Dr Matthew Oldfield is a Lecturer in Mechanical Engineering within the Department of Mechanical Engineering Sciences. He joined the University of Surrey in 2016 and is part of the Centre for Biomedical Engineering.
Dr Oldfield gained an MEng in Mechanical Engineering from the University of Liverpool. Staying at the University, he obtained his PhD on the 'Harmonic Excitation of Bolted Joints' in the Structural Mechanics group. Following his PhD, he spent two years working in the Civil Service before re-entering academic life at Brunel University. While there, in two postdoctoral positions, he investigated the optimal design of inertial, piezoelectric MEMs devices and the use of pattern recognition techniques in the dynamic analysis of structures.
After leaving Brunel University, Dr Oldfield took a position in the Mechatronics in Medicine Laboratory at Imperial College. In a group focussing on medical applications of robotic technology, his main work was numerical modelling in a team developing a steerable needle for use in neurosurgery. He investigated finite element modelling of needle cutting and the interactions allowing a soft surgical catheter to navigate tissue. The experimental validation of this work has also led to an interest in digital image correlation measurement techniques and the properties of soft solids. Dr Oldfield was part of a team that developed methods to analyse the insertion mechanics of a needle, inside a soft tissue phantom, with sub-millimetre resolution. While with the Mechatronics in Medicine Laboratory, he also led a project team that produced a novel retraction device for thyroid surgery performed with the daVinci robot.
Following seven years at Imperial College, Dr Oldfield left to take up his position, here, at the University of Surrey.
Postgraduate research supervision
PhD student in Biomechanics
EngD students as part of the Centre for Doctoral Training in Micro- and NanoMaterials and Technologies
Courses I teach on
with a Biologically Inspired Steerable Needle,Bioinspiration & Biomimetics 13 (2) 026009 IOP Publishing
mechanical challenges. In the present paper, an adaptive finite element algorithm is developed to simulate the penetration of a
steerable needle in brain-like gelatine material, where the penetration path is not predetermined. The geometry of the needle
tip induces asymmetric tractions along the tool?substrate frictional interfaces, generating a bending action on the needle
in addition to combined normal and shear loading in the region where fracture takes place during penetration. The fracture
process is described by a cohesive zone model, and the direction of crack propagation is determined by the distribution of
strain energy density in the tissue surrounding the tip. Simulation results of deep needle penetration for a programmable
bevel-tip needle design, where steering can be controlled by changing the offset between interlocked needle segments, are
mainly discussed in terms of penetration force versus displacement along with a detailed description of the needle tip trajectories. It is shown that such results are strongly dependent on the relative stiffness of needle and tissue and on the tip offset.
The simulated relationship between programmable bevel offset and needle curvature is found to be approximately linear,
confirming empirical results derived experimentally in a previous work. The proposed model enables a detailed analysis of
the tool?tissue interactions during needle penetration, providing a reliable means to optimise the design of surgical catheters
and aid pre-operative planning.
Current assessment methodologies ensure safety standards are met, but detailed evaluation of components requires transportation from site. Minimising transport of equipment would reduce costs and fuel usage, and also save lives.
The current work considers the use of digital image correlation (DIC) for non-destructive evaluation (NDE), with a particular focus on the assessment of combat helmets.
To optimize component loading, the use of pressure differentials and single-point mechanical loading were trialed. Finite element analysis (FEA) suggests pressure differentials produce a greater likelihood for the detection of component damage via surface strain discontinuities. By contrast, single point loading produces highly concentrated strain in the region of contact, whilst minimal strains result for the rest of the component.
The optimization of component speckling has also been considered, leading to the development of a novel approach using customisable transfer paper, which can be printed with a pattern specific to a given test geometry. This allows greater standardization, faster application, and increased accuracy, compared with traditional approaches, such as spray painting a speckle pattern.
Following these experiments, NDE of an entire military helmet was investigated using a portable test rig. With the method for helmet loading in concept stage, proof that the technique can detect damage is presented via five case studies. The variety of materials and testing processes show the novel approach for component speckling has direct use for the completion of, and external to, the primary goal of the project: ?to develop a DIC technique with the potential for portable damage detection of helmets?.