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Evaluation of computationally optimized design variants for additive manufacturing using a fuzzy multi-criterion decision-making approach

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Abstract

The additive manufacturing industry requires effective and standardized methods for selecting design variants generated through computational tools. To address this need and overcome the current barriers in the industry, a decision support system based on quantitative metrics is necessary. This research aims to establish multiple criteria for evaluating design variations in additive manufacturing, considering both opportunistic and constraint-based approaches. The multi-criterion decision-making process integrates four distinct metrics that capture aspects such as geometric complexity, cost–benefit, and the additional cost associated with support structures. To facilitate the evaluation of design variants in metal additive manufacturing using laser powder bed fusion, a fuzzy power Maclaurin symmetric mean operator is employed for metric aggregation. The proposed approach is demonstrated by assessing topologically optimized design variants of an airplane bearing bracket and an engine bracket. The ranking and selection of design variants using this approach resulted in significant cost reductions, with a 50% reduction for the airplane bracket and a 75% reduction for the engine bracket, compared to the original designs manufactured using additive manufacturing techniques.

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Abbreviations

\(c_{\mathit e\mathit s}\)  :

External shape complexity metric

\(c_{\mathit i\mathit s}\)  :

Internal structure complexity metric

\(C_{After\mathit\;TO}\)  :

Processing cost in AM after optimization \((USD)\)

\(C_{Before\mathit\;TO}\)  :

Processing cost in AM before optimization \((USD)\)

\(C_{\mathit b\mathit r}\)  :

Cost benefit ratio

\(C_{\mathit i}\)  :

Incremental cost \((USD)\)

\({\mathit{(C_S)}}_{\mathit A\mathit f\mathit t\mathit e\mathit r\mathit\;\mathit T\mathit O}\)  :

Processing cost of support structure after optimization (\(USD\))

\({\mathit{(C_S)}}_{\mathit B\mathit e\mathit f\mathit o\mathit r\mathit e\mathit\;\mathit T\mathit O}\)  :

Processing cost of support structure before optimization (\(USD\))

\(y_{\mathit i\mathit,\mathit j}\)  :

Value of each criterion in the decision matrix

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Authors and Affiliations

  1. Centre for Smart Manufacturing, Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing, Kancheepuram, Chennai, India

    Jayakrishnan Jayapal & Senthilkumaran Kumaraguru

  2. School of Interdisciplinary Design and Innovation, Indian Institute of Information Technology Design and Manufacturing, Kancheepuram, Chennai, India

    Sudhir Varadarajan

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  1. Jayakrishnan Jayapal
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  2. Senthilkumaran Kumaraguru
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  3. Sudhir Varadarajan
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by JJ and SK. The first draft of the manuscript was written by JJ, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Senthilkumaran Kumaraguru.

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Table 7

Table 7 Data used for the calculation of cost–benefit ratio and incremental cost
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Jayapal, J., Kumaraguru, S. & Varadarajan, S. Evaluation of computationally optimized design variants for additive manufacturing using a fuzzy multi-criterion decision-making approach. Int J Adv Manuf Technol 129, 5199–5218 (2023). https://doi.org/10.1007/s00170-023-12641-1

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