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Increasing product diversity, rising performance and reliability demands, and industry competitiveness are some of the many reasons that increase the need of more complex product designs in almost all sectors. The complexity of parts increases with their geometrical features to be designed and manufactured. Researchers agreed that it can be qualitatively evaluated and expressed with terms like low, medium, high, and very high. However, it might be evaluated differently, depending on the designer's considerations, domain and experience. Quantitative evaluation of a design complexity is, therefore, indispensable and expedites the decision-making about the selection of the manufacturing process. However, having a well-defined and unambiguous metric for quantitative evaluation is challenging. Most of existing metrics are not objective and are only valid for their specific applications. This paper presents a novel, unambiguous, and generalized approach for shape complexity evaluation. The developed metric enables determining if the selected part should be produced by conventional methods such as machining, or by non-conventional methods such as additive manufacturing. In order to ensure its objectivity, only geometrical features have been considered. The metric was tested through 25 different part designs of varying complexity. The investigations showed an accordance between the qualitatively evaluated shape and the calculated complexity factor. Also, the comparison of the results with other metrics showed the weakness of the latter and the efficiency and reliability of our metric. The results have been also validated by 50 experts from 23 countries. Based on these results, a threshold between machining and additive manufacturing is fixed allowing an easier decision-making. [ABSTRACT FROM AUTHOR]

The dimensional and geometric inspection of non-rigid mechanical parts still presents a real challenge to the automotive and aerospace industry. Much research has been devoted to automating the inspection of these parts in order to eliminate the cost of specialized fixtures that cause productivity problems for manufacturers. A registration step between the part'31s nominal CAD model and its corresponding scan is required for an automatic inspection. The coherent point drift (CPD) is one of the existing algorithms for registration and is widely used in imaging applications. It has been recently adapted with regard to the mechanical field for the fixtureless dimensional inspection problems of deformable bodies, notably the IDB-ACPD algorithm. In this paper, an improvement of the optimization phase by reformulating the objective function of the algorithm is put forward. A correction matrix associated with the importance of each measurement point, and therefore with the local rigidity of the part during the registration operation, was introduced and incorporated. In addition, the measurement points representing the same rigidity have been clustered based on mechanical metrics using a fuzzy c-mean algorithm. The effectiveness of the proposed approach is demonstrated on different case studies from the transport industry and the results provide an improved registration of non-rigid parts leading to better dimensional and geometric errors detection. [ABSTRACT FROM AUTHOR]

The improvement of the simulation process requires an integration of the design and analysis models. There are two essential tasks in the design analysis process: (i) computer-aided design (CAD) which provides the geometric description of the model and (ii) the finite element method (FEM) used for mechanical behaviour simulations. The interoperability between these two tasks reduces costs and improves product quality through the acceleration of design analysis loops. Our activity fits into this research orientation by providing a method to link the FE analysis and the CAD model. This is done by reconstructing the CAD model from the FE analysis results (deformed mesh). This paper proposes a method to update the CAD geometry from the deformed mesh. This approach allows for rebuilding the CAD model after analysis by extracting geometric information from the deformed mesh. An illustration of the developed method is discussed at the end of this paper. [ABSTRACT FROM AUTHOR]