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Research Papers

The Strategy of Datum Reference Frame Selection Based on Statistical Learning

[+] Author and Article Information
Yanlong Cao

State Key Laboratory of Fluid Power
and Mechatronic Systems,
College of Mechanical Engineering,
Zhejiang University,
Hangzhou 310027, China;
Key Laboratory of Advanced Manufacturing
Technology of Zhejiang Province,
College of Mechanical Engineering,
Zhejiang University,
Zheda Rd. 38,
Hangzhou 310027, China
e-mail: sdcaoyl@zju.edu.cn

Qijian Zhao

Key Laboratory of Advanced Manufacturing
Technology of Zhejiang Province,
College of Mechanical Engineering,
Zhejiang University,
Zheda Rd. 38,
Hangzhou 310027, China
e-mail: zqj@zju.edu.cn

Ting Liu

Key Laboratory of Advanced Manufacturing
Technology of Zhejiang Province,
College of Mechanical Engineering,
Zhejiang University,
Zheda Rd. 38,
Hangzhou 310027, China
e-mail: tingliu@zju.edu.cn

Lifei Ren

Key Laboratory of Advanced Manufacturing
Technology of Zhejiang Province,
College of Mechanical Engineering,
Zhejiang University,
Zheda Rd. 38,
Hangzhou 310027, China
e-mail: renlifei@zju.edu.cn

Jiangxin Yang

Key Laboratory of Advanced Manufacturing
Technology of Zhejiang Province,
College of Mechanical Engineering,
Zhejiang University,
Zheda Rd. 38,
Hangzhou 310027, China
e-mail: yangjx@zju.edu.cn

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received February 15, 2017; final manuscript received February 5, 2018; published online March 15, 2018. Assoc. Editor: Yan Wang.

J. Comput. Inf. Sci. Eng 18(2), 021002 (Mar 15, 2018) (9 pages) Paper No: JCISE-17-1037; doi: 10.1115/1.4039380 History: Received February 15, 2017; Revised February 05, 2018

A datum selection strategy based on statistical learning is proposed. The datum selection is an important part of tolerance specification which is the base of geometric tolerance selection and tolerance principle selection. The problem of datum selection is to deduce the datum reference frame (DRF) based on geometrical, contact, and positioning characteristics. Currently, heuristic rules are used for DRF selection, leading to suboptimal choice of DRF in many cases. The proposed strategy formulates normalized vectors computed from the geometric, contact, and positioning characteristics of surfaces. The surfaces of different parts can be compared by their normalized vectors. Then the statistical learning method is used for building a classifier which can discriminate datum feature vectors based on training samples. Finally, a case study is given to verify the strategy and the different algorithms are compared and discussed.

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Figures

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Fig. 1

Flow diagram of tolerance specification

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Fig. 2

Surface A and B of two parts of different size

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Fig. 3

Probability density functions of p (where the left one is p∼N(0.7,0.32) and the right one is p∼N(0.3,0.32))

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Fig. 4

DRF selection based on statistical learning

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Fig. 5

Steps of DRF selection (where M means the matrix which consists of all the feature vectors of a part)

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Fig. 6

Parts to be specified: fork 174

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Fig. 7

Parts to be specified: frame 172

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Fig. 8

Surface number in parts to be specified: Surfaces number in fork 174

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Fig. 9

Surface number in parts to be specified: Surfaces number in frame 172

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