Optimising additive manufacturing for fine art sculpture and digital restoration of archaeological artefacts

Additive manufacturing (AM) has shown itself to be beneficial in many application areas, including product design and manufacture, medical models and prosthetics, architectural modelling and artistic endeavours. For some of these applications, coupling AM with reverse engineering (RE) enables the utilisation of data from existing 3D shapes. This thesis describes the application of AM and RE within sculpture manufacture, in order to optimise the process chains for sculpture reproduction and relic conservation and restoration. This area poses particular problems since the original artefacts can often be fragile and inaccessible, and the finishing required on the AM replicas is both complex and varied. Several case studies within both literature and practical projects are presented, which cover essential knowledge of producing large scale sculptures from an original models as well as a wide range of artefact shapes and downstream finishing techniques. The combination of digital technologies and traditional art requires interdisciplinary knowledge across engineering and fine art. Also, definitions and requirements (e.g. ‘accuracy’), can be applied as both engineering and artistic terms when specifications and trade-offs are being considered. The thesis discusses the feasibility for using these technologies across domains, and explores the potential for developing new market opportunities for AM. It presents and analyses a number of case study projects undertaken by the author with a view to developing cost and time models for various processes used. These models have then been used to develop a series of "process maps", which enable users of AM in this area to decide upon the optimum process route to follow, under various circumstances. The maps were validated and user feedback obtained through the execution of two further sculpture manufacturing projects. The thesis finishes with conclusions about the feasibility of the approach, its constraints, the pros and cons of adopting AM in this area and recommendations for future research.