SMEs have been rather slow in adopting tools and techniques used in larger companies for improving their innovative performance, even if they are very well aware of the importance of innovation, due to difficulties in applying them in their practices. Furthermore, initiatives on improving the innovation within the SMEs in the past, have addressed ways of improving the product innovation process, through a wide spectrum of methods, techniques and tools without quantifying the degree of change of 'innovativeness'. The approach presented in this paper, addresses both these issues. In the first part of this paper, the most commonly used measures of innovation are presented, and the difficulties in applying them to SMEs are described. In the second part a new methodology is presented, which is based on measuring and benchmarking innovation with fuzzy logic, through an innovation survey. This is achieved by addressing three inter-related, but separately measurable, aspects of a company's innovation process-the products developed; the innovation process utilized; the way the product innovation process is project managed. The approach aims at improving the iterative process of innovation in a SME, by assessing innovation and determining a product innovation profile. Finally an example based on data from 100 companies coming from the creative industries sector is presented.
This study investigates the mechanical response of antibacterial PA12/TiO
2
nanocomposite 3D printed specimens by varying the TiO
2
loading in the filament, raster deposition angle, and nozzle temperature. The prediction of the antibacterial and mechanical performance of such nanocomposites is a challenging field, especially nowadays with the covid-19 pandemic dilemma. The experimental work in this study utilizes a fully factorial design approach to analyze the effect of three parameters on the mechanical response of 3D printed components. Therefore, all combinations of these three parameters were tested, resulting in twenty-seven independent experiments, in which each combination was repeated three times (a total of eighty-one experiments). The antibacterial performance of the fabricated PA12/TiO
2
nanocomposite materials was confirmed, and regression and arithmetic artificial neural network (ANN) models were developed and validated for mechanical response prediction. The analysis of the results showed that an increase in the TiO
2
% loading decreased the mechanical responses but increased the antibacterial performance of the nanocomposites. In addition, higher nozzle temperatures and zero deposition angles optimize the mechanical performance of all TiO
2
% nanocomposites. Independent experiments evaluated the proposed models with mean absolute percentage errors (MAPE) similar to the ANN models. These findings and the interaction charts show a strong interaction between the studied parameters. Therefore, the authors propose the improvement of predictions by utilizing artificial neural network models and genetic algorithms as future work and the spreading of the experimental area with extra variable parameters and levels.
This paper presents rapid prototyping and reverse engineering techniques applied to create an implant for the surgical reconstruction of a large cranial defect. A series of computed tomography (CT) images was obtained and purpose built software was used to extract the cranial geometry in a point cloud. The point cloud produced was used for: (a) the creation of a stereolithographic (STL) physical model for direct assessment of the cranial defect; and (b) the creation of a 3D mould model for the fabrication of the patient-specific implant.
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