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Manufacturing motor core lamination from thin non-oriented silicon steel sheet direct by pulsed laser cutting using multi-quality optimized process parameters

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Abstract

Based on the experimental results, this study develops a multi-objective optimization scheme to obtain the set of optimal process parameters for pulsed laser direct cutting of non-oriented silicon steel sheets for manufacturing motor core laminations. The experiments were conducted in the nital solvent, which was the one with the smallest thermal effect of laser cutting among the candidate environments of air, deionized water, alcohol, lubricant oil, sodium chloride solution, and nital solution. The laser was a pulsed Nd:YAG nanosecond laser and the thickness of the silicon steel was 100 μm. The effects of three laser process parameters, laser power (P), cutting speed (v), and pulse repetition rate (f) on the cutting qualities of the heat-affected zone (HAZ), cutting time (TC), and multiple dimensional accuracies of the cut core lamination were examined. The inspected geometric accuracies included the errors in inner and outer core diameters (ED1 and ED2), the error in tooth width (EL), and the roundness of inner and outer diameters (C1 and C2). The developed multi-objective optimization model was a PSI-based ELM-GA scheme consisting of the extreme learning machine (ELM) model for connecting the inputs and the outputs, the preference selection index (PSI) method for obtaining the weighted multi-objective function, and the genetic algorithm (GA) for process optimization. Through employing the predicted optimal process parameter set, the validation experiment showed that the errors between the prediction and the experimental result for the seven qualities of HAZ, ED1, ED2, C1, C2, EL, and TC were 4.04%, 6.25%, 4.02%, 0.48%, 2.14%, 5.09%, and 1.25%, respectively. The HAZ and geometric accuracy of the cut core laminations were qualified for the subsequent lamination assembly. Consequently, the merit of direct formation of ready-for-assembly core laminations without the need for any post-processing renders the proposed laser cutting scheme an economical and effective approach for manufacturing motor core laminations from thin silicon steel sheets.

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Data availability

The data and material are available.

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Abbreviations

SS:

Silicon steel

TSSS:

Thin silicon steel sheet

HAZ:

Heat-affected zone

AI:

Artificial intelligence

MRA:

Multiple regression analysis

ANN:

Artificial neural network

ELM:

Extreme learning machine

GA:

Genetic algorithm

E D1 :

Error of inner diameter

E D2 :

Error of outer diameter

E L :

Error of tooth width

C 1 :

Roundness of inner diameter

C 2 :

Roundness of outer diameter

T C :

Cutting time

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Funding

The Ministry of Science and Technology of Taiwan provided essential support for this project under grant numbers MOST 110–2221-E-008–043-MY2, MOST 111–2622-E008-018, and MOST 111–2218-E-008–006. Professor Jeng-Rong Ho was the recipient of all these grants.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Central University, 300 Zhongda Road, Zhongli District, Taoyuan City, 32001, Taiwan

    Chih-Kuang Lin, Pi-Cheng Tung & Jeng-Rong Ho

  2. Faculty of Mechanical Engineering, Can Tho University, Ninh Kieu District, 3/2 Street, Can Tho City, Vietnam

    Hoai-Tan Nguyen & Van-Cuong Nguyen

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  1. Hoai-Tan Nguyen
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Contributions

Hoai-Tan Nguyen: investigation, experiment, data curation, writing original draft, and visualization.

Chih-Kuang Lin: conceptualization, methodology, discussion, and modification.

Pi-Cheng Tung: conceptualization, methodology, discussion, and modification.

Van-Cuong Nguyen: conceptualization, methodology, discussion, and modification.

Jeng-Rong Ho: resource, conceptualization, methodology, writing, review, editing, supervision, and funding acquisition.

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Correspondence to Jeng-Rong Ho.

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Nguyen, HT., Lin, CK., Tung, PC. et al. Manufacturing motor core lamination from thin non-oriented silicon steel sheet direct by pulsed laser cutting using multi-quality optimized process parameters. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-13661-1

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