APEM
jowatal
Advances in Production Engineering & Management
Volume 11 | Number 1 | March 2016 | pp 59-69 http://dx.doi.Org/10.14743/apem2016.1.210
ISSN 1854-6250
Journal home: apem-journal.org Professional paper
Aluminium hot extrusion process capability improvement using Six Sigma
Ketan, H.a*, Nassir, M.a
aMechanical Engineering Department, University of Baghdad, Baghdad, Iraq
A B S T R A C T
A R T I C L E I N F O
In this work, the Six Sigma (Define-Measure-Analyse-Improve-Control) DMAIC methodology has been followed to explain the original problem of lowering extrusion process variation and improving the process capability based on the determined Critical Quality Characteristics (CQC). The extrusion process charter worksheet is recognized, a SIPOC (Supplier-Input-Process-Output-Customer) chart is constructed and a Pareto chart is drawn in the Define phase of the methodology. Measurement data are collected, verifying process stability and verifying process normality by using X-R charts and normality test, respectively. Process capacity index, sigma levels, defects per million opportunities (DPMO) determination in the measure phase using a Histogram. During Analyse phase, Cause and Effect diagram are established to determine their likelihood for the root cause of aluminium extrusion defective products. The suggested solutions are installed in the improve phase. In the Control phase, all tools are applied in the Measure phase are repeated to determine the improvement level. The DMAIC methodology has been applied in the (Ur state company for engineering industries)/(aluminium extrusion factory). The Minitab 16 Software is used for calculations and plot charts. The results for the internal dimension (X1) of the corner section product indicate a reduction in DPMO from 536804 to 185795.09, sigma level is improved from 1.4 to 2.4, process yield (Y) is improved from 46 % to 81 %, and profit is improved from ID 127.000 to ID 223.000 per 1000 kg. © 2016 PEI, University of Maribor. All rights reserved.
Keywords:
Aluminium extrusion process
Six Sigma
DMAIC
Critical quality characteristics Profit
*Corresponding author: hussket@yahoo.com (Ketan, H.)
Article history: Received 21 April 2014 Revised 3 September 2015 Accepted 20 October 2015
1. Introduction
Six Sigma was used by Motorola in 1987 [1]. Therefore of a series of changes in the quality area beginning in the late 1970s, with determined ten-fold advance drives [2]. The top-level organization management along with CEO Robert Galvin developed a theory called Six Sigma [3, 4]. From 1987 to 1997, Motorola got a fivefold increase in sales with income climbing nearly 20 percent per year, cumulative investments at $14 billion and stock price gain compounded to a once a year rate of 21.3 % [5].
In 1994, Six Sigma was started as a business initiative to produce high-level results, improve work processes, expand all employees' skills and change the culture [6]. GE determined in 1995 to apply Six Sigma throughout the entire companies. CEO Jack Welch led the organization during this implementation, and many distributions of GE experienced notable improvements in quality through those years [7]. Universal Electric reported that $300 million supplied in 1997 in Six Sigma send between $400 million and $500 million savings, with further incremental limits of $100 million to $200 million [8, 9].
59
Ketan, Nassir
In early 1997, Samsung and LG set in Korea started to establish Six Sigma under their comp organizations. The outcomes were surprisingly good in those organizations. For example, Samsung SDI, which is an organization under Samsung set, reported that the cost investments by Six Sigma planner totalled $150 million [10, 11]. Sigma is the letter in the Greek alphabet utilized to indicate standard deviation, a statistical mensuration of variation, the exclusion to expected results. The standard deviation can be thought of like a comparison among expected results in a group of procedures, against those that not succeed. The measurement of standard deviation illustrates that rates of defects, or exceptions, are calculable [7, 12]. Six Sigma is arithmetical term that refers to 3.4 defects per million (or 99.99966 % accuracy), which is as close as anyone is probable to obtain to perfect [5, 13]. A defect any effect that falls short of the customer's requirements or expectations [7].
Six Sigma methods use defects per unit (DPU) like a measurement tool. DPU is a good method to determine the quality of product or a process. The defects are generally relation between the time and the cost. The sigma value additional shows the frequency at which failures happen; as a result, as upper sigma value means the lower defect possibility. The defect is definite as the displeasure of the customer. Therefore, as sigma level raises, cycle time and cost reduce and at the same time customer satisfaction raises [6].
In Six Sigma method there are two tools namely: DMAIC and DFSS. The overall method to solve problem by DMAIC method consist of: translation of a practical problem into a statistical problem, discover a statistical solution, and then translation of that statistical solution into a practical solution and implementation appropriately in the industry [14].
Gijo et al. [15] shows the application of the Six Sigma method in decreasing defects in a fine grinding process of an automotive company, The DMAIC (Define-Measure-Analyse-Improve-Control) method to solve the original problem of decreasing process improving and variation the process yield. The purpose of the Six Sigma method resulted in decrease of defects in the fine grinding process from 16.6 % to 1.19 %.
Hung et al. [16] showed how a food company in Taiwan can use a systematic and disciplined method to go towards the aim of Six Sigma quality level. The DMAIC phases are used to reduce the defect rate of small custard buns by 70 % from the baseline to its entitlement. After the development actions were implemented through a six-month period this fell to under 0.141 %. Mandahawi et al. [17] studies a procedure development study applied at a local paper manufacturing support on customized lean Six Sigma method. The DMAIC methodology and various lean tools are used to streamline processes and enhance production. Gupta [18] showed a quality development study applied at a yarn manufacturing company's foundation on Six Sigma methodologies. The DMAIC task management-methodology and various tools are used to streamline processes and enhance production. Defects rate of textile goods in the yarn manufacturing process is so essential in industry point of view.
2. DMAIC methodology phases application
DMAIC is closed-loop method that removes non-productive steps, oftentimes concentrate on new measurements, and used technology for continuous development. Achievement of DMAIC method took place in five phases. Problem classification and definition takes in defining phase. After recognizing main processes, their performance is determined by measure phase with the assist of data collection. Origin causes of the problem are establishing out in the analysis phase. Solutions to implement problem and solving them are in improving phase. Development is maintained in control phase. The following case is taken from production line that produces aluminium products in in the (Ur state company for engineering industries) and particularly to (aluminium extrusion factory) [14].
60
Advances in Production Engineering & Management 11(1) 2016
Aluminium hot extrusion process capability improvement using Six Sigma
2.1 Define phase
Define the extrusion process at dissimilar angles with the help of tools as the extrusion process charter worksheet and Pareto chart as shown below.
Drafting the extrusion process charter worksheet
This extrusion process charter worksheet outlines the purpose, objectives, and scope of the project as shown in Table 1.
Table 1 Extrusion process worksheet
Project title	Extrusion process capability improvement using Six Sigma
Business case	Extrusion factory in Ur state company for engineering industries produces varying amounts
of typical aluminium products. Depending on the data recorded for the marketing department as shown in Table 2. It illustrates the increase of defects percentage due to the appearance of defects in products so that the records for the year 2012 will be taking due to the lack of the production rate.
Problem statement Appearance defects in aluminium products and this has led to lower production rate as shown in the Table 2.
Goal statement Improve extrusion process capability to reduce extrusion defects that appear frequently and in large quantities in the typically aluminium products, increase production costs, reduce inventory planes, raise profit and get better satisfaction for customer.
Table 2 Sales of aluminium products for years 2010 to 2012
Y Production quantity Production sold Production defective Annual income ear_(x 1000 kg)_(x 1000 kg)_(%)_(x ID 1000)
2010	67.837	60.836	10	270.325
2011	92.342	82.854	10.5	365.000
2012	313.005	268.501	14	1.182.00
Developing process map (SIPOC Diagram)
The SIPOC diagram of this work describing the supplier, input, process, output and customer are as shown in Table 3.
Table 3 Supplier-Input-Process-Output-Customer (SIPOC) diagram
Supplier
Input
Process
Output
Customer
1	- Raw material store
2	- Dies store
3	- Adjuvants store (Graphite pens)
4	- Sutton company
1	- Aluminum alloy 1 6063.	2
-	Dimensions (Billet 3 diameter 0178-198 4 mm and length L 5 400-700 mm) 6
-	Standard specifica- 7 tion
(ASTM-B221 Iraqi standard 1730)
2	- Physical properties
3	- Chemical composition
4	- Hydraulic fluid
-	Preheating process
-	Extrusion process
-	Quenching process
-	Stretching process
-	Cut-off process Artificial aging Packaging process
1	- Square section
2	- Rectangular section
3	- Joint section
4	-Swing doors section
5	- Structural section
6	- Furniture section
7	-T-section
8	- Angles section
9	- Round section
10	- Corner section
1	- Directorate General of Electricity Distribution Rusafa
2	- Directorate General of Electricity Distribution Karkh
3	- Directorate General of Electricity Distribution Euphrates
4	- Directorate General of Electricity Distribution South
5	- Directorate General of Electricity Distribution center
6	- Directorate General for power
Project selection
In this step the aluminium products and extrusion defects are selected and by using the data in the records of quality control department in extrusion factory for the year 2012. Initially, Pareto
Advances in Production Engineering & Management 11(1) 2016	61
Ketan, Nassir
chart in Fig. 1 should be used to select only the vital aluminium products that have the highest cumulative percentage as a key product. Finally, Pareto chart in Fig. 2 should be used to select only the vital extrusion defects that have the highest cumulative percentage as a key defect.
According to results from the Pareto charts in Fig. 1 and Fig. 2, the corner section product has the polygon with high defective rate (0.32) with percent (17.8), and the dimensional deflection defect has the high defective count (19923) with percent (45.2), respectively.
Percent 17.8 12 11.5 10.5 10 9.4	9.3	7.7 7.3	4.4
Cum	17.8 29.8 41.3 51.9 61.9 71.3 80.6 88.3 95.6 100
Fig. 1 Pareto chart of aluminium product type
Percent	45.2 20.7 15.7 12.2 1.7 1.6 1.2 O.S 0.9
Cum«	45.2 55.84 81.5 93.7 95.3 97 98.15 99.1 lOO
Fig. 2 Pareto chart of defect type
2.2 Measure phase
In this phase, corner section product is selected to execute the research methodology based on the results of Pareto charts in the previous phase. Critical Quality Characteristic (X1) with dimension specification (36.7± 046) for corner section product in Fig. 3 is selected, due to its importance. Since any deviation from the required specification of (X1) will lead to the emergence of more defect products rejected by the customer. Measurements of 15 samples have been taken, each sample consist of 5 items from the packaging operation.
62
Advances in Production Engineering & Management 11(1) 2016
Aluminium hot extrusion process capability improvement using Six Sigma
m	Charact^ii Eti l E	S^n	T-si^fani^ [mm)
i	S^diííD tiidth	Til	
2	TLe inaxal ±LV.=j:=iir. DCTfc= E EU±aB	HI	3 6.7±lis
3	K^v-tí™-- Ihnin u	T1	1.15=»"
Fig. 3 Corner section of the product
Analyzing the samples data by X-R charts to determine if the extrusion process is under statistical control or not. Minitab 16 software is used to draw X-R charts as shown in Fig. 4.
Xbar-R Chart of The internal dimension of section (X1)
£
JS 36.2-Q.
s
S 36.0-
UC L=36.6248
X=36.2112
LC L=35.7976
8
Sample
36.6
S 36.4
35.8
8, 1.2' C
ê
Sample
UCL=1.516
Fig. 4 X-R charts for internal dimension of section (X1) before improvement
1.6
R = 0.717
0.4
0.0
LC L= 0
In Fig. 4 we can notice internal dimension (X1) of corner section product in stable state because no points out of the control limits of X-R charts.
The Anderson-Darling test is used to determine the normality of internal dimension (X1) samples data of corner section product. Minitab 16 software is used for this purpose and the results are shown in Fig. 5. It is appear that (X1) samples data is normally distributed because the P-value of 0.212 is bigger than the critical value of 0.05.
Advances in Production Engineering & Management 11(1) 2016
63
Ketan, Nassir
Normality Test for internal dimension of section (X1)
Normal - 95% CI
Observed Values
Fig. 5 Normality test for internal dimension of section (X1) before improvement
Based on the results of normality test, it is found that data for X1 are normally distributed. Therefore, process capability can be measured by using process capability analysis using histogram as shown in Fig. 6. Sigma level, yield (Y), defects per 1000 kg, and profit can be calculated
by using following equations and the results in Table 4 as follows [2]:
Sigma level = 3 • Cpk + 1.5	(1)
7 = e~DPM0	(2)
Defects per 1000 kg = DPMO ■ W	(3)
W= V ■ p	(4)
V = A-L	(5)
Profit = (1-Defectsper1000kg)-Profit margin	(6)
L is length of corner section product (6 m), A is area of corner section product (165 mm2), V is volume of corner section product, Wis weight of corner section product, p is alloys 6063 density (2685 kg/m3), DPMO is defects per million opportunities, and profit margin is ID 275.000.
Table 4 Results for calculations extrusion process measures of internal dimension (X1) of corner section of the product before improvement
Extrusion process measures	Measure value
Cp	0.46
Cpk	-0.03
Sigma level	1.4
DPMO	536804
Yield (Y)	46 %
Defects per 1000 kg	0.536804
Profit per 1000 kg	ID 127.000
	0.329759
X	36.2112
64
Advances in Production Engineering & Management 11(1) 2016
Aluminium hot extrusion process capability improvement using Six Sigma
Process Capability of Internal dimension of section (X1)
Process Data	
LSL	36.24
Target	36.7
USL	37.16
Sample M ean	36.2112
Sample N	75
StDev	0.329754
LSL	Target
Process C apability	
Cp	0.46
CPL	-0.03
CPU	0.96
Cpk	-0.03
35.2
35.6
36.0
36.4
36.8
37.2
Exp. Overall Performance PPM < LSL 534798.50 PPM > USL 2005.50 PPM Total 536804.00
Fig. 6 Process capability of internal dimension of section (X1) before improvement
2.3. Analysis phase
This phase includes causes and effect diagram tool for analysis the previous results obtained from measure phase.
Cause and effect analysis
This step expresses the possible causes identified which have the most impact on the extrusion process. Fig. 7 for dimensional deflection defect presents a chain of causes and effects.
Cause and Effect diagram of Dimensional deflection
Dies
Machines
Deviation in backer part of die ..Weakness in the process o fins tailing die parts Ins u f ficent refininE d i es
Materials
Difference in extrosionprocess ten^serature •
.from the s tanc ard temperature (500*0)
Difference in extrusion.speed from the standard extrusion speed (25.4mm-'sec)
roduct family to familyvariation
Supplier to supplier varia tian
\ Tools used away from allowing
Further decline in die and the front face of die is broken
v The mandrel part in hollow extrusion is not properly centered
Old equipment
Machine vibration
Unskilled worker in extrusion factory
Not familiar with the process and equipment
Absence of engineering staff responsible for quality control in extrusion factory
Man power
Dim en sional ~~^ d eflection
Inspector to inspectorvariation
Measurement system ins abiHtv
Difference in machining allow an ce
Measurements
Fig. 7 Process capability of internal dimension of section (X1) before improvement
2.4 Improve phase
The improve phase is the fourth step in DMAIC methodology phases and its objective is to implement and find measures that would solve the aluminium products defects. Cause and suggested solution are shown in Table 5.
Advances in Production Engineering & Management 11(1) 2016
65
Ketan, Nassir
Table 5 Cause and suggested solution
Cause
Suggested solution
1	- Difference in extrusion process temperature from the standard temperature.
2	- Unskilled workers in extrusion factory.
3	- Absence of engineering staff to monitor the production line in every step of the extrusion process.
4	- Further decline in die and the front face of die is broken.
5	- Deviation in backer.
6	- Weakness in the process of assembly die parts.
7	- Insufficient refining dies.
1	- Monitoring the extrusion process temperature (control the die temperature and billet preheating temperature) by thermocouple device as shown in Fig. 8.
2	- Workers must engage in training sessions before overseeing the extrusion process.
3	- Creating a staff of quality control specialist.
4	- Replacement of the old die with a new die and check the front face of the die.
5	- Checking the process of assembly and grinding of die parts (mandrel and backer) as shown in Fig. 9, Fig. 10, and Fig. 11.
Fig. 8 Thermocouple device
Fig. 9 Parts of corner section die
Fig. 10 Corner section die after assembly
Fig. 11 Corner section die after grinding process
2.5 Control phase
The extrusion process will be test by finding the values of PCIs (Process capability indices), Sigma level, DPMO, Yield (Y) and profit after improvement. Therefore, new data of 15 samples with sample size 5 have been collected from the aluminium extrusion process. Then the entire steps in measure phase are repeated. The collected data and the details of the steps and calculations are shown in Table 6 and Figs. 12, 13, and 14.
Fig. 12 X-R charts for internal dimension of section (X1) after improvement
66	Advances in Production Engineering & Management 11(1) 2016
Aluminium hot extrusion process capability improvement using Six Sigma
Normality Test for internal dimension of section (XI)
Normal - 9S% CI
Fig 13 Normality test for internal dimension of section (X1) after improvement
Process Capability of Internal dimension of section (X1)
USL
Process	Data
LSL	36.24
Target	36.7
USL	37.16
Sample Mean	36.4297
Sample N	75
StDev	0.212094
Process Capability Cp 0.72 CPL 0.30 CPU 1.15 Cpk 0.30
36.0 36.2 36.4 36.6 36.8 37.0
Exp. Overall Performance PPM < LSL 185507.58 PPM > USL 287.51 PPM Total 185795.09
Fig 14 Process capability of internal dimension of section (X1) after improvement
Table 6 Results for calculations extrusion process measures of internal dimension of section (X1) after improvement
Extrusion process measures
Measure value
Cp Cpk Sigma level
DPMO Yield (Y) Defect per 1000 kg Profit per 1000 kg E X
0.72 0.3 2.4 185795.09 81 % 0.18579509 223.000 0.212094 36.4297
Advances in Production Engineering & Management 11(1) 2016
67
Ketan, Nassir
3.	Results and discussion
The results of extrusion process measures PCIs (Process capability indices), sigma levels, and DPMO values before and after improvement shown in Table 4 and Table 6. The improvement of performance measures are as following: Cp value has been increased from 0.5 to 0.74 which means that the process capability is sufficient and the specification width greater than the process spread. The value of Cpk has increased from -0.032 to 0.306 which means that the standard deviation has decreased from 0.3082 to 0.208125. The process yield is increase to 36 % items without defects. The value of sigma level has increased from 1.4 to 2.42 which means reduction in defect products, so that DPMO value has been reduced from 536804 to 185795.09 and the profit increased from ID 127.000 to ID 226.000 per 1000 kg.
4.	Conclusions and recommendations for future work
The conclusions and recommendations that are drawn from this work are as follows:
•	Profits of implementation DMAIC methodology are accomplished in expression of cost decrease and remove aluminium products defects.
•	The values for process capability measures (Cp, Cpk) indicate the ability to process improves or not. If the values are less than 1.0 as for CQC (X1) this situation point out the process mean deviation for aluminium product design specification (target value).
•	The extrusion process mean increased, the extrusion process dispersion decreased and the process extrusion very nearer to target value.
•	Based on the results, the sigma levels values increased depending on the implemented suggested solution. Therefore, this improvement is not sufficient to reach the value of six sigma level.
•	The results prove that the DMAIC methodology is effective in estimation, analysis and improvement process capability of data that are normally distributed.
•	Study the process capability improvement (DMAIC methodology) by using simulation technique to test and improve the effectiveness of suggested solution before they are implemented.
•	The possibility of the DMAIC methodology application in the other aluminium products and other product defects were not able to study in this work due to the limitation of research time.
Acknowledgement
Our grateful to the staff members of quality control, dies manufacturing departments and staff of extrusion factory in UR state company for engineering industries in Iraq for their support to accomplish this research work. Finally, thanks are devoted to head and staff members of the department of mechanical engineering of Baghdad University for all facilities offered.
References
[1]	Garcia, F.R. (2014). Six Sigma implementation within the building construction industry: A case study of the research building construction, Master of science thesis, University Polytechnic of Valencia, School of Architecture, Valencia, Spain.
[2]	Desale, S.V., Deodhar S.V. (2013). Lean Six Sigma principal in construction: A literature review related to conclusions, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, No. 5, 531-535.
[3]	El Santty, M.I., Tharwat A.A., Zein Eldin, R.A. (2013). Multi objective Six Sigma methodology: Application on chromojet printing, American Journal of Research Communication, Vol. 1, No. 7, 100-115.
[4]	Park, S.H. (2003). Six Sigma for quality and productivity promotion, Productivity Series 32, Asian Productivity Organization, Tokyo, Japan.
[5]	Khan, O.H. (2005). A study of critical success factors for Six Sigma implementation in UK organizations, Master of science dissertation, Bradford University, School of Management, UK.
[6]	Aksoy, B., Orbak, A.Y. (2009). Reducing the quantity of reworked parts in a robotic arc welding process, Quality and Reliability Engineering International, Vol. 25, No. 4, 495-512, doi: 10.1002/qre.985.
68
Advances in Production Engineering & Management 11(1) 2016
Aluminium hot extrusion process capability improvement using Six Sigma
[7]	Thomsett, M.C. (2004). Getting started in Six Sigma, John Wiley & Sons, Inc., Hoboken, New Jersey, USA.
[8]	Farahmand, K., Grajales, J.V.M., Hamidi, M. (2010). Application of Six Sigma to gear box manufacturing, International Journal of Modern Engineering, Vol. 11, No. 1, 12-19.
[9]	Basu, R., Wright, J.N. (2003). Quality beyond Six Sigma, Butterworth-Heinemann, Oxford, UK.
[10]	Jodi, M.R. (2014). Casting process improvements based on Six Sigma with simulation modelling, Ph.D. thesis, Baghdad University, Mechanical department, Iraq.
[11]	Prasad, K.G.D., Subbaiah, K.V., Padmavathi, G. (2012). Application of Six Sigma methodology in an engineering educational institution, International Journal of Emerging Sciences, Vol. 2, No. 2, 222-237.
[12]	Mahanti, R., Antony, J. (2005). Confluence of Six Sigma, simulation and software development, Managerial Auditing Journal, Vol. 20, No. 7, 739-762, doi: 10.1108/02686900510611267.
[13]	Habibi, A., Rezapour, A. (2015). The ability of implementation of Six Sigma in government hospitals of Babol, DU Journal, Humanities and Social Sciences, Vol. 8, No. 6, 41-63.
[14]	Khekale, S.N., Chatpalliwar, A.S., Thakur, N.V. (2010). Minimization of cord wastages in belt industry using DMAIC, International Journal of Engineering Science and Technology, Vol. 2, No. 8, 3687-3694.
[15]	Gijo, E.V., Scaria, J., Antony, J. (2011). Application of Six Sigma methodology to reduce defects of a grinding process, Quality and Reliability Engineering International, Vol. 27, No. 8, 1221-1234, doi: 10.1002/qre.1212.
[16]	Hung, H.C., Sung, M.H. (2011). Applying Six Sigma to manufacturing processes in the food industry to reduce quality cost, Scientific Research and Essays, Vol. 6, No. 3, 580-591.
[17]	Mandahawi, N., Fouad, R.H., Obeidat, S. (2012). An application of customized Lean Six Sigma to enhance productivity at a paper manufacturing company, Jordan Journal of Mechanical & Industrial Engineering, Vol. 6, No. 1, 103-109.
[18]	Gupta, N., Bharti, P.K. (2013). Implementation of Six Sigma for minimizing the defects rate at a yarn manufacturing company, International Journal of Engineering Research and Applications, Vol. 3, No. 2, 1000-1011.
Advances in Production Engineering & Management 11(1) 2016
69