Simulation of Flexible Manufacturing System using Adaptive Neuro Fuzzy Hybrid Structure for Efficient Job Sequencing
Rajkiran Bramhane1, Arun Arora2, H. Chandra3
1Rajkiran Bramhane, Student of M.E (Prod. Engg.), Bhilai Institute of Technology, Durg, Chhattisgarh, India.
2Dr.Arun Arora, Principal, Department of Mechanical Engineering, Bhilai Institute of Technology,Durg, Chhattisgarh, India.
3Dr.H.Chandra, Sr. Asso. Prof., Department of Mechanical Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India.
Manuscript received on July 05, 2014. | Revised Manuscript received on July 11, 2014. | Manuscript published on July 15, 2014. | PP: 21-27 | Volume-2 Issue-8, July 2014. | Retrieval Number: H0691072814/2014©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley
©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Flexible Manufacturing Systems (FMS) basically belongs to a category of productive systems in which the main characteristic is the simultaneous execution of several processes and sharing a finite set of resource. Analysis and modeling of flexible manufacturing system (FMS) includes priority analysis of machining jobs and machining routing for efficient profit and production. Flexible manufacturing system (FMS) job Priority calculation becomes exceptionally complex when it comes to contain frequent variations in the part designs of incoming jobs. This paper focuses on priority analysis of variety of incoming jobs into the system efficiently and maximizing system utilization and throughput of system where machines are equipped with different tools and tool magazines but multiple machines can be assigned to single operation. For the complete analysis of the proposed work, a cloud of four incoming jobs have been considered. The Jobs have been assigned the priority according to Slack per Remaining Operations. Usually the probability of incoming job priority is calculated based on three parameters based strategy. In this work an adaptive Neuro fuzzy inference system (ANFIS) is developed to calculate the priority of incoming jobs based on Slack per Remaining Operations (S/RO) parameter. Four horizontal CNC lathe machines have been utilized for this work. Therefore, in this paper, an ANFIS system is developed to generate best priority of incoming jobs. The results obtained clearly indicate the higher efficiency of the proposed work to decide the priority of the incoming jobs.
Keywords: Flexible manufacturing system (FMS), adaptive Neuro fuzzy inference system (ANFIS), Slack per Remaining Operations (S/RO), Incoming job priority.