Koçak, Muhammed TayyipBayraklılar, Mehmed SaidKuncan, Melih2024-01-242024-01-242024Koçak, M. T., Bayraklılar, M. S., & Kuncan, M. (2024). Material selection for artificial femur bone using PROMETHEE-GAIA method. Journal of Testing and Evaluation, 52(2). https://doi.org/10.1520/JTE202303870090-3973https://doi.org/10.1520/JTE20230387https://hdl.handle.net/20.500.13055/641When replacing bones and implants, choosing the right materials for the artificial bone and orthopedic implants is crucial to the procedure’s success. In this work, a thorough assessment of the literature was followed by a thorough and rigorous evaluation of prospective materials for prosthetic femurs using a multicriteria decision-making process known as PROMETHEE-GAIA (Preference Ranking Organization METHod for Enrichment Evaluation and Geometric Analysis for Interactive Assistance). The proposed approach was validated using a total of 12 assessment parameters, including density, tensile strength, and ultimate tensile strength, and 17 candidate materials. The significance of the chosen criteria is well described. These 17 candidate implant materials and the 12 assessment criteria were used to develop a choice matrix. Rankings over the prepared matrix were produced using the PROMETHEE-GAIA program. Ti-6Al-7Nb, Ti-6Al-4V, and ASTM F1537, Standard Specification for Wrought Cobalt-28Chromium-6Molybdenum Alloys for Surgical Implants (UNS R31537, UNS R31538, and UNS R31539), Co-Cr-W emerged as the top contenders and were demonstrated as possible materials for effective artificial femur materials because of the assessment. With a large number of pertinent criteria and a wide range of materials, this study offers a framework for the selection of implant materials. It also emphasizes how choosing materials carefully may increase the durability and efficiency of orthopedic implants.eninfo:eu-repo/semantics/closedAccessPROMETHEE-GAIAArtificial Femur BoneMaterial SelectionMulticriteria Decision-MakingMaterial selection for artificial femur bone using PROMETHEE-GAIA methodArticle10.1520/JTE20230387522Q3WOS:0011695553000012-s2.0-85183315349Q3