There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

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

Additive manufacturing of metallic components – Process, structure and properties

Primary austenitic solidification is related to increased hot cracking susceptibility in welding of austenitic stainless steels. It is also recognised that high cooling rates and rapid solidificati ...

Influence of low energy laser welding on solidification and microstructure of austenitic stainless steel welds

Pore Formation and Composition Change During Continuous Wave Nd:YAG Laser Welding of Aluminum Alloys 5182 and 5754

Friction stir welding does not generate fumes and there is no loss of volatile alloying elements. This remarkable process maintains the solid state during welding and yet is able to fabricate structurally sound joints. A robust spinning-tool rubs against the abutting parts that require joining, generates heat by friction, thus softening the metals without melting it. The translation of the spinning-tool along the joint line forges the pieces together under great pressure. It is almost like locally and forcefully mixing the two sides of the joint together without changing the overall shape although the flow pattern during this mixing is asymmetrical. The lack of melting means that it is possible to join dissimilar metals or very difficult metals that cannot otherwise be joined. In spite of its relatively recent invention, the process is now ubiquitous in the welding of aluminium alloys in particular, but with many efforts to extend its horizon, for example, to the microelectronics field.

Stirring Solid Metals to Form Sound Welds

Laser melting, such as that encountered during additive manufacturing (AM), produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model validation of the process itself and resulting material produced necessitates the ability to characterize these temperature fields. However, well-established means to directly probe material temperature below the surface of an alloy while it is being processed are limited. To address this gap in characterization capabilities, we present a novel means to extract subsurface temperature distribution metrics, with uncertainty, from in situ synchrotron X-ray diffraction measurements to provide quantitative temperature evolution during laser melting. Temperature distribution metrics are determined using Gaussian Process Regression supervised machine learning surrogate models trained with a combination of mechanistic modeling (heat transfer and fluid flow) and X-ray diffraction simulation. Trained surrogate model uncertainties are found to range from 5% to 15% depending on the metric and current temperature. The surrogate models are then applied to experimental data to extract temperature metrics from an Inconel 625 nickel superalloy wall specimen during laser melting. Maximum temperatures of the solid phase in the diffraction volume through melting and cooling are found to reach the solidus temperature as expected, with mean and minimum temperatures found to be several hundred degrees less. The extracted temperature metrics near melting are determined to be more accurate due to the lower relative levels of mechanical elastic strains. However, uncertainties for temperature metrics during cooling are increased due to the effects of thermomechanical stress.

Combining Synchrotron X-ray Diffraction, Mechanistic Modeling, and Machine Learning for In Situ Subsurface Temperature Quantification during Laser Melting

Tarasankar Debroy

Papers

Influence of low energy laser welding on solidification and microstructure of austenitic stainless steel welds

Pore Formation and Composition Change During Continuous Wave Nd:YAG Laser Welding of Aluminum Alloys 5182 and 5754

Stirring Solid Metals to Form Sound Welds

Combining Synchrotron X-ray Diffraction, Mechanistic Modeling, and Machine Learning for In Situ Subsurface Temperature Quantification during Laser Melting

Grain Growth in the Heat-Affected Zone of Ti-6Al-4V Welds: Measurements and Three Dimensional Monte Carlo Simulation