Parallel implementation of metaheuristics for optimizing tool path computation on CNC machining

Rico-Garcia, Hector; Sánchez-Romero, José-Luis; Migallón, Héctor; Rao, R. Venkata

doi:10.1016/j.compind.2020.103322

Cited by 19 publications

(8 citation statements)

References 25 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 [8] Discrete Teaching Learning Based Optimization (DTLBO) is applied for sequencing the holes and optimizing the tool travel time. The problem solved with DTLBO is compared with commercially available software CAMotics.…”

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%

Section: Methodsmentioning

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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“…Surface roughness for plasma arc cutting of AISID2 steel [3] and electric discharge machining (EDM) of pure magnesium [4] were minimized by TLBO via the searching of optimal machining parameters. The tool path computation of CNC machining in [5] was formulated as a discrete optimization problem and a discrete TLBO variant was implemented via parallel computing to determine an optimized path with minimum global distance. Apart from the original TLBO, substantial amounts of TLBO variants have also been developed via various enhancement schemes to solve different challenging optimization problems and real-world applications.…”

Section: ) Single Objective Optimizationmentioning

confidence: 99%

“…Although numerous works related to TLBO were proposed by different researchers since its inception, some common drawbacks and technical challenges can be observed from these studies. First of all, it is noteworthy that the related works of [1][2][3][4][5][31][32][33][34][35] focused on applying the original TLBO to solve different real-world applications, particularly on the machining optimization problems. Despite having relatively good performances in solving these problems, the original TLBO tends to suffer with drastic performance degradation when dealing with more complex optimization problems with explosive numbers of local optima in fitness landscapes.…”

Section: B Challenges Of Existing Workmentioning

confidence: 99%

Production Wastage Avoidance Using Modified Multi-Objective Teaching Learning Based Optimization Embedded With Refined Learning Scheme

Natarajan

Kaviarasan²,

Ang

et al. 2022

IEEE Access

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Teaching learning-based optimization (TLBO) is a popular algorithm used to solve various optimization problems. Nevertheless, conventional TLBO and some improved variants tends to suffer with premature convergence due to rapid loss of population diversity, especially when handling the challenging optimization problems. Furthermore, it is not practical to tackle real-world multiobjective problems using prior approach given the frequent changes of customers' requirements. Motivated by these challenges, an improved variant known as Modified Multi-objective Teaching Learning Based Optimization-Refined Learning Scheme (MMTLBO-RLS) was proposed as a posterior approach to solve challenging multiobjective optimization problems, including the prediction of optimum turning parameters to machine Polyether ether ketone material (PEEK). Substantial modifications were introduced for teacher and learner phases of MMTLBO-RLS to achieve better balancing of exploration and exploitation searches without incurring excessive computational cost. For modified teacher phase of MMTLBO-RLS, each learner was guided by a unique teacher solution and unique mean position to perform searching with better diversity. Meanwhile, two new learning strategies are incorporated into the modified learner phase of MMTLBO-RLS, enabling all learners to enhance their knowledge more efficiently based on their learning preferences. A systematic approach was followed to develop modelling equations required for optimization. The developed algorithm was then employed in single objective optimization as well as multiobjective optimization to cater its performances in any real-world environment. The prediction model reports that surface roughness of 1.1042μm and material removal rate of 22.8991 cm 3 /minute can be achieved. The predicted results differ from validation results by less than 2.69% in any case of optimization. A benchmarking on the performance of MMTLBO-RLS in solving CEC 2009 multiobjective benchmark functions was further carried out with other seven meta-heuristic algorithms. The superior performance of MMTLBO-RLS proves that it is not only suitable to be used in industries to produce the parts of PEEK with supportive quality and quantity, but it is also able to solve other multiobjective optimization problems with competitive performances.

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A Variant of Parallel-Hybrid Genetic Algorithm for Large-Scale Traveling Salesman Problem

Tapia,

Martínez-Vargas,

Cosío-León

et al. 2023

Handbook of Formal Optimization

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Parallel implementation of metaheuristics for optimizing tool path computation on CNC machining

Cited by 19 publications

References 25 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

Production Wastage Avoidance Using Modified Multi-Objective Teaching Learning Based Optimization Embedded With Refined Learning Scheme

A Variant of Parallel-Hybrid Genetic Algorithm for Large-Scale Traveling Salesman Problem

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