realize OSP for SHM.

In structural dynamics a structure’s dynamic properties are often determined from its frequency-response functions (FRFs). Commonly, FRFs are determined by measuring a structure’s response while it is subjected to controlled excitation. Impact excitation performed by hand is a popular way to perform this step, as it enables rapid FRF acquisition for each individual excitation location. On the other hand, the precise location of impacts performed by hand is difficult to estimate and relies mainly on the experimentalist’s skills. Furthermore, deviations in the impact’s location and direction affect the FRFs across the entire frequency range. This paper proposes the use of ArUco markers for an impact-pose estimation for the use in FRF acquisition campaign. The approach relies on two dodecahedrons with markers on each face, one mounted on the impact hammer and another at a known location on the structure. An experimental setup with an analog trigger is suggested, recording an image at the exact time of the impact. A camera with a fixed aperture is used to capture the images, from which the impact pose is estimated in the structure’s coordinate system. Finally, a procedure to compensate for the location error is presented. This relies on the linear dependency of the FRFs in relation to the impact offset.

The vibrational characteristics of the Hybrid III and NOCSAE headforms are not well understood. It is hypothesized that they may perform differently in certain loading environments due to their structural differences; their frequency responses may differ depending on the impact characteristics. Short-duration impacts excite a wider range of headform frequencies than longer-duration (padded) impacts. While headforms generally perform similarly during padded head impacts where resonant frequencies are avoided, excitation of resonant frequencies during short-duration impacts can result in differences in kinematic measurements between headforms for the matched impacts. This study aimed to identify the natural frequencies of each headform through experimental modal analysis techniques. An impulse hammer was used to excite various locations on both the Hybrid III and NOCSAE headforms. The resulting frequency response functions were analyzed to determine the first natural frequencies. The average first natural frequency of the NOCSAE headform was 812 Hz. The Hybrid III headform did not exhibit any natural frequencies below 1000 Hz. Comparisons of our results with previous studies of the human head suggest that the NOCSAE headform’s vibrational response aligns more closely with that of the human head, as it exhibits lower natural frequencies. This insight is particularly relevant for assessing head injury risk in short-duration impact scenarios, where resonant frequencies can influence the injury outcome.

The study focuses on harnessing recycled materials to create sustainable and efficient composites, addressing both environmental issues related to waste management and industrial requirements for materials with improved vibration damping properties. The research involves the analysis of the physico-mechanical properties of the obtained composites and the evaluation of their performance in practical applications. Composite materials were tested in terms of their tensile strength and vibration damping capabilities, considering stress–strain diagrams, vibration amplitudes, frequency response functions (FRFs) and vibration modes. The research results have shown that by adding PVC and FA to the rubber-based matrix composition, the stiffness decreases and elasticity increases. The use of FA in the structure of composite materials causes an increase in the vibration damping possibilities due to the fact that it contributes to the chemical properties of the analyzed composite materials. Additionally, the use of PVC results in increased material elasticity, as evidenced by the higher damping factor compared to materials containing only rubber. Simultaneously, the addition of FA and PVC in specific proportions (60 phr) can lead to a decrease in stiffness and a greater increase in the damping factor. The incorporation of PVC and fly ash (FA) particles into rubber-based matrix composites reduces their stiffness and increases their elasticity. These effects are due to the fact that FA particles behave as extensions of chemical bonds during traction, which contributes to the increase in yield elongation. In addition, the use of flexible PVC increases the elasticity of the material, which is evidenced by the increase in the damping factor.

In the present study, the optimization design of the cubic nonlinear damping is conducted for a floating raft isolation system, which can be simplified as a multiple degree-of-freedom system with double parallel freedom inputs. The direct relationship between the power transmissibility and cubic nonlinear damping parameters is derived by taking advantage of the output frequency response function (OFRF) approach. The design requirements are proposed in order to achieve low resonant peak values, low power transmissibility over the high frequency range. The detailed step-by-step design process of the floating raft isolation system applied in a practical vessel is provided. A polynomial function, in terms of the required frequency range, is developed and validated from the simulation data. The case application indicates that the design method determined by the OFRF approach can effectively realize the design requirements of a floating raft isolation system, which can deal with the relationship of the power transmissibility and nonlinear damping parameters.

The dynamic behavior of the large-pitch screw during turning affects the stability of the cutting process, which in turn impacts the machining quality of the large-pitch screw. The large-pitch screw turning system among the machine tool, cutting tool, and the workpiece is taken as the present research object, and the frequency response function modeling of the large-pitch screw turning process system is carried out. The concept of generalized modal field and generalized stiffness field of large-pitch screw turning process system is introduced. Considering the dynamic change of the whole process system with the change of tool position, the dynamic characteristic information of the processing system is obtained and analyzed and ultimately reflects the inherent properties of the large-pitch screw turning process system and the ability to resist deformation. The cutting stability prediction model based on support vector machines (SVM) is established, and the average prediction error is 5.04%. The artificial bee colony algorithm is used to optimize the cutting parameters, and finally, the optimization method of large-pitch thread cutting stability based on SVM is proposed. This method can reduce the cutting vibration and effectively improve the cutting stability.

Accurate information on vehicle parameters is essential for vehicle design, vehicle-bridge interaction analysis, and road maintenance. Currently, most vehicle parameters identification (VPI) methods are deterministic and rely on strict experimental conditions. This paper proposes a simple method based on Bayesian framework to identify uncertain vehicle parameters, which only requires the vibration response of the vehicle under arbitrary excitation. First, the dynamics of the vehicle is analyzed and the link between excitation and response is established using the vehicle frequency response function. Subsequently, the likelihood function is formulated based on the difference function between the measured responses and the expected responses of updated model. Notably, this method introduces a creative probabilistic form of the frequency-domain response assurance criterion. Furthermore, a numerical simulation of the vehicle driving over bumps is performed to assess the influence factors on VPI, such as noise pollution and the excitation correlation between the front and rear wheel. At last, the parameters of a van are identified through field test and the reliability of the results is demonstrated.

Measuring the Frequency Response Functions (FRF) at the tool-tip is essential for the identification of chatter-free machining conditions. The tool-tip FRF in CNC machines are usually measured by impulse hammer tests in idle conditions, and the measured FRF remain relatively unchanged under operational conditions. This method is not effective in robotic machining, because the robot’s vibration response in idle and operational conditions are significantly different. The robot’s vibration response is pose-dependent and nonlinear and therefore strongly dependent on the operational conditions. This paper presents new methods for measuring the TCP (tool-tip) FRF of machining robots under operational conditions. In-process FRF are measured by leveraging the milling forces as the excitation source, and two approaches are proposed to achieve broadband, uncorrelated, and sufficiently exciting forces: i) milling of porous materials to generate randomized cutting forces, and ii) milling of a homogeneous material with spindle speed sweep. In the latter approach, the periodic content of cutting forces is used for excitation while in the former approach excitation by the random content is considered. A table dynamometer is used to measure the excitation (milling) forces and accelerometers are used to measure the resulting vibrations. The measured in-process FRF are then used to develop the chatter stability lobes diagram of the process, which determine the chatter-free combinations of the cutting depth and spindle speed for milling. Chatter experiments are conducted to confirm that the stability diagrams are more accurate when the presented in-process FRF measurements are used instead of the FRF measured in idle conditions.

The purpose of this study was to use a hybrid frequency response function–based substructuring synthesis method to predict the structure-borne noise of a vehicle frame resulting due to excitation by the engine and to develop a noise reduction method for optimizing the subframe model. The finite element models of the subframe were first verified by comparing their natural frequencies and frequency response functions with those of the real subframe models. Several substructures were then defined and the vehicle interior noises were predicted using the hybrid frequency response function–based substructuring synthesis method. Based on the prediction results, different target frequency ranges were selected, and different methods for optimizing the subframe model to reduce noise were investigated within the selected frequency ranges. Finally, the subframe modes within the selected frequency ranges were analyzed and new noise reduction models were developed by modifying the thickness of parts of the original subframe.

Frequency response functions (FRFs) are one of the most useful methods for representing machine tool dynamics under force excitation. FRFs are usually obtained empirically through output measurements, and force excitations are controlled by an external device such as hammers or shakers. This study offers an operational identification method that utilizes the calculation of force applied during the process as an input for FRF identification. Force excitation is provided through the face milling of a thin-walled workpiece, and acceleration measurements are taken during the process. The FRF is calculated at a designated position by sampling workpiece-cutting tool contacts as individual tap tests and substituting a force calculation as input. Force coefficients need to be known for the force calculation. An experimental force coefficient identification method is proposed. In that case, a similar thin-walled workpiece at a point with known FRF and acceleration measurements is utilized. Results are confirmed with FRFs obtained in the same location for both FRF identification and force coefficient identification approaches.