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A hybrid approach of process reasoning and artificial intelligence-based intelligent decision system framework for fatigue life of belt grinding

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Abstract

Belt grinding is widely used as the final step in the fabrication of fatigue-resistant surfaces of nickel-based superalloy components, and fatigue life after grinding is one of the most concerning issues. However, the response mechanism of fatigue life under different grinding parameter excitation conditions is not well understood for a long time. In this study, a system framework of fatigue life prediction for nickel-based superalloy abrasive belt based on process reasoning and artificial intelligence algorithm is proposed. Based on the process reasoning method, the mathematical relationship between grinding parameters and fatigue life is established. The equation is solved by RNN and LSMT algorithms embedded in the system, and the excitation response model of process parameters to fatigue life is obtained. The results show that the prediction accuracy of the system is high. The mean squared error (MSE) of the LSTM algorithm is below 0.0441, and the R-squared can be above 0.9956. In addition, experimental verification has been carried out, the observation of the specimen section shows that the process parameters have an effect on the initiation position, distribution, and crack length of the fatigue crack source, which are related to the stress concentration and residual stress distribution at the depth of the grinding scratches. Furthermore, using Spring Boot framework, an intelligent decision-making system based on this system framework is developed by using java and python.

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Data availability

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

Abbreviations

u(t):

Response

K t :

Stress concentration factor

I(t) :

Incentive factor

n :

State of stress

u rest :

Initial parameter

λ :

Ratio of unevenness spacing to unevenness height

C :

Material property parameter

ρ :

Micro-crack root radius

R :

Material property parameter

σ rs :

Residual compressive stress

Ra:

Roughness

σ es :

Tensile stress

Rs:

residual stress

K′ :

corrected fatigue stress concentration factor

w :

Weight

\(\overset{\bullet }{y}\) :

Calculating output values

s:

excitation parameter

A :

Coefficient of Ra

a :

crack size

B :

Coefficient of σrs

N :

Number of fatigue cycles

h :

Step size

M :

Material property parameters

L :

Loss function

ΔK :

Magnitude of the strength stress factor

h s :

Hidden layer output

a c :

Critical crack size

f :

Function of component geometry and crack size

σ max :

Maximum cyclic stress

K C :

Mracture toughness of the material

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (No. 52175377), Natural Science Foundation of Chongqing (No. CSTB2022NSCO-LZX0080), Innovation Group Science Fund of Chongqing Natural Science Foundation (No. cstc2019jcyj-cxttX0003), and Basic Research Funds for Central Universities (No. 2023CDIXY-026 and 2023CDIXY-026)

Author information

Authors and Affiliations

  1. College of Mechanical and Vehicle Engineering, Chongqing University, No. 174, Shazhengjie street, Shapingba, Chongqing, 400044, China

    Youdong Zhang, Guijian Xiao, Hui Gao, Bao Zhu & Yun Huang

  2. State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, No. 174, Shazhengjie street, Shapingba, Chongqing, 400044, China

    Guijian Xiao & Yun Huang

  3. Chongqing Midea General Refrigeration Equipment Co.,Ltd., No. 15, Rose Road, Nan’an, Chongqing, 400044, China

    Ji Ma

Authors
  1. Youdong Zhang
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  2. Guijian Xiao
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  3. Ji Ma
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  4. Hui Gao
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Contributions

Youdong Zhang: methodology, validation, and writing—original draft preparation. Guijian Xiao: conceptualization and funding acquisition. Bao Zhu: data curation. Hui Gao: investigation. Yun Huang: supervision.

Corresponding author

Correspondence to Guijian Xiao.

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Written informed consent was obtained from individual or guardian participants.

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Manuscript was approved by all authors for publication.

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The authors declare no competing interests.

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Appendix

Appendix

v s

v f

a p

Workpiece 1

Workpiece 2

Workpiece 3

Mean value

11

3

0.2

189,937

152,414

170,418

170,923

16

3

0.2

167,503

150,433

103,826

140,587.3

21

3

0.2

122,701

152,603

163,102

146,135.3

18

6

0.08

132,011

123,806

187,104

147,640.3

18

6

0.12

141,129

102,178

127,724

123,677

18

6

0.16

103,438

91,648

45,348

80,144.7

10

10

0.2

175,099

97,336

140,117

137,517.3

10

14

0.2

111,837

107,709

76,678

98,741.3

10

18

0.2

58,583

110,941

70,408

79,977.3

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Zhang, Y., Xiao, G., Ma, J. et al. A hybrid approach of process reasoning and artificial intelligence-based intelligent decision system framework for fatigue life of belt grinding. Int J Adv Manuf Technol 130, 311–328 (2024). https://doi.org/10.1007/s00170-023-12597-2

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  • DOI: https://doi.org/10.1007/s00170-023-12597-2

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