The existing surface reconstruction algorithms currently reconstruct large amounts of mesh data. Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. This method was verified using an online personalized customization system that was developed with WebGL and oriented toward 3D printing. The experimental results indicate that this method can generate a lightweight 3D scanning mesh rapidly and efficiently in a web environment.

Knowledge recommendation is an important means of knowledge reuse that can improve the efficiency and quality of product design. However, at present, there is no good way to fully consider the personalized demands of designers while ensuring the applicability of the recommendation results. Previous studies have usually been based on the similarity between tasks and knowledge or use collaborative filtering technology to accomplish knowledge recommendation. However, these methods do not consider the personal experience of designers and the characteristics of knowledge. This paper proposes a knowledge recommendation approach that integrates the degree of correlation between knowledge and tasks, the feedback-based personal experience, the collective experience of designers, and the degree of demand for knowledge based on the forgetting curve. A knowledge assistance score is generated based on these factors, and the knowledge recommendation list is obtained by ranking the knowledge in descending order of this score. Finally, the approach is applied to a machine shop layout design task and a computer numerical control (CNC) machine tool's spindle design and bearings selection task. The experimental results on two tasks demonstrate that the proposed approach outperforms three baselines on three ranking oriented evaluation metrics. This approach can effectively shorten the time for designers to acquire knowledge by recommending applicable knowledge to assist designers in completing design tasks with high quality and efficiency.

According to the theory of a complex network, the overall characteristics are derived from complex interactions among its components, that is, network structure. For its service network, its overall characteristics are its functions. In view of the coordinated linkage between the 3D printing industry and the logistics industry, 3D printing is deeply integrated with Internet technology, digital casting technology and Internet + service platforms. We propose a hyper-network model of raw material suppliers, semi-finished product processors and finished product manufacturers that realize on-demand customization. To analyse, design, and build the cloud 3D printing manufacturing capability service competition network, we propose a 3D printing service node structure model based on a super network using a reference and cloud manufacturing service model. The model's mathematical structure is given in detail. Under this condition, it is necessary to discuss the interaction between the logistics service level, productivity level and product processing quantity. The process of the proposed model and method is given, and the validity is proven with a case study of a Cloud 3D printing service hyper-network in service-oriented manufacturing systems.