Generation of Optimal Sequence of Machining Operations in Setup Planning by Genetic Algorithms

Kumar, Chandan; Deb, Sankha

doi:10.1142/s0219686712500059

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…Matlab 2016b was used for simulation of both heuristics and their comparison. Crossover and mutation rate are stochastic parameters for GA. For the present case, crossover and mutation rate was selected to be 0.9 and 0.1 respectively which has been recommended in literature [3,52]. While comparing the process plans obtained with GA, it was observed that process plans obtained by Kumar & Deb [53] were unable to maintain precedence constraints which resulted in infeasible plans.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment

Khan

Ghafoor

Zahid³

2021

IEEE Access

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The need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focused on mass customization requires computer aided planning techniques that are able to cope with custom changes in products and explores intelligent algorithms for efficient scheduling solutions to reduce lead time. This problem has been categorized as NP-Hard in literature and is addressed by providing intelligent heuristics that focus on reducing machining time of the products at hand. However, as 70% of the lead time is consumed in non-value added tasks, it is fundamental to provide modular solutions that can reduce this time and handle part variety. To address the subject, this paper focuses on the generation of automated process plans for a single machine problem while focusing on reducing time lead time. Two evolutionary algorithms (EAs) have been proposed and compared to answer complex problem of process planning. A modified genetic algorithm (GA) has been proposed in addition to cuckoo search (CS) heuristic for this discrete problem. On testing with selected benchmark part ANC101, significant improvement was seen in terms of convergence with proposed EAs. Moreover, a novel Precedence Group Algorithm (PGA) is proposed to generate quality input for heuristics. The algorithm produces a set of initial population which significantly effects the performance of proposed heuristics. For the discrete constrained process planning problem, GA outperforms CS providing 10% more feasible scheduling options and three times lesser run time as compared to CS. The proposed technique is flexible and responsive in order to accommodate part variety, a necessary requirement for reconfigurable systems.

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Section: Experiments and Resultsmentioning

confidence: 99%

Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment

Khan

Ghafoor

Zahid³

2021

IEEE Access

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“…Reduction in product cost and responsiveness can be observed by customizing the machining capabilities at product design stage and then the reuse of these capabilities at reconfiguration stage. Kumar and Deb [17] carried out an analysis by minimizing weighted function in case of a simultaneous set up and tool change. The results not only gave the optimal solution for each parameter but also optimized the overall effect.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimum machine capabilities for reconfigurable manufacturing systems

Asghar

Zaman

Baqai

et al. 2018

Int J Adv Manuf Technol

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This is an author-deposited version published in: https://sam.ensam.eu Handle ID AbstractReconfigurable manufacturing systems constitute a new manufacturing paradigm and are considered the future of manufacturing because of their changeable and flexible nature. In a reconfigurable manufacturing environment, basic modules can be rearranged, interchanged or modified, to adjust the production capacity according to production requirements. Reconfigurable machine tools have modular structure comprising of basic and auxiliary modules that aid in modifying the functionality of a manufacturing system. As the product's design and its manufacturing capabilities are closely related, the manufacturing system is desired to be customizable to cater for all the design changes. Moreover, the performance of a manufacturing system lies in a set of planning and scheduling data incorporated with the machining capabilites keeping in view the market demands. This research work is based on the coevolution of process planning and machine configurations in which optimal machine capabilities are generated through the application of multi-objective genetic algorithms. Furthermore, based on these capabilities, system is tested for reconfiguration in case of production changeovers. Since, in a reconfigurable environment, same machine can be used to perform different tasks depending on the required configuration, the subject research work assigns optimum number of machines by minimizing the machining capabilities to carry out different operations in order to streamline production responses. An algorithm has also been developed and verified on a part family. As a result of the proposed methodology, an optimized reconfigurable framework can be achieved to realize optimal production of a part family. Finally, the proposed methodology was applied on a case study and respective conclusions were drawn.

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“…Choice of constraints again depends on the problem and the availability of machining data. For example, Qiao et al (2000) used various process planning rules including precedence rules, clustering rules, adjacent order rules, and so on; Kumar and Deb (2012) have attempted to optimise operation sequences in set-up planning based on minimising the number of set-up changes and tool changes, subject to various machining precedence constraints. GAs have also been used for solving optimisation problems in production planning and control such as process route optimisation (Bo et al, 2006), multi-objective scheduling (Azadeh et al, 2010) and facility layout planning (Kundu and Dan, 2012).…”

Section: Review Of Previous Researchmentioning

confidence: 99%

“…One of the major challenges for optimisation here lies in minimising the overall manufacturing cost. Several researchers have approached the problem of minimising the cost by minimising changes in parameters like machine changes, tool changes, and set-up changes (Ma et al, 2000;Krishna and Rao, 2006;Kumar and Deb, 2012). We have adopted a similar approach by assuming that, in a particular industry and while operating a particular machine, the manufacturing cost increases with increase in the machining time for a part, provided other factors like machine set-up time, number of operators, etc., are held constant.…”

Section: Introductionmentioning

confidence: 99%

An intelligent methodology for optimising machining operation sequence by ant system algorithm

Singh

Deb

2014

IJISE

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The paper describes an intelligent ant system-based algorithm for automatic generation of optimal sequence of machining operations required to produce a part, based on minimising the number of tool changes and setup changes subject to satisfying all precedence constraints during manufacturing. The MATLAB programme for the algorithm uses a list of machining operations, tool approach directions, and the precedence constraints between the operations as inputs. It generates only feasible sequences of operations and finds out an optimal sequence among them. The concept of specific selection of a starting node at the beginning of each ant cycle and introducing a precedence check in the transition rules reduces the computation time significantly. A comparative study shows that for a demonstration run, the proposed ant system-based approach performed faster than previously developed methodologies for ant colony optimisation as well as a genetic algorithm-based optimisation techniques.

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Generation of Optimal Sequence of Machining Operations in Setup Planning by Genetic Algorithms

Cited by 12 publications

References 11 publications

Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment

Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment

Optimum machine capabilities for reconfigurable manufacturing systems

An intelligent methodology for optimising machining operation sequence by ant system algorithm

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