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Summary Addressing service control factors, rapid manufacturing environment change, difficulty of resource allocation evaluation, resource optimization of 3D cloud printing service in a cloud manufacturing environment, and other characteristics, this paper proposes an evaluation indicator system of innovative new product development 3D printing order task execution. The evaluation indicator has eight dimensional components, including Time (T), Quality of Service (Q), Matching (Mat), Reliability (R), Flexibility (Flex), Cost (C), Fault tolerance (Ft), and Satisfaction (Sa). It constructs a type of optimal selection model based on a Multi‐Agent 3D Cloud Printing Service Quality Evaluation and a framework of cloud service evaluation of an AHP‐TOPSIS evaluation model based on Pareto optimization, and it designs an algorithm involving hybrid multi‐objective particle swarm optimization (PSO) based on the Baldwin Effect Model. In addition, this paper verifies the effectiveness of the algorithm through an example and offers a case study designed to test its feasibility and effectiveness.

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.

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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. INDEX TERMS Collaborative filtering, degree of assistance, degree of correlation, degree of demand, knowledge recommendation, ontology model.