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1 Breakthrough Improvement in quality & manufacturing cost of MIG welding process by using Classical Design of experiment Jahir Ahamad Pinjari Sharda Motor Industries Ltd (SMIL), Chakan [email protected] 1.0 Abstract: In automotive industry welding is considered as both critical and as special process. SMIL with its major product range is having almost 70 % processes as welding. SMILs vision till 2022 is to increase its foot print from current 45 % to 70 % (w.r.t. exhaust market of four wheeler) and to meet BSVI requirements. Achieving quality at lowest processing cost is key factor to achieve companies vision. We have challenge achieving multiple quality characteristics from welding process. Some of them are variable type and many are attribute data. Few characteristics need destructive testing. Several welding companies use trial and error, one factor at a time (OFAT) methodology including SMIL. We had feeling that our process is at optimum as parameters were set during development after several trials. These trials were taken by internal welding experts along with welding expert coming from foreign country. After various trials parameters were set and important parameters’ specification is derived. DOE is not attempted in past due to insufficient knowledge. We had assumption that process experts are setting parameters at optimum. MSES stand for Mahindra Supplier Evaluation Standard. Long term strategic suppliers of Mahindra and Mahindra Limited get opportunity for MSES evaluation. Mahindra team assigns their pilot who invests time in coaching, reviewing at shop floor, guiding us to improve our processes and make our organization more effective and efficient. As a part of supplier capability building various trainings organized by Mahindra team helps us to improve our competence which results in meeting customer quality and cost expectations. Mahindra team helps us to implement better system and improve our processes. This helps us to compete with MNCs. SMIL is selected for MSES A drive from Mahindra &Mahindra. One of the requirement is to implement DOE for process parameter dominant processes. SMIL team attended training of Mr.Mahesh Hegde (LearnEx Consulting) is introduced as TQM Guru for our MSES A

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Breakthrough Improvement in quality & manufacturing cost of MIG

welding process by using Classical Design of experiment

Jahir Ahamad Pinjari

Sharda Motor Industries Ltd (SMIL), Chakan

[email protected]

1.0 Abstract:

In automotive industry welding is considered as both critical and as special process. SMIL

with its major product range is having almost 70 % processes as welding. SMIL’s vision till

2022 is to increase its foot print from current 45 % to 70 % (w.r.t. exhaust market of four

wheeler) and to meet BSVI requirements.

Achieving quality at lowest processing cost is key factor to achieve companies vision. We

have challenge achieving multiple quality characteristics from welding process. Some of

them are variable type and many are attribute data. Few characteristics need destructive

testing. Several welding companies use trial and error, one factor at a time (OFAT)

methodology including SMIL.

We had feeling that our process is at optimum as parameters were set during development

after several trials. These trials were taken by internal welding experts along with welding

expert coming from foreign country. After various trials parameters were set and important

parameters’ specification is derived. DOE is not attempted in past due to insufficient

knowledge. We had assumption that process experts are setting parameters at optimum.

MSES stand for Mahindra Supplier Evaluation Standard. Long term strategic suppliers of

Mahindra and Mahindra Limited get opportunity for MSES evaluation. Mahindra team

assigns their pilot who invests time in coaching, reviewing at shop floor, guiding us to

improve our processes and make our organization more effective and efficient. As a part of

supplier capability building various trainings organized by Mahindra team helps us to

improve our competence which results in meeting customer quality and cost expectations.

Mahindra team helps us to implement better system and improve our processes. This helps

us to compete with MNCs.

SMIL is selected for MSES A drive from Mahindra &Mahindra. One of the requirement is to

implement DOE for process parameter dominant processes. SMIL team attended training of

Mr.Mahesh Hegde – (LearnEx Consulting) is introduced as TQM Guru for our MSES A

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journey by Mahindra Team, who is training and guiding TQM implementation for all MSES A

suppliers. We were trained along with other suppliers and learnt about how to plan, design,

conduct and analyse experiments. We got opportunity to learn Full factorial 2 level Design,

General Full factorial, Fractional Factorial, Plackett- Burman, Response Surface Methods,

Mixture DOE and Taguchi.

DOE team was formed by SMIL and detailed experimental plan made, among 16 affecting

factors, “Experimental”, “Control / Must be OK” and “Noise factors” are identified.

Finally 4 experimental factors are selected to meet 5 responses. Full factorial DOE is

conducted. We have studied totally 10 responses. Detailed analysis helped us to set process

parameters at optimum level. Quality trend improved for OK ratio (first time right) from 80 to

98%, estimated saving in input process parameters (Power) and consumables (Weld wire,

Gas) for this plant is @ Rs 24 Lacs/annum.

Likewise all systemic documents ex. PFMEA, Control plan & SOP & Managing documents

are updated. New product Development procedure revised adding DOE study as mandatory

tool for future products.

2.0 What is design of experiment?

It is a scientific way to set process at optimum level.

Experiment Design

The formal plan for conducting the experiment is called the “experiment design”

(also the “experiment pattern”)

Planning experiments, execution of experiments, collecting data, analysis of data

using statistical techniques and making inference about the real world.

It is about understanding the real world, not just explaining the data. It is about

understanding why a change in process parameter is affecting product parameter

that way.

Classical DOE is a tool to design experiment by systematically choosing responses, factors,

levels, blocks, Treatments, making experimental plan considering randomization, replication

etc and then conducting these experiments and analysing the data by using defined

statistical procedures such that meaningful conclusions made.

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3.0 Need for DOE

Experiment is to KNOW Something. DOE is not necessary for known and proven things.

Product Quality and processing cost of several processes like casting, injection moulding,

heat treatment, welding, painting, machining, plating, chemical process are mainly

dependent on process parameters. These process parameters are conventionally set by

people with technical knowledge by taking various trials. Final specification of these

processes are set and mentioned in control plan. Process experts tend to set the process

parameters based on the best of their technical knowledge and trials. However, it becomes

difficult to establish the exact relationship with output quality parameters and input process

parameters. This is due to various “interaction effects” and complicated relationships

among several process parameters and output product parameters.

When these parameters are not at optimum there will be chronic problems in quality

resulting into rejection, rework on routine basis. Cost of manufacturing also de-pends on

consumable items like tool consumption, gas, power, material etc. So it becomes very

important to set process parameters at optimum.

Changing One Factor at A time Vs DOE

In DOE decision about each factor is made considering all experiments, by averaging factor

high and low level effects by nullifying effects of other factors. This helps to make right

decision even if there is special cause in one of the experiment.

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4.0 Experiment Planning

Brainstorming is carried out to list response and factors using Cross Functional Team. The

team was including experienced members from manufacturing engineering, Quality,

Maintenance and Process engineering through Brainstorming & Ishikawa diagram we have

identified number of factors affecting output of MIG welding process. Those factors then

further categorised into Experimental Factors, Control Factors & Noise Factors.

85% of success of experiment is based on planning and only 15% success is based

on conducting and analysing the experiment. Very good statistical analysis cannot save

us from poorly planned experiment. If we miss factors, incorrectly select levels, do not

identify possible special causes which can influence the experimental results, do not plan

sufficient number of trials then when fail to make right decisions. So planning is considered

as most important phase of experiment

Experimental Factors are those which can be selected for experimentation with Two or

more levels, person during experimentation can control them & wants manipulate during

experiment to know the effect of the same on output.

Control Factors or must be OK factors are those factors which are kept constant throughout

the experiment. Person doing experiment can control them & does not want to manipulate

during experiment as effect of the same on the output is not the interest of the

experimentation.

Noise factors are those which are uneconomical to control, or may be or may not be having

direct impact of output of process & which we don’t want to control as they are uneconomical

to control.

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5. 1 Experimental Planning sheet

Sl. No.

Factor UOM Present Status Range

Nature offactor

(E,C,N) 1 2 3 Remark Control Method

1 Current Amp 140-180 100 -150

E 100 150 Weld penetration, spatters , burn

though

2 Voltage Volt 20 -24 18-22 E 18 22 Spatter, Weld bead etc.

3 Gas flow LPM 15 -20 10' - 20'

E 10 20 Blow Hole, Porosity, discontinuous welding etc.

4 Drive Speed HZ 15 -20 13-15 E 13 15 Weld penetration.,

Weld bead etc.

5 Stick Out mm 15 10

C Weld penetration, Blow hole porosity

etc.

Template for maintaining the gap between nozzle and workpiece

6 Torch Angle

Degree 45 45

C Weld Bead, welding shift etc.

Template for maintaining the torch angle

7 Clean Part Ok

No Oil and dust

No Oil and dust

C Blow hole, porosity, weld penetration etc.

verified before conducting trial for each run

8 Fixture condition Ok

free from

spatter

free from

spatter

C Welding Shift, part orientation etc.

verified before conducting trial for each run

9 Environment - Ok Ok

N verified before conducting trial for each run

10 Air Pressure Bar 4 to 5 4 to 5

C Insufficient clamping,weld shift etc.

verified before conducting trial for each run

11 Operator Skill Level 3 3

C As per Required skill operator ensured for eachtrial run

12 Weld Wire Type Flux core

Flux core C

verified before conducting trial for each run

13 Weld Wiredia. mm 1.2 1.2 C

Weld Bead , Cycle time

verified before conducting trial for each run

14 Nozzle cleaning Ok

free from

spatter

free from

spatterC

Blow hole, porosity etc.

Ensured before each trial run

15 Liner cleaning Ok Ok Ok C Discontinuous

welding Ensured before each trial run

16 Earthen Ok Firm Firm C Discontinuous welding

Ensured before each trial run

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Check sheet has been made to verify the control condition before conducting each trial of an

experiment.

5.2 Experiment design in MINITAB:

There were 4 experimental factors with 2 levels are selected in our experiment on MIG

welding, so total experiments are 16

6.0 Conducting practical experiment:

Before conducting experiment in practical, we checked and ensured that all must OK factors

are in control. Samples are numbered according to their run order. Experimental factors set

by adjusting those process parameters as per operation adjustment worksheet with trained

operator. During experiment the responses measurable in experiment are captured at same

time, like Weld wire consumption and Cycle time etc. for each experiment.

After conducting practical trial all parts are visually inspected for attribute responses like

Blow Hole, Porosity and Spatter etc. and made a note of each response for all parts. Those

parts are further given for destructive testing of Weld to get variable responses like Lug

length, throat height, Weld penetration etc.

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Flow of conducting experiment

After getting results of experiment as weld penetration report, responses are put in MINITAB

in respective column.

7.0 Analyse Experiment:

Step 1• Set Parameter as

per DOE experiment in Machine

• Keep Control Factor constant

Step 2•Conduct trial as

per run order

Step 3•Record variable

response during experiment

Step 4•Inspect part for

Aesthetic Weld Defect

Step 5•Test Samples in

lab •Generate weld

test report

Pareto Chart for standardized

effectResidual plot Main Effect Plot

Interaction plotContour PlotResponse Optimizer

Multi Response optimizer

For Each Response

For All Responses

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Example: Individual response analysis for weld penetration

a. Pareto Chart for standardized effect

Inference of this chart gives standardized effect of particular factor on expected

response.

Pareto chart of the effects to determine the magnitude and the importance of an

effect. The chart displays the absolute value of the effects and draws a reference line

on the chart. Any effect that extends past this reference line is potentially important.

As shown in chart current is having significant impact on Weld wire consumption

Estimated Effects and Coefficients for Weld Penetration (coded units)

Term Effect Coef SE Coef T P

Constant 46.38 5.036 9.21 0.000

Current 22.11 11.06 5.036 2.20 0.049

Gas Flow -16.12 -8.06 5.036 -1.60 0.135

Current*Gas flow

-23.65 -11.82 5.036 -2.35 0.037

S = 20.1456 PRESS = 8658.02 R-Sq = 51.79% R-Sq(pred) = 14.29% R-Sq(adj) = 39.74%

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b. Residual Plots

In the normal probability plot of the effects, points that do not fall near the line usually

signal important effects. Important effects are larger and generally further from the

fitted line than unimportant effects. Unimportant effects tend to be smaller and

cantered around zero.

c. Main Effect plot

Inference of main effects gives main effect of particular experiment factor on

response

As current increasing from 100 to 150 A weld penetration increases from 35 to 60 % -

Significant effect

As Gas flow increases from 10 to 20 weld penetration decreases from 55 to 35 % -

Significant Effect

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d. Interaction plot

Inference of Interaction plot gives effect of interaction of two factors on response

At current 100 A- Increasing gas flow from 10 to 20 decreases weld penetration

significantly.

At Voltage 18 V - Increasing gas flow from 10 to 20 decreases weld penetration

significantly.

At Drive speed – 15 Hz increasing gas flow from 10 to 20 decreases weld penetration

significantly.

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e. Contour Plot

To get weld penetration value 50 %

Parameter setting: Current – 120 AMP, Gas Flow – 10.0 LPM

Inference of contour plot gives a operating band where we can get optimum setting

for expected response, also we can get intangible benefits out of it.

f. Response optimizer

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Result optimizer inference gives optimal solution for the input variable

combinations. The optimization plot is interactive; we can adjust input variable

settings on the plot to search for more desirable solutions.

For different expected response same method can be followed for getting

optimum setting different for different responses

8.0 Multi Response study:

By using contour plot & result optimizer for multiple response we can get

optimum setting of multiple input variables for all expected variable responses

in one DOE experiment.

a. Overlaid Contour Plot for Multiple Responses

Multi response contour plot gives a operating band where we can get optimal

setting of input variable to achieve multiple reponses.

As shown in example at current 120, Voltage 20, keeping drive speed 14 &

gas flow 15lpm constant we can get weld penetration 47%,Lug length 5.82,

Weld wire consumption 8.93, throat height 3.15, power consumption 0.20

Rs/joint.

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b. Response Optimizer for Multi response :

For optimizing the response value responses had been given weightages

based on product quality & cost

Considering product quality as prime importance weld penetration, throat

height, lug length were given higher importance i.e.10,9 & 8 respectively

for achieving the goal of Target value, maximizing or minimizing it.

Response optimizer (Multi-Response) plot -

Result optimizer gives optimal solution for multiple input variable combinations to

adjust input variable setting for more desirable solution in terms of multiple responses

at a time.

CurHighLow1.0000

DNew

d = 1.0000MaximumWeld Pen

y = 50.6109

d = 1.0000MinimumWeld Wiry = 8.4206

d = 1.0000MaximumThroat H

y = 3.5709

d = 1.0000MinimumPower Coy = 0.2113

d = 1.0000MaximumLug Leng

y = 6.0568

1.0000DesirabilityComposite

10.020.0

13.015.0

18.022.0

100.0150.0

Voltage Drive Sp Gas FlowCurrent

125.7716] [18.0] [13.0] [11.3415]

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9.0 Benefits in MIG welding by implementing DOE

Parameter Before DOE After DOE Improvement

Straight Through

Ratio 80% 98% 22.5 % Improved

Weld Penetration 26.34 - 103.2% 54.12% - 55.56 % Consistent

Lug length inconsistent 5.5-6.5 Consistent Throat Height Inconsistent 3-3.4 Consistent

Weld Wire

Consumption 9.79 gm/Joint 8.57 gm/Joint 14.23 % Reduction

Power Consumption Rs. 0.5 / Joint Rs. 0.2 / Joint 60% Reduced

Shielding Gas

Consumption 18 lpm 12 lpm 33.3% Reduced

For SMIL Chakan plant expected future saving 24Lacs/Annum

For SMIL Group expected future saving 1.25 Cr./Annum

10.0 Conclusion

DOE techniques offer a structured approach for changing many factor settings within

a process at once and observing the data collectively for improvement. DOE analyses not

only yield a significance test of the factor levels but also give a prediction model for the

response. In DOE the effects of several independent factors (variables) can be considered

simultaneously in one experiment without evaluating all possible combinations of factor

levels.

Maximum numbers of weld joints are there in exhaust system for different variants &

we will be generating benchmarking library for each joint which be a guideline in upcoming

projects to follow.

DOE training to other members:

DOE training has been imparted in plant for members of Production, Quality & New

Product development department, total 19 members covered.

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Future Plans:

Future plan is to conduct Design of experiment on all products joints available in

plant on SPM & Robotic joints. Parallel the learning would be implemented across SMIL

group companies.

Key learning from this entire exercise is “We must follow Scientifically Proven Methods” As

told by Dr.Deming “Before doing your best, you must know what is the best method” We

were doing our best way of optimization in the past and learnt new way of doing it. There is

enormous scope among Indian Industries to apply this technique which can deliver large

benefit by improving quality and reducing processing cost.

References

Note: All the theory part of DOE is mentioned in this paper is taken from “Training Material

of Design of Experiments – Mahesh Hegde – LearnEx Consulting”

11.0 Acknowledgments

I sincerely thank Mr Mahesh Hegde for sharing, teaching & mentoring DOE method. It has

provided immense help to implement DOE fruitfully.

Thanks to Mr Jagannath Burli & Mr Sagar Vispute SQD-CDMM-Mahindra and Mahindra

Automotive sector for driving implementation of TQM as a part of supplier capability building

initiatives through training, workshops & projects. & Thanks to Mr Prashant Patil, Mr Sachin

Jagtap, Mr Sandeep Rokade & team SMIL for support on conducting experiment .