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Control over scale, dynamic, environment, and geometric errors in 5-axis machine tool are required to realize a high precision machine tool. Especially geometric errors such as translational, rotational, offset, and squareness errors are important factors which should be considered in the design stages of the machine tool. In this paper, geometric errors are evaluated for different configurations of 5-axis machine tool, namely, 1) table tilting, 2) head tilting, and 3) universal and their error synthesis models are derived. The proposed model is different from the conventional error synthesis model since it considers offset and offset errors. The volumetric error is estimated for every configuration with random geometric errors. Finally, the best configuration, the critical design parameter and error are suggested.

CAD reconstruction of anatomical regions from computerized tomography (CT) scans is a very common approach in orthopaedic biomechanics. The CAD model is discretized into finite volume sub-domains and finite element (FE) analyses are performed in order to predict the distribution of stresses generated by applied loads. However, quality and reliability of numerical results depend on the level of accuracy reached in the meshing process. This paper analyzes some critical parameters that may affect the overall efficiency of the CT–FEM transformation process: scan threshold range, object size, and complexity. An optimization procedure for minimizing geometric errors on size and shape of reconstructed objects is presented. Finally, accuracy of stress predictions is evaluated for FE models that include known amounts of geometric errors. Compression and bending loads are considered. Results show that geometric and stress errors rapidly decrease as the objects to be reconstructed become larger in size. Optimal threshold ranges can be identified clearly for both an epoxy-resin benchmark model and a real bone specimen cut from a human lumbar vertebra. This allows geometric errors to be reduced significantly.