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

Singh, Sneha; Deb, Sankha

doi:10.1504/ijise.2014.060654

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…This single objective optimisation problem has also been formulated as a 0-1 non-linear mathematic model with consideration of tool air-cutting time and tool switch time [26]. For the tool change cost, a single objective optimisation problem for minimising it by feature sequencing was developed subjected to various manufacturing precedence constraints [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach

Tang

Liu

et al. 2018

Energy Conversion and Management

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Minimising the energy consumption of tool change and tool path of machining by sequencing the features Abstract: A considerable amount of energy is consumed by machine tools during the run-time operations such as tool change and tool path. The value of this part of energy is affected by the processing sequence of features of a part (PSFP) because the tool path and tool change plan will vary based on the different PSFP. This paper firstly aims to understand the relationship between the PSFP and the energy consumption of tool change and tool path during the feature transitions. Then, a model is introduced for the single objective optimisation problem that minimises the energy consumption of machine tools during the feature transitions which include all the tool path and tool change operations. Finally, optimisation approaches including depth-first search and genetic algorithm are modified and applied to find the optimal PSFP which results in the minimisation of the energy consumption of feature transitions (EFT). In the case study, the optimal and nearoptimal sequences of features, in terms of the minimum EFT, of a 15 features part which is processed by a machining centre have been found. The optimal PSFP achieves a 28.60% EFT reduction, which validates the effectiveness of the developed model and optimisation approaches. Besides, a 27.95% time reduction of feature transitions benefits from the EFT minimisation.

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Section: Literature Reviewmentioning

confidence: 99%

Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach

Tang

Liu

et al. 2018

Energy Conversion and Management

View full text Add to dashboard Cite

show abstract

“…This single objective optimisation problem has also been formu-13 lated as a 0-1 non-linear mathematic model with consideration of tool air-cutting time and tool 14 switch time [26]. For the tool change cost, a single objective optimisation problem for minimising it 15 by feature sequencing was developed subjected to various manufacturing precedence constraints [27]. 16 Bhaskara Reddy [28] proposed a feature precedence graph to identify manufacturing precedence 17…”

Section: Introductionmentioning

confidence: 99%

Minimising the energy consumption of tool change and tool path of machining by sequencing the features

Liu

Peng

et al. 2018

Energy

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and tool change plan will vary based on the different PSFP. This paper firstly aims to un-6 derstand the relationship between the PSFP and the energy consumption of tool change 7 and tool path during the feature transitions. Then, a model is introduced for the single ob-8 jective optimisation problem that minimises the energy consumption of machine tools 9 during the feature transitions which include all the tool path and tool change operations. 10Finally, optimisation approaches including depth-first search and genetic algorithm are 11 modified and applied to find the optimal PSFP which results in the minimisation of the 12 energy consumption of feature transitions (EFT). In the case study, the optimal and near-13 optimal sequences of features, in terms of the minimum EFT, of a 15 features part which 14 is processed by a machining centre have been found. The optimal PSFP achieves a 15

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“…For the generation of the cylindrical and prismatic pieces cutting process, several authors have focused on the use of rules-based Primitive Form Features (PFF), which contain the information required for their manufacture, and then use Artificial Intelligence techniques (IA), among them the CBR, in order to detect the level of similarity between two EFPs belonging to different pieces (phase of retrieval in the CBR). Basically, the geometric coincidence of the part to be fabricated with the existing ones in the base of cases is verified, without considering technological attributes as the precision and type of material [5][6][7][8][9]. Hsin-chi Chang, Wen F. Lu and Xiaoqing Frank Liu [10], worked on the planning of the process of cutting rotational symmetrical parts, and introduced significant improvements on previous systems when considering the precision, material and the cutting process history.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Adaptation of Axisymmetric Solid Parts in Machining Process Planning by Case Based Reasoning

García¹,

Lancho²,

Aleaga³

et al. 2017

IJIES

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The goal of this paper is the adaptation of plans of manufacturing processes of Axisymmetric Solid Parts under the approach of Case Based Reasoning (CBR). To this end, a data model for feature-based representation has been developed, which contains the geometric, dimensional and technological information required for the preparation of the one-piece process plan. As a result, an algorithm to generate new parts process plans from a similar plan retrieved from the case base is presented, which is based on the specific information of the Primitive Form Features (PFF) and considers the possible interaction between neighboring PFFs. The computer support of the developed algorithm was implemented in AutoLISP as programming language of the CAD system used and the interface was created in OpenDCL.

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

Cited by 7 publications

References 30 publications

Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach

Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach

Minimising the energy consumption of tool change and tool path of machining by sequencing the features

Intelligent Adaptation of Axisymmetric Solid Parts in Machining Process Planning by Case Based Reasoning

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