This document provides an introduction to design of experiments. It discusses how industrial engineering draws upon various fields like mathematics, physical sciences, and social sciences. It also discusses key concepts like systems, factors and treatments, the P diagram, and noise factors. The goal of experimental design is to specify, predict, and evaluate results from integrated systems through designing and analyzing experiments.
Experiments
A Quick History of Design of Experiments
Why We Use Experimental Designs
What is Design of Experiment
How Design of Experiment contributes
Terminology
Analysis Of Variation (ANOVA)
Basic Principle of Design of Experiments
Some Experimental Designs
Approaches to Experimentation
What is Design of Experiments
Definition of DOE
Why DOE
History of DOE
Basic DOE Example
Factors, Levels, Responses
General Model of Process or System
Interaction, Randomization, Blocking, Replication
Experiment Design Process
Types of DOE
One factorial
Two factorial
Fractional factorial
Screening experiments
Calculation of Alias
DOE Selection Guide
Basic Concepts of Experimental Design & Standard Design ( Statistics )Hasnat Israq
This gives the basic description of Design and Analysis of Experiment . This is one of the most important topic in Statistics and also for Mathematics and for Researchers-Scientists
Basic Concepts of Standard Experimental Designs ( Statistics )Hasnat Israq
This gives the basic description of Design and Analysis of Experiment . This is one of the important topic in Statistics and also for Mathematics and for Researchers - Scientists . Good Luck .
1. Write test cases from given software models using the following test
design techniques. (K3)
a equivalence partitioning;
b boundary value analysis;
c decision tables;
d state transition testing.
2. Understand the main purpose of each of the four techniques, what level and type of testing could use the technique, and how coverage may be measured. (K2)
3. Understand the concept of use case testing and its benefits.
backlink:
http://sif.uin-suska.ac.id/
http://fst.uin-suska.ac.id/
http://www.uin-suska.ac.id/
Specification based or black box techniquesmuhammad afif
In this section, look for the definitions of the glossary terms: boundary value analysis, decision table testing, equivalence partitioning, state transition testing and use case testing
Factorial design - Dr. Manu Melwin Joy - School of Management Studies, Cochin...manumelwin
In statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors.
Experiments
A Quick History of Design of Experiments
Why We Use Experimental Designs
What is Design of Experiment
How Design of Experiment contributes
Terminology
Analysis Of Variation (ANOVA)
Basic Principle of Design of Experiments
Some Experimental Designs
Approaches to Experimentation
What is Design of Experiments
Definition of DOE
Why DOE
History of DOE
Basic DOE Example
Factors, Levels, Responses
General Model of Process or System
Interaction, Randomization, Blocking, Replication
Experiment Design Process
Types of DOE
One factorial
Two factorial
Fractional factorial
Screening experiments
Calculation of Alias
DOE Selection Guide
Basic Concepts of Experimental Design & Standard Design ( Statistics )Hasnat Israq
This gives the basic description of Design and Analysis of Experiment . This is one of the most important topic in Statistics and also for Mathematics and for Researchers-Scientists
Basic Concepts of Standard Experimental Designs ( Statistics )Hasnat Israq
This gives the basic description of Design and Analysis of Experiment . This is one of the important topic in Statistics and also for Mathematics and for Researchers - Scientists . Good Luck .
1. Write test cases from given software models using the following test
design techniques. (K3)
a equivalence partitioning;
b boundary value analysis;
c decision tables;
d state transition testing.
2. Understand the main purpose of each of the four techniques, what level and type of testing could use the technique, and how coverage may be measured. (K2)
3. Understand the concept of use case testing and its benefits.
backlink:
http://sif.uin-suska.ac.id/
http://fst.uin-suska.ac.id/
http://www.uin-suska.ac.id/
Specification based or black box techniquesmuhammad afif
In this section, look for the definitions of the glossary terms: boundary value analysis, decision table testing, equivalence partitioning, state transition testing and use case testing
Factorial design - Dr. Manu Melwin Joy - School of Management Studies, Cochin...manumelwin
In statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors.
Formulation and development is a process of selection of component and processing.
Now days computer tools used in the formulation and development of pharmaceutical product.
Various technique, such as design of experiment are implemented for optimization of formulation and processing parameter.
Many times finding the correct answer is not simple and straight forward in such cases use of computer tools (optimization procedure) for best compromise is the smarter way to solve problem.
Here is a piece of detailed information about the experimental design used in the field of statistics. This also features some information on the three most widely accepted and most widely used designs.
Guidelines to Understanding Design of Experiment and Reliability Predictionijsrd.com
This paper will focus on how to plan experiments effectively and how to analyse data correctly. Practical and correct methods for analysing data from life testing will also be provided. This paper gives an extensive overview of reliability issues, definitions and prediction methods currently used in the industry. It defines different methods and correlations between these methods in order to make reliability comparison statements from different manufacturers' in easy way that may use different prediction methods and databases for failure rates. The paper finds however such comparison very difficult and risky unless the conditions for the reliability statements are scrutinized and analysed in detail.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
2. Industrial Engineering
...is concerned with the design, improvement, and
installation of integrated systems of men,
materials, information, energy, and equipment. It
draws upon specialized knowledge and skill in the
mathematical, physical and social sciences
together with the principles and methods of
engineering analysis and design to specify, predict
and evaluate the result to be obtained from such
systems
2
4. What is SYSTEM ?
A set of interdependent things (parts or elements) forming a unified
whole and performing a set of rules to carry out a specific purpose.
An organized, purposeful structure that consists of interrelated and
interdependent elements (components, entities, factors, members,
parts etc.). These elements continually influence one another (directly
or indirectly) to maintain their activity and the existence of the system,
in order to achieve the goal of the system.
(http://www.businessdictionary.com/definition/)
A regularly interacting or interdependent group of items forming a
unified whole (https://www.merriam-webster.com/dictionary/)
A set of things working together as parts of a mechanism or an
interconnecting network; a complex whole
(https://en.oxforddictionaries.com/definition/)
A set of connected things or devices that operate together
(http://dictionary.cambridge.org/dictionary/english/)
5. What is SYSTEM ?
(a) a functional perspective,
(b) a structural perspective,
(c) a hierarchical perspective
5
8. Process Improvement - SIPOC
Diagram
Customers
Process
Suppliers
Inputs Outputs
S I P O C
8
9. Process Improvement - SIPOC
Diagram
Customers
Process
Suppliers
Inputs Outputs
Process Management
and Improvement
Supplier
Performance
Input
Measures
Process
Measures
Output
Measures
Customer
Feedback
Process
Changes
9
11. References
Montgomery, DC, Design and Analysis of Experiments,
John Wiley & Sons
Hicks, CR & Turner, KV, Fundamental Concepts in the
Design of Experiments. Oxford University Press
Cochran, WG & Cox, G, Experimental Designs, John
Wiley & Sons
Fisher, RA, The Design of Experiments, Oliver and Boyd
Taguchi, G, Systems of Experimental Design, Unipub
Kraus International
Sudjana. Desain dan Analisis Eksperimen, Tarsito
Suwanda, Desain Eksperimen untuk Penelitian Ilmiah,
Alfabeta
11
13. Learning Outcomes
Students can comprehend the basic principles of
experimental design, such as randomization,
replication and local control (blocking, balancing
and grouping).
Students can construct an appropriate design of
experiments with a minimal risk of bias
Students can analyze the results of experiments
with statistical inference
13
14. Deductive – Inductive Reasoning
Theory Observation
General Specific
Population Sample
Hypothesis Prediction
Estimation Hypothesis
R
E
A
S
O
N
I
N
G
INDUCTIVE
DEDUCTIVE
14
15. Deductive – Inductive Reasoning
DEDUCTIVE
REASONING
INDUCTIVE
REASONING
PREMISES Stated as facts or
general principles
Based on
observations of
specific cases
CONCLUSION Conclusion is more
special than the
information the
premises provide. It is
reached directly by
applying logical
rules to the premises
Conclusion is more
general than the
information the
premises provide. It is
reached by
generalizing the
premises information
VALIDITY If the premises are
true, the conclusion
must be true
If the premises are
true, the conclusion is
probably true
USAGE More difficult to use
(mainly in logical
problems). One needs
facts which are
definitely true
Used often in
everyday life (fast and
easy). Evidence is
used instead of proved
facts.
15
17. Engineering Method
Scientific Method
•Identify
problem
•Explore
requirements
•Trace
constraints
•Diagnose
causes
•Define
objectives
•Plan program
schedule
•Drawings
•Schematics
•Models
•Algorithms
•Proof of
concepts
•Prototypes
•Experiments
•Validation
and
verification
•Summary
results
•Conduct
implementa-
tion
Phase 1
Idea
Phase 2
Concept
Phase 3
Planning
Phase 4
Design
Phase 5
Development
Phase 6
Launch
Step 1
Ask A
Question
Step 2
Do
Background
Research
Step 3
Construct
A Hypothesis
Step 4
Test
Hypothesis
Step 5
Analyze Data &
DrawConclusion
Step 6
Communicate
17
18. Observations and Experiments
In an observational study, the engineer observes
the process or population, disturbing it as little as
possible, and records the quantities of interest.
In a designed experiment the engineer makes
deliberate or purposeful changes in the controllable
variables of the system or process, observes the
resulting system output data, and then makes an
inference or decision about which variables are
responsible for the observed changes in output
performance.
18
22. Random Experiments
Designed experiment is an experiment in which
the tests are planned in advance and the plans
usually incorporate statistical models
Random experiment is an experiment that can
result in different outcomes, even though it is
repeated in the same manner each time.
Outcome is an element of a sample space.
Event is a subset of a sample space.
Sample space is the set of all possible outcomes
of a random experiment.
22
23. Factors and Treatment
Factors are the potential sources of variability that
influence the performance of a process or system.
Treatments are specific levels of the design
factors (factors of interest). They are deliberate
changes of a set of design factors at various level
to observe the changes in the system
performance.
Factor level is the settings (or conditions) used
for a factor in an experiment.
23
24. Factors and Treatment
Effects are the impact of treatment to response
variables. They are the mean change to the
response due to the presence of the treatment.
Interaction is interdependence of several factors.
Two factors are said to interact if the effect of one
variable is different at different levels of the other
variables. In general, when variables operate
independently of each other, they do not exhibit
interaction. An interaction is the failure of one
factor to produce the same effect on the response
at different levels of another factor.
24
25. Factors and Treatment
The potential design factors are those factors
that the experimenter may wish to vary in the
experiment.
Design factors are the factors actually selected for
study in the experiment.
Held-constant factors are variables that may exert
some effect on the response, but for purposes of the
present experiment these factors are not of interest, so
they will be held at a specific level.
Allowed-to-vary factors are variables that are usually
nonhomogeneous, but for ignoring this unit-to-unit
variability, it relies on randomization to balance out any
effect.
25
26. Factors and Treatment
Nuisance factors may have large effects that
must be accounted for, yet the experimenter may
not be interested in them in the context of the
present experiment.
A controllable nuisance factor is one whose levels
may be set by the experimenter
An uncontrollable nuisance factor is a nuisance
factor that is uncontrollable in the experiment, but it can
be measured. An analysis procedure called the analysis
of covariance can be used to compensate for its effect.
A noise factor is a factor that varies naturally and
uncontrollably in the process.
26
27. Factors and Treatment
Fixed effect factor is a design factor of
experiment with specific treatment at certain
levels. All the levels of interest for the factor are
included in the experiment.
Random effect factor is a design factor of
experiment with treatment by random sample from
some population of factor levels. There may be
unknown levels between treatment (level numbers
are only nominal).
27
31. P Diagram
Response variables (y) are the dependent variables (that are affected
some factors) as observed output characteristics (that are designed to meet
the target).
Signal factors (M) are the parameter values set by the user at specified
point or within an acceptable range to attain the desired output.
Control factors (Z) are the parameter values set by the engineer at least at
two-levels to select the best level for the desired output.
Noise factors (X) are not controllable by the engineer or the user. However,
for the purpose of optimization, these factors may be set at one or more levels.
Scaling factors (R) are special cases of control factors that are adjusted to
achieve the desired functional relationship as a ratio between the signal factor
and the response.
Leveling factors (D) are special cases of control factors that are adjusted
to achieve the desired functional relationship as a constant between the signal
factor and the response.
31
32. Noise or Nuisances Factors
External noise factors are sources of variation that are external to the
product or process. They include environmental noise factors and load-related
noise factors. The environmental noise factors are temperature, humidity, dust,
electromagnetic interference, etc. The load-related noise factors are the period
of time the product works continuously, the pressures to which it is subjected
simultaneously..
Internal noise factors are sources of variation that are internal to the
product or process. They include time-dependent deterioration factors such as
wear of components, spoilage of materials, fatigue of parts, and operational
errors, such as improper settings on product or equipment.
Unit-to-unit noise factors are inherent random variations in the process or
product caused by variability in raw materials, machinery and human
participation.
32
34. Sources of Errors
random error is an uncontrollable difference from one
trial to another due to environment, equipment, or other
issues that reduce the repeatability of an observation
systematic error is a reproducible deviation of an
observation that biases the results, arising from
procedures, instruments, or ignorance
illegitimate error is an error introduced when an
engineer does mistakes, blunders, or miscalculations (e.g.
measures at the wrong time, notes the wrong value)
34
35. Measurement Errors
unusual value (outlier) is an observation in a
sample that are so far from the main body of data
that they give rise to the question that they may be
from another population.
missing value is any relevant data which are
missing, since there may be transcription or
recording errors or may not have been collected
and archived.
bias is an effect that systematically distorts a
statistical result or estimate, preventing it from
representing the true quantity of interest.
35
36. Errors on Statistical Analysis
Type I Error () is rejecting the true null
hypothesis
Type II Error () is failing to reject the false
null hypothesis
36
52. Elements of Design of
Experiments
1. Conjecture or hypothesis
2. Response variable
3. Factors, levels and ranges
4. Treatments of factors
5. Blockings
6. Tools and methods for experiments and
measurements
7. Effect models (independent or interaction factors)
8. Replication, randomization and local factor
52
53. Tentative Empirical Model
Linear
Quadratic
Cubic
Polynomial
2
2
1
1
0 x
x
y
2
2
22
2
1
11
2
1
12
2
2
1
1
0 x
x
x
x
x
x
y
3
2
222
3
1
111
2
2
22
2
1
11
2
2
1
1
0 ...
... x
x
x
x
x
x
y
k
k
x
x
x
x
y 2
...
2
1
...
1
2
2
1
1
0 ...
53
55. Best-guess Experiments
Advantages
The experimenter reasonably
selects an arbitrary combination
of the design factors, test them,
and see what happens
The experimenter switches the
levels of one or two (or perhaps
several) factors for the next test,
based on the outcome of the
current test.
There is a great deal of
technical or theoretical
knowledge of the system, as
well as considerable practical
experience.
Disadvantages
The approach could be
continued almost indefinitely.
The initial best-guess does not
produce the desired results. So
the experimenter has to take
another guess at the correct
combination of factor levels.
This could continue for a long
time, without any guarantee of
success.
The initial best-guess produces
an acceptable result. And the
experimenter is tempted to stop
testing, although there is no
guarantee that the best solution
has been found.
55
56. One-factor-at-a-time (OFAT)
Experiments
Advantages
The experimenter selects a
starting point, or baseline set of
levels, for each factor, and then
successively varying each factor
over its range with the other
factors held constant at the
baseline level.
The experimenter analyzes how
the response variable is
affected by varying each factor
with all other factors held
constant.
The interpretation is
straightforward, conclude the
interaction.
Disadvantages
It assumes factors were
independent. If the
experimenter varies a factor, he
assumes that the other factors
have virtually no effect.
It fails to consider any possible
interaction between the factors.
A factor may produce the
different effect on the response
at different levels of another
factor.
If the interactions between
factors occur, it will usually
produce poor results
56
57. Statistically-designed (Factorial)
Experiments
Advantages
All possible combinations of the
design factors across their
levels are used in the design
A reasonable plan would be at
each combination of factor
levels
The experimental design would
enable the experimenter to
investigate the individual effects
of each factor (or the main
effects) and to determine
whether the factors interact.
Disadvantages
The number of factors of
interest increases, the number
of runs required increases
rapidly.
57
66. Experimental Units
Experimental unit or trial is a single testing in
scientific investigation through observations or
experiments that is reproducible in the same
condition or treatment to observe the response
variable. It is an entity which is the primary unit of
interest in a specific research objective for
researcher to make inferences about (in the
population) based on the sample (in the
experiment). Thus it needs adequate replication of
experimental units. The sample size is the number
of experimental units per group.
66
67. Basic Principles of Experimental
Design
Replication, to provide an estimate of
experimental error;
Randomization, to ensure that this estimate is
statistically valid; and
Local control, to reduce experimental error by
making the experiment more efficient
67
68. Basic Principles of Experimental
Design
Replication is an independent repeat run of each factor
combination. It is the repetition of experiment under identical
conditions. It refers to the number of distinct experimental
units under the same treatment.
Replikasi bermanfaat untuk mendapatkan data yang homogen.
Replikasi meningkatkan akurasi taksiran response dengan memetakan
confidence interval pada significance level tertentu.
Replikasi membantu mendeteksi outlier akibat kekeliruan eksperimen,
kekeliruan pengukuran atau faktor pengganggu lainnya.
68
69. Basic Principles of Experimental
Design
Randomization is the cornerstone underlying the use of
statistical methods in experimental design to randomly
determine the order in which the individual runs of the
experiment are to be performed. Through randomization,
every experimental unit will have the same chance of
receiving any treatment.
Pengacakan bermanfaat untuk memastikan setiap percobaan bersifat
independen, dan pengaruh faktor pengganggu terkurangi.
Pengacakan mengurangi resiko bias eksperimentasi akibat faktor
pengganggu memberikan pengaruh sama dan berulang pada
perlakuan eksperimentasi yang sama.
Pengacakan membantu menambah keyakinan dari analisa statistik
hasil eksperimentasi
69
70. Basic Principles of Experimental
Design
Local control is the control of all factors except the design
factors which are investigated. It refines the relatively
heterogeneous experimental subset into homogeneous
subset by removing extraneous sources of variability. It refers
to the amount of balancing, blocking and grouping of the
experimental units.
Pengelompokan (grouping) yaitu penempatan sekumpulan percobaan
yang homogen dalam kelompok-kelompok yang mendapatkan
perlakuan yang sama.
Pemblokan (blocking) yaitu pengalokasian percobaan dalam blok,
agar setiap blok berisikan percobaan-percobaan bersifat homogen.
Penyeimbangan (balancing) yaitu pengendalian proses
pengelompokan dan pemblokan agar percobaan dalam konfigurasi
atau formasi yang seimbang.
70
71. Engineering Method
Scientific Method
•Identify
problem
•Explore
requirements
•Trace
constraints
•Diagnose
causes
•Define
objectives
•Plan program
schedule
•Drawings
•Schematics
•Models
•Algorithms
•Proof of
concepts
•Prototypes
•Experiments
•Validation
and
verification
•Summary
results
•Conduct
implementa-
tion
Phase 1
Idea
Phase 2
Concept
Phase 3
Planning
Phase 4
Design
Phase 5
Development
Phase 6
Launch
Step 1
Ask A
Question
Step 2
Do
Background
Research
Step 3
Construct
A Hypothesis
Step 4
Test
Hypothesis
Step 5
Analyze Data &
DrawConclusion
Step 6
Communicate
71
72. Keep The Following Points In
Mind
Use your non-statistical knowledge of the
problem.
Keep the design and analysis as simple as
possible.
Recognize the difference between practical and
statistical significance.
Experiments are usually iterative.
72