Affiliations: [a] Department of Industrial Engineering, The University of Jordan, Amman, Jordan
| [b] Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, Jordan
| [c] Department of Oral and Maxillofacial Surgery, Oral Medicine and Periodontology, The University of Jordan, Amman, Jordan
| [d] Collage of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait
| [e] Department of Automatic Control and Systems Engineering, The University of Sheffield, UK
| [f] Printie 3D Company, Amman, Jordan
Correspondence:
[*]
Corresponding author. Wafa’ H. AlAlaween, Department of Industrial Engineering, The University of Jordan, Amman, Jordan. Tel.: +962777788268; E-mail: w.alaween@ju.edu.jo.
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.
Keywords: Fuzzy logic, particle swarm optimization, radial based integrated network, right-first-time production
