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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

Prior work has demonstrated greater antipathogenic efficacy concerning the nanostructured copper cold spray coatings versus conventional copper cold spray coatings, while both the nanostructured and conventional cold spray coatings maintain greater contact killing/inactivation rates relative to other thermal spray deposition methods. Recent work has more heavily focused upon the nanostructured cold spray coatings greater efficacy. However, the antimicrobial efficacy of conventional copper cold spray coatings may be improved upon by way of identifying processing parameters that yield microstructures with the greatest concentration of atomic copper ion diffusion pathways. Since ideal processing parameters for a given application can be computed in silico via finite element analysis methods, the fundamental computational frameworks for doing so using the Johnson–Cook and Preston–Tonks–Wallace plasticity models. Modeled single-particle impact morphology outputs were compared with experimental microstructures using scanning electron microscopy and optical microscopy. The computed von Mises flow stresses associated with the two plasticity models were compared with traditionally static nanoindentation data as well as dynamic spherical nanoindentation stress–strain curves. Continued work with the finite element analysis framework developed herein will enable the best cold spray parameters to be identified for optimized antimicrobial properties as a function of deformation-mediated microstructures while still maintaining the structural integrity of the deposited material. Subsequent work will extend the finite element analysis models to multi-particle impacts when spray-dried and gas-atomized copper powder particles have been appropriately meshed.

Finite Element Modeling of Single-Particle Impacts for the Optimization of Antimicrobial Copper Cold Spray Coatings

Heat treaters need an effective simulation tool to predict the carburization performance of a variety of steels. The tool is needed to not only predict the carbon profile but also optimize the process in terms of the cycle time and the total cost. CarbTool© has been developed to meet these needs for gas and vacuum carburization. In this article, CarbTool© predictions were compared with industrial experimental results for three types of steels (AISI 8620, 5120, and 4320), heat treated by both gas and vacuum carburizing processes. Based on the agreement of model predictions and experimental results, it is found that CarbTool© could be used to predict the carbon concentration profile for a variety of alloys in both gas and vacuum carburizing processes.

Modeling of Carbon Concentration Profile Development During Both Atmosphere and Low Pressure Carburizing Processes

Each year millions of dollars are lost as a result of distortion, cracking and mechanical property variations due to unexpected problems in the quenching process. A thorough understanding of the variations in quenching fluid’s performance as a function of the medium’s physical properties, system variables and part geometry is not currently available. In this paper the results of a series of experiments using four different quench probes in a wide variety of quenching media and process parameters will be presented and discussed. The experimental results are presented in terms of the calculated effective heat transfer coefficients h(T) as a function of metal surface temperature. The data is stored in a new database QuenchPAD[1] that can effectively used via a Decision Support System called QuenchMiner[1].

/pdf/quenching-understanding-controlling-and-optimizing-the-2z0ykw6yy1.pdf

Quenching-Understanding, Controlling and Optimizing the Process

The U.S. spends over $125 billion a year in prevention of
touch surface infections [1-5]. Copper cold spray coatings have
been identified as having greater antimicrobial effectiveness than
other additive methods...

https://biomedres.us/pdfs/BJSTR.MS.ID.003768.pdf

Richard D. Sisson

Papers

Finite Element Modeling of Single-Particle Impacts for the Optimization of Antimicrobial Copper Cold Spray Coatings

Modeling of Carbon Concentration Profile Development During Both Atmosphere and Low Pressure Carburizing Processes

Quenching-Understanding, Controlling and Optimizing the Process

The Effect of Corrosion on Conventional and Nanomaterial Copper Cold Spray Surfaces for Antimicrobial Applications

An initial microstructural analysis of A36 steel from WTC building 7