International Journal on Interactive Design and Manufacturing (IJIDeM)

Reducing Welding Repair Requirements in Refinery Pressure Vessel Manufacturing: ACase Study Applying Six Sigma Principles

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Full Title: Reducing Welding Repair Requirements in Refinery Pressure Vessel Manufacturing: ACase Study Applying Six Sigma Principles

Article Type: Original Paper

Corresponding Author: David WoodDWA Energy LimitedLincoln, UNITED KINGDOM

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Corresponding Author's Institution: DWA Energy Limited

Corresponding Author's SecondaryInstitution:

First Author: Ahmadreza Rezaei, MSc

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Order of Authors: Ahmadreza Rezaei, MSc

Mohammad Ehsanifar

David Wood

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Abstract: High welding intensity is an integral part of the refinery pressure vessel manufacturingprocess. Weld defects are also responsible for a substantial part of the reworkrequirements, waste and operating costs associated with such manufacturing. Seekingways to reduce welding defects and improve the efficiency of the manufacturingprocess is therefore critical for profitability and fulfills customer expectations of productreliability. A problem-solving project applying six sigma principles, and specifically itsdefine, measure, analysis, improve and control (DMAIC) methodology, successfullyidentified, quantified and reduced weld defects in pressure vessel manufacture for theMachine Sazi Arak Company leading to ongoing process improvement gains. Failuremodes and effect analysis and customized experiments were able to quantify theimpacts of identified root causes on the slag inclusion, lack of fusion and porosity welddefects. This lead to improvement solutions being successfully tested andimplemented. These improvement initiatives significantly reduced the average numberof the welding repairs required, increased the sigma level of weld defects by up to 19%reduced welding process costs by up to 350% and increased process yield by 1.12%.Consequently, the profitability and efficiency of pressure vessel manufacture was muchimproved by adopting the six-sigma problem-solving approach.

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Reducing Welding Repair Requirements in Refinery Pressure Vessel

Manufacturing: A Case Study Applying Six Sigma Principles

Ahmadreza Rezaei Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak,

Iran Rezaeeah@msa.ir

Mohammad Ehsanifar

Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran

M-Ehsanifar@iau-arak.ac.ir ORCID: orcid.org/ 0000-0001-9817-0881

David A. Wood*

DWA Energy Limited, Lincoln, United Kingdom (*corresponding author) Tel: +44 1522 789095 dw@dwasolutions.com

ORCID: orcid.org/0000-0003-3202-4069

Title Page

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Reducing Welding Repair Requirements in Refinery Pressure Vessel

Manufacturing: A Case Study Applying Six Sigma Principles

Abstract:

High welding intensity is an integral part of the refinery pressure vessel manufacturing process.

Weld defects are also responsible for a substantial part of the rework requirements, waste and

operating costs associated with such manufacturing. Seeking ways to reduce welding defects and

improve the efficiency of the manufacturing process is therefore critical for profitability and fulfills

customer expectations of product reliability. A problem-solving project applying six sigma

principles, and specifically its define, measure, analysis, improve and control (DMAIC)

methodology, successfully identified, quantified and reduced weld defects in pressure vessel

manufacture for the Machine Sazi Arak Company leading to ongoing process improvement gains.

Failure modes and effect analysis and customized experiments were able to quantify the impacts

of identified root causes on the slag inclusion, lack of fusion and porosity weld defects. This lead

to improvement solutions being successfully tested and implemented. These improvement

initiatives significantly reduced the average number of the welding repairs required, increased the

sigma level of weld defects by up to 19% reduced welding process costs by up to 350% and

increased process yield by 1.12%. Consequently, the profitability and efficiency of pressure vessel

manufacture was much improved by adopting the six-sigma problem-solving approach.

Keywords: weld defect reduction; six-sigma problem solving; root-cause analysis; critical-to-quality factors; failure modes and effect analysis; manufacturing process improvement.

1. Introduction

The thickness of metal sheets used in heavy-wall refinery pressure vessel fabrication involves a

high volume of welding in the manufacturing process. As the number of welds increases the

likelihood of defects and rework increases exponentially. Extensive research studies and analysis

have been conducted to address and attempt to resolve the technical and technological issues

associated with weld defects. Some studies have led to improved standards (e.g., standards of

the American Society of Mechanical Engineers - ASME) and guidelines published as journal

articles [1], [2], [3]. However, such standards and best practice guidance only address the

technical solutions, whereas the purpose of this article is to provide a method that reduces welding

defects based on process improvement.

Blinded Manuscript Click here to view linked References

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The desire to reduce manufacturing costs, production time and also to increasing quality and

productivity organizations commonly engage in improvement initiatives. Increasing customer

awareness with respect to quality and changes within the market requires strategic and planning

attention focused on long-term, medium-term and short-term quality improvements. Such a focus

is likely over time to increase an organizations competitiveness and reputation within a targeted

industrial sectors. Organizations often apply the well-known Total Quality Management (TQM)

principles to achieve ongoing improvements [4] and as a long-term quality-improvement strategy.

As part of such a strategy, in order to achieve short-term and medium-term quality improvements,

the data-driven Six Sigma (6σ) techniques can be usefully applied [5].

The methodologies that make up the now widely applied Six-Sigma technique were developed

for Motorola in the 1980s and successfully expanded into a more comprehensive business

strategy by general Electric in the 1990s [6]. They were further expanded in the 2000’s to combine

with lean production speed approaches to reduce waste and packaged as a lean six sigma

method [7]. However, the techniques have received mixed reviews over the past decade. On the

one hand, it is used extensively by many top manufacturing companies and is credited with saving

them hundreds of billions of U.S. dollars over recent decades [8]. On the other hand, while

focusing on driving defects down some criticize the technique for being too narrow to be a core

strategy as it diverts attention away from the innovation needed to develop new competitive

products and disruptive technologies [9]. However, considering six-sigma techniques as quality-

improvement tools, rather than applying them more broadly as a strategic philosophy, has much

merit and a proven track record.

There are many published examples demonstrating how Six Sigma can be successfully applied

to eradicate waste, reduce repair frequency, lower operating costs and/or improve overall

product quality for small and large organizations. In the automobile industry, Rajeshkumar and

Sambhe [10] used implemented six sigma to improve customer satisfactions and simultaneously

achieve significant financial savings for a small car accessories unit in India. Gupta [11]

demonstrated that six-sigma methodologies applied in a cotton mill successfully reduced

defects in yarn packaging. Valles et al [12] in a semi-conductor manufacturing company were

able to reduce by up to 50% the electrical defects incurred by applying six-sigma techniques. In

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the food industry in Taiwan, Hung & Sung [13] were able to reduce process variation and the

high prevailing defect rate to achieve tangible cost savings by applying six-sigma methods.

Six Sigma techniques integrate a number of tools commonly applied for quality management,

problem solving and risk management within an effective logic framework that tends to improve

outcomes. central to the Implementation of Six Sigma and Lean Six Sigma methodologies is a

sequence of processes focused on improving existing products: Define, Measure, Analysis,

Improve and Control (DMAIC) [14]; or, for creating new products: Define, Measure, Analysis,

Design test and Verify (DMADV). It is the former of these sets of processes that we focus upon in

the pressure vessel welding repair case study presented here.

Several six-sigma studies have addressed the improvement of welding processes and reducing

welding defects. Soni et al [15] were able to increase welding process yield and improve

profitability by using six sigma approach. In the aerospace industry, Shinde et al [16] were able

to improve tungsten inert gas welding and eliminate the root causes of defects identified through

implementing a six-sigma methodology. For metal inert gas welding Dhmija et al [17] were able

to reduce welding waste and weld-repair requirements using six-sigma methods. Also Nicole C.

Anderson, Jamison V. Kovach [18] used lean six sigma to reduce welding defects in turnaround

projects.

Using six sigma approach in order to reduce rework and welding defects in refinery pressure

vessels has not significantly developed. Rimawan and A. Haryono [19] applied DMAIC

methodology to improve submerged arc welding. They could improve the welding process by

Non-numerical and non-statistical methods.

In this article we use wide range of statistical tools which applied in six sigma such as FMEA to

filter the causes, R&R gage to validating data and DOE to reach root causes. These tools help

make the results credible and reliable. Also we indicate real data and real achievements.

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2. Case Study: Reducing Weld Defects and Associated Repairs in Refinery Pressure

Vessels

2.1 Company studied

The Machine Sazi Arak Company (MSAC) is a significant oil and gas refinery equipment

manufacturing company operating in Iran and around the Middle East. It produces a wide range

of steel pressure vessel for the refining sector with extensive welding requirements during their

manufacture.

2.2 Problem definition

Reducing the cost of weld repairs is a key objective for MSAC as that is recognized as a major

sources of waste. Many of the rework requirements associated with pressure vessel

manufacturing is well known as being associated with welding defects [20]. Several

researchers have conducted studies focused on reducing welding defects by implementing

complex technical solutions [21-22]. In refinery pressure vessel manufacturing significant

thicknesses of metal components are required, leading to large welding volumes. Consequently,

the probability of welding defects occurring increases. Research and development in welding

processes is vital in achieving quality improvements of manufactured pressure vessels. The

high welding intensity associated with these vessels results in a high average of welding defects

for these vessels. Seeking ways to mitigate this is therefore a priority. Here we apply six-sigma

techniques to study and resolve this problem.

3. Define, Measure, Analysis, Improve and Control Methodology

Central to the implementation of a six-sigma approach is the application of the Define, Measure,

Analyze, Improve and Control (DMAIC) methodology [23-24]. This methodology consists of five

phases (Table1Error! Reference source not found.)Error! Reference source not found.:

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Table1: A generic summary explaining the DMAIC Methodology and identifying the inputs and outputs associated with its different phases [23-24].

Phase Explanations Inputs outputs

Define

Identify reasons for raising the issue and problem, the current situation, goals and constraints, within the

framework of the project and definition of key project activities

Problem within the existing

process(es)

The economic benefits;

problem definition; critical quality

characteristics; process map;

voice of the customer

Measurement

Evaluation of the current process performance to identify potential causes of the problem and filter

them

Critical characteristic

quality process map

The initial sigma level potential causes statistical data

Analysis Statistical analysis of data to

examine potential root/contributing causes

Potential causes via statistical

data analysis

Statistical analysis of the root causes

Improvement To positively impact root causes,

multiple creative solutions are considered

The root causes

based on statistical analysis

Improved process

Control In the last phase of the project

improved process is controlled and standardized

Improved process

Standard Improved process

Secondary sigma level

3.1 Define phaseError! Reference source not found.

As a first step, the following items need to be defined and specified:

Project charter including the background and reasons for conducting the project, its

definition, current status and performance, future objectives, constraints and a business

case (Table 2);

Process map describing the process(es) being targeted (Figure 1; Table 3);

Voice of the customer (VOC) statement (see contractual commitment); and,

Critical-to-quality (CTQ) characteristics that will help to achieve positive outcomes (Table

3)

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Table 2. A summary of the project charter for the six-sigma project to reduce repairs due to weld defects

Project title: Implementation of six-sigma project to reduce the number of repairs due to weld defects

Background and reasons for the project definition: Because of the costly and time-consuming repairs causing by defects and to achieve optimal welding process and weld quality, the project was defined

Project goal (present situation and future goal): The goal of this project is to reduce average number of welding repairs

Business case: 4.6% reducing in welding repairs cause to reduce welding time and cost. Amount of this cost reducing is near 64000USD

Finish Date Start Date Phase

2013/11/10 2013/10/10 Define

2014/02/01 2013/11/10 Measure

2014/03/30 2014/02/01 Analyze

2014/05/15 2014/03/30 Improve

2015/06/15 2014/05/15 Control

Figure 1. Welding process flow diagram. (NDT: Non Destructive Test including: Visual (VT) & Ultra sonic (UT) & Radiographic (RT) & Magnetic (MT) Tests.

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Table 3 Welding process map contributing to the suppliers, inputs, processes, outputs, and customers (SIPOC) six-sigma component. (WPS= welding procedure system; PQR= procedure qualification record; NDTMAP = non-destructive testing map)

Suppliers, Inputs, Processes, Outputs and Customers

SUPPLIERS INPUTS PROCESSES OUTPUTS CUSTOMERS

Employer Employer’s technical

documentation

Developing welding technical documentation (WPS,PQR,NDTMAP)

Welding technical document

Manufacturing and quality control unit

Procurement Raw materials

(pipe and plate)

Shot blasting Raw materials

cleaned and rust removed

First operations unit

Shot Blast Unit

Raw materials

Fabrication Prepared work

pieces for assembly

Assembling shop

Operators

Machines

First Operation

Prepare work pieces for assembly Assembly

Assembled plate or pipe

Welding preparation

group Operators

Tools

Fitters Assembled

plate or pipe Preparation for welding Assembled plate

or pipe and ready for welding

Welding group Tools

Welding Preparation

Group

Welding operators

Welding operation Welded plate or

pipe Quality control

Welding materials Welding

equipment Measurement

tools Welding technical document

Welding Group

Operator Non-destructive testing

NDT(VT,UT,RT)

Welded plate or pipe which has

been tested

Assembly shop

Equipment for radiography

Material

Quality Control

Similar to welding

operation Welding operation

Welded plate or pipe

Welded plate or pipe

Heat Treatment

Shop

Operator

Heat Treatment

Welded plate or pipe which has

been heat treated

Quality control Equipment

Quality Control

Operator MT

Correct welded plate or pipe

Assembling shop Equipment

Assembling Shop

Correct Welded Plate

or Pipe Finish Product Customer

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The voice of the customer (VOC) for this project is expressed in terms of the contractual

commitment for MSAC to supply the National Iranian Oil Company (customer) with products that

require no more than 6% welding repairs.

3.2 Measurement Phase

As a first step, the potential causes of welding defects were surfaced by conducting a brain

storming session addressing each of the critical to quality issues. The causes identified were

then filtered by applying cause-and-effect analysis. The causes were then scored and ranked

applying failure modes and effects analysis (FMEA). The outcome of that combined analysis

provided the initial sigma level of the current welding process (es) based on statistical data.

3.2.1 Brainstorming Session

This initial free-thinking session was attended by the six-sigma evaluation team, the workshop’s

experienced welders, welding engineers, members of the management board and welding

inspectors. The identified causes were then compiled into affinity groups using an affinity

diagram; viz. a visual analytical tool that arranges ideas into subgroups with common themes

and/or relationships.

3.2.2 Cause and Effect Analysis

The affinity groups were then reorganized into a cause-and-effect (or Ishikawa fishbone) diagram.

This structured graphical representation helped to distinguish and classify cause and effect

relationships associated with weld defects. This requires some of repetitive items identified in the

affinity groups (i.e., from the brainstorming session) to be filtered out so that focus is placed upon

the key causes and their respective relationships. Figure 2 provides an example cause-and-effect

diagram for the slag-inclusion defect in welds of pressure vessels.

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Figure 2. Cause-and-effect fish-bone (Ishikawa) diagram for weld defects.

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3.2.3 Failure modes and effect analysis

The causes identified by the cause-and-effect analysis were then evaluated quantitatively and

ranked by failure modes and effect analysis (FMEA) [25-26]. This technique was initially

progressed through further brainstorming team meetings focused on each cause, ranking them

for severity of impact, likelihood of occurrence and control of causes. The team was collectively

obliged to reach a consensus on the ranking for each cause based on those three criteria

(severity, occurrence and control). The three rankings obtained for each cause criteria (severity,

occurrence and control) were then multiplied together to derive an overall ranking score for

comparative purposes. The derived FMEA ranking matrix for the porosity defect in pressure

vessel welds is presented as an example in Table 4.

Table 4. The Failure modes and effect analysis (FMEA) matrix for welds.

FMEA Matrix

Team: Welding Defects Reduction Group

CTQ: POROSITY Weld Defect Type Considered

Risk Rating RPN

Control Rating DET

Current Control

Occurrence Rating OCC

Potential Causes Cause NO.

Severity Rating SEV

The potential failure modes

Process duty

720 10 QA 9 Using an

unsuitable electrode

X1

8

POROSITY

Welding Defects

Reduction

504 9 - 7

Lack of education,

knowledge and technical literacy

within the welding team

X2

448 8 QC and Welder

7

Dirty welding surfaces

(including the work piece and

clothing and equipment )

X3

432 6

Welder testing, Welder

Certificate, NDT report

9 Welder

proficiency X4

392 7 QC,

Electrode keeper

7 Non-baking electrode

X5

320 8 - 5 Unusual time pressure to

finish the job X6

288 6 QC, Welder,

Skillful 6

Not setting welding

parameters such as ampere

X7

280 7 QC 5 Inappropriate

use of the oven X8

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3.2.4 Data Gathering

In order to apply a six-sigma approach and DMAIC methodology it is necessary to gather

quantitative data and validate that data as being reliable and, in some cases, repeatable. The six-

sigma team used two types of data gathering methods for the pressure-vessel-weld-defect

project:

1- Available reports detailing past weld failures

2- Direct observation of the welding process and occurrences of defects

Data were collected from welding reports extending back over a period of one year. These reports

included a monthly radiography report classified by the specific workshop of origin, the specific

welder involved and the specific type of weld defect. In addition, direct observation was

documented by completing welding fact checklists while observing actual welds being conducted.

This directly observed information included specifically monitoring for the key causes identified

by the FMEA analysis. This was achieved by using basic information to help identify and track

for those specific FMEA-identified causes via carefully drafted checklist questions.

3.2.5 Data validating

Following the established DMAIC methodology, a measurement system analysis (MSA) was

adopted for data validation. Gage repeatability and reproducibility (Gage R&R) [27], a statistical

tool that measures the amount of variation in the measurement system arising from the

measurement device and the people conducting the measurement, was used to validate the weld-

defect data. Applying gage R&R, the weld-defect data were checked and compared with existing

standards. The interpretation of X-ray films of weld was carried out by two independent inspectors

in such a way that each inspector reviewed the film twice using two distinct methods.

1- visual scrutiny, without any additional information

2- Evaluating historical information and using it to conduct statistical analysis of variance

(ANOVA) (Table 5).

These two approaches to data interpretation have made data validation possible.

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Table 5. Statistical analysis of variance (ANOVA) of historical weld data and weld failure information. Gage R&R using Minitab software to determine the data validation.

Source DF Seq SS

Adj SS Adj MS F P

Films No. 9 78.05 78.1062 8.6785 173.91 0

Inspector 1 0.0513 0.0567 0.0567 1.14 0.29

Gauge 1 0.0554 0.0554 0.0554 1.11 0.29

Repeatability 68 3.3933 3.3933 0.0499

Total 79 81.55

Alpha to remove interaction term = 0.25 Variance Components

Source VarComp %Contribution (of

VarComp)

Total Gage R&R 0.05021 4.44

Repeatability 0.0499 4.41

Reproducibility 0.00031 0.03

Inspector 0.00017 0.02

Gauge 0.00014 0.01

Part-To-Part 1.08129 95.56

Films No. 1.08129 95.56

Total Variation 1.1315 100

Gage Evaluation

Source Std Dev

(SD) Study Var (6 * SD)

%Study Var (%SV)

Total Gage R&R 0.22408 1.34451 21.07

Repeatability 0.22339 1.34032 21

Reproducibility 0.01767 0.10602 1.66

Inspector 0.0132 0.07922 1.24

Gauge 0.01174 0.07045 1.1

Part-To-Part 1.03985 6.2391 97.76

Films No. 1.03985 6.2391 97.76

Total Variation 1.06372 6.38232 100

Number of Distinct Categories = 6 (component parts of the process that can be distinguished)

The validation data obtained from the Minitab software (Table 5) indicates a standard deviation

Of 22% (or gage R&R of 0.22), which as it is less than 30%, is considered to validate the data

across six distinct categories of weld defects.

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3.3 Analysis Phase

Those causes of weld defect evaluated for which the quantitative data showed had negligible

effects on the number of weld defects observed were disregarded at this stage and not taken

forward for more detailed analysis. Screening of those extracted causes from the measurement

phase was finalized using a design of experiments (DOE) technique [28]. This determined the

root causes that have major influence on the developments of weld defects in pressure vessels.

In the DOE method one influencing factor at a time is changed and the analysis reconducted to

provide statistically valid data regarding its impact on weld defects. In this way, the effect of each

factor (cause) on the output (weld defect) could be observed and quantified. This method, in which

effect of variation of one parameter is investigated in turn, is sometimes referred to as the

extraction-experiment method. In applying this approach to the weld-defect study, the effects of

changes in the identified key causes likely to lead to weld defects were evaluated one by one.

Consequently, one identified cause from the FMEA analysis was changed while the other

potential causes remained unchanged. The outputs from the extraction experiments were then

reanalyzed by the ANOVA statistical method. Here, we present as an example of the results of

the extraction experiments, the statistical analysis for two causes identified for the slag-inclusion

weld defect.

3.3.1 Statistical analysis of the effect of monetary incentives paid to enhance good

welding performance

In this experiment some of the company’s welders were randomly selected. They were asked to

weld 10 meters of a work piece, including 5 repetitions with 3 different levels of monetary

incentive. These work pieces produced were then evaluated by non-destructive testing (NDT

Radiography) to establish defects associated with each work piece. The results are shown in

Table 6.

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Table 6. Experiment results of welding failures occurring during repeated incentivized tests.

The number of defects in 10 meters of welding for each repetition

Test5 Test4 Test3 Test2 Test1 Incentive amount (USD)

4 1 3 3 1 0

4 2 2 0 1 20

0 1 1 1 0 40

0 2 0 0 0 120

The Table-6 observations were subjected to a one-way analysis of variance yielding the results

listed in Table 7.

Table 7 ANOVA results. (DF: Degree of Freedom, SS: Sequential sums of squares, MS: mean squares, F: Fisher distribution, P: P-value)

One-way ANOVA: response versus factor

Source DF SS MS F P

factor 3 13.8 4.6 3.61 0.037

Error 16 20.4 1.27

Total 19 34.2

S = 1.129 R-Sq = 40.35% R-Sq. (adj) = 29.17%

The F-test statistics (Table 7) is used to establish the significance of the observation (Table 6).

The F-test considers that the test statistics has an F-distribution if the null hypothesis applies. The

F-test measure is typically used when comparing different statistical models fitted to a data set,

The F-test value derived from statistical analysis is 3.61 (Table 7). This value is compared for (3,

16) degrees of freedom with the value of 3.24 for 0.05 probability of occurrence. As F-test value

is greater than F0.05, 3, 16 (i.e., 3.61>3.24) this suggests that the null hypothesis is likely to occur by

chance with less than 0.05 probability. We therefore conclude that changing the monetary

incentive paid to welders does indeed cause a significant change in the number of weld defects

that materialize. The variation in observed values for this extraction experiment is shown in a box-

plot (Figure 3).

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Figure 3. Box plot showing number of defects associated with incentivized good welding performance.

3.3.2 Statistical analysis of effect of different beveling methods on number of

welding defects

For this experiment, randomly selected welders welded 10 meters of 10 work pieces which had

been beveled with two different methods. These work pieces were then evaluated by non-

destructive testing (NDT Radiography) to determine the number of defects developed in those

work pieces. The results for this extraction experiment are listed in Table 8.

In bevel method “A” involves operator with average skill, beveling the edge of the metal using gas

cutting equipment. In bevel method “B” beveling is conduct using a more-skilled operator and a

gas cutting machine.

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Table 8. Number of weld defects applying two different beveling methods.

The number of defects in 10 meters of welding for each repetition

Test5 Test4 Test3 Test2 Test1 Bevel

method

1 1 3 3 5 A

1 0 0 1 1 B

The results of analysis of variance using the Minitab statistical software are shown in Table 9.

Table 9. ANOVA results. (DF: Degree of Freedom, SS: Sequential sums of squares, MS: mean squares, F: Fisher distribution, P: P-value)

One-way ANOVA: response1 versus cutting type

Source DF SS MS F P

factor 1 10 10 6.45 0.035

Error 8 12.4 1.5

Total 9 22.4

S = 1.245 R-Sq = 44.64% R-Sq(adj) = 37.72%

The F-test value derived from statistical analysis is 6.45 (Table 9). This value is compared for

(1,8) degrees of freedom with the value of 5.32 for 0.05 probability of occurrence. As the F-test

value is greater than F0.05,1,8 (i.e., 6.45>5.32) this suggests that the null hypothesis is likely to

occur by chance with less than 0.05 probability. We therefore conclude that the beveling method

does indeed cause a significant change in the number of weld defects that materialize. The

variation in observed values for this extraction experiment is shown in a box-plot (Figure 4).

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Figure 4. Box plot showing number of defects associated with two distinct beveling methods.

Using the same method for each cause identified by the FMEA results for each critical-to-quality

(CTQ) objective, several causes were distinguished for consideration for adjustments, i.e., as

inputs for the six-sigma improvement phase.

3.4. Improvement Phase

The improvement phase was again initiated with a brainstorming session focused on root causes.

From this session potential improvement plans were formulated based on creative ideas raised

by the employees (mainly the welders and workshop supervisors). The creative ideas were then

further assessed and rated based on the criteria: 1- ease of implementation 2- speed of

implementation 3- technical viability 4- costs to implement 5- impact on weld performance 6-

customer expectation (VOC).

For this propose, a prioritization matrix of solutions was developed for each root cause. The matrix

for one of the root cause (i.e., the bevel method employed) is shown in Table 10.

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Table 10. Alternatives to apply in order to potentially improve bevel weld performance

Prioritization matrix of solutions for method of bevel of welding edge

Score VOC (0.6)

Impact (1.65)

Cost (1.5)

Technical (0.3)

Speed (1.25)

Ease (0.2)

Potential Solutions

120.2 =20*0.6 =18*1.65

=23*1.5 =10*0.3 =28*1.25

=30*0.2 Bevel by using gas cutting and cutting

expert

123.5 =25*0.6 =25*1.65

=25*1.5 =20*0.3 =15*1.25

=25*0.2 Bevel by using the existing bevel

machine

103.1 =17*0.6 =26*1.65

=7*1.5 =25*0.3 =24*1.25

=10*0.2 Buying a new bevel machine

The right-hand column in Table 10 calculates the sum of the scores assigned to each criterion.

The numbers in parenthesis in the column headings represent the weight assigned to each

criterion in calculating the total score for each potential solution. The total score achieved for each

solution is equal to the sum of the weights of each criteria multiplied by the criteria score. The

potential solution that achieved the highest score in Table 10 is "bevel by using the existing bevel

machine". Consequently, that was the solution selected through which to initiate improvements.

A prioritization matrix of solutions with scoring was prepared, similar to Table 10, for all the root

causes and all the alternative solutions identified that could potentially lead to improvement in the

associated weld defects. Prior to rolling out the high-scoring solutions on a plant-wide scale at

this stage the solutions were initially adopted as part of pilot schemes so that further information

could be gathered on their effectiveness.

The reasons for this cautious approach in restricting implementation of improvement actions to a

pilot trial to start with, are:

Understanding implementing problems before full-scale roll-out

Implementing at full-scale avoiding problems

Ensuring the selected solution complies with the key objectives

Verifying that the expected results do actually materialize

Surfacing potential unknown / unexpected performance issues

Consequently, the selected improvements were implemented initially in only one of the workshops

(i.e., Heavy Metal Working) and on a single sample project (ISOICO Clad Drums) as pilot-

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improvement initiative. Once the pilot initiative was implementation, actions were closely

monitored and the new sigma performance level and process efficiency were assessed and

compared to the former performance levels. As this comparison was positive the improvement

initiatives were subsequently rolled out across all welding operations.

3.5 Control Phase

Following the implementation of the improvement solutions, the welding processes needed to be

carefully controlled to avoid returning to former standards and performance gains failing to be

maintained. The six-sigma control phase is a final essential part of the DMAIC methodology and

its importance was originally recognized as valuable by General Electric in the 1990s [6] .

For the weld-defect-in-pressure-vessel project the control phase adopted to techniques to control

the improvement solutions and ensure that they were effective and remained effective over time,

these were:

Standardization and documentation of methods; and,

Statistical process Control (SPC)

The interfaces between the sequential steps in the DMIAC methodology flow logically from one

to the next in that the results (output) from one step become the inputs for next step. This means

that the results of each step can be considered on a stand-alone basis or the results from all

former steps completed can contribute or are available for review for the later steps. Hence, the

control phase has the benefit of being able to call upon the results of all the former steps which

are now completed with results available to use as input.

3.5.1. Define the Results of Previous Steps: Critical-to-Quality (CTQ)

CTQ are measureable characteristics of a defined problem. Which can be established commonly

by breaking down the main problem into smaller components and contributing factors. One such

approach is to use a Pareto chart to display the quantity of welding defects to determine and rank

the causes in terms of their CTQ contributions. The Pareto analysis for the weld-defect results

(Figures 5 and 6) identify that slag inclusion, lack of fusion and porosity are the main causes of

weld defects in the pressure vessel constructed by the company.

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Figure 5. Pareto plot of main welding defects occurring. (C2 indicates the fractional contribution of each type of welding defect)

Figure 6. The three main welding defects measured become the priorities for improvement. The main objective of “Welding repair reduction” is subdivided into three quantifiable SMART (Specific, Measurable, Attainable, Realistic and Time bounded) components.

3.5.2 Measure the Performance of Improvement Solutions Implemented

The causes of weld defects established from the earlier FMEA method are used as the basis for

clarifying what is required to reduce weld defects of each of the three priority defect types

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identified from the Pareto plot (Figure 5). Table 11 illustrates this approach applied just to the Slag

Inclusion-type weld defect.

Table 11. FMEA matrix for Slag Inclusion weld defect.

FMEA Matrix

Team: Welding Defects Reduction Group

CTQ: Slag Inclusion Weld Defect Type Considered

Risk Rating RPN

Control Rating DET

Current Control

Occurrence Rating OCC

Potential Causes Cause NO.

Severity Rating SEV

The potential failure modes

Process duty

36 2 Operator

3

The high stick out value in the saw method causes a

high sedimentation rate at the low ampere and causes the

adhesion of the flux

Z1

6

Slag

Inclusion

Welding Defects

Reduction

432 9 - 8

Lack of education, knowledge and

technical literacy in welding team

Z2

432 9 QC and

Management

8

Lack of proper rewards and

punishment in high & low-quality execution for

welders

Z3

324 6

Welder testing, Welder

Certificate, NDT report

9 Welder proficiency Z4

288 8 Operator

6

Absence of correct and complete

gouging

Z5

240 8 - 5 Unusual time

pressure to finish the job

Z6

324 9 QC, Welder,

Skillful 6

Not setting welding parameters such

as Ampere Z7

192 8 Maintenance and Operator

4

Failure of Rotating

machine during the welding

Z8

270 9 5

Tight welder spot and hard access to

welding spot

Z9

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Complementing the FMEA improvement matrix is a baseline measure of the primary sigma level

of performance against which improvements are to be measured. The primary sigma level used

in this project is the defects per million opportunities (DPMO) metric for measuring the weld

failure rate, calculated using the following formula:

DPMO = (Number of Defects*106) / (Number of failure opportunities*Number of checked units)

By using the DPMO formula in this project, the primary sigma level was calculated, as displayed

in Figure 7.

Figure 7. Quantification of weld defects to establish the primary sigma level of failure using the DPMO metric.

Using the DPMO metric, the primary sigma level for the process (i.e., the baseline for performance

measurement) was calculated to be 3.7. A high value of the primary sigma level indicates a

performance of high process efficiency. The higher that baseline value the harder it is to achieve

performance improvements by implementing process changes aimed at achieving even higher

efficiency.

3.5.3 Performance Focus Associated with the Instigation of Improvement Initiatives

The root causes leading to welding defects were established for each CTQ cause through the

combination of the earlier analysis steps in applying the DMIAC methodology. These root causes

are summarized in Table 12. Paying close attention to these root causes is a key part of the

improvement initiation process.

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Table 12. Critical-to-quality (CTQ) identifies the root causes of the weld defects.

Root Causes CTQ

Lack of appropriate incentives for welders in high quality performance

SLAG INCLUSION

Lack of education, knowledge and technical literacy in welding team

Unusual time pressure to finish the job from managers

No uniform bevel at the welding edge of the work piece

No standard setting of welding parameters such as amperage

Unusual time pressure to finish the job from managers

LACK OF FUSION

Sharp angle between cladding and base metal

Failure to adhere to preheat

Welder proficiency

Using unsuitable electrodes

POROSITY Dirty welding surfaces (including the work piece and clothing and

equipment )

Non-baking electrodes

3.5.4 Calculating Process Performance Following Implementation of Improvements Initiatives

Process efficiency of the improved welding process was monitored and analyzed to calculate the

secondary sigma level and to quantify process cost reductions and associated Increases in

product profits for the higher quality pressure vessels constructed. Process efficiency was shown

to have increased from 98.7% to 99.83% and sigma level to have increased from 3.7 to 4.4 (Figure

8). The improved solution established was observed to increase process yield by up to 1.12%,

reduce the sigma level of weld defects by up to 19% and reduce welding process costs by up to

350%.

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Figure 8. Improved weld defect performance expressed in terms of the second sigma level.

4. Discussion

In the traditional approach to improving welding performance the focus is typically on finding

technical and technological solutions. However, this tends to lead to intermittent step-change

improvements which can be difficult to sustain, i.e., they maybe unstable over time. On the other

hand, tackling welding performance improvement to achieve long-term reductions in welding

defects by applying six sigma principles can lead to more continuous, stable and logical

adjustments to the welding methodology. Such an approach helps to identify and maintain

sustainable improvements in welding performance by securing benefits at the engineering and

operations levels from the lessons learned and information provided at each step in the DMAIC

(Define, Measure, Analyze, Improve and Control) sequence.

The methodology described here could be beneficially applied to a wide range of sheet metal

welding manufacturing processes, such as pressure vessels, bridges, and metal-frame support

structures that use high-thickness sheet metal and involve a high volume of welding. Based on

the results and successful implementation of the study presented here, MSAC plan to establish

future six sigma projects to reduce other types of rework in sheet-metal cutting and assembly.

5. Conclusion

Refinery pressure vessels require extensive welding in their construction and the efficiency in

terms of minimum weld defects has a significant bearing on the profitability that can be achieved

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from their manufacture. Applying six sigma principles, and specifically its define, measure,

analysis, improve and control (DMAIC) methodology, successfully identified, quantified and

reduced weld defects in pressure vessel manufacture for the Machine Sazi Arak Company. The

highest ranking critical-to-quality types of weld defects in the pressure vessel manufacture were

identified as slag inclusion, lack of fusion and porosity defects and their contributions to overall

weld defect performance quantified. The root causes of these specific weld defects were initially

identified with the aid of brainstorming and evaluated in detail by failure modes and effect analysis

and customized experiments. The analytical results were used to identify and refine suitable

solutions to eliminate some of the root causes of weld defects and positively adjust the efficiency

of the welding processes involved in pressure vessel manufacture. Implementation of these

improvement solutions increased process yield by up to 1.12%, improved the sigma level of weld

defects by up to 19% and reduced welding process costs by up to 350%.

Acknowledgements

The authors acknowledge the support and motivation of the MSA management.

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