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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Hundreds of different types of coatings are used to protect a variety of structural engineering materials from corrosion, wear, and erosion, and to provide lubrication and thermal insulation. Of all these, thermal barrier coatings (TBCs) have the most complex structure and must operate in the most demanding high-temperature environment of aircraft and industrial gas-turbine engines. TBCs, which comprise metal and ceramic multilayers, insulate turbine and combustor engine components from the hot gas stream, and improve the durability and energy efficiency of these engines. Improvements in TBCs will require a better understanding of the complex changes in their structure and properties that occur under operating conditions that lead to their failure. The structure, properties, and failure mechanisms of TBCs are herein reviewed, together with a discussion of current limitations and future opportunities.

http://science.sciencemag.org/content/sci/296/5566/280.full.pdf

Thermal Barrier Coatings for Gas-Turbine Engine Applications

Hydrogen effects on the age hardening behavior of 2024 aluminum

The accurate assessment of the compatibility of metals and alloys with in-service hydrogen environments requires the ability to extrapolate short term laboratory test data to long term engineering applications. This extrapolation frequently requires the use of tensile and subsized fracture mechanics specimens to predict the behavior of large scale structural components. Both macroscopic and microscopic stress states in the test specimens may be quite different from those in the structural component; thus it is vital to understand the potential roles of stress state in the hydrogen embrittlement process. This paper demonstrates the importance of stress state in the assessment of hydrogen compatibility and shows that for safe, efficient materials utilization design data must be based on the results of several types of mechanical tests.

Importance of stress state on hydrogen embrittlement

Hydrogen suppression of “ductile” processes

A 6061 aluminum alloy probe was quenched using a Center for Heat Treating Excellence probe-quenching system in distilled water while varying bath temperature and the agitation level. Time versus temperature data were collected during the quench using a thermocouple embedded inside the probe. The surface heat transfer coefficients as a function of temperature were calculated using the Newtonian cooling approximation. The maximum heat transfer coefficient values ranged from 1054 W/m2⋅K for 100°C water with no agitation to 3822 W/m2⋅K for 5°C water with the agitation of 1850 r/min. The data also showed that at higher levels of agitation and lower bath temperatures, the maximum heat transfer coefficient increased.

The Effect of Agitation and Quenchant Temperature on the Heat Transfer Coefficients for 6061 Aluminum Alloy Quenched in Distilled Water

Design-driven materials engineering is gaining wider acceptance with the advancement and refinement of commercially available thermodynamic software as well as enhanced computing power. Computationally designed materials are a significant improvement over the more common and resource-intensive experimental approach to materials design by way of trial and error. While not entirely eliminating experimental methods for alloy design, thermodynamic and kinetic models provide accurate predictions of phases within a given alloy, which enables material properties to be calculated. Accordingly, the present paper introduces a new technique that offers a systematic method of material design by way of utilizing commercial computational software, which has been termed the elemental impact factor. In turn, the present manuscript considers Al 6061 as a proof-of-concept metallic alloy system for elemental impact factor substantiation. Effects of chemical composition on resultant equilibrium and metastable material phases as well as properties can be efficiently assessed with the elemental impact factor framework for metallurgical materials design. Desired phases or properties may be produced by adding elements with a positive elemental impact factor, while deleterious phases or undesired properties may be reduced by adding elements with a negative elemental impact factor. Therefore, the elemental impact factor methodology was presented and then demonstrated herein with examples that showcase the technique’s potential applications and utility for integrated structure-processing-property-performance analysis.

Richard D. Sisson

Papers

Hydrogen effects on the age hardening behavior of 2024 aluminum

Importance of stress state on hydrogen embrittlement

Hydrogen suppression of “ductile” processes

The Effect of Agitation and Quenchant Temperature on the Heat Transfer Coefficients for 6061 Aluminum Alloy Quenched in Distilled Water

On the Elemental Impact Factor, a Method to Determine an Alloy's Compositional Influences upon Phase Stability and Metallurgical Material Properties.