Evaluation of cloud 3D printing services oriented toward the product life cycle based on a triangular fuzzy number complementary judgment matrix

In recent years, the realm of information technologies has undergone continual evolution, marked by significant progress in cloud computing, big data analytics, and the Internet of Things (IoT). Concurrently, cutting-edge manufacturing technologies, typified by Industry 4.0 and smart manufacturing, particularly embodied in 3D printing, have played a pivotal role in meeting the escalating demands for personalized and customized manufacturing services. As a result, the need for cloud-based 3D printing services has increased significantly. Furthermore, it is now crucial in this field to thoroughly and impartially evaluate the reputation, performance, quality, and transactional behavior of various cloud 3D printing services. The fundamental objective of this study is to establish an efficient and rigorous methodology for evaluating the quality of cloud 3D printing services. To achieve this objective, we have meticulously devised a systematic approach. First, we have intricately formulated a hierarchical evaluation index system for cloud 3D printing services, integrating the BOCR (Benefits, Opportunities, Costs, and Risks) model. This systematic framework elucidates a myriad of evaluation criteria, encompassing credit evaluation indicators specific to cloud 3D printing service providers and indispensable Quality of Service (QoS) metrics. Subsequently, we introduce an innovative cloud 3D printing service evaluation methodology grounded in the product lifecycle perspective. Within this contextual framework, we have crafted a sophisticated credit evaluation algorithm and model tailored explicitly for cloud 3D printing service providers. This approach meticulously determines the weights associated with credit evaluation indicators, ensuring a robust and precise assessment. Furthermore, we have engineered a cloud 3D printing QoS evaluation model based on complementary judgment matrices employing triangular fuzzy numbers (TFN). This advanced model significantly broadens the dimensions of QoS evaluation, offering a comprehensive and nuanced perspective. Moreover, we present a pioneering multi-attribute evaluation methodology designed for the comprehensive assessment of platform performance, adding an additional layer of depth to our evaluation framework. The rationality and efficacy of our research methodology are scrupulously validated through a meticulously designed series of case studies. Notably, the fuzzy analytic hierarchy process (FAHP) algorithm, a core component of our approach, has demonstrated exceptional problem-solving capabilities and unparalleled optimization of performance. This methodological innovation underscores its practical feasibility and effectiveness in real-world applications. Upon rigorous analysis, our proposed cloud 3D printing service evaluation methodology stands as a comprehensive and sophisticated tool for evaluating the creditworthiness and QoS performance of service providers. Rooted in the robust foundations of FAHP and TFN, this method not only provides reliable decision support to 3D printing businesses but also serves as a catalyst for enhancing product quality and manufacturing efficiency.

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