BackgroundMedical 3D printing has brought the manufacturing world closer to the patient’s bedside than ever before. This requires hospitals and their personnel to update their quality assurance program to more appropriately accommodate the 3D printing fabrication process and the challenges that come along with it.ResultsIn this paper, we explored different methods for verifying the accuracy of a 3D printed anatomical model. Methods included physical measurements, digital photographic measurements, surface scanning, photogrammetry, and computed tomography (CT) scans. The details of each verification method, as well as their benefits and challenges, are discussed.ConclusionThere are multiple methods for model verification, each with benefits and drawbacks. The choice of which method to adopt into a quality assurance program is multifactorial and will depend on the type of 3D printed models being created, the training of personnel, and what resources are available within a 3D printed laboratory.

Abstract-The XCS Learning Classifier System has traditionally used roulette wheel selection within its genetic algorithm component. Recently, tournament selection has been suggested as providing a number of benefits over the original scheme, particularly a robustness to parameter settings and problem noise. This paper revisits the comparisons made between the behavior of tournament and roulette wheel selection within XCS in a number of different situations. Results indicate that roulette wheel selection is competitive in terms of performance, stability and generated solution size if the appropriate parameters are used.

OntoREM is an Ontology‐driven Requirements Engineering Methodology (process, methods and tools) that aims to improve the quality of requirements while also reducing the time and cost needed to develop, maintain and re‐use requirements. In order to evaluate the potential of such an ontology‐driven approach, OntoREM was applied to the aircraft operability (AO) domain and generic AO requirements for the wing design were developed. These requirements were subsequently compared to corresponding AO requirements that were developed for the wing design of two different development contexts for which a traditional requirements process was applied. Similarly, the elapsed process times to develop the requirements were compared, as measured for OntoREM and estimated for the traditional requirements process. The preliminary outcomes of this case study strongly suggest that the application of the OntoREM approach led to better quality requirements in considerably less time and hence at less cost per requirement. Further saving potential exists through increasing automation of OntoREM. In addition, there are several, additional advantages of applying this methodology that, for example, enable the reuse of domain ontologies including requirements in even less time i.e. at lower cost in the future, not to mention the advantages of explicitly capturing domain knowledge. An industrial scale pilot application of OntoREM has been proposed in order to mature the methodology and its underlying IT infrastructure for the anticipated global use in the context of entire aircraft development programmes in order to prepare for full integration into the Airbus Product Development Process (PDP).

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This paper discusses links that may be made between process models and UML software specification techniques, working from an argument that the whole complexity of organisational activity cannot be captured by UML alone. The approach taken is to develop a set of use cases which would be capable of providing information support to a pre-defined organisational process. The nature of the thinking which is necessary to derive the use cases is outlined, using the pre-defined process as a case study. The grouping of transactions and state changes into Use Cases is shown to require design choices which may vary between particular organisational contexts. Conclusions are drawn about the direction of further investigation of links between process modelling and UML.