Random fiber network loaded by a point force

This article presents the displacement field produced by a point force acting on an athermal random fiber network (the Green function for the network). The problem is defined within the limits of linear elasticity and the field is obtained numerically for nonaffine networks characterized by various parameter sets. The classical Green function solution applies at distances from the point force larger than a threshold which is independent of the network parameters in the range studied. At smaller distances, the nonlocal nature of fiber interactions modifies the solution.

Error Sensing Strategy for Active Control of Low-Frequency Sound Absorption of Micro-Perforated Panel Absorber by Using a Point Force Controlled Thin Plate

This paper presents an analytical investigation on constructing an error sensing strategy of a new type of active MPPA. The proposed active MPPA is composed of MPP, air cavity, and point force-controlled backing panel, which can actively improve the low-frequency sound absorption of the MPPA. Constructing an appropriate error sensing strategy for obtaining an error signal that is highly correlated with the sound absorption coefficient of the active MPPA is a key problem encountered in practical implementation. The theoretical model of the active MPPA is firstly established using the modal analysis approach. Then, the active control performance and surface impedance characteristics in the controlled condition are analyzed in detail. Finally, the error sensing strategy of the active MPPA is constructed by measuring the surface average impedance ratio with an acoustic vector sensor (AVS). Simulation results show that, due to the antisymmetric property of the vibration of the backing panel on the resonant frequency, the surface impedance of the active MPPA after control also has symmetry or antisymmetry properties. Hence, the surface average impedance ratio of the active MPPA can be measured by using the limited number of acoustic vector sensors (sensing pressure and particle velocity). This variable is also highly correlated with the sound absorption coefficient of the active MPPA and thus can be used to construct the cost function (error signal). The active control result obtained by the proposed error sensing strategy is in good agreement with the theoretically optimal result, which validates the feasibility of this approach.

Dynamic Analysis of Working Device of Excavator under Limit Digging Force

In order to more accurately analyze the dynamic characteristics of the working device of the hydraulic excavator. The load changes on the two digging trajectories were calculated and analyzed by using the limit digging force model and the theoretical digging force model, respectively. The rigid-flexible coupling model of the working device was established in ADAMS. Taking the limit digging force (LDF) and the theoretical digging force (TDF) as the external load of the working device, the dynamic simulation of the hinge force of the working device in the two trajectories was carried out, and the structural strength analysis of the bucket was carried out by using ANSYS. The results show that the tangential force of the LDF is generally larger than that of the TDF, the hinge point force of the working device changes dynamically with the external load of the tooling, and the influence of the LDF on the hinge point force is greater than that of the TDF. When the LDF is taken as the external load, the structural strength of the bucket meets the operational requirements.

CNN-GRU Network-based Force Prediction Approach for Variable Working Condition Milling Clamping Points of Deformable Parts

Improper clamping is one of the major causes of part deformation. Improving the fixture arrangement through force analysis of clamping points is an effective means to suppress or improve machining deformation. However, the existing research focuses on the monitoring and off-line optimization of the clamping point force, which has a certain lag on the machining deformation control, and it is difficult to predict the clamping point force due to the time-varying coupling effect of multiple factors such as process parameters, cutting force and clamping point force in the machining process. Inspired by the excellent performance of convolutional neural networks and gated recurrent networks in feature extraction and learning of temporal association laws, this paper proposes a CNN-GRU-based method for predicting the force state of clamping points under variable working conditions. Firstly, a force prediction model of clamping point during milling process with variable working conditions is established. Secondly, a convolutional neural network is designed to extract the features of dynamic coupled machining conditions. Then, a network of gated recurrent units is constructed to learn the temporal correlation law between the machining conditions and the forces on the clamping points to achieve force prediction of the clamping points during machining. Finally, it was verified by the milling process of the piston skirt. The results show that CNN-GRU can effectively predict the clamping force. In addition, CNN-GRU has higher computational efficiency and accuracy compared with CNN-LSTM, CNN-RNN and CNN-BP.

Are the Parameters of Novel Two-Point Force-Velocity Model Generalizable in Leg Muscles?

The two-point force-velocity model allows the assessment of the muscle mechanical capacities in fast, almost fatigue-free conditions. The aim of this study was to investigate the concurrent validity of the two-point parameters with directly measured force and power and to examine the generalization of the two-point parameters across the different functional movement tests of leg muscles. Twelve physically active participants were tested performing three functional lower limb maximal tests under two different magnitudes of loads: countermovement jumps, maximal cycling sprint, and maximal force under isokinetic conditions of the knee extensors. The results showed that all values from the two-point model were higher than the values from the standard tests (p < 0.05). We also found strong correlations between the same variables from different tests (r ≥ 0.84; p < 0.01), except for force in maximal cycling sprint, where it was low and negligible (r = −0.24). The results regarding our second aim showed that the correlation coefficients between the same two-point parameters of different lower limb tests ranged from moderate to strong (r −0.47 to 0.72). In particular, the relationships were stronger between power variables than between force variables and somewhat stronger between standard tests and two-point parameters. We can conclude that mechanical capacities of the leg muscles can be partially generalized between different functional tests.

Directed force propagation in semiflexible networks

We consider the propagation of tension along specific filaments of a semiflexible filament network in response to the application of a point force using a combination of numerical simulations and analytic theory.

Granular impacts: how far the Boussinesq model can go?

A key problem on granular impacts deals with the determination of the mechanical response of the grains due to the impact of the intruder. This topic has been poorly addressed in the literature so far, a gap to which this study aims to contribute by measuring the pressure distribution at the bottom of a loose and dry sandy bed, impacted by a heavy sphere of fixed diameter. Exploring different bed thicknesses and intruder’s dropping height, we have found that the structure of this distribution is very similar to the Boussinesq model, initially proposed for a static point-force acting over an isotropic-elastic medium. This surprising result opens up many challenging questions that could help validate or refute this model in other scenarios.