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A hybrid immune genetic algorithm with tabu search for minimizing the tool switch times in CNC milling batch-processing

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Abstract

In order to enhance the machining efficiency, batch-processing is widely used in computer numerical control (CNC) milling machining. Each job in a batch requires a set of different tools to be processed, so tool switching is needed during processing. However, the frequent tool switching not only affects the machining efficiency, but also affects the life of the machine spindle. In order to solve this problem, a hybrid immune genetic algorithm with tabu search (HIGATS) integrated is proposed to minimize the tool switch times. In HIGATS, a well-designed encoding/decoding scheme is developed to represent the solution and evaluate the fitness; a novel constructive heuristic is used for initializing population; in order to balance the intensification and diversification, tabu search is integrated into genetic algorithm, and also, a problem-specific greedy immune operator is applied to intensify the searching ability. Simulation experiments are conducted to verify the performance of HIGATS by comparing it with other five algorithms. The results and analyses demonstrate that HIGATS outperforms the other five algorithms in minimizing the tool switch times.

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Data availability

The datasets used or analyzed in this study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Abbreviations

F :

Fitness value

t s :

Time required to switch a tool

C :

The capacity of tool magazine

B :

The batch size

N :

Total number of jobs in a batch

T j,i :

The ith tool of job j

S j,i :

The start processing time of Tj,i

E j,i :

The end processing time of Tj,i

NT j :

Number of tools of job j

M :

Maximum number of tools of all jobs require, \(M = \mathop {\max }\limits_{{1 \le j \le N}} \left\{ {NT_{j} } \right\}\)

T j :

M-Dimensional single row matrix of tool sequence for job j

S :

M × N Tool-status matrix, the element in the matrix is specified by xj,i

x j,i :

Tool switching status, where xj,i = 1 if tool i of job j need switch, and 0 otherwise

T :

Total tool switch times of all jobs

PopSize :

Population size

l :

Length of chromosome

I max :

Maximum number of iterations

I best :

Iterations of best fitness

T Non-OPT :

Total tool switch times of non-optimization method

Pc :

Crossover probability

Pm :

Mutation probability

m :

Integer random variable

r :

Real random variable

R :

Number of independent runs

CNC:

Computer numerical control

GA:

Genetic algorithm

MGA:

Modified genetic algorithm

TS:

Tabu search

MTS:

Modified Tabu search

HGATS:

Hybrid genetic algorithm with tabu search

HIGATS:

Hybrid immune genetic algorithm with tabu search

CAD:

Computer-aided design

CAM:

Computer-aided manufacture

Non-OPT:

Non-optimization method

OPT:

Optimization method

ATC:

Automatic tool changer

RND:

Initial population completely randomly

TFH:

Top-First Heuristic

ARPD:

Average Relative Percentage Deviation

INIT:

Initialization method

SOPR:

Selection operator

COPR:

Crossover operator

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Acknowledgements

This research work was supported by the National Natural Science Foundation of China under Grant No. 51875422.

Funding

This research work was supported by the National Natural Science Foundation of China under Grant No. 51875422.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Wuhan University of Science and Technology, No. 947, Heping Road, Qingshan District, Wuhan, China

    Shuangyuan Shi & Hegen Xiong

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  1. Shuangyuan Shi
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Contributions

All authors contributed to the study conception and design. Data collection, code implementation and result analysis were performed by HX and SS. The first draft of the manuscript was written by SS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Hegen Xiong.

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Shi, S., Xiong, H. A hybrid immune genetic algorithm with tabu search for minimizing the tool switch times in CNC milling batch-processing. Appl Intell 52, 7793–7807 (2022). https://doi.org/10.1007/s10489-021-02869-3

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