Design and optimization of concurrent tolerance in mechanical assemblies using bat algorithm

Kumar, Lalitesh; Padmanaban, K.; Kumar, Saurabh; Balamurugan, C.

doi:10.1007/s12206-016-0521-y

Cited by 27 publications

(13 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. (Kumar, et al, 2016) utilized BA to optimize the tolerance based on parallel objectives to minimize the cost of manufacturing, present worth of expected quality loss and quality loss and obtained better results than existing methods. Ref.…”

Section: Methodsmentioning

confidence: 99%

Deploying NSBA algorithm for bi-objective manufacturing cells considering percentage utilisation of machines

Martinsen

Ghosh

2020

IJISTA

View full text Add to dashboard Cite

Percentage Utilization of Machines is considered as an important production factor for manufacturing Cell Formation Problem (CFP) in Cellular Manufacturing (CM). This recently developed concept correctly emphasize ration data in context of CM. In this paper, a utilization based bi-objective mathematical model is developed, which minimizes the total machine utilization induced by bottleneck machines and number of voids. Thereafter, a new data generating algorithm is introduced. The abovementioned bi-objective CFP is solved using a Non-Dominated Sorting Bat Algorithm (NSBA), which is compared with published Multi-Objective Bat Algorithm (MOBA) successfully. Statistical tests are conducted and data consistency is confirmed on obtained results. The computational experiments depict that the Pareto solutions of NSBA are 35.7% improved. The contribution of this research is threefold. First, an accurate bi-objective mathematical expression is developed for utilization based CFPs. Second, a novel data generating algorithm is stated. Third, NSBA technique is successfully tested.

show abstract

Section: Methodsmentioning

confidence: 99%

Deploying NSBA algorithm for bi-objective manufacturing cells considering percentage utilisation of machines

Martinsen

Ghosh

2020

IJISTA

View full text Add to dashboard Cite

show abstract

“…Later, the best network model was defined as the objective function, for which GA was applied to obtain optimum results. The first case study is a gearbox assembly (Kumar et al ., 2016) consisting of rotational parts and second, a two pin-two hole assembly (Saravanan et al ., 2014) with prismatic and rotational parts.…”

Section: Case Studymentioning

confidence: 99%

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

Saravanan

Jerald

Rani

2020

AIEDAM

View full text Add to dashboard Cite

The objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

show abstract

“…BA was used to solve the 2 Mathematical Problems in Engineering reliability redundancy allocation problem (RAP) and showed that the BA was competitive with the best known heuristics for redundancy allocation [4]. BA was used to design and optimize the concurrent tolerance in mechanical assemblies [32]. It was found that the BA produced better results than other methods (such as EA and GA) in the initial generations of concurrent tolerance problems.…”

Section: Literature Reviewmentioning

confidence: 99%

GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

Chen

Sun

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions.

show abstract

Design and optimization of concurrent tolerance in mechanical assemblies using bat algorithm

Cited by 27 publications

References 26 publications

Deploying NSBA algorithm for bi-objective manufacturing cells considering percentage utilisation of machines

Deploying NSBA algorithm for bi-objective manufacturing cells considering percentage utilisation of machines

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

Contact Info

Product

Resources

About