Selecting an appropriate additive manufacturing (AM) process or machine to fabricate an end-use product is an important issue in design for AM. One of many types of approaches for AM process selection is based on multi-criteria decision making (MCDM). Most of the MCDM based approaches have an advantage in taking into account the relative importance of performance parameter types and a few of them also consider the interrelationships of performance parameter types. Each of these approaches can work well in its specific context. They are however not entirely satisfactory, as they do not have the capabilities to reduce the influence of the deviation of performance parameter values on the decision-making result and to capture the risk attitudes of users in their decision-making models. In this paper, an MCDM approach based on fuzzy Archimedean weighted power Bonferroni aggregation operators with such capabilities is proposed for AM process selection. A fuzzy Archimedean weighted power Bonferroni mean operator and a fuzzy Archimedean weighted power geometric Bonferroni mean operator are firstly constructed. Based on these operators, an MCDM approach for selection of AM processes are then developed. After that, four practical examples are adopted to illustrate the developed approach and a set of sensitivity analysis experiments on the basis of these examples are carried out. Finally, qualitative and quantitative comparisons between the approach and the existing MCDM based approaches are reported to demonstrate its feasibility, effectiveness, and advantages.

选择合适的增材制造（AM）工艺或机器来制造最终产品是AM设计中的重要问题。用于AM流程选择的多种方法之一是基于多准则决策（MCDM）的。大多数基于MCDM的方法在考虑性能参数类型的相对重要性方面均具有优势，并且其中一些还考虑了性能参数类型的相互关系。这些方法中的每一种都可以在其特定的上下文中很好地工作。但是，它们并不完全令人满意，因为它们不具有减少性能参数值的偏差对决策结果的影响并无法在其决策模型中捕获用户的风险态度的能力。在本文中，提出了一种基于模糊阿基米德加权功率Bonferroni集合算子的MCDM方法，用于AM过程选择。首先构造了模糊阿基米德加权幂Bonferroni均值算子和模糊阿基米德加权幂Bonferroni均值算子。基于这些运算符，然后开发了用于选择AM流程的MCDM方法。之后，采用四个实际例子来说明所开发的方法，并在这些例子的基础上进行了一组灵敏度分析实验。最后，据报道，该方法与现有基于MCDM的方法之间进行了定性和定量比较，以证明其可行性，有效性和优势。