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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

https://researchrepository.wvu.edu/cgi/viewcontent.cgi?article=2473&context=faculty_publications

Exploration of high entropy ceramics (HECs) with computational thermodynamics - A case study with LaMnO3±δ

Isothermal, multiphase diffusion was investigated with infinite diffusion couples in the Cu-Ni-Zn system at 775°C. The couples were assembled with disks of a β (bcc) ternary alloy sandwiched between disks of selected binary, Cu-Ni α (fcc)alloys and were annealed for 2 hr to 2 days. The diffusion structures were investigated metallographically and by electron probe analysis. The couples developed multiphase structures that showed transitions from planar interfaces to nonplanar morphologies with changes in composition of the α -terminal alloys. The various structures are described by the aid of composition paths. The velocity of planar interfaces is discussed in terms of intrinsic fluxes in the adjacent phases.

Diffusion Structures in Multiphase Cu-Ni-Zn Couples

The demand for high performance lithium-ion batteries (LIBs) is increasing due to widespread use of portable devices and electric vehicles. Silicon (Si) is one of the most attractive candidate anode materials for next generation LIBs. However, the high-volume change (>300%) during lithium ion alloying/de-alloying leads to poor cycle life. When Si is used as the anode, conductive carbon is needed to provide the necessary conductivity. However, the traditional carbon coating method could not overcome the challenges of pulverization and unstable Solid Electrolyte Interphase (SEI) layer during long-term cycling. Since 2010, Si/Graphene composites have been vigorously studied in hopes of providing a material with better cycling performance. This paper reviews current progress of Si/Graphene nanocomposites in LIBs. Different fabrication methods have been studied to synthesize Si/Graphene nanocomposites with promising electrochemical performances. Graphene plays a key enabling role in Si/Graphene anodes. However, the desired properties of graphene for this application have not been systematically studied and understood. Further systematic investigation of the desired graphene properties is suggested to better control the Si/Graphene anode performance.

https://www.mdpi.com/2311-5629/4/1/18/pdf

Current Progress of Si/Graphene Nanocomposites for Lithium-Ion Batteries

Cast aluminum alloys are usually subject to solution treatment, quenching, and aging hardening for improved mechanical properties. Cooling rate during quenching plays an important role in residual stress, distortion, and mechanical property distributions in the resultant cast aluminum components. As the cooling rates of work pieces heavily depend on the interfacial heat transfer coefficient (HTC) between work pieces and quenchants, it is important to understand how HTC varies with different quenching conditions so that optimal quenching process can be achieved. In this study, a quenching system and an experimental procedure of obtaining HTC are presented. A series of experiments have been conducted to study the variations of HTC with respect to air temperature, air humidity, air velocity, and part orientation.

An Experimental Study of Heat Transfer During Forced Air Convection

Experimental data in many domains serves as a basis for predicting useful trends. If the data and analysis are available over the Web this promotes E-Business by connecting clientele worldwide. This paper describes such a predictive tool "QuenchMiner™" in the domain "Materials Science". Data mining, more specifically the "Apriori Algorithm", is used to derive association rules that represent relationships between input conditions and results of domain experiments. This enables the tool to answer questions such as "Given cooling medium and agitation during material heat treatment, predict cooling rate". This allows users to perform case studies on the Web and use their results to optimize the involved processes, thus increasing customer satisfaction. Another interesting aspect is predicting material microstructure during heat treatment. Microstructure controls material properties such as hardness. Hence its prediction helps in making decisions about materials selection. Microstructure prediction has similarities to an artificial intelligence process called "Game-of-Life". Some challenges in our work are incorporating domain expert judgement while mining association rules, simulating microstructure evolution under different conditions, and dealing with uncertainty. These challenges and associated research issues are outlined here. To the best of our knowledge, this is the first tool performing Web-based predictive analysis in Materials Science.

/pdf/apriori-algorithm-and-game-of-life-for-predictive-analysis-1qt8o7yqbc.pdf

Richard D. Sisson

Papers

Exploration of high entropy ceramics (HECs) with computational thermodynamics - A case study with LaMnO3±δ

Diffusion Structures in Multiphase Cu-Ni-Zn Couples

Current Progress of Si/Graphene Nanocomposites for Lithium-Ion Batteries

An Experimental Study of Heat Transfer During Forced Air Convection

Apriori algorithm and game-of-life for predictive analysis in materials science