Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector

Peizhi Shi; Qunfen Qi; Yuchu Qin; Paul J. Scott; Xiangqian Jiang

doi:10.1109/TII.2020.3030620

Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector

Peizhi Shi, Qunfen Qi^*, Yuchu Qin, Paul J. Scott, Xiangqian Jiang

^*Corresponding author for this work

Department of Mechanical Engineering

Research output: Contribution to journal › Article (Academic Journal) › peer-review

32 Citations (Scopus)

Abstract

In Industrie 4.0, machines are expected to become autonomous, self-aware and self-correcting. One important step in the area of manufacturing is feature recognition that aims to detect all the machining features from a 3-D model. In this research area, recognizing and locating a wide variety of highly intersecting features are extremely challenging as the topology information of features is substantially damaged because of the feature intersection. Motivated by the single shot multibox detector (SSD), this article presents a novel deep learning approach named SsdNet to tackle the machining feature localization and recognition problem. The typical SSD is designed for 2-D image objection detection rather than 3-D feature recognition. Therefore, the network architecture and output of SSD are modified to fulfil the purpose of this research. In addition, some advanced techniques are also utilized to further enhance the recognition performance. Experimental results on the benchmark dataset confirm that the proposed method achieves the state-of-the-art feature recognition performance (95.20% F-score), localization performance (90.62% F-score), and recognition efficiency (243.85 ms per model).

Original language	English
Article number	9222288
Pages (from-to)	3292-3302
Number of pages	11
Journal	IEEE Transactions on Industrial Informatics
Volume	17
Issue number	5
Early online date	13 Oct 2021
DOIs	https://doi.org/10.1109/TII.2020.3030620
Publication status	E-pub ahead of print - 13 Oct 2021

Bibliographical note

Funding Information:
Manuscript received May 12, 2020; revised September 9, 2020; accepted October 7, 2020. Date of publication October 13, 2020; date of current version February 22, 2021. This work was supported in part by the EPSRC UKRI Innovation Fellowship (Ref. EP/S001328/1), in part by EPSRC Future Advanced Metrology Hub (Ref. EP/P006930/1), and in part by EPSRC Fellowship in Manufacturing (Ref. EP/R024162/1). Paper no. TII-20-2436. (Corresponding author: Qunfen Qi.) The authors are with the EPSRC Future Advanced Metrology Hub, School of Computing and Engineering, University of Huddersfield, Hud-dersfield HD1 3DH, U.K. (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]).

Publisher Copyright:
© 2005-2012 IEEE.

Keywords

3-D feature localization
Deep learning
feature recognition
Industrie 4.0
single shot multibox detector (SSD)

Access to Document

10.1109/TII.2020.3030620Licence: CC BY

Handle.net

Persistent link

Cite this

@article{06d6b16942f149fd8a87b030f6a967b6,

title = "Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector",

abstract = "In Industrie 4.0, machines are expected to become autonomous, self-aware and self-correcting. One important step in the area of manufacturing is feature recognition that aims to detect all the machining features from a 3-D model. In this research area, recognizing and locating a wide variety of highly intersecting features are extremely challenging as the topology information of features is substantially damaged because of the feature intersection. Motivated by the single shot multibox detector (SSD), this article presents a novel deep learning approach named SsdNet to tackle the machining feature localization and recognition problem. The typical SSD is designed for 2-D image objection detection rather than 3-D feature recognition. Therefore, the network architecture and output of SSD are modified to fulfil the purpose of this research. In addition, some advanced techniques are also utilized to further enhance the recognition performance. Experimental results on the benchmark dataset confirm that the proposed method achieves the state-of-the-art feature recognition performance (95.20% F-score), localization performance (90.62% F-score), and recognition efficiency (243.85 ms per model).",

keywords = "3-D feature localization, Deep learning, feature recognition, Industrie 4.0, single shot multibox detector (SSD)",

author = "Peizhi Shi and Qunfen Qi and Yuchu Qin and Scott, {Paul J.} and Xiangqian Jiang",

note = "Funding Information: Manuscript received May 12, 2020; revised September 9, 2020; accepted October 7, 2020. Date of publication October 13, 2020; date of current version February 22, 2021. This work was supported in part by the EPSRC UKRI Innovation Fellowship (Ref. EP/S001328/1), in part by EPSRC Future Advanced Metrology Hub (Ref. EP/P006930/1), and in part by EPSRC Fellowship in Manufacturing (Ref. EP/R024162/1). Paper no. TII-20-2436. (Corresponding author: Qunfen Qi.) The authors are with the EPSRC Future Advanced Metrology Hub, School of Computing and Engineering, University of Huddersfield, Hud-dersfield HD1 3DH, U.K. (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]). Publisher Copyright: {\textcopyright} 2005-2012 IEEE.",

year = "2021",

month = oct,

day = "13",

doi = "10.1109/TII.2020.3030620",

language = "English",

volume = "17",

pages = "3292--3302",

journal = "IEEE Transactions on Industrial Informatics",

issn = "1551-3203",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

number = "5",

}

TY - JOUR

T1 - Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector

AU - Shi, Peizhi

AU - Qi, Qunfen

AU - Qin, Yuchu

AU - Scott, Paul J.

AU - Jiang, Xiangqian

N1 - Funding Information: Manuscript received May 12, 2020; revised September 9, 2020; accepted October 7, 2020. Date of publication October 13, 2020; date of current version February 22, 2021. This work was supported in part by the EPSRC UKRI Innovation Fellowship (Ref. EP/S001328/1), in part by EPSRC Future Advanced Metrology Hub (Ref. EP/P006930/1), and in part by EPSRC Fellowship in Manufacturing (Ref. EP/R024162/1). Paper no. TII-20-2436. (Corresponding author: Qunfen Qi.) The authors are with the EPSRC Future Advanced Metrology Hub, School of Computing and Engineering, University of Huddersfield, Hud-dersfield HD1 3DH, U.K. (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]). Publisher Copyright: © 2005-2012 IEEE.

PY - 2021/10/13

Y1 - 2021/10/13

N2 - In Industrie 4.0, machines are expected to become autonomous, self-aware and self-correcting. One important step in the area of manufacturing is feature recognition that aims to detect all the machining features from a 3-D model. In this research area, recognizing and locating a wide variety of highly intersecting features are extremely challenging as the topology information of features is substantially damaged because of the feature intersection. Motivated by the single shot multibox detector (SSD), this article presents a novel deep learning approach named SsdNet to tackle the machining feature localization and recognition problem. The typical SSD is designed for 2-D image objection detection rather than 3-D feature recognition. Therefore, the network architecture and output of SSD are modified to fulfil the purpose of this research. In addition, some advanced techniques are also utilized to further enhance the recognition performance. Experimental results on the benchmark dataset confirm that the proposed method achieves the state-of-the-art feature recognition performance (95.20% F-score), localization performance (90.62% F-score), and recognition efficiency (243.85 ms per model).

AB - In Industrie 4.0, machines are expected to become autonomous, self-aware and self-correcting. One important step in the area of manufacturing is feature recognition that aims to detect all the machining features from a 3-D model. In this research area, recognizing and locating a wide variety of highly intersecting features are extremely challenging as the topology information of features is substantially damaged because of the feature intersection. Motivated by the single shot multibox detector (SSD), this article presents a novel deep learning approach named SsdNet to tackle the machining feature localization and recognition problem. The typical SSD is designed for 2-D image objection detection rather than 3-D feature recognition. Therefore, the network architecture and output of SSD are modified to fulfil the purpose of this research. In addition, some advanced techniques are also utilized to further enhance the recognition performance. Experimental results on the benchmark dataset confirm that the proposed method achieves the state-of-the-art feature recognition performance (95.20% F-score), localization performance (90.62% F-score), and recognition efficiency (243.85 ms per model).

KW - 3-D feature localization

KW - Deep learning

KW - feature recognition

KW - Industrie 4.0

KW - single shot multibox detector (SSD)

UR - http://www.scopus.com/inward/record.url?scp=85101754086&partnerID=8YFLogxK

U2 - 10.1109/TII.2020.3030620

DO - 10.1109/TII.2020.3030620

M3 - Article (Academic Journal)

AN - SCOPUS:85101754086

SN - 1551-3203

VL - 17

SP - 3292

EP - 3302

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

IS - 5

M1 - 9222288

ER -

Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector

Abstract

Bibliographical note

Keywords

Access to Document

Handle.net

Other files and links

Fingerprint

Cite this