Ramin Amali

Improvements in the accuracy of an Inverse Problem Engine's output for the prediction of below-knee prosthetic socket interfacial loads

The monitoring of in-service loads on many components has become a routine operation for simple and well-understood cases in engineering. However, as the complexity of the structure increases so does the difficulty in obtaining an acceptable understanding of the real loading. It has been shown that it is possible to solve these problems by interfacing traditional analysis methodologies with more modern mathematical methods (i.e. artificial intelligence) in order to create a hybrid analysis tool. It has, however, been recognised that an Artificial Neural Network (ANN) predicts poorly in the high and low ranges of the envelope of which it is trying to predict. This paper presents results of research to develop the ANN Difference Method to improve the accuracy of the Inverse Problem Engines output. This method has been applied to accurately predict the complex pressure distribution at the residual limb/socket interface of a lower-limb prosthesis. It has been shown that application of the ANN Difference Method to the output of a backpropagation neural network can reduce inherent errors that exist at the low and high ends of the ANN solution envelope. This powerful approach can offer load information at high speed once the relationship between the loading and response of the component has been established through training the ANN. Utilising an experimental technique combined with an ANN can provide in-service loads on complex components in real time as part of a sophisticated embedded system.

A novel approach for assessing interfacial pressure between the prosthetic socket and the residual limb for below knee amputees using artificial neural networks

So far the study of interfacial pressures between residual limb and the prosthetic socket has not led to the design of any useful tool that can assist the prosthetist to fit a prosthesis. Researchers at the UWE Bristol have found a novel methodology that can revolutionise this process. It is based on the combined application of a hybrid numerical method and experimental finite element analysis for stress analysis. This paper represents part of the development process of this tool and discusses the step forward from a two-dimensional analysis, discussed previously (2000), into a 3D symmetrical shell analysis which is intended to simulate a structured and ideal socket. The authors feel this is a logical step, which is necessary for understanding the relationship between surface stress/strain, and internal load, which causes those surface stresses. This paper emphasises the significance of this statement because it requires no knowledge of tissue properties.

A photoelastic clinical study of the static load distribution at the stump/socket interface of PTB sockets.

It is recognized that the assessment of prosthetic socket fit is based largely on the subjective clinical judgement of the prosthetist. This study assesses a novel technique, photoelasticity, for use as a tool for the qualitative and quantitative assessment of socket fit. Photoelasticity is a visual technique that produces contours of principal stress or strain differences. The colour and/or distance between the contours can be qualitatively or quantitatively assessed, using a polariscope, to give a full-field analysis of the stresses on the socketss surface. This paper presents qualitative photoelastic socket surface contour data gathered during several prosthesis fitting sessions for two male trans-tibial amputees. Results are compared with the actual known contact regions at the stump/socket interface to determine if a relationship exists. This comparison of results has then been used to conclude the suitability of photoelasticity as a tool for the assessment of socket fit and recommendations are made as to the future developments of the technique. A direct relationship between the stump/socket contact regions and the qualitative photoelastic contours was demonstrated. Given further development this photoelastic technique may therefore be suitable for qualitative analysis of the interactions between the stump and prosthetic socket.

Detection of Damage in Composite Materials Using Classification and Novelty Detection Methods

The increased use of composite materials in engineering applications, and their susceptibility to damage means that it’s imperative that robust testing technique are developed to help in the detection of damage. Many of the detection techniques currently available are highly complex, difficult to conduct and rely on human interpretation of data. Simple testing methods are available but are too unreliable to be used effectively. This investigation explores the development of simple testing methods which use classification and novelty detection methods to detect the presence of damage in composite materials, making the process of damage detection much quicker, simpler and more versatile.

Harnessing electric energy from vehicle induced wind gust

This paper is focused on simple demonstration of yet untapped possibility to generate electricity from vehicle induced wind gust (VIWG). As such enormous energy resource continues to waste daily along millions of worlds vehicle paths, this paper through simple demonstrative design, seeks to redirect researchers and engineers towards a new thinking in the area of VIWG resource. Here, a self-sustaining apparatus designed around the features of simple ratchet mechanism demonstrates the ability to harness energy from the kinetic energy inherent in VIWG. For ease of reference this apparatus is referred to as Alternative Power Generating Machine (APGM). Data culled from a research work of the Ministry of Transportation, Ontario, Canada served as the source of basic data for the detailed design of the APGM. The simplicity of the APGM holds its key to the market. APGM could sustain facilities such as modern LED-based street lighting technologies, traffic lights or be utilized for electric car/battery recharging units. The machine is very easy to install and would operate effectively at safe distance from the curb of selected vehicle path. The current model of the APGM presented in this paper is a first attempt, hence has lots of room for future improvement. Recommendations are suggested to advance the current model in order to multiply the current power output. This work without doubt, would open new window into many possibilities in alternative and greener energy source.

Application of Artificial Neural Network to Predict Static Loads on an Aircraft Rib

Aircraft wing structures are subjected to different types of loads such as static and dynamic loads throughout their life span. A methodology was developed to predict the static load applied on a wing rib without load cells using Artificial Neural Network (ANN). In conjunction with the finite element modelling of the rib, a classic two layer feed-forward networks were created and trained on MATLAB using the back-propagation algorithm. The strain values obtained from the static loading experiment was used as the input data for the network training and the applied load was set as the output. The results obtained from the ANN showed that this method can be used to predict the static load applied on the wing rib to an accuracy of 92%.

Modelling process of electrical contact rivet through finite element simulation

Reliability of the electrical contact riveting process is studied with the aid of non-linear finite element analysis modelling and experimental techniques. The modelling is performed within ALGOR platform and its validity is assessed through a purpose built riveting test rig. A close correlation is observed between the results of FE model and the test rig.