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. 2024 May 8;10(10):e30867.
doi: 10.1016/j.heliyon.2024.e30867. eCollection 2024 May 30.

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application

Syed Hammad Mian  1 , Emad Abouel Nasr  2 , Khaja Moiduddin  1 , Mustafa Saleh  2 , Mustufa Haider Abidi  1 , Hisham Alkhalefah  1
Affiliations

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application

Syed Hammad Mian et al. Heliyon. .

Abstract

Objective: The objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials.

Methods: It integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments.

Results: The findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches.

Conclusions: To ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

Keywords: Additive manufacturing; CRITIC; Chang's extent analysis method; Integrative weights; Multi-criteria decision making; Simple ranking process.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Key factors in the orthosis design [15].
Fig. 2
Fig. 2
Assessment of consolidative multi-criteria decision-making algorithms.
Fig. 3
Fig. 3
Procedure for estimation of combined weights.
Fig. 4
Fig. 4
Strategy adopted for data collection.
Fig. 5
Fig. 5
FDM 3D Printer used in Experiments and fabricated standard specimens.
Fig. 6
Fig. 6
(a) Universal testing machine; (b) Tensile testing set up; (c) Compression testing set up.
Fig. 7
Fig. 7
CMM set up deployed to measure the 3D printed parts.
Fig. 8
Fig. 8
Surface characterization of fabricated specimens (a) PLA; (b) PETG.
Fig. 9
Fig. 9
Analysis of rankings influenced by the criteria weights for (a) SRP; (b) MOORA (Ratio System Approach; (c) MOOSRA; (d) MARCOS; (e) TOPSIS.
Fig. 10
Fig. 10
Dependability of C-MCDM methods on criteria weights.
Fig. 11
Fig. 11
Influence of least desired material removal on the ranks of (a) SRP; (b) MOORA-Ratio System; (c) MOORA – Full Multiplicative; (d) MOOSRA; (e) MARCOS; (f) TOPSIS.
Fig. 12
Fig. 12
Influence of least significant criterion removal on the ranks of (a) SRP; (b) MOORA-Ratio System; (c) MOOSRA; (d) MARCOS; (e) TOPSIS.
Fig. 13
Fig. 13
Similarity Analysis using correlation coefficients (a) SRC; (b) WRC; (c) RSC.
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