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A novel tolerance geometric method based on machine learning

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Abstract

In most cases, designers must manually specify geometric tolerance types and values when designing mechanical products. For the same nominal geometry, different designers may specify different types and values of geometric tolerances. To reduce the uncertainty and realize the tolerance specification automatically, a tolerance specification method based on machine learning is proposed. The innovation of this paper is to find out the information that affects geometric tolerances selection and use machine learning methods to generate tolerance specifications. The realization of tolerance specifications is changed from rule-driven to data-driven. In this paper, feature engineering is performed on the data for the application scenarios of tolerance specifications, which improves the performance of the machine learning model. This approach firstly considers the past tolerance specification schemes as cases and sets up the cases to the tolerance specification database which contains information such as datum reference frame, positional relationship, spatial relationship, and product cost. Then perform feature engineering on the data and established machine learning algorithm to convert the tolerance specification problem into an optimization problem. Finally, a gear reducer as a case study is given to verify the method. The results are evaluated with three different machine learning evaluation indicators and made a comparison with the tolerance specification method in the industry. The final results show that the machine learning algorithm can automatically generate tolerance specifications, and after feature engineering, the accuracy of the tolerance specification results is improved.

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Abbreviations

DRF:

Datum reference frame

CAT:

Computer-aided tolerancing

GPS:

Geometrical product specification and verification

TTRS:

Technologically and topologically related surfaces

MGDE:

Minimum geometric datum elements

L:

Feature vector for geometric tolerance selection

l:

Characteristic parameter in the feature vector

n:

Number of characteristic parameters

d:

Normalized size of the feature

V:

Volume of the feature

S:

Surface area of the feature

c:

Detection cost

p:

Machining accuracy

m:

Instrument uncertainty

u:

Manufacturer’s expectation of product cost

Q:

Training set

yn :

Class label

yxn :

Model output result

P:

Parts to be specified

r:

Shape tolerance tendency parameter

SR:

Spatial relationship between the feature to be specified and the main datum

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Acknowledgements

This project was supported by National Natural Science Foundation of China (51505506); Henan universities key scientific research projects (19A460035, 20A460031, 20A460033); The Science and Technology Key Project of the Department of Education of Henan Province (Grant No. 202102210068). Applied Research Project of Independent Innovation in Zhongyuan Universtiy of Technology (K2018YY001).

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Authors and Affiliations

  1. Zhongyuan University of Technology, Henan, 450007, China

    Lu-jun Cui, Man-ying Sun, Yan-long Cao, Wen-han Zeng & Shi-rui Guo

  2. Institute of Mechanical Manufacturing Technology, China Academy of Engineering Physics, Mianyang, 621900, China

    Qi-jian Zhao

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  1. Lu-jun Cui
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  2. Man-ying Sun
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Correspondence to Man-ying Sun.

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Cui, Lj., Sun, My., Cao, Yl. et al. A novel tolerance geometric method based on machine learning. J Intell Manuf 32, 799–821 (2021). https://doi.org/10.1007/s10845-020-01706-7

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