TY - GEN
T1 - Intelligent Sequence Optimization Method for Hole Making Operations in 2M Production Line
AU - Ahammed, Thanveer
AU - Qudeiri, Jaber Abu
AU - Mourad, Abdel Hamid
AU - Ziout, Aiman
AU - Safieh, Faris
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - This paper is concerned with the optimization of the tool path in a production line consisting of two machine tools. Existing computer – numerical control (CNC) time estimation methods are based either for a single machine or a single operation. As current methods don’t illustrate the necessity of the multiple operations with more than one machine, this paper presents a new method for CNC machining time estimation which predicts the optimal tool path sequence with minimal time for a 2M production line. The optimized sequence is determined by employing the most reliable hybrid method i.e., Genetic Algorithm (GA). Attention was focusing on the hole making operations where a hole may need multiple cutting tools to get the process finished. Each of the machines can do certain set of operations. So the non-productive time between two machines should be minimized and it is obtained by this intelligent sequence optimizer. This proposed technique is developed on a modified travel salesman problem algorithm with preceding constraints. The work also introduces a computational program based on this methodology. The numerical simulation conducted in this research shows that the proposed approach is feasible and practical. It is beneficial especially in real-time manufacturing process outlining and scheduling multiple systems.
AB - This paper is concerned with the optimization of the tool path in a production line consisting of two machine tools. Existing computer – numerical control (CNC) time estimation methods are based either for a single machine or a single operation. As current methods don’t illustrate the necessity of the multiple operations with more than one machine, this paper presents a new method for CNC machining time estimation which predicts the optimal tool path sequence with minimal time for a 2M production line. The optimized sequence is determined by employing the most reliable hybrid method i.e., Genetic Algorithm (GA). Attention was focusing on the hole making operations where a hole may need multiple cutting tools to get the process finished. Each of the machines can do certain set of operations. So the non-productive time between two machines should be minimized and it is obtained by this intelligent sequence optimizer. This proposed technique is developed on a modified travel salesman problem algorithm with preceding constraints. The work also introduces a computational program based on this methodology. The numerical simulation conducted in this research shows that the proposed approach is feasible and practical. It is beneficial especially in real-time manufacturing process outlining and scheduling multiple systems.
KW - CNC
KW - Optimization
KW - Production line
KW - Tool path
UR - http://www.scopus.com/inward/record.url?scp=85075271769&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075271769&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30577-2_29
DO - 10.1007/978-3-030-30577-2_29
M3 - Conference contribution
AN - SCOPUS:85075271769
SN - 9783030305765
T3 - Lecture Notes in Electrical Engineering
SP - 339
EP - 355
BT - Proceedings of ICETIT 2019 - Emerging Trends in Information Technology
A2 - Singh, Pradeep Kumar
A2 - Panigrahi, Bijaya Ketan
A2 - Suryadevara, Nagender Kumar
A2 - Sharma, Sudhir Kumar
A2 - Singh, Amit Prakash
PB - Springer
T2 - 1st International Conference on Emerging Trends in Information Technology, ICETIT 2019
Y2 - 21 June 2019 through 22 June 2019
ER -