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

VOLUME 34B, APRIL 2003—267 T. HONG, formerly Graduate Student with the Department of Materials Science and Engineering, The Pennsylvania State University, is Senior Project Engineer with the Technical Center, Caterpillar Inc., Peoria, IL 61656. T. DEBROY, Professor, is with the Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802. Contact e-mail: debroy@psu.edu Manuscript submitted January 22, 2001. Communications

Nonisothermal growth and dissolution of inclusions in liquid steels

Weld pool development during GTA and laser beam welding of Type 304 stainless steel

In situ X-ray diffraction methods have been developed at Lawrence Livermore National Laboratory for direct observation of microstructural evolution under quasi-steady state and transient welding conditions. Using intense highly collimated synchrotron radiation, the crystal structures in the weld heat affected and fusion zones are probed in real time to monitor solidification and solid state phase transformations during welding. Here the authors review recent work on the development and use of the time resolved X-ray diffraction (TRXRD) technique during transient welding and illustrate its unique capabilities to: directly observe the solidification mode; discover, in real time, the definitive sequence of phase transformations that lead to the final microstructure; and provide quantitative kinetic data of phase transformations through synthesis of TRXRD data with the temperature history obtained through heat transfer modelling. The TRXRD technique has been used to investigate welding induced phase ...

Time resolved X-ray diffraction observations of phase transformations in transient arc welds

During high power density laser welding of mild steel, the keyhole depth, liquid metal flow, weld geometry and weld integrity are affected by base-metal sulfur content and oxygen (O2) present in the atmosphere or shielding gas. The role of these surface-active elements during keyhole-mode laser welding of steels is not well understood. In order to better understand their effects, welding of mild steel specimens containing various concentrations of oxygen and sulfur are examined. In addition, a numerical model is used to evaluate the influence of the surface-active elements on heat transfer and fluid flow in keyhole-mode laser welding. Increase in base-metal sulfur concentration or O2 content of shielding gas results in decreased weld widths. Sulfur results in a negligible increase in penetration depth whereas the presence of O2 in shielding gas significantly affects the weld penetration. It has earlier been proposed that oxygen, if present in the shielding gas, can get introduced into the weld pool resulting in formation of carbon monoxide (CO) at the keyhole surface and additional pressure from CO can result in increased penetration. Numerical modelling has been used in this work to understand the effects of formation of CO on the keyhole and weld geometries.

Role of surface-active elements during keyhole-mode laser welding

The results of numerical simulation of heat transfer and fluid flow in the weld pool strongly depend on the physical processes considered and the input data used in the model. The aim of this paper is to examine the impact of the thermophysical properties on the results of such calculations. The effects of various thermophysical properties such as the viscosity, thermal diffusivities of both the solid and liquid, the temperature coefficient of surface tension and the energy absorption coefficient on the depth and the diameter of the weld pool, and the weld pool surface velocities and temperature distributions are analyzed

Tarasankar Debroy

Papers

Nonisothermal growth and dissolution of inclusions in liquid steels

Weld pool development during GTA and laser beam welding of Type 304 stainless steel

Time resolved X-ray diffraction observations of phase transformations in transient arc welds

Role of surface-active elements during keyhole-mode laser welding

Role of thermophysical properties in weld pool modeling