Fuzzy particle swarm for the right-first-time of fused deposition

Wafa'H H. Alalaween, Abdallah H. Alalawin, Saif O. Abuhamour, Belal M.Y. Gharaibeh, Mahdi Mahfouf, Ahmad Alsoussi, Ashraf E. Abukaraky

Research output: Contribution to journalArticlepeer-review

Abstract

Right-first-time production enables manufacturing companies to be profitable as well as competitive. Ascertaining such a concept is not as straightforward as it may seem in many industries, including 3D printing. Therefore, in this research paper, a right-first-time framework based on the integration of fuzzy logic and multi-objective swarm optimization is proposed to reverse-engineer the radial based integrated network. Such a framework was elicited to represent the fused deposition modelling (FDM) process. Such a framework aims to identify the optimal FDM parameters that should be used to produce a 3D printed specimen with the desired mechanical characteristics right from the first time. The proposed right-first-time framework can determine the optimal set of the FDM parameters that should be used to 3D print parts with the required characteristics. It has been proven that the right-first-time model developed in this paper has the ability to identify the optimal set of parameters successfully with an average error percentage of 4.7%. Such a framework is validated in a real medical case by producing three different medical implants with the desired mechanical characteristics for a 21-year-old patient.

Original languageAmerican English
Pages (from-to)11977-11991
Number of pages15
JournalJournal of Intelligent and Fuzzy Systems
Volume45
Issue number6
DOIs
StatePublished - 2 Dec 2023

Keywords

  • Fuzzy logic
  • particle swarm optimization
  • radial based integrated network
  • right-first-time production

Fingerprint

Dive into the research topics of 'Fuzzy particle swarm for the right-first-time of fused deposition'. Together they form a unique fingerprint.

Cite this