Mehmood Nasir, Umer Muhammad, Asgher Umer
Business and Engineering Management Department/Sir Syed, CASE Institute of Technology, Islamabad, Pakistan.
Quality Assurance & NUST International Office Directorate (QA & NIO Dte), National University of Science and Technology (NUST), Islamabad, Pakistan.
SN Appl Sci. 2023;5(2):61. doi: 10.1007/s42452-022-05271-x. Epub 2023 Jan 20.
In drilling process almost seventy percent time is spent in tool switching and moving the spindle from one hole to the other. This time travel is non productive as it does not take part in actual drilling process. Therefore, this non productive time needs to be optimized. Different metaheuristic algorithms have been applied to minimize this non productive tool travel time. In this study, two metaheuristic approaches, shuffled frog leaping algorithm (SFLA) and ant colony optimization (ACO) have been hybridized. In industry, the CAM softwares are employed for minimization of non productive tool travel time and it is considered that the path obtained by using the CAM softwares is the optimized path. However this is not the case in all problems. In order to show the contribution of the SFLA-ACO algorithm and to prove that results achieved through CAM softwares are not always optimized, hybrid SFLA-ACO algorithm has been applied to two drilling problems as case studies with the main objective of minimization of non productive tool travel time. The drilling problems which are taken from the manufacturing industry include ventilator manifold problem and lift axle mounting bracket problem. The results of hybrid SFLA-ACO algorithm have been compared with the results of commercially available computer aided manufacturing (CAM) software. For comparison purpose, the CAM softwares used are Creo 6.0, Pro E, Siemens NX and Solidworks. The comparison shows that the results of proposed hybrid SFLA-ACO algorithm are better than commercially available CAM softwares in both real world manufacturing problems.
Different optimization techniques are being used for optimization of drilling tool path problems. In this paper two techniques SFLA and ACO has been combined to form a hybrid SFLA-ACO algorithm and has been applied to the real world industrial problems.Two real world problems have been taken from the local manufacturing industries. In both the problems the objective is to optimize the tool traveling time through hybrid SFLA-ACO and compare it with CAM software.Four CAM softwares have been used for comparison purpose. The problems undertaken are solved through these CAM software and compared with the results of hybrid SFLA-ACO results. As result of comparison it is found that in both the problems the performance of hybrid SFLA-ACO algorithm remains outclass. This signifies that results of CAM software in case of optimization of drilling tool path are not always optimal and these can be improved by using different optimization techniques.
在钻孔过程中,几乎70%的时间都花在了刀具切换以及将主轴从一个孔移动到另一个孔上。这段移动时间是无生产效率的,因为它并未参与实际的钻孔过程。因此,需要对这段无生产效率的时间进行优化。已应用不同的元启发式算法来最小化这种无生产效率的刀具移动时间。在本研究中,将两种元启发式方法,即洗牌蛙跳算法(SFLA)和蚁群优化算法(ACO)进行了混合。在工业中,计算机辅助制造(CAM)软件被用于最小化无生产效率的刀具移动时间,并且人们认为使用CAM软件获得的路径就是优化路径。然而,并非所有问题都是如此。为了展示SFLA - ACO算法的贡献,并证明通过CAM软件获得的结果并非总是最优的,已将混合SFLA - ACO算法作为案例研究应用于两个钻孔问题,主要目标是最小化无生产效率的刀具移动时间。从制造业选取的钻孔问题包括呼吸机歧管问题和提升轴安装支架问题。已将混合SFLA - ACO算法的结果与市售计算机辅助制造(CAM)软件的结果进行了比较。为了进行比较,所使用的CAM软件有Creo 6.0、Pro E、西门子NX和Solidworks。比较结果表明,在这两个实际制造问题中,所提出的混合SFLA - ACO算法的结果均优于市售CAM软件。
不同的优化技术正被用于优化钻孔刀具路径问题。本文将两种技术SFLA和ACO相结合,形成了混合SFLA - ACO算法,并已应用于实际工业问题。从当地制造业选取了两个实际问题。在这两个问题中,目标都是通过混合SFLA - ACO来优化刀具移动时间,并将其与CAM软件进行比较。已使用四种CAM软件进行比较。所承担的问题通过这些CAM软件求解,并与混合SFLA - ACO的结果进行比较。比较结果发现,在这两个问题中,混合SFLA - ACO算法的性能均出类拔萃。这表明在钻孔刀具路径优化的情况下,CAM软件的结果并非总是最优的,并且可以通过使用不同的优化技术来改进。