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<p>Sustainable material selection is difficult because mechanical performance, environmental impact, cost, circularity, manufacturability, and service reliability often conflict while available data are incomplete or reported as literature ranges. This paper proposes a reliability- and uncertainty-aware multi-criteria decision model for early-stage sustainable manufacturing material selection. The framework combines source-traceable screening data, scenario priorities, entropy-based objective weights, Weibull-type service reliability, TOPSIS closeness, VIKOR compromise utility, functional-unit checking, rank-reversal testing, parameter sensitivity, and Monte Carlo rank probabilities. The main novelty is that reliability is not treated only as a static criterion; it also modifies effective decision scores, allowing premature degradation to reduce the practical sustainability of a material. A component-anchored example compares AA6061-T6 aluminum, recycled AA6061, AZ31 magnesium alloy, HSLA steel, CFRP laminate, and glass-fiber reinforced polypropylene. Results show that CFRP laminate is preferred in weight-critical scenarios, whereas recycled AA6061 becomes the preferred compromise when reliability, low-carbon production, cost, and circularity are emphasized. The model provides a transparent and reproducible decision-support tool for manufacturers selecting materials under uncertainty.</p>

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Sustainable Manufacturing Material Selection Using a Reliability- and Uncertainty-Aware Multi-Criteria Decision Model

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Mohammad M. Hamasha

摘要

Sustainable material selection is difficult because mechanical performance, environmental impact, cost, circularity, manufacturability, and service reliability often conflict while available data are incomplete or reported as literature ranges. This paper proposes a reliability- and uncertainty-aware multi-criteria decision model for early-stage sustainable manufacturing material selection. The framework combines source-traceable screening data, scenario priorities, entropy-based objective weights, Weibull-type service reliability, TOPSIS closeness, VIKOR compromise utility, functional-unit checking, rank-reversal testing, parameter sensitivity, and Monte Carlo rank probabilities. The main novelty is that reliability is not treated only as a static criterion; it also modifies effective decision scores, allowing premature degradation to reduce the practical sustainability of a material. A component-anchored example compares AA6061-T6 aluminum, recycled AA6061, AZ31 magnesium alloy, HSLA steel, CFRP laminate, and glass-fiber reinforced polypropylene. Results show that CFRP laminate is preferred in weight-critical scenarios, whereas recycled AA6061 becomes the preferred compromise when reliability, low-carbon production, cost, and circularity are emphasized. The model provides a transparent and reproducible decision-support tool for manufacturers selecting materials under uncertainty.