Method for selecting the optimal technology in metal additive manufacturing using an analytical hierarchical process

Description

The research hypothesis of this study revolves around employing Multi-Criteria Decision Analysis (MCDA) techniques, particularly the Analytical Hierarchical Process (AHP), to optimize technology selection in metal additive manufacturing. The data collected and analyzed includes the results of the survey and criteria evaluation relevant to the decision-making process, such as reliability, finishing of the part after printing, complexity of post-processing, sustainability of the process, user preferences, machine price, manufacturing cost, and productivity. The AHP methodology involves constructing a hierarchy structure wherein the goal or objective, criteria, and alternatives are systematically organized. Pairwise comparisons are then made among criteria and alternatives, using a relative importance scale ranging from 1 to 9. These comparisons are recorded in a positive reciprocal matrix, which is then normalized to obtain numerical weights for decision-making. The priority vector or normalized principal eigenvector is computed, representing the relative importance of criteria, and the maximum eigenvalue is determined. Finally, a global ranking of decision alternatives is analyzed based on additive aggregation and normalization of the sum of local priorities of criteria and alternatives.

Steps to reproduce

The data for this research was gathered using the Analytical Hierarchical Process (AHP) methodology, a well-established technique for decision-making in complex scenarios. The following outline describes the process followed to arrive at the data: Hierarchy Structure Design: A hierarchical structure was designed to organize the decision-making process. This structure included the goal or objective at the top level, criteria and sub-criteria at the intermediate level, and alternatives at the bottom level. The hierarchy was logically organized to facilitate systematic assessment. Pairwise Comparisons: Pairwise comparisons were conducted among criteria and alternatives to determine their relative importance. Participants were asked to compare each pair of criteria and alternatives using a scale ranging from 1 to 9, with 1 indicating equal importance and 9 indicating extreme importance. These comparisons were recorded in a positive reciprocal matrix. Normalization of Comparison Matrices: The comparison matrices were normalized to obtain numerical weights for decision-making. This involved computing the priority vector or normalized principal eigenvector, which represents the relative importance of criteria. The maximum eigenvalue was also determined to assess the consistency of judgments. Global Ranking of Alternatives: Based on the normalized weights obtained from the priority vector, a global ranking of decision alternatives was analyzed using additive aggregation and normalization techniques. This allowed for the determination of the most optimal alternative(s) based on the selected criteria. In terms of instruments and software, the AHP process typically involves using specialized software tools or spreadsheets designed for decision analysis. These tools facilitate the organization of the hierarchical structure, computation of pairwise comparisons, normalization of matrices, and determination of priority vectors and eigenvalues. To reproduce the research, one would need to follow the outlined steps of the AHP methodology, ensuring consistency in pairwise comparisons and adherence to the hierarchical structure. Specialized software such as Expert Choice, SuperDecisions, or dedicated spreadsheets for AHP can be used to streamline the process and perform the necessary calculations accurately. Additionally, clear documentation of the criteria, alternatives, and judgment scales used in the pairwise comparisons is essential for reproducibility and transparency in research.

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