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Turn-Milling Combined NC machine tool is different from traditional machine tools in structure and process realization. As an important means in the design stage, the analysis method of geometric accuracy error is also different from the traditional method. The actual errors and the error compensation values are a pair of "symmetry" data sets which are connected by the movement of machine tools. The authors try to make them more consistent through this work. The geometric error terms were firstly determined by topological structure analysis, then based on homogeneous coordinate transformation and multibody system theory, the geometric error model was established. With the interval theory, the function rule of sensitivity of geometric error sources to spatial errors was analyzed in detail, and the global maximum interval sensitivity of nine geometric error sources was extracted, providing a theoretical basis for error compensation and precision distribution. The geometric error sensitivity analysis method proposed in this paper can accurately evaluate the influence weights of each error term on the machining accuracy, and identify the important sensitive error terms with great influence on the machining accuracy from many error terms.

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Intelligent manufacturing is becoming more and more attractive for industrial societies especially after the introduction of industry 4.0 where most of industrial operations are to be carried by robots equipped with intelligent capabilities. This explicitly implies that the manufacturing systems will entirely be integrated and all manufacturing functions including quality control and management will have to be made as much intelligent as possible in operating with minimum human intervention. This Chapter will present a brief overview of some implications about intelligent quality systems. It intends to provide the readers of the book to understand how the concept of artificial intelligence is to be embedded into quality functions. It is known that the interoperability is the rapid transformation requirement of industry specific operations. This requires the integration of quality functions to other manufacturing functions for sharing the quality related knowledge with other manufacturing functions in order to sustain total intelligent collaboration. Achieving this, on the other hand, ensures the improvement of manufacturing processes for better performance in an integrated manner. Note that, although some general information about intelligent manufacturing systems are given, this chapter is particularly focused on discussing intelligent quality related issues.

In the process of product manufacturing, control of assembly error will directly affect product operating behavior. When product running, operating loads will lead to change of assembly relation of product parts, affecting product behavior. Based on Jacobian-Torsor method, the Jacobian-Torsor tolerance model, considering contribution of operating loads, was extended and corrected, the assembly error (assembly relation change) resulted from operating loads can be calculated. Variation of running behavior with assembly error was divided to three phases: compensation phase, rapid loss phase and total loss phase. Based on changing curve of product behavior, function of behavior loss was constructed to describe behavior loss resulting from assembly error of a part of product. The conception and calculating method of behavior loss index (BLI) are given to reflect behavior changing status of whole product under certain assembly accuracy. Combined with extended Jacobian-Torsor method, the calculated results can be used to predict product behavior change led by operating loads. The prediction can help to know next measurement adopted in product design phase. An example is given to demonstrate calculating procedure of given method.

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