Search results

This paper aims to focus on the attempts to implement the challenges of teaching enterprise to science and engineering students by the embedding approach chosen by the White Rose Centre for Enterprise (WRCE), one of the centres formed under the Science Engineering Challenge in the UK.

WRCE's objective was to have departmental science and engineering staff teach enterprise modules as part of their overall departmental teaching and to have such modules integrated into the course provision. The WRCE approach emphasized the value of giving students some real life or simulated “real” experience and of developing a strand or track of enterprise through the years of the course.

The general propositions of WRCE are reviewed in the light of the outcomes in a number of departments, but most specifically within the Department of Mechanical Engineering at Sheffield, which would claim to be one of the most successful departmental interventions of WRCE. Whilst good examples of embedded modules exist, the successful results in Mechanical Engineering appear to depend on the appointment of non‐traditional staff, the integration of enterprise in a track broader than enterprise and leadership of that track that has the confidence and resource to deliver its agenda in part using external rather than internal teaching.

The embedding of enterprise learning into an engineering curriculum and a mechanical engineering degree program in particular, is discussed from the perspective of the programme leadership, the sponsoring body and the implementing instructor.

The purpose of this paper is to detail the design and first use of a force transducer device to study the development of forces during the laser-powder bed fusion (L-PBF) process from which residual stresses can be inferred.

The proposed novel device consists of an array of load cells for in-situ measurement of forces over time during the L-PBF additive manufacturing process. Measurements of the developed forces layer by layer were recorded in a first build using a 67-degree rotating scan strategy using Inconel 625 build material.

Preliminary experimental results from in-situ measurements using a 67-degree rotating scan strategy showed that the forces induced in the first five layers represented approximately 80% of the maximum on completion of the build and were distributed such as to induce concave deformation of the part, i.e. tension in the centre and compression at the edges of the part.

This paper describes a novel device for in-process measurement of the spatial distribution and time-varying nature of the forces induced during the L-PBF process as well as an evaluation of the residual forces following the completion of the build.

The purpose of this paper is to study the feasibility on the use of alternative parameters for representing acoustic emission (AE) and acousto-ultrasonic (AU) signals, using a wavelet-based approach and the computation of Chebyshev moments.

Two tests were performed, one on AE artificial signals generated on a CFRP plate and one on an AU setup used for actively detecting impact damage. The waveforms were represented using a data reduction technique based on the Daubechies wavelet and an image processing technique using Chebyshev moments approximation, to get 32 descriptors for each waveform.

The use of such descriptors allowed in the AE case to verify that the moments are similar when the waveforms are similar; in the AU setup the correlation coefficient of the descriptors with respect to a reference data set was found to be linked to the delimitation size.

Such a data reduction while retaining all the useful information will be positive for wireless sensor networks, where power consumption during data transmission is key. With having to send only a reliable set of descriptors and not an entire waveform, the power consumption is believed to be reduced.

This paper is a preliminary study that fulfils a need for a more reliable data reduction for ultrasonic transient signals, such as those used in AE and AU.

This paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes.

A system identification method based on the support vector machine (SVM) is developed and applied to the nonlinear dynamics of an aeroelastic aircraft. In the proposed non-parametric gray-box method, force and moment coefficients are estimated based on the state variables, flight conditions and control commands. Then, flight parameters are estimated using aircraft equations of motion. Nonlinear system identification is performed using the SVM network by minimizing errors between the calculated and estimated force and moment coefficients. To that end, a least squares algorithm is used as the training rule to optimize the generalization bound given for the regression.

The results confirm that the SVM is successful at the aircraft system identification. The precision of the SVM model is preserved when the models are excited by input commands different from the training ones. Also, the generalization of the SVM model is acceptable at non-trained flight conditions within the trained flight conditions. Considering the precision and generalization of the model, the results indicate that the SVM is more successful than the well-known methods such as artificial neural networks.

In this paper, both the simulated and real flight data of the F/A-18 aircraft are used to provide aeroelastic models for its lateral-directional dynamics.

This paper proposes a non-parametric system identification method for aeroelastic aircraft based on the SVM method for the first time. Up to the author’s best knowledge, the SVM is not used for the aircraft system identification or the aircraft parameter estimation until now.