Multi-level fuzzy comprehensive evaluations for rapid manufacturing systems

Abstract

Recently, the performance evaluations of raid manufacturing systems (RMSs) has become more and more crucial, because it is the foundation for investment and implementation of RMSs. However, due to various complex and vague factors involved in RMSs, it is hard to evaluate exactly. To measure a RMS roundly and objectively, a multi-level fuzzy comprehensive evaluation approach based on analytic hierarchy process (AHP) is proposed in this paper. Firstly, a hierarchical and comprehensive evaluation index system for RMSs is extracted. Then, based on the structure, a multi-level fuzzy comprehensive evaluation model is developed, in which AHP is incorporated to determine factor weights. Finally, an example is used to illustrate the proposed approach, and results indicate that the proposed approach is able to evaluation performances of RMSs effectively and reasonably.

An additive manufacturing process selection approach based on fuzzy Archimedean weighted power Bonferroni aggregation operators

“…Because of such advantage, the use of the techniques to solve AM process selection issue has been gained importance and popularity during the past two decades [6]. A number of researchers, which include Braglia and Petroni [20], Xu et al [21], Byun and Lee [22], Mahesh et al [23], Wilson and Rosen [24], Rao and Padmanabhan [25], Armillotta [26], Borille et al [27], Lokesh and Jain [28], Rao and Patel [29], Zhou and Chen [30], Chakraborty [31], Khrais et al [32], Vahdani et al [33], Ic [34], Mahapatra and Panda [35], Roberson et al [36], Liao et al [37], Vinodh et al [38], Zhang and Bernard [39], Zhang et al [40], Mancanares et al [41], Kek [42], Zheng et al [43], Anand and Vinodh [44], uz Zaman et al [45], Wang et al [46], and Kadkhoda-Ahmadi et al [47] have successively presented their approaches in this period. The specific techniques adopted in these approaches, the data sources of the approaches, the way the approaches deal with the relative importance of the criteria for selecting AM processes, and the way the approaches deal with the interrelationships between criteria are summarised in Table 1.…”

“…For the quantification of the collected information, there are a number of mathematical tools which have been used, such as FN [23,25,29,30,32,38], TFN [44], FN and TFN [22,33,42], interval number [24], ratio [31], grey number [35], mean and deviation [36], deviation and grey number [39], rough number and triangular rough number [43], and Latin hypercube lattice [46]. Choosing the mathematical tools to quantify the performance information should be carried out according to the technical basis of an AM process selection approach.…”

“…Thus, the TFNs ∆i,q = <αi,q, βi,q, γi,q> can be transformed into the FNs <ĩ,q>. Based on this, the fuzzy decision matrix is constructed as: (30) where j = 1, 2, …, k−1, k, k+1,…, n.…”

“…In practical application of the approach, users can also directly assign a relatively large value (e.g. 10,20,30) to λ. According to the result of the experiment, users can assign 10 or 20 to λ.…”

Selection of additive manufacturing processes

“…Fernandez et al (2005) regarded an AM machine together with a certain material as a solution. Some considered the printing service providers and treat each service provider as one solution (Liao et al, 2014;Zhou and Chen, 2010).…”