title: Evaluation of post-weld heat treatments for corrosion protection in friction stir welded 2024 and 7075 aluminum alloys authors: Widener, Christian Aragon
abstract: This dissertation presents the results of an investigation into the corrosion resistance of friction stir welding (FSW) for aerospace structures. Two of the most common aerospace aluminum alloys, 2024 and 7075, were investigated. In the as-welded condition, both alloys were found to be highly susceptible to exfoliation corrosion, and 7075 was found to be susceptible to stress corrosion cracking as well. The goal of this research was to identify proper initial temper selection and postweld aging treatments for enhancing the corrosion resistance of both 2024 and 7075 alloys, and their dissimilar joints. A large number of heat treatments were investigated for 7075 in the T6 and T73 tempers, including retrogression re-aging (RRA). Heat treatments were also investigated for 2024-T3 and 2024-T81. Samples were evaluated for resistance to exfoliation corrosion using optical microscopy. Microhardness, electrical conductivity, tension, and fatigue crack propagation tests were also performed on the samples. Beneficial heat treatments were found for both alloys as well as for their dissimilar joints.
description: "December 2005."

title: Optimization of the infusion process using adaptive control coupled with genetic algorithm in resin transfer molding authors: Kashani, Pooria; Rodriguez, Alejandro; Minaie, Bob
abstract: To account for the irregularities in the filling pattern during Resin Transfer Molding (RTM), adaptive control can be used to regulate the filling pattern such that the last point to fill coincides with the preset exit vent location to avoid dry spot formation. In this work, Genetic Algorithm (GA) was selected as a robust search method to optimize the location of the gates and the sensors. Results obtained show that GA was able to use less than 5% of all possible arrangements to find the optimal solutions. In addition, the solutions found by GA were always in the top 0.4% of all possible combinations. These results could provide useful information for optimum arrangements and they could lead to more efficient and intelligent processing
description: Paper presented to the 3rd Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Hughes Metropolitan Complex, Wichita State University, April 27, 2007.

Safe Universities

Alphabetically

Apply to these Universities if you are an average student with reasonable GPA, TOEFL and GRE scores.

AUBURN UNIVERSITY - AL

BOISE STATE UNIVERSITY - ID

BRIGHAM YOUNG UNIVERSITY - UT

CALIFORNIA STATE UNIVERSITY, CHICO - CA

CALIFORNIA STATE UNIVERSITY, FRESNO - CA

CALIFORNIA STATE UNIVERSITY, FULLERTON - CA

CALIFORNIA STATE UNIVERSITY, LONG BEACH - CA

CALIFORNIA STATE UNIVERSITY, LOS ANGELES - CA

CALIFORNIA STATE UNIVERSITY, NORTHRIDGE - CA

CALIFORNIA STATE UNVIERSITY, SACRAMENTO - CA

CITY COLLEGE, CUNY - NY

CLARKSON UNIVERSITY - NY

CLEVELAND STATE UNIVERSITY - OH

COLLEGE OF ST. SCHOLASTIC - MN

COLORADO STATE UNIVERSITY

FLORIDA A&M UNIVERSITY - FL

FLORIDA ATLANTIC UNIVERSITY - FL

FLORIDA INTERNATIONAL UNIVERSITY - FL

FLORIDA STATE UNIVERSITY - FL

GRADUATE SCHOOL & UNIVERSITY CENTER, CUNY - NY

HOWARD UNIVERSITY - DC

ILLINOIS INSTITUTE OF TECHNOLOGY

IOWA STATE UNIVERSITY - IA

KANSAS STATE UNIVERSITY - KS

LAMAR UNIVERSITY - TX

LOUISIANA STATE UNIVERSITY AND A&M COLLEGE - LA

MCNEESE STATE UNIVERSITY - LA

MERCER UNIVERSITY - GA

MISSISSIPPI STATE UNIVERSITY - MS

MONTANA STATE UNIVERSITY - MT

NORTHERN ILLINOIS UNIVERSITY - IL

OKLAHOMA STATE UNIVERSITY - OK

PORTLAND STATE UNIVERSITY - OR

ROCHESTER INSTITUTE OF TECHNOLOGY - NY

SAN DIEGO STATE UNIVERSITY - CA

SAN JOSE STATE UNIVERSITY - CA

SOUTH DAKOTA STATE UNIVERSITY - SD

SOUTHERN ILLINOIS UNIVERSITY, CARBONDALE - IL

SOUTHERN ILLINOIS UNIVERSITY, EDWARDSVILLE - IL

STEVENS INSTITUTE OF TECHNOLOGY - NJ

TENNESSEE TECHNOLOGICAL UNIVERSITY - TN

UNIVERSITY OF AKRON - OH

UNIVERSITY OF ALABAMA - AL

UNIVERSITY OF ALABAMA, BIRMINGHAM - AL

UNIVERSITY OF ALABAMA, HUNTSVILLE - AL

UNIVERSITY OF ARKANSAS - AR

UNIVERSITY OF CENTRAL FLORIDA - FL

UNIVERSITY OF FLORIDA - FL

UNIVERSITY OF HOUSTON - TX

UNIVERSITY OF IDAHO - ID

UNIVERSITY OF ILLINOIS, CHICAGO - IL

UNIVERSITY OF KANSAS - KS

UNIVERSITY OF LOUISIANA, LAFAYETTE - LA

UNIVERSITY OF LOUISVILLE - KY

UNIVERSITY OF MISSOURI, COLUMBIA - MO

UNIVERSITY OF MISSOURI, ROLLA - MO

UNIVERSITY OF NEVEDA, RENO - NV

UNIVERSITY OF NEW MEXICO - NM

UNIVERSITY OF NEW ORLEANS - LA

UNIVERSITY OF NORTH CAROLINA - NC

UNIVERSITY OF NORTH DAKOTA - ND

UNIVERSITY OF NORTH TEXAS - TX

UNIVERSITY OF NORTHERN COLORADO - CO

UNIVERSITY OF NORTHERN IOWA - IA

UNIVERSITY OF OKLAHOMA - OK

UNIVERSITY OF PUERTO RICO, MAYAGUEZ - PR

UNIVERSITY OF SOUTH ALABAMA - AL

UNIVERSITY OF SOUTH FLORIDA - FL

UNIVERSITY OF TENNESSEE, MEMPHIS - TN

UNIVERSITY OF TEXAS AT EL PASO - TX

UNIVERSITY OF TEXAS AT SAN ANTONIO - TX

UNIVERSITY OF TEXAS, ARLINGTON - TX

UNIVERSITY OF TOLEDO - OH

UNIVERSITY OF UTAH - UT

UNIVERSITY OF WYOMING - WY

UTAH STATE UNIVERSITY - UT

WASHINGTON STATE UNIVERSITY - WA

WAYNE STATE UNIVERSITY - MI

WEST VIRGINIA UNIVERSITY - WV

WESTERN MICHIGAN UNIVERSITY - MI

WESTERN WASHINGTON UNIVERSITY - WA

WICHITA STATE UNIVERSITY - KS

WRIGHT STATE UNIVERSITY - OH

YOUNGSTOWN STATE UNIVERSITY - OH

 

Difficult Universities

Apply to these Universities if you are in the top 10% of your class with high GPA and above average scores of TOEFL and GRE.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

STANFORD UNIVERSITY

UNIVERSITY OF CALIFORNIA - BERKELEY

CALIFORNIA INSTITUTE OF TECHNOLOGY

UNIVERSITY OF MINNESOTA - MINNEAPOLIS

PRINCETON UNIVERSITY

PURDUE UNIVERSITY - WEST LAFAYETTE

CORNELL UNIVERSITY

UNIVERSITY OF ARIZONA - AZ

ARIZONA STATE UNIVERISTY, TEMPE - AZ

CASE WESTERN RESERVE UNIVERSITY - OH

BROWN UNIVERSITY

UNIVERSITY OF MICHIGAN - ANN ARBOR

UNIVERSITY OF CALIFORNIA - LOS ANGELES

UNIVERSITY OF ILLINOIS - URBANA CHAMPAIGN

UNIVERSITY OF WISCONSIN - MADISON

NORTHWESTERN UNIVERSITY

RENSSELAER POLYTECHNIC INSTITUTE

UNIVERSITY OF TEXAS - AUSTIN

LEHIGH UNIVERSITY

JOHNS HOPKINS UNIVERSITY

UNIVERSITY OF PENNSYLVANIA

CARNEGIE MELLON UNIVERSITY

CASE WESTERN RESERVE UNIVERSITY

COLUMBIA UNIVERSITY

GEORGIA INSTITUTE OF TECHNOLOGY

UNIVERSITY OF CALIFORNIA - DAVIS

RICE UNIVERSITY

VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY

PENNSYL. STATE UNIVERSITY - UNIVERSITY PARK

OHIO STATE UNIVERSITY - COLUMBUS

IOWA STATE UNIVERSITY - AMES

UNIVERSITY OF WASHINGTON - SEATTLE

NORTH CAROLINA STATE UNIVERSITY - RALEIGH

YALE UNIVERSITY

STATE UNIVERSITY OF NEW YORK - STONY BROOK

UNIVERSITY OF ILLINOIS - CHICAGO

UNIVERSITY OF HOUSTON - VARSITY PARK

UNIVERSITY OF MARYLAND - COLLEGE PARK

UNIVERSITY OF NOTRE DAME

MICHIGAN STATE UNIVERSITY

UNIVERSITY OF ROCHESTER

UNIVERSITY OF IOWA - IOWA CITY

UNIVERSITY OF VIRGINIA

OKLAHOMA STATE UNIVERSITY

STATE UNIVERSITY OF NEW YORK - BUFFALO

POLYTECHNIC UNIVERSITY - BROOKLYN CAMPUS

RUTGERS UNIVERSITY - NEW BRUNSWICK

OREGON STATE UNIVERSITY

UNIVERSITY OF FLORIDA

DREXEL UNIVERSITY

UNIVERSITY OF NEW HAVEN

UNIVERSITY OF MISSISSIPPI

UNIVERSITY OF CONNECTICUT - STORRS

 

In my Six Hats, Six Coats and Six Ring metaphors I have been discussing this diagram:

It can also be presented as follows:

It has been a challenge to the traditional Data, Information, Knowledge and Wisdom hierarchy by introducing not one, but three forms of data.  I call them Blue Data, Red Data and Black Data.

Blue Data or Reflex Data is data that affects the system, but is not necessarily captured at all. It influences a transaction, but requires techniques external to regular data capture to record. Some of the most subtle aspects of design influence this level of a transaction.  This is when a customer leaves a website because the Flash presentation takes too long to load.

Red Data or Intuitive Data is registered by the system, but does not generate any exceptions or variances. Although the data affects the bottom line it does not register in the cognitive-physical or cognitive levels of the system.  This is "business as usual" data.

Black Data or Exception Data is registered by higher levels of the system.  This is data that calls for physical-cognitive response outside the domain of normal operation.  An example would be a customer having to make two orders of nine units and one order of seven units because the system cannot capture more than nine units in one transaction.

I believe that for the Data, Information, Knowledge and Wisdom model to be complete it has to recognize the significance of Blue Data, Red Data and Black Data. For business to truly be successful, it not only has to ascend the DIKW hierarchy, it has to descend the hierarchy below the traditional definition of data and recognize all of data's facets.

And what is Information, Knowledge and Wisdom, but higher forms of data?

A steam trap releases condensate (liquid) and most also purge air. The type of steam trap (inverted bucket, float and thermostatic, impulse, thermodynamic, thermostatic, etc.) will determine the effectiveness of air venting. Steam traps basically close to prevent steam from passing, and open to allow condensed steam (condensate) to pass.

An air trap is most often used on an air compressor system. This type of trap is usually a simple float-type trap that purges condensed air (condensdate) and closes to prevent loss of air.

There is one type of trap that will perform both functions with one simple modification. An F&T trap, or float and thermostatic trap, will purge steam condensate and air. The same trap less the thermostatic element (now called a float trap) will purge condensate only.

In the last post I discussed the Six Rings metaphor which revealed that both the Six Hats and the Six Coats metaphors were both cognitive hierarchies. In this post I will discuss leadership or emphasis within a project or system.

First, we have the Six Hats which we can express as a Venn diagram:

We can also express the Six Coats as a Venn diagram:

These diagrams illustrate an equal emphasis on all the Hats and all the Coats. However, no system is completely balanced. An actual system might have a Six Hats Venn diagram like the following:

In this example the emphasis is predominantly on the Blue Hat, Operational.

An actual system may also have a Six Coats Venn diagram as follows:

In this example it is the Black Coat, Functional that has emphasis.

Putting the Hat and Coat together we can say that the emphasis of the system or leadership of the project or business is Blue Hat, Black Coat or Operational/Functional. Here's a Mix Thirty-Six diagram of the emphasis:

You can see that the Blue Hat row and the Black Coat column are larger.  If this were a computing project we could say that the operational perspective and functional focus are leading the effort. We are likely to get an effective transaction system at the expense of everything else. We hope that that is what the other systems that interact with ours will want.

This is a very simple example, but by analyzing the Mix Thirty-Six of external and internal systems we can realize beneficial systems. And everything is ultimately a system within its own right.

In my last post I revealed a ring metaphor that positioned Transaction, Intuition, Data, Information, Knowledge and Wisdom. Now that we have made the transition with a majority of the concepts from tetrads to hexads, we can now explore how the Six Hats, Six Coats metaphor can be shifted into the Six Rings metaphor. First, lets call up the Six Hats for review:

As I discussed earlier, Conceptual is the Creative perspective, Contextual is the Compatibility perspective, Logical is the Reliability perspective, Physical is the Economy perspective, Mechanical is the Intuitability perspective and Operational is the Convenience perspective.

And now let's take this metaphor and shift it into the Six Rings metaphor:

As you can see I have given Creativity the highest order and Convenience the lowest.

With that done, let's take another look at the Six Coats metaphor:

Motivational is the Goal focus.  Spatial is the Network focus.  Formal is the Data focus.  Functional is the Process focus.  Personal is the People focus.  Temporal is the Time focus.

Now let's take the Six Coats metaphor and shift it to the Six Rings metaphor:

In the Six Rings metaphor I give Goals the highest order and Time the lowest order.

As you can see in these representations of the Six Hats and Six Coats as rings, there are other implications when we look at the Mix Thirty-Six:

When we look at the Mix Thirty-Six the Blue Hat, Blue Coat requires the least cognitive effort, while the Green Hat, Green Coat requires the greatest. However, the Mix Thirty-Six describes a team and a network. Leadership and communication can follow different emphases and paths between Green Hat, Green Coat and Blue Hat, Blue Coat. And the best Green Hat, Green Coat and the best Blue Hat, Blue Coat has worn the entire Mix Thirty-Six. We will explore this more in future posts by introducing additional metaphors.

Further reading: Six Hats, Six Coats , Mix Thirty-Six and TIDIKW

In an earlier post I created a variation on this ring diagram:

I came up with the two lower levels of the ring diagram, reflex and intuition, after having given considerable thought to the Six Hats metaphor:

And the Six Rings metaphor:

I could see that these hexads revealed two levels that were rarely discussed in the context of Data, Information, Knowledge and Wisdom. I began to wonder how to correctly define them. After reading Edward de Bono's Six Thinking Hats concept and John Zachman's Framework which both contained six perspectives (Zachman considered Mechanical and Operational out of context, but made a point of including them) I began to look at the Red Hat and the Blue Hat in a new light.

Edward de Bono called the Red Hat the "intuitive" hat. Zachman referred to it as the implementation perspective. The Blue Hat de Bono called the "process" hat, Zachman called it the "operational" perspective. The Blue Hat I call the Operational or "reflex" hat. In this post I am going to discuss the Red Hat, which I call the Mechanical or "intuition" hat.

I find that intuition is not thoroughly discussed or well understood in most of the literature. However, Zachman's framework gave me an alternative insight. In Zachman's implementation perspective, design is translated into formally documented goals, network configurations, data definition language, program code, personnel roles, and system schedules. In otherwords, the mechanisms which the system enforceably observes. Any operations that fall outside of the implementation are treated as exceptions and flagged for handling at higher levels. The implementation defines intuitive behavior.

Intuitive behavior in us as persons is often called habit or subconscious behavior. When we train ourselves in any way we are implementing design. The literature says that it takes roughly twenty-one days to implement any habit. Habit has a motivational, spatial, formal, functional, personal and temporal component, what I describe as the Six Coats:

When we work within the boundaries of well developed habit we experience the phenomenon called flow. Flow is the handling of events without the occurrence of exceptions to our habits. One of the commonly used examples is a rally during tennis. The two well trained players play within their intuitive boundaries for a prolonged period of time. There is little cognition regarding the return of the ball. In fact, cognition may be focused elsewhere.

Walking is another example of intuition. It is possible to perform many cognitive-physical and cognitive actions while walking. And the flow of walking is rarely broken, even when negotiating a busy sidewalk or corridor.

One aspect of intuition that hasn't been recognized and which de Bono and Zachman lead me to consider is that intuition observes a hierarchy:

There are high level intuitions and low level intuitions. We can have intuition about our sleeping habits, which is a temporal intuition focus. We can have intuition about walking, which is primarily a functional intuition focus. We can also have intuition about formal (data), spatial (networks) and motivational (goals) focuses. The higher the intuitive focus the more training it requires. This is also where the concept of "naturals" and the "refined" comes into play.

Naturals, are individuals who seem to have an intuitive focus mastered without training. Child prodigies are an example of naturals. However, there is nothing saying that any intuitive focus cannot be be trained to a level exceeding that of a natural. In this case we have the refined performer. Considering these two extremes, we can say that intuitive people can be born or made.

The intuitive difference between genders is another issue. Women are regarded as having superior intuitive abilities. This is attributed to a greater "white matter" content in women's brains which emphasizes associations as in languages, while men have a greater "grey matter" content which emphasizes entities as in mathematics. However, association intuition and entity intuition both have been exhibited. Also, as in naturals and the refined, intuitive talents can be both born or made across genders.

I have shown that intuition operates below the Data level where exceptions are not handled, but passed upward cognitively when outside of the intuitive flow. I have also explained how intuition observes a hierarchy giving it a dimensionality that is often overlooked. Finally, I have demonstrated that intuition is the product of both innate ability and trained habit.

All systems have an intuitive level where the day passes and few events actually register cognitively. At this time we wonder where the day went. We have come to refer to this state as "business as usual", but it can be the time of high productivity. Continual interruption of our intuitive processes can actually be counterproductive as it takes time to restore flow, to refocus.

Intuition does have a place in the DIKW hierarchy. But it requires us to descend the hierarchy to place it rightfully at the foundation where we can perform without cognitive registration.

It's like the dancer said, "I was a good dancer until I started to think about it."

 

 

Martensite transformation usually occurs on cooling, with the volume fraction of martensite depending upon the temperature as shown in the first two examples below. Martensite transformation is often thought only to depend upon the temperature, but has been shown to also depend upon the time. It should therefore be represented by it's own C-curve on a time-temperature-transformation graph. The strong temperature dependence may be caused by the large driving force necessary before transformation can proceed.

The Koistinen-Marburger equation

This equation due to Koistinen[1] and Marburger can be used to estimate the fraction of martensite as a function of temperature.

Steels were quenched to various temperatures and the austenite volume fraction determined by X-ray diffraction. The results plotted logarithmically against Ms-Tq, where Ms is the martensite start temperature and Tq is the quench temperature.

V_gamma = exp [-b(Ms-Tq)] where b is 1.1x10^-2

Magee[2] later showed that an equation of this form can be justified based on martensite nucleation theory.

Rearranging the formula is simple to give the volume fraction of austenite transforming to martensite on cooling to room temperature as a function of the MS temperature of the steel.

Khan and Bhadeshia [3] have produced a modified equation which also considered autocatalytic nucleation of martensite to allow better matching to dilatometry results. This changed the goodness of the fit by regression from 0.9 to 0.94, when analysing the data of volume fraction against temperature obtained from their own experiments. This work used one alloy (300M) but changed the starting conditions by partial transformation to bainite.

[1] D. P. Koistinen and R. E. Marburger, Acta Metallurgica, 7, p59-60, 1959
[2] C. L. Magee, The nucleation of martensite, In "Phase transformations", p115-156, Ed: H. I. Aaronson and V. F. Zackay, ASM, Metals Park, Ohio, 1970.
[3] S. A. Khan and H. K. D. H. Bhadeshia "Kinetics of Martensitic Transformation in Partially Bainitic 300M Steel" Materials Science and Engineering A, Vol. A129, 1990, pp. 257-272.

The Six Hats, Six Coats metaphor allows you to manage your Perspectives and Focuses for any project. In fact, it allows you to pose thirty-six questions that can make or break a system. In this post I am going to translate the Six Hats and Six Coats into those questions. So, hang on, we're going to eat the whole enchilada.

1. Conceptual
1.1 Motivational: What goals are available to achieve?
1.2 Spatial: What networks are available to support?
1.3 Formal: What data are available to verify?
1.4 Functional: What processes are available to perform?
1.5 Personal: What people are available to serve?
1.6 Temporal: What schedules are available to meet?

2. Contextual
2.1 Motivational: What goals are we compatible with achieving?
2.2 Spatial: What networks are we compatible with supporting?
2.3 Formal: What data are we compatible with verifying?
2.4 Functional: What processes are we compatible with performing?
2.5 Personal: What people are we compatible with serving?
2.6 Temporal: What schedules are we compatible with meeting?

3. Logical
3.1 Motivational: What goals can we reliably achieve?
3.2 Spatial: What networks can we reliably support?
3.3 Formal: What data can we reliably verify?
3.4 Functional: What processes can we reliably perform?
3.6 Personal: What people can we reliably serve?
3.7 Temporal: What schedules can we reliably meet?

4. Physical
4.1 Motivational: What goals can we economically achieve?
4.2 Spatial: What networks can we economically support?
4.3 Formal: What data can we economically verify?
4.4 Functional: What processes can we economically perform?
4.5 Personal: What people can we economically serve?
4.6 Temporal: What schedules can we economically meet?

5. Mechanical
5.1 Motivational: What goals can we intuitively achieve?
5.2 Spatial: What networks can we intuitively support?
5.3 Formal: What data can we intuitively verify?
5.4 Functional: What processes can we intuitively perform?
5.5 Personal: What people can we intuitively serve?
5.6 Temporal: What schedules can we intuitively meet?

6. Operational
6.1 Motivational: What goals can we actually achieve?
6.2 Spatial: What networks can we actually support?
6.3 Formal: What data can we actually verify?
6.4 Functional: What processes can we actually perform?
6.5 Personal: What people can we actually serve?
6.6 Temporal: What schedules can we actually meet?

So, there you have it. Thirty six questions to lead you through the life of a project. As I pointed out in Good Design, your emphasis will probably vary based on how these Focuses interplay as will your Perspectives. However, an complete oversight in any of these Focuses or Perspectives will most likely result in failure or diminished gains. Of course there are many more or even fewer questions you can ask, but I have found this batch to be a healthy standard.