NDT&E International 37 (2004) 301–307
www.elsevier.com/locate/ndteint
Automatic inspection of gas pipeline welding defects
using an expert vision system
H.I. Shafeeka, E.S. Gadelmawlab,*, A.A. Abdel-Shafyb, I.M. Elewab
b
a
Mechanical Engineering Department, Higher Technological Institute, Tenth of Ramadan City, Egypt
Production Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Received 21 July 2003; revised 14 October 2003; accepted 15 October 2003
Abstract
Automatic inspection of welded gas pipelines is desirable because human inspectors are not always consistent evaluators. In addition,
automatic inspection decreases the cost of inspection process and improves the inspection quality. In this paper, an expert vision system for
automatic inspection of gas pipeline welding defects from radiographic films is presented. The proposed system has been established in the
Metrology lab, Mansoura University, Faculty of Engineering. The software, named AutoWDI, is fully written in lab using Microsoft Visual
Cþ þ and is ready to run on any Windows environment. The proposed vision system is used to capture images for the radiographic films then
applies various image processing and computer vision algorithms to recognize the defects and to make acceptance decisions according to
international standards. The expert system is based on a knowledge base, which was gathered from specialists, textbooks and international
standards. The proposed system is capable of identifying and testing the main types of welding defects (11 defects) in gas pipelines welded by
shielded metal arc welding.
q 2003 Elsevier Ltd. All rights reserved.
Keywords: Expert system; Welding defects; Quality control; Computer vision; Radiography
1. Introduction
The reliable detection of flaws by radiography is one of
the most important tasks in non-destructive testing (NDT).
Improvements in these methods are necessary, because the
human factor still has great influences on the evaluation.
Inspection of welding defects of gas pipelines is vital
because some welding defects such as sharp weld cups and
weld roof angle are contributing to failure in welding joints
[1,2]. Human inspection of gas pipeline welding defects is a
hard and difficult task when a great number of welds are to
be counted and inspected. In addition, human visual
inspection can only catch around 60 –75% of the signification defects [3]. Therefore, in order to lower the cost of
inspection process and to improve the inspection quality, it
is necessary to automate the inspection process [3,4]. More
than 125 nondestructive testing technologies have been
identified [5]. One of the most important areas of NDT is the
welding inspection of oil and gas pipelines [6]. The film
* Corresponding author. Tel.: þ 20-122808003; fax: þ 20-402977854.
E-mail address: esamy@mans.edu.eg (E.S. Gadelmawla).
0963-8695/$ - see front matter q 2003 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ndteint.2003.10.004
radiography method has been the cornerstone of NDT for
the last 50 years [7].
On the other hand, developments in image processing,
computer vision, artificial intelligence and other related
fields have significantly improved the capability of visual
inspection techniques [8,9]. It was reported that about 60–
90% of all existing machine vision applications were
classified as automated visual inspection [10]. Therefore,
various algorithms were used to identify welding defects
from radiographic films. These algorithms include image
processing/computer vision [11 –13], neural networks [14,
15] and fuzzy algorithms [15,16].
2. Problem description
Automated visual inspection can be classified into two
stages: measurement and quality control. In the measurement stage, the system is concerned with making accurate
measurements of critical dimensions. In the quality control
stage, the system emulates a human inspector who assesses
manufactured parts for integrity and completeness. The first
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Nomenclature
AutoWDA Automatic Welding Defect Assessment
AutoWDI Automatic Welding Defect Inspection
stage has been recovered in a previous paper related to
AutoWDA software [13]. This paper aims to extend and
develop the previous software (AutoWDA) to recover the
quality control stage. This could be done by building an
expert vision system to identify the most common welding
defects and to make acceptance decisions according to
international standards of welds.
3. Proposed system
Fig. 1 shows a photograph for the proposed vision
system. It consists of two parts: hardware and software. The
hardware includes an IBM compatible personal computer
(5) with Windows operating system, frame grabber (4) with
resolution up to 760 £ 570 pixels and a CCD (Charged
Couple Device) camera (3). The software is especially
written to identify the most common welding defects (11
defects) of gas pipelines radiographic films and to make an
acceptance decision for each defect according to international standards of welds. It was totally developed in-lab
using Visual Cþ þ 6.0 and it is ready to run under Windows
operating systems. The proposed software is named
AutoWDI (Automatic Welding Defects Inspection) and it
was developed such that it can be used independently
without referring to any other software. In addition, it
supports many different image file formats such as BMP,
TIFF, GIF, JPG, PCX and TGA.
DIA
ADA
Defect Identification Algorithm
Acceptance Decision Algorithm
4. Procedures of working
Referring to Fig. 1, the procedures of working could be
summarized as follows:
1. Setting the radiographic film (1) on the backlighting table
(2) under the CCD camera (3).
2. Capturing an image for the radiographic film and saving
it to a BMP file using the capturing software (6) provided
with the frame grabber (4).
3. Opening the captured image by the AutoWDI software
and then specifying a window around the whole welding
area.
4. Applying the AutoWDI algorithms to detect, identify and
inspect the defects.
5. Image processing and computer vision algorithms
Fig. 2 shows the main algorithms used by the AutoWDI
software to detect, identify and inspect the welding defects
from the radiographic captured images. The proposed
system is capable of identifying 11 welding defects, which
are hollow bead, porosity, scattered porosity, burn through,
incomplete fusion of root pass, external undercut, slag line,
slag inclusions, longitudinal cracks, transverse cracks and
base metal cracks.
Fig. 1. A photograph of the proposed vision system.
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303
in the filed of weld defects inspection. An identification tree
was build from these expertises as shown in Fig. 3. The
identification process is based on estimating three main
factors for the defects, which are shape, orientation and
location. In addition, some secondary factors were used to
finalize the identification process such as number of defects,
defect width, defect size and defect straightness.
5.2.1. Estimating the defects shape
The DIA classifies the shape of the defects into three
categorizes, which are circular, rectangular and irregular.
Two factors were introduced to identify the shape of regions
[17]. The first factor called form factor ðFf Þ and is used to
measure the circularity of regions based on its area and
perimeter. The form factor Ff is calculated using Eq. (1).
Ff ¼
ðPerimeterÞ2
4pArea
ð1Þ
For circular regions, the form factor Ff is equal to 1.0. For
regions extracted by image processing algorithms, it is
difficult to obtain a value of 1.0 for the form factor.
Therefore, a tolerance value of ^ 10% is used to identify the
circular defects. The tolerance values could be changed by
the user through a settings dialog box.
The second factor used to identify the shape of defects
called rectangularity factor ðRf Þ. It is used to measure the
rectangularity of regions based on the area of the region and
the area of the minimum rectangle enclosing it. The
rectangularity factor Rf is calculated by Eq. (2).
Rf ¼
Fig. 2. Block diagram of the main algorithms of the AutoWDI software.
5.1. The AutoWDA algorithms
The AutoWDA algorithms were described in details in a
previous paper for the same authors [13]. These algorithms
include converting color images to gray images, extracting
the gray level histogram, calculating a suitable Threshold
and applying image enhancement algorithms to obtain the
optimum image. In addition, it includes the segmentation
process, edge detection, and feature extraction algorithms.
The main objective of these algorithms was to extract the
welding defects from captured images and to calculate
necessary information for each defect. The extracted
information includes width, height, area, perimeter and
dimension of the minimum box enclosing each defect. Once
the defects are extracted and its information is calculated,
the Defect Identification Algorithm is used to classify these
defects.
5.2. The defect identification algorithm
The Defect Identification Algorithm (DIA) is used to
identify the extracted defects based on the human expertise
Ar
Amin
ð2Þ
where Ar is the calculated area of the defect and Amin is the
area of the minimum rectangle that encloses the defect. The
rectangularity factor Rf is equal to 1.0 for actual rectangular
regions.
To identify the shape of the defect, the form factor ðFf Þ is
calculated first. If Ff lies between 0.90 and 1.10, the defect
shape is considered circular. Otherwise, the rectangularity
factor ðRf Þ is determined. If Rf lies between 0.90 and 1.10,
the defect shape is considered rectangular, else, it is
considered irregular.
5.2.2. Estimating the defects orientation
The defect orientation is required for elongated defects
only. The orientation of each defect is calculated based on
its length and width. If the length (size in X direction) is
greater than the width (size in Y direction), the orientation is
considered horizontal; otherwise it is considered vertical.
5.2.3. Estimating the defects location
The location of defects is needed to identify both
irregular and elongated defects. It is calculated according
to the position of the defects in the captured image as
shown in Fig. 4. Defects location could be classified into
four regions, which are Inside Weld (IW), Center of
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Fig. 3. Classifying the defects using the identification tree.
Weld (CW), Edge of Weld (EW) and Base Metal (BM).
For defects, which lie at the EW and the CW regions, a
tolerance zone equal to 5% of the weld height (size in Y
direction) is taken above and below both the edge of
weld and the center of weld, respectively. The width of
weld is calculated from the window specified by the user.
The location of each defect could be estimated by
comparing the coordinates of the minimum rectangle
enclosing the defect with the coordinates of the four
mentioned regions.
5.3. The acceptance decision algorithm
The Acceptance Decision Algorithm (ADA) is used to
make an acceptance decision about the identified defects.
After the defect type is identified, its dimensions are
compared with international standards to make the acceptance decision. The proposed system supports the most
international standards codes (API 1104, ASME, DIN, BS,
ABS, AWS, and JIS) for the acceptance criteria of weld
defects. In addition, any new code can be added easily to the
system through a user-friendly dialog box. The default code
is the American Petroleum Institute (API).
6. Experimental study
Fig. 4. Classification of welding defects location.
To verify the proposed system, five clear radiographic
films for each type of the defects mentioned in Section 5 (11
defects) were obtained from the PETROJET Company and
inspected by both the system and the specialists of the
company. The specialists’ identification and decision are the
same as the results obtained from the system. Figs. 5– 8
show four samples from the tested radiographs, which have
different shapes according to the identification tree (circular,
irregular, elongated horizontal and elongated vertical). Each
figure shows: (a) part of the original radiographic film, (b)
the defects after applying the enhancement and segmentation algorithms, (c) the detected defects, and (d) the results
obtained from the AutoWDI software. The obtained results
include the defects measurements, information, identification and decision. The estimated information includes
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305
Fig. 5. Example for detecting and identifying circular defects (Prosity).
the form factor ðFf Þ; the rectangularity factor ðRf Þ; shape,
orientation and the location.
7. Discussions
The results obtained by both the proposed system and the
specialists proved that the identification tree of the defects
has been successfully defined and the proposed algorithms
(DIA and ADA) are capable of identifying and inspecting all
defects, which are predefined by the identification tree.
Using such expert system could decrease the cost of the
inspection process by using human inspectors with reasonable knowledge instead of specialists for the general
inspection processes. In addition, the enhancement algorithms provided with the proposed system could reduce
Fig. 6. Example for detecting and identifying irregular defects (Burn Through).
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Fig. 7. Example for detecting and identifying elongated (horizontal) defects (Incomplete Fusion).
Fig. 8. Example for detecting and identifying elongated (vertical) defects (Transverse Crack).
the inspection time, especially for defects, which are not
clear in the radiographic films.
8. Conclusions
An expert vision system for the automatic identification
and inspection of gas pipeline welding defects has been
introduced. The system is capable of identifying and
inspecting the most common welding defects (11 defects)
in radiographic films. Two algorithms were introduced for
the identification and inspection process. The first algorithm
(DIA) is used to identify the type of defects based on a
predefined identification tree, which was gathered from
specialists, textbooks and international standards. The
second algorithm (ADA) is used to make an acceptance
decision for the identified defects according to international
standards. In addition, any new standards or customized
codes could be added to the system. The inspected defects
were identified and inspected correctly as reported by
H.I. Shafeek et al. / NDT&E International 37 (2004) 301–307
the specialists of weld inspection. The proposed system is
considered quite cheap compared with the commercial
automatic inspection systems and eliminates the need for
image interoperation by a skilled inspectors.
9. Future work
Currently, we are working in a project to apply the
proposed system in the interpretation and inspection of
radiographic films in the field. The aim of this work is to test
and develop the proposed system so that it can be used to
store the inspection results of radiographic films to a
database system. In addition, other information about the
inspected welds and the welders will be stored to the
database for further analysis in the company such as
evaluating the welders’ performance and the common
defects produced by each welder.
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