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The purpose of this paper is to carry out an assembly tolerance analysis by means of a combined Jacobian model and skin model shape. The former is based on small displacements modeling of points using 6 × 6 transformation matrices of open kinematic chains in robotics. The latter easily models toleranced features with all kinds of geometric deviations.

This paper presents the procedure of performing tolerance analysis by means of the Jacobian model and skin model shape for assemblies. The point cloud-based discrete representative is able to model the actual toleranced surfaces instead of the ideal or associated ones in an assembly, which brings the simulation tools closer to reality.

The proposed method has the advantage of skin model shape which is suitable for geometric tolerances management along the product life cycle and contact analysis of kinematic small variations, as well as, with the Jacobian, enabling transformation of locally expressed parts deviations to globally expressed functional requirements. The result of the case study shows the accuracy of the method.

The proposed approach has not been developed fully; other functional features such as the pyramid are still ongoing challenges.

It is an effective method for supporting design, manufacturing and inspection by providing a quantitative analysis of the effects of multi-tolerances on the final functional key characteristics and for predicting the quality level.

The paper is original in taking advantages of both Jacobian model and skin model shape to consider all geometric tolerances in assembly.

Metamodeling is an effective method to approximate the relations between input and output parameters when significant efforts of experiments and simulations are required to collect the data to build the relations. This paper aims to develop a new sequential sampling method for adaptive metamodeling by using the data with highly nonlinear relation between input and output parameters.

In this method, the Latin hypercube sampling method is used to sample the initial data, and kriging method is used to construct the metamodel. In this work, input parameter values for collecting the next output data to update the currently achieved metamodel are determined based on qualities of data in both the input and output parameter spaces. Uniformity is used to evaluate data in the input parameter space. Leave-one-out errors and sensitivities are considered to evaluate data in the output parameter space.

This new method has been compared with the existing methods to demonstrate its effectiveness in approximation. This new method has also been compared with the existing methods in solving global optimization problems. An engineering case is used at last to verify the method further.

This paper provides an effective sequential sampling method for adaptive metamodeling to approximate highly nonlinear relations between input and output parameters.

Adaptive slicing is a key step in three-dimensional (3D) printing as it is closely related to the building time and the surface quality. This study aims to develop a novel adaptive slicing method based on ameliorative area ratio and accurate cusp height for 3D printing using stereolithography (STL) models.

The proposed method consists of two stages. In the first stage, the STL model is sliced with constant layer thickness, where an improved algorithm for generating active triangular patches, the list is developed to preprocess the model faster. In the second stage, the model is first divided into several blocks according to the number of contours, then an axis-aligned bounding box-based contour matching algorithm and a polygons intersection algorithm are given to compare the geometric information between several successive layers, which will determine whether these layers can be merged to one.

Several benchmarks are applied to verify this new method. Developed method has also been compared with the uniform slicing method and two existing adaptive slicing methods to demonstrate its effectiveness in slicing.

Compared with other methods, the method leads to fewer layers whilst keeping the geometric error within a given threshold. It demonstrates that the proposed slicing method can reach a trade-off between the building time and the surface quality.

The aim of this paper was to attempt to investigate the transformation of traditional Chinese medicine (TCM) industry in Guangdong Province of China by applying a perspective of sectoral system of innovation (SSI). TCM industry in China has experienced an evolution path from low-tech to modern industry.

An analytical framework of sectoral system innovation for explaining the change in TCM industry in Guangdong Province has been conducted.

It shows that during the successful transformation of the TCM industry in Guangdong from low-tech to modern sector, national and provincial institution are acting as main drivers. Knowledge integration is the decision factor of modernization and innovation strategy as an actor that makes the transformation adjust and operate efficiently. Other actors, such as demand and external networks interplay together and led to a gradual organizational, structural and institutional change and modernization of TCM industry.

SSI analyses of TCM in China have never been conducted before, this paper also contributes to enrich the experience of low-tech industry transformation and provide references to other low-tech industries around the world.

The purpose of this paper is to propose a novel mathematical model to present the three-dimensional tolerance of a discrete surface and to carry out an approach to analyze the tolerance of an assembly with a discrete surface structure. A discrete surface is a special structure of a large surface base with several discrete elements mounted on it, one, which is widely used in complex electromechanical products.

The geometric features of discrete surfaces are separated and characterized by small displacement torsors according to the spatial relationship of discrete elements. The torsor cluster model is established to characterize the integral feature variation of a discrete surface by integrating the torsor model. The influence and accumulation of the assembly tolerance of a discrete surface are determined by statistical tolerance analysis based on the unified Jacobian-Torsor method.

The effectiveness and superiority of the proposed model in comprehensive tolerance characterization of discrete surfaces are successfully demonstrated by a case study of a phased array antenna. The tolerance is evidently and intuitively computed and expressed based on the torsor cluster model.

The tolerance analysis method proposed requires much time and high computing performance for the calculation of the statistical simulation.

The torsor cluster model achieves the three-dimensional tolerance representation of the discrete surface. The tolerance analysis method based on this model predicts the accumulation of the tolerance of components before their physical assembly.

This paper proposes the torsor cluster as a novel mathematical model to interpret the tolerance of a discrete surface.