Optimization of robotic assembly sequences using immune based technique

Biswal, Bibhuti Bhusan; Deepak, B. B. V. L.; Rao, Yogesh

doi:10.1108/17410381311318882

Cited by 18 publications

(8 citation statements)

References 13 publications

(9 reference statements)

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“…Even though the method is applied for both ASP and plant assignment, but the algorithm has not considered the stability of the sequence by which the generated sequence is not an optimal assembly sequence. Meanwhile, the algorithms like Artificial Immune System (AIS), 23,24 FPA, MA, and IHSA play an important role in achieving the optimal assembly sequences.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal robotic assembly sequence planning using stability graph through stable assembly subset identification

Murali

Deepak

Raju³

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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Assembly sequence planning is one of the multi-model optimization problems, in which more than one objective function has to be optimized at a time to obtain the quality assembly sequence. Moreover obtaining the feasible sequences from the possible finite set of sequences is a difficult task as the assembly sequence planning problem is N-P hard combinatorial problem. To solve the assembly sequence planning problem, researchers have developed various techniques to obtain the optimum solution. The developed methodologies have many drawbacks like struck at local optima, poor performance, huge search space and many more. To overcome these difficulties, the current research work aims to use stability graph to generate stable assembly subsets for obtaining the optimum assembly sequences. In the proposed methodology, to reduce the search space and to obtain the quality assembly sequences, stability graph is considered. Moreover, the fitness of assembly subsets is evaluated according to the user weights at each level before proceeding to the higher levels. Due to this, the higher fitness value subsets are eliminated at each stage by which time of execution will reduce enormously. The proposed methodology has implemented on various industrial products and compared the results with the various well-known algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal robotic assembly sequence planning using stability graph through stable assembly subset identification

Murali

Deepak

Raju³

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…As before, the focus is on the process and not the product design. Alternative assembly line arrangements with stochastic operation times were evaluated using a computer simulation approach by Das et al (2012).This research, once again deals with the assembly process and line optimization by Assembly process optimization Asadi et al (2017) Link between product design and assembly system flexibility Demonstrative case study Biswal et al (2013) Robotic assembly sequences Optimization study Chen et al (2008) Assembly sequence planning Three stage integrated approach using neural networks Choi et al (2009) Multi criteria assembly sequence planning Genetic algorithms Cohen (2013) Determination of number of stations per section of an assembly line Assembly line segmentation Model Cohen (2015) Integrated modeling of manual and automatic assembly Quantitative methodology Das et al (2010) Assembly line balancing with variable operation times Computer simulation approach Das et al (2012) Alternative assembly line arrangements with stochastic operation times…”

Section: Research On Product Assemblymentioning

confidence: 99%

Ease of product assembly through a time-based design methodology

Desai

2019

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Purpose This paper aims to present a design methodology to enable product design for ease of assembly. It is corroborated by means of a case study. The methodology is based on standard time data. This enables quick computation of assembly time as well as comparing different design options for ease of assembly. Design/methodology/approach Component design that is easy to assemble is likely to take less time and vice versa. Assembly time is a function of product design attributes such as geometric shape, weight, center of gravity, type of material, number of fasteners and types of fasteners. The methodology uses standard data to achieve its objective. Numeric scores are developed for each design feature based on the aforementioned design attributes. This enables not only computation of assembly time for a brand new product but also comparison of two or more alternative design configurations from the point of view of ease of assembly. Findings The value of the system is corroborated by means of case studies of actual product designs. It is demonstrated that changing any of the underlying design attributes (such as type of fastener used, number of fasteners used, material of the component and component shape) is likely to result in changing the amount of time taken to assemble the product. The scoring system facilitates the quick computation of assembly time Originality/value The amount of time to assemble a product before the product is ever designed is facilitated by this system. Assembly time is a direct function of product design attributes. Process time is calculated using standard data, specifically, the Methods Time Measurement (MTM) system. This is accomplished by converting design features into time measurement units (TMUs). Assembly cost can then be easily computed by using assembly time as the basis. The computation of assembly time and cost is important inasmuch as its role in influencing productivity. This is of obvious value not only to the designer but the company as a whole.

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“…Chang et al 84 proposed AIS algorithm based on the clonal selection operation to generate the optimal assembly sequence. Biswal et al 85 proposed immunebased optimal ASP by considering the clonal section and affinity maturation to obtain the optimal assembly sequence.…”

Section: Reviewmentioning

confidence: 99%

Assembly sequence planning using soft computing methods: A review

Deepak

Murali

Bahubalendruni³

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part E:

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The implementation of artificial intelligence techniques is increasing rapidly in recent years to solve numerous engineering problems. Assembly sequence planning is one of the prominent complex combinatorial problem draw attention of industrial engineers to economize the overall manufacturing cost by minimizing the assembly time and energy. Due to large search space and multiple assembly predicate criteria, researchers are motivated towards efficient utilization of AI techniques to address the problem. Literature review on various artificial intelligence techniques for obtaining the optimal assembly sequence planning are analyzed and the limitations of the existed methodologies are discussed in detail. This review provides an outlook for the researchers on various artificial intell1igent techniques which will be useful to carry out research for obtaining the optimum assembly sequence planning while qualifying various assembly predicate criteria.

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Optimization of robotic assembly sequences using immune based technique

Cited by 18 publications

References 13 publications

Optimal robotic assembly sequence planning using stability graph through stable assembly subset identification

Optimal robotic assembly sequence planning using stability graph through stable assembly subset identification

Ease of product assembly through a time-based design methodology

Assembly sequence planning using soft computing methods: A review

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