Analytic temperature evaluation and process forces estimation for the RARR process of flat rings

Two different analytical models are derived for the estimation of the temperature drop in the two deformation stages of radial-axial ring rolling process of flat rings. The temperature estimation, along with previous results regarding geometry, strain and strain rate, is used to calculate the variation of the flow stress of the material during the process and accordingly to derive the process forces, utilizing different forces estimation models already available in the literature

Ground reaction forces estimation using IMU-based kinematics and OpenSim Moco

openThe state-of-the-art method to measure GRF is based on force plates (FP) often integrated with motion capture (MoCap) in motion analysis laboratories. Despite the high accuracy, reliability, and repeatability of laboratory data in combination with musculoskeletal modeling it requires a controlled environment and highly skilled operators. In addition, it was observed that subjects may change their movement strategy when walking on a (limited environment – nonecological) treadmill instead of an open field overground. To overcome the need for a laboratory environment, several methods were developed to measure GRF using mobile devices such as wearable sensors. For instance, methods based on insoles (limitations, wear and tear, cons) that directly measure GRF or methods based on inertial measurement units (IMUs) that measure the motion of body segments and estimate GRF using musculoskeletal models (MSK) and/or machine learning methods1. Therefore, this thesis project aims to 1) develop a workflow to estimate the ground reaction force based on a novel foot contact sphere MSK model combined with kinematics: • OpenSim Moco – Full body model • Contact sphere properties: position, thickness, friction, etc • IK -> torques (as low as possible) • 2) evaluate its accuracy compared to the state-of-the-art method - MoCap and ML - during walkin

Real-time wave excitation forces estimation: An application on the ISWEC device

Optimal control strategies represent a widespread solution to increase the extracted energy of a Wave Energy Converter (WEC). The aim is to bring the WEC into resonance enhancing the produced power without compromising its reliability and durability. Most of the control algorithms proposed in literature require for the knowledge of the Wave Excitation Force (WEF) generated from the incoming wave field. In practice, WEFs are unknown, and an estimate must be used. This paper investigates the WEF estimation of a non-linear WEC. A model-based and a model-free approach are proposed. First, a Kalman Filter (KF) is implemented considering the WEC linear model and the WEF modelled as an unknown state to be estimated. Second, a feedforward Neural Network (NN) is applied to map the WEC dynamics to the WEF by training the network through a supervised learning algorithm. Both methods are tested for a wide range of irregular sea-states showing promising results in terms of estimation accuracy. Sensitivity and robustness analyses are performed to investigate the estimation error in presence of un-modelled phenomena, model errors and measurement noise

Wheel Forces Estimation via Adaptive Sub-Optimal Second Order Sliding Mode Observers

In this work a system for the estimation of the forces (both longitudinal and lateral) exerted between the tires and the road is presented. Starting from two of the most commonly used descriptions of the vehicle dynamics, the single-corner and the single-track models, a system composed of Sub-Optimal Second Order Sliding Mode observers in a cascade structure plus an adaptive element is developed and verified to be effective in conditions in which the effect of the weight transfer can be neglected. One notable property of this approach is that only standard sensors, which are present in most of the stock cars, are exploited. The practical implementation is done using a switched/time-based adaptation law for the gains of the observers, in order to be able to track the quantities in a wide range of conditions while keeping the chattering low. Simulation results are presented in IPG Car-Maker

Excitation forces estimation for non-linear wave energy converters: A neural network approach

Investigating optimal control algorithms is a continuing concern within the Wave Energy field. A considerable amount of literature has been published on optimal control architectures applied to Wave Energy Converter (WEC) devices. However, most of them requires the knowledge of the wave excitation forces acting on the WEC body. In practice such forces are unknown and an estimate must be used. In this work a methodology to estimate the wave excitation forces of a non-linear WEC along with the achievable accuracy, is discussed. A feedforward Neural Network (NN) is applied to address the estimation problem. Such a method aims to map the WEC dynamics to the wave excitation forces by training the network through a supervised learning algorithm. The most challenging aspects of these techniques are the ability of the network to estimate data not considered in the training process and their accuracy in presence of model uncertanities. Numerical simulations under different irregular sea conditions demonstrate accurate estimation results of the NN approach as well as a small sensitivity to changes in the plant parameters relative to the case study presented

MusIC MAKES THE MUSCLES WORK TOGETHER

International audienceThis short communication aims at presenting an interpolation and correction method for muscle forces estimation, called the MusIC method

External grind-hardening forces modelling and experimentation

Grind hardening process utilizes the heat generated in the grinding area for the surface heat treatment of the workpiece. The workpiece surface is heated above the austenitizing temperature by using large values of depth of cut and low workpiece feed speeds. However, such process parameter combinations result in high process forces that inhibit the broad application of grind hardening to smaller grinding machines. In the present paper, modelling and predicting of the process forces as a function of the process parameters are presented. The theoretical predictions present good agreement with experimental results. The results of the study can be used for the prediction of the grind hardening process forces and, therefore, optimize the process parameters so as to be used with every size grinding machine

Estimation of unsteady aerodynamic forces using pointwise velocity data

A novel method to estimate unsteady aerodynamic force coefficients from pointwise velocity measurements is presented. The methodology is based on a resolvent-based reduced-order model which requires the mean flow to obtain physical flow structures and pointwise measurement to calibrate their amplitudes. A computationally-affordable time-stepping methodology to obtain resolvent modes in non-trivial flow domains is introduced and compared to previous existing matrix-free and matrix-forming strategies. The technique is applied to the unsteady flow around an inclined square cylinder at low Reynolds number. The potential of the methodology is demonstrated through good agreement between the fluctuating pressure distribution on the cylinder and the temporal evolution of the unsteady lift and drag coefficients predicted by the model and those computed by direct numerical simulation.Comment: In revie

Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

The estimation of muscular forces is useful in several areas such as biomedical or rehabilitation engineering. As muscular forces cannot be measured in vivo non-invasively they must be estimated by using indirect measurements such as surface electromyography (sEMG) signals or by means of inverse dynamic (ID) analyses. This paper proposes an approach to estimate muscular forces based on both of them. The main idea is to tune a gain matrix so as to compute muscular forces from sEMG signals. To do so, a curve fitting process based on least-squares is carried out. The input is the sEMG signal filtered using singular spectrum analysis technique. The output corresponds to the muscular force estimated by the ID analysis of the recorded task, a dumbbell weightlifting. Once the model parameters are tuned, it is possible to obtain an estimation of muscular forces based on sEMG signal. This procedure might be used to predict muscular forces in vivo outside the space limitations of the gait analysis laboratory.Postprint (published version

Evaluation of five traction models for agricultural tractors in Colombia

The behavior of traction devices of agricultural tractors has been modeled by analytical techniques and through the use of empirical equations, the latter methodology has shown good results and numerous applications. This article presented the evaluation of four empirical traction models and one semi-empirical in order to establish the model that best fit to Colombian agricultural soil mechanization and to propose a tool that better assess the traction behavior of tractors in the field. Taking into account all terrains conditions evaluated, the model that best adjusted was the Gee-Clough and collaborators model and it was possible to explain the 90% of the draft forces measured. Also the model of Evans and others, using improved prediction coefficients of Deere Group Research model, which fit in soils with vegetable cover, got a coefficient of determination of 94% in draft forces estimation under these conditions. All comparable observations were made with tractors in 2WD (two wheel drive) mode, it was suggested that tests with tractors in 4WD (four wheel drive) mode should be run