This document discusses the application of design of experiments (DOE) in welding technology. It explains why industrial engineers need to study DOE to push quality higher and focus on quality management. It also describes different types of experimental design methods like full factorial, fractional factorial, and Taguchi's method. Finally, it provides an overview of various welding processes classified based on the energy source used like gas, arc, resistance, solid state, and radiant energy welding.

1. Application of DOE in welding
technology
NAME :- DHRUV PATEL
NUMBER :- 11BIE024
INDUSTRIAL ENGINEER

2. Design of Expriment
• This techniques enables designers to determine simultaneously the
individual and interactive effects of many effect that could affect the
output result in any design.
• DOE is systematic approach to engineering problem solving that
applies principles and techniques at the data collection stage so as to
ensure the generation of valid engineering conclusion.

3. Why industrial engineer have to study the
DOE?
• Push quality issue further and further high.
• Need to focus on quality management.
• Continously study on process purformance

4. Exprimentation
• A test or a series of tests in which purposeful changes are made to
input variables of a process or system to observe and identify the
reasons for changes that may be observed in the output response.
• It is plays an important role in 1) product design
2) product development
3) product improvement
• Complexity :- because k factor and p response then k*p entites are
there.
• Experimental error :- when the variability is not expressed by known
influences then it is experimental error.

5. Types of DOE methods
1. Full factorial method
2. Fraction factorial method
3. One factor at a time
4. Taguchi’s experimental design
5. Six-sigma
6. Annova
7. Latin square
8. screening

6. Full factorial design
• Study of two or more factor effect then factorial design are the most
efficient way of doing this.
• Widely used in manufacturing company.
• Full factorial have 22 and 23 factorial design. 23 means 2 is the level
and 3 is the factor.
• Using this method we can find…
1. Main effect
2. Effect which influence variability
3. Minimizing the variability
4. Way to achieve target value

7. 22 factorial design
• In this level for factors is 2. 1) high and 2) low.
• The 2 factors which are affecting the output at two levels (high and
low) is considering in the calculation.

8. 23 factorial design
• In this design the level is two high and low but the factors are three.

9. Fraction factorial design
• When the factors which are affecting is more than two then we have
to go for the fraction factorial method.
• In this design the confounding concept is used. In confounding
method we know that one factor is confounded by another factor or
factors.
• Generally the 27−4 and 24−1 models are used in experiment.
• Need for fraction factorial :- when the 24 design is there then the 4
factors are affecting and there is 2 level so there is total 16 replicate
we have to observe so it is not economic. To reduce the replicates we
have to apply the fraction factorial. so the no. of replicate is 8 (in this
case it is half fraction factorial).

10. Annova
• Analysis of variances
• Why we use? :- to test appropriate hypothesis about the treatment
mean and to estimate the treatment mean.
• Assumption :- model errors are assumed to be normally and
independently distributed random variable with mean zero and
variance σ2. Variance being constant for all level of the factor.
• This method is based on the calculation of sum of squares, mean
square and the f-table.
• If the f-value is fall between the f-table then we have to accept the
null hypothesis.

11. Taguchi method
• Taguchi has found a new method of conducting the design of experiments
which are based on well defined guidelines. This method uses a special set
of arrays called orthogonal arrays. These standard arrays stipulates the way
of conducting the minimal number of experiments which could give the full
information of all the factors that affect the performance parameter.
• Objective function :- 1) nominal is best
2) larger is the best
3) smaller is the best
• According to the control factor we have to select the orthogonal array and
conduct the experiment.

12. Welding process
• Welding is a process of permanent joining two materials with suitable
combination of temperature, pressure and metallurgical conditions.
• Depending upon the combination of temperature and pressure a
wide range of welding processes has been developed.
• Classification of welding processes (based upon source of energy)
1. Gas welding
2. Arc welding
3. Resistance welding
4. Solid state welding
5. Radiant energy welding

13. Types of welding processes
• Oxyacetylene gas welding
• Submerged arc welding
• Sheet metal arc welding
• Gas tungsten arc welding (TIG)
• MIG welding
• Plasma arc welding
• Spot welding
• Friction stir welding