Additive manufacturing process selection for automotive industry using Pythagorean fuzzy CRITIC EDAS

Area of Science:

• Engineering
• Materials Science
• Decision Science

Background:

• Additive manufacturing (AM) offers significant potential for product and process innovation across industries, particularly in the automotive sector.
• The selection of appropriate AM technologies is complex due to numerous alternatives and inherent decision-making uncertainties.
• Existing decision-making methods struggle with the ambiguity and subjectivity present in evaluating AM alternatives.

Purpose of the Study:

• To develop and apply an integrated fuzzy multi-criteria decision-making (MCDM) approach for assessing additive manufacturing alternatives.
• To address the inherent uncertainty and subjectivity in selecting AM technologies for the automotive industry.
• To provide a robust framework for decision-makers in choosing the most suitable AM solutions.

Main Methods:

• Utilized Pythagorean fuzzy sets to effectively manage ambiguity and uncertainty in decision-making processes.
• Employed the Criteria Importance Through Inter-criteria Correlation (CRITIC) technique to objectively determine criterion significance levels.
• Applied the Evaluation based on Distance from Average Solution (EDAS) method for prioritizing additive manufacturing alternatives.

Main Results:

• The proposed Pythagorean fuzzy MCDM approach successfully assessed and prioritized various additive manufacturing alternatives.
• Sensitivity analysis demonstrated the robustness of the model against variations in criterion and decision-maker weights.
• Comparative analysis validated the reliability and effectiveness of the developed methodology.

Conclusions:

• The integrated Pythagorean fuzzy MCDM approach provides a powerful tool for uncertain decision-making in selecting additive manufacturing technologies.
• This methodology enhances the selection process for additive manufacturing alternatives in the automotive industry.
• The study contributes a novel framework for tackling complex, multi-criteria selection problems under uncertainty.