The increasing use of biodegradable devices in tissue engineering and regenerative medicine means it is essential to study and understand their degradation behaviour. Accelerated degradation systems aim to achieve similar degradation profiles within a shorter period of time, compared with standard conditions. However, these conditions only partially mimic the actual situation, and subsequent analyses and derived mechanisms must be treated with caution and should always be supported by actual long-term degradation data obtained under physiological conditions. Our studies revealed that polycaprolactone (PCL) and PCL-composite scaffolds degrade very differently under these different degradation conditions, whilst still undergoing hydrolysis. Molecular weight and mass loss results differ due to the different degradation pathways followed (surface degradation pathway for accelerated conditions and bulk degradation pathway for simulated physiological conditions). Crystallinity studies revealed similar patterns of recrystallization dynamics, and mechanical data indicated that the scaffolds retained their functional stability, in both instances, over the course of degradation. Ultimately, polymer degradation was shown to be chiefly governed by molecular weight, crystallinity susceptibility to hydrolysis and device architecture considerations whilst maintaining its thermodynamic equilibrium.

Making models based on patient data is an obvious application for rapid prototyping technology. Whilst many doctors and surgeons have used this technology successfully, it is by no means a standard for diagnosis or integration with medical procedures. There are numerous reasons for this related to complexity, cost, speed and other performance criteria. This paper will illustrate a number of instances where RP and associated technology has been successfully used for medical applications. These examples will serve to illustrate the diversity of tasks in which RP can benefit the medical community and form the basis for discussion on how medical RP technology should develop.

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Selective laser sintering (SLS) has been investigated for the production of bioactive implants and tissue scaffolds using hydroxyapatite (HA) reinforced polyethylene (HDPE) composites with the aim of achieving the rapid manufacturing of customised implants. Single layer and multilayer block specimens made of HA-HDPE composites with 30 vol% and 40 vol% HA were sintered successfully using a CO 2 laser sintering system. Laser power and scanning speed had a significant effect on the sintering behaviour. The degree of particle fusion and porosity were influenced by the laser processing parameters, hence control can be attained by varying these parameters. Moreover, the SLS processing allowed exposure of HA particles on the surface of the composites and thereby should provide bioactive products. Pores existed in the SLS fabricated composite parts and at certain processing parameters a significant fraction of the pores were within the optimal sizes for tissue regeneration. The results indicate that the SLS technique has the potential not only to fabricate HA-HDPE composite products, but also produce appropriate features for their application as bioactive implants and tissue scaffolds.

Hydroxyapatite, a ceramic with which natural bone inherently bonds, has been incorporated into a polymer matrix to enhance the bioactivity of implant materials. In order to manufacture custom-made bioactive implants rapidly, selective laser sintering has been investigated to fabricate hydroxyapatite and polyamide composites and their properties investigated. One objective of this research was to identify the maximum hydroxyapatite content that could be incorporated into the matrix, which was sintered at various parameters. The study focused on investigating the control of porosity and pore size of the matrix by manipulating the selective laser sintering parameters of the laser power and laser scan speed. The interception method was used to analyse the internal porous morphology of the matrices which were crosssectioned through the vertical plane. Most notably, all structures built demonstrated interconnection and penetration throughout the matrix. Liquid displacement was also used to analyse the porosity of the matrices. The laser power showed a negative relationship between porosity and variation in parameter values until a critical power value was reached. However, the same relationship for laser scan speed matrices was inconsistent. The effects of the laser power and laser scanning speed on the features of porous structures that could influence cell spreading, proliferation, and bone regeneration are presented.

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Selective laser sintering (SLS) is a manufacturing technique which enables the final product to be made directly and rapidly, without tooling or additional machining. For biomedical applications, SLS permits the fabrication of implants and scaffolds with complex geometry accurately and economically. In this study, hydroxyapatite-reinforced polyethylene and polyamide composites were fabricated using SLS. The SLS samples were characterized in terms of their internal structure, morphology, and porosity. The mechanical properties were examined by dynamic mechanical analysis. The effects of SLS processing conditions, including particle size and laser power, were investigated, and the results were compared with conventional compression-molded and machined specimens. The internal structure of sintered samples was porous, with open interconnected pores, and the pore size was up to 200 microm. Particle size and laser energy play a key role in the final density and mechanical properties of the sintered components. In the parameter range used, the use of smaller particles produced higher density and stiffness, and the laser-induced energy could also be varied to optimize the manufacturing process. This study demonstrated that high-HA-content reinforced polymer composite can be successfully manufactured by SLS with controlled porosity features.

Purpose -The empirical modelling of major rapid prototyping (RP) processes such as fused deposition modelling (FDM), selective laser sintering (SLS) and stereolithography (SL) has attracted the attention of researchers in view of their contribution to the overall cost of the product. Empirical modelling techniques such as artificial neural network (ANN) and regression analysis have been paid considerable attention. In this paper, a powerful modelling technique using genetic programming (GP) for modelling the FDM process is introduced and the issues related to the empirical modelling of RP processes are discussed. The present work aims to investigate the performance of various potential empirical modelling techniques so that the choice of an appropriate modelling technique for a given RP process can be made. The paper aims to discuss these issues. Design/methodology/approach -Apart from the study of applications of empirical modelling techniques on RP processes, a multigene GP is applied to predict the compressive strength of a FDM part based on five given input process parameters. The parameter setting for GP is determined using trial and experimental runs. The performance of the GP model is compared to those of neural networks and regression analysis. Findings -The GP approach provides a model in the form of a mathematical equation reflecting the relationship between the compressive strength and five given input parameters. The performance of ANN is found to be better than those of GP and regression, showing the effectiveness of ANN in predicting the performance characteristics of the FDM part. The GP is able to identify the significant input parameters that comply with those of an earlier study. The distinct advantages of GP as compared to ANN and regression are highlighted. Several vital issues related to the empirical modelling of RP processes are also highlighted in the end. Originality/value -For the first time, a review of the application of empirical modelling techniques on RP processes is undertaken and a new GP method for modelling the FDM process is introduced. The performance of potential empirical modelling techniques for modelling RP processes is evaluated. This is an important step in modernising the era of empirical modelling of RP processes.