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Research Papers

Computer-Aided Inspection Planning: A Multisensor High-Level Inspection Planning Strategy

[+] Author and Article Information
Sif Eddine Sadaoui

LURPA ENS Cachan,
Université Paris-Sud,
Université Paris-Saclay,
61 Avenue du président Wilson,
Cachan 94230, France
e-mail: sif-eddine.sadaoui@ens-paris-saclay.fr

Charyar Mehdi-Souzani

LURPA ENS Cachan,
Université Paris-Sud,
Université Paris 13,
Sorbonne Paris Cité,
Université Paris-Saclay,
61 Avenue du président,
Wilson Cachan 94230, France
e-mail: charyar.souzani@ens-paris-saclay.fr

Claire Lartigue

LURPA ENS Cachan,
Université Paris-Sud,
Université Paris-Saclay,
61 Avenue du président,
Wilson Cachan 94230, France
e-mail: claire.lartigue@ens-paris-saclay.fr

1Corresponding author.

Manuscript received June 20, 2018; final manuscript received November 3, 2018; published online February 4, 2019. Assoc. Editor: Kristina Wärmefjord.

J. Comput. Inf. Sci. Eng 19(2), 021005 (Feb 04, 2019) (13 pages) Paper No: JCISE-18-1143; doi: 10.1115/1.4041970 History: Received June 20, 2018; Revised November 03, 2018

Computer-aided inspection planning (CAIP) has gained significant research attention in the last years. So far, most CAIP systems have focused on the use of a touch probe mounted on a coordinate measuring machine (CMM). This article investigates multisensor measurement aiming to perform automatic and efficient inspection plans. High-level inspection planning, which deals with sequencing of measuring operations, is the main concern of inspection planning. This paper presents an automatic approach to generate inspection sequences by combining laser sensor and touch probe, and by giving preference to the measurement using the laser sensor if quality requirements are satisfied. The proposed approach consists of three steps. In the first step, recognition of inspection data from the computer-aided design (CAD) part model is carried out based on the concept of inspection feature (IF), and the extracted information is stored in a database. In the second step, a list of privileged scanner orientations is proposed by analyzing the accessibility of both sensors. In the third step, a sequence of operations is generated iteratively. For a given scanner orientation, the ability of the laser sensor is assessed according to an original process based on fuzzy logic model. If the laser sensor does not meet the ability requirements, touch probe ability is assessed. The proposed approach is implemented and tested on a part defined by its CAD model and specifications.

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References

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Figures

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Fig. 1

Overview of the proposed approach

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Fig. 2

Case study, part model with specifications

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Fig. 3

(a) View of the CMM and (b) 3D scanner KA50 (composed of a touch probe and a laser-sensor) mounted on Renishaw PH10 head

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Fig. 4

(a) Example of IFs with their MFs and (b) IF and MF types

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Fig. 5

Relational schema of the database

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Fig. 6

MFs and their attributes: (a) point, (b) line, (c) circle, (d) plane, (e) cone, (f) cylinder, (g) sphere, and (h) curves

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Fig. 7

List of scanner orientations identification on the case study: (a) inspection features, (b) voxelization of data, (c) directions of view, (d) list 1, (e) list 2, and (f) list of scanner orientation

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Fig. 8

Algorithm of feature group generation

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Fig. 9

Laser ability evaluation principle

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Fig. 10

Fuzzy sets and membership functions of the input variables

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Fig. 11

Laser ability in function of laser uncertainty and admissible uncertainty

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Fig. 12

(a) Configuration of laser beam orientation and cameras orientations, (b) laser beam interruption, and (c) collision issue modeling

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Fig. 13

Laser ability modeling

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Fig. 14

Results of laser ability evaluation to measure a free-form feature (specification type: form, IT = 0.15 mm), and semisphere (specification type: position, IT = 0.1 mm): (a) laser ability for each facet and (b) partitioning of partially measured feature

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Fig. 16

Accessibility of the touch probe to measure a free-form feature

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Fig. 17

Results of operation sequence generation: green, measured surfaces with laser sensor; blue, measured surfaces with touch probe; and red, nonmeasured surfaces: (a) laser ability evaluation at Ornt1, (b) after one iteration (Ornt1), (c) after two iterations (Ornt2), and (d) after three iterations (Ornt3)

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