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

https://iopscience.iop.org/article/10.1088/2515-7639/ac09fb/pdf

The case for digital twins in metal additive manufacturing

2. L.W. Helle: J. South African Institute of Mining and Metallurg,~, 1981, pp. 329-37 3. J. Szekely, T. Lehner, and C.W. Chang: lronmaking and Steel- making, 1979, vol. 6, pp. 285-93 4. T. Usui, K. Yamada, andY. Miyashita: Proceedings, SCAN1NJECTII, International Conference on Injection Metallurgy, 1980, MEFOS, Lule~, Sweden, pp. 12:1-12:24. 5. O. Haida, T. Emi, S. Yamada, and F. Sudo: Proceedings, SCAN- INJECT H, International Conference on Injection Metallurgy, 1980, MEFOS, Lule~, Sweden, pp. 20:1-20:20. 6. Q. Ymg, L Yun, and L. Lm: Proceedings, SCANINJECT 11l, Inter- national Conference on Injection Metallurgy, 1983, MEFOS, Lule~, Sweden, pp. 21:1-21.16. 7 M. Sano and K. Moil: Proceedings, SCANINJECT 11I, lnternattonal Conference on Injection Metallurgy, 1983, MEFOS, Luleh, Sweden, pp. 6:1-6:17. 8. M. Matway, H. Henein, R.J. Fruehan, and J. lsaacs: Proceedings, Fourth Process Technology Conference, ISS-AIME, Warrendale, PA, 1984, pp. 39-43. 9. Y. Sundberg. Scand. J. Metallurgy, 1978, vol. 7, pp 81-87 10. N.J. Themelis and P. Goyal: Can. Met. Quart, 1983. vol 22, pp. 313-20 11. Y. Sahai and R.I.L. Guthne: Metal1. Trans. B, 1982, vol. 13B, pp. 125-27. 12. Y Sahal and R.I.L Guthne: Metall. Trans. B, 1982, vol 13B, pp. 203-11. 13. D. Mazumdar and R. I. L. Guthrie: Metall. Trans. B, 1985, vol. 16B, pp. 83-90. 14. S. Asai, T Okamoto, J C. He, and I Muchi: Transactions, ISIJ, 1983, vol. 23, pp. 43-50. 15. O. Halda and J.K. Brimacombe: Proceedmgs. SCANINJECT 111. International Conference on Injection Metallurgy, 1983, MEFOS, Lule~., Sweden, pp. 5:1-5.17. 16. L.H. Lehrer: Ind. Eng. Chem , Proc. Des. and Dev.. 1968, vol 7, pp. 226-39. 17. T.A. Engh, K. Larson, and K. Venas: Ironmakmg and Steelmaking, 1979, vol. 5, pp. 268-73

Absorption of CO2 laser beam by AISI 4340 steel

In order to improve the reliability of numerical calculations of heat and fluid flow in fusion welding, a model was proposed in part I of this paper to determine the uncertain welding input parameters from a limited volume of experimental data. The application of the model for the complex gas metal arc (GMA) fillet welding of mild steel is reported in this part of the paper. The values of the arc efficiency, effective thermal conductivity and effective viscosity were evaluated as a function of the heat input. A vorticity-based mixing-length hypothesis was also used to independently calculate the values of the effective viscosity and effective thermal conductivity. Good agreement was obtained between the values of these parameters calculated using the two techniques, indicating the effectiveness of the proposed model in improving the reliability of heat and fluid flow calculations in fusion welding. The shape and size of the fusion zone, finger penetration characteristic of the GMA welds and solidified free surface profile computed using the optimized values of the uncertain welding parameters were in fair agreement with the corresponding experimental results for various welding conditions. In particular, the computed values of the leg length, the penetration depth and the actual throat agreed well with those measured experimentally for various heat inputs.

Improving reliability of modelling heat and fluid flow in complex gas metal arc fillet welds—part II: application to welding of steel

Laser and hybrid laser­arc welding present significant opportunities for thick sec­ tion welding of nickel­based alloys during the construction and repair of nuclear power plant components. However, the impact of these welding processes on the fu­ sion zone geometry and defect levels in Alloy 690 are not well understood. A series of laser and hybrid laser­gas metal arc welds were fabricated with varying laser powers and welding speeds. The internal macroporosity remaining after welding was charac­ terized using x­ray computed tomography. While the porosity levels attributed to key­ hole instability and collapse remained high in the laser welds for all power levels, the addition of the arc in the hybrid laser­arc welds inhibited the formation of porosity at laser powers in excess of 4 kW. A well­tested three­dimensional heat transfer and fluid flow model was used to determine both the geometry of the fusion zone and the region of mixing of the filler metal within the weld pool for various welding vari­ ables. By correlating the geometries of the weld pool and the volume of the filler metal mixing region with the experimentally determined porosity, significant insight can be obtained about the mechanism of porosity reduction in hybrid laser­arc weld­ ing. At lower laser powers, a combination of high­speed filler metal addition and small pool size prevented the bubbles from escaping. The experimental and calculated results show that porosity in Alloy 690 hybrid welds can be eliminated if the laser heat input and arc conditions are properly selected to avoid the bubble being trapped in the weld pool. The mechanistic understanding uncovered in the work is used to develop a process map showing the important combination of weld­ ing variables for producing porosity­free hybrid welds.

Porosity in thick section alloy 690 welds - Experiments, modeling, mechanism, and remedy

Exposure of a falling stream of 1 μm average size α‐quartz particles to a continuous wave or pulsed CO2 laser beam in air resulted in the formation of a complete series of high‐pressure phases of silica: coesite, stishovite, and apparently even denser forms with α‐PbO2 and Fe2N structures. Since the laser exposure technique works with the carbon black to diamond transition, the technique is confirmed as a simple and generally applicable means to achieve the same effects as exposure to several hundred kilobars pressure.

Tarasankar Debroy

Papers

The case for digital twins in metal additive manufacturing

Absorption of CO2 laser beam by AISI 4340 steel

Improving reliability of modelling heat and fluid flow in complex gas metal arc fillet welds—part II: application to welding of steel

Porosity in thick section alloy 690 welds - Experiments, modeling, mechanism, and remedy

High-pressure phases of SiO2 made in air by Fedoseev-Derjaguin laser process