Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents’ dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.

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.

To study the variation in resource allocation efficiency in a cloud 3D printing (C3DP) manufacturing grid, C3DP task modeling for a complex network model was established based on the dynamic coupling of nodes, taking cloud manufacturing resource characteristics such as distribution and diversity into account. Based on this idea, we propose an architecture of C3DP task modeling for complex networks based on the dynamic coupling of nodes. First, the model calculates the initial tasks and services by evaluating the quality of the manufacturing resources, and then, according to the strength of the correlations among various tasks and among services and tasks, a multiplex network model based on cloud tasks and services is constructed. Then, according to the description and its constraint structure definition of the C3DP order tasks, by using the intercept method for complex networks, this paper identifies the work breakdown structure of coupling task sets, simplifies the optimization process, and proposes a C3DP order task (C3DPOT) network modeling method. Third, on the basis of the formal description of C3DP service resources, an architecture of C3DP order task methods for complex networks based on the dynamic coupling of nodes is proposed. Finally, a Service_Net model example is designed to verify the feasibility of the method.