Tool path optimization in CNC punching machine for sheet metal manufacturing

Nguyen, Thanh-Hai; Pham, Huy-Tuan; Nguyen, Thi Hong-Minh

doi:10.1109/icsse.2017.8030901

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…In M1, the cost of the tour started at 1100mm and ended at 4117mm after a total of 108 iterations. In M2, the cost of the tour started at 7750mm and ended at 4900mm.The optimized cost of the tour for the omega plate problem is 4117mm and the optimal sequence by using proposed hybrid algorithm is [1,2,3,4,5,6,7,8,13,18,17,14,15,16,25,24,23,22,21,20,19,12,11,10,9, and 1], which has been obtained through memeplex 2 (M2).…”

Section: B Formulation Of Problem and Results Obtainedmentioning

confidence: 99%

“…In [6] cncKad Program is used to find the shortest path for drilling. The results are compared with already established results in the literature by using ACO and GA.…”

Section: Methodsmentioning

confidence: 99%

“…Archives for Technical Sciences 2022, 26 (1), [1][2][3][4][5][6][7][8][9][10][11][12] Technical Institute Bijeljina, Archives for Technical Sciences. Year XIV -N 0 26.…”

mentioning

confidence: 99%

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Application of Hybrid Sfla and Aco Algorithm to Omega Plate for Drilling Process Planning and Cost Management

Mehmood¹,

Umer²,

Asgher³

2022

AFST

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Tool traveling and tool switching time constitute almost seventy percent (70%) of the total time consumed in drilling process. This fact that 70% of the total time is nonproductive and does not add any value to the job, grabs attention of the researchers and the industrialist for optimization. A literature on drilling process revealed that very few studies have been done on hybridization of metaheuristics for optimization of tool travel time. This research gap is the motivation of the present study. In this study, two metaheuristic approaches – the shuffled frog leaping algorithm (SFLA) and ant colony optimization (ACO) were hybridized. With respect to hybridization of SFLA and ACO, this study signifies its originality and novelty in which main objective is to minimize the tool travel time. The literature review also revealed that the shortest path generated through commercially available software is not optimal all the time. This aspect emphasizes the application of metaheuristic algorithms on the real-world industrial problems. In this study, the proposed hybrid algorithm was applied to drilling of omega plate which is used in automobile manufacturing industry. The results of the proposed hybrid algorithm were compared with those of manual drilling path and software generated path. The results obtained through proposed hybrid algorithm were improved by 11.1% when compared to results of manual drilling path. The results of proposed algorithm were also better than results of commercial software Creo 6.0 and Siemens NX by 5.9% each. This showed that hybrid algorithm outperformed the commercially available software. This not only validates the efficacy of proposed hybrid algorithm, but also indicates the significance of the metaheuristic algorithm applications in industrial optimization problems.

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Section: B Formulation Of Problem and Results Obtainedmentioning

confidence: 99%

“…In [6] cncKad Program is used to find the shortest path for drilling. The results are compared with already established results in the literature by using ACO and GA.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Hybrid Sfla and Aco Algorithm to Omega Plate for Drilling Process Planning and Cost Management

Mehmood¹,

Umer²,

Asgher³

2022

AFST

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“…The results produced by GA have outperformed the results of WinCAM, CAMConcept and Catia V5. Nguyen et al [ 7 ] have used two metaheuristcs approaches, ACO and GA. The results achieved are them compared waith cncKad Program.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of hybrid SFLA-ACO algorithm and CAM softwares for optimization of drilling tool path problems

2023

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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. Article highlights 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.

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Multi-Hole Drilling Tool Path Planning and Cost Management through Hybrid SFLA-ACO Algorithm for Composites and Hybrid Materials

2022

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In the process of drilling multiple holes in composites and hybrid materials, almost 70% of the time is consumed in tool traveling and tool changing. Recently, researchers have focused on this consumption of time for optimization of the tool path. A literature review revealed the following research gap: little work has been performed on the hybridization of metaheuristics. In the present study, the hybridization of SFLA and ACO metaheuristic algorithms is carried out, which is based on this research gap. The hybridization of SFLA and ACO metaheuristic algorithms provides originality and novelty in this study. The main objective of this study is to minimize the tool path in drilling problems. The proposed algorithm was applied to five benchmark multi-hole drilling problems and one industrial problem from the literature. The outcome of this work is evaluated with the results of dynamic programming (DP), ACO, an immune-based evolutionary approach (IA), and a modified SFLA for five benchmark problems. The accuracy of the results was improved by 2.27% using the proposed hybrid algorithm, indicating that the proposed algorithm is superior to DP, ACO, IA, and the modified SFLA. Additionally, the results of the proposed hybrid algorithm for an example industrial problem from the literature were compared with those of the SFLA and modified SFLA. The proposed algorithm reduced the total cost by 6.17% and 3.76% compared with the SFLA and modified SFLA, respectively. Thus, the efficacy of the proposed hybrid algorithm was confirmed, along with its applicability.

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Tool path optimization in CNC punching machine for sheet metal manufacturing

Cited by 3 publications

References 5 publications

Application of Hybrid Sfla and Aco Algorithm to Omega Plate for Drilling Process Planning and Cost Management

Application of Hybrid Sfla and Aco Algorithm to Omega Plate for Drilling Process Planning and Cost Management

Application of hybrid SFLA-ACO algorithm and CAM softwares for optimization of drilling tool path problems

Multi-Hole Drilling Tool Path Planning and Cost Management through Hybrid SFLA-ACO Algorithm for Composites and Hybrid Materials

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