TY - JOUR
T1 - Fuzzy particle swarm for the right-first-time of fused deposition
AU - Alalaween, Wafa'H H.
AU - Alalawin, Abdallah H.
AU - Abuhamour, Saif O.
AU - Gharaibeh, Belal M.Y.
AU - Mahfouf, Mahdi
AU - Alsoussi, Ahmad
AU - Abukaraky, Ashraf E.
N1 - Publisher Copyright:
© 2023 - IOS Press. All rights reserved.
PY - 2023/12/2
Y1 - 2023/12/2
N2 - 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.
AB - 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.
KW - Fuzzy logic
KW - particle swarm optimization
KW - radial based integrated network
KW - right-first-time production
UR - https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs232135?resultNumber=0&totalResults=526&start=0&q=fuzzy+particle&resultsPageSize=10&rows=10
UR - http://10.3233/JIFS-232135
U2 - 10.3233/JIFS-232135
DO - 10.3233/JIFS-232135
M3 - Article
AN - SCOPUS:85179548608
SN - 1064-1246
VL - 45
SP - 11977
EP - 11991
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
IS - 6
ER -