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

Recent developments in advanced aircraft aluminium alloys

Thermophysical Properties Research Literature Retrieval Guide Edited by Y. S. Touloukian, J. K. Gerritsen and N. Y. Moore Second edition, revised and expanded. Book 1: Pp. xxi + 819. Book 2: Pp.621. Book 3: Pp. ix + 1315. (New York: Plenum Press, 1967.) n.p.

/pdf/heat-and-mass-transfer-1yaijfx8vp.pdf

Heat and Mass Transfer

A significant increase in the research activity dedicated to high manganese TWIP steels has occurred during the past five years, motivated by the breakthrough combination of strength and ductility possessed by these alloys. Here a review of the relations between microstructure and mechanical properties is presented focusing on plasticity mechanisms, strain-hardening, yield stress, texture, fracture and fatigue. This summarized knowledge explains why TWIP steel metallurgy is currently a topic of great practical interest and fundamental importance. Finally, this publication indicates some of the main avenues for future investigations required in order to sustain the quality and the dynamism in this field.

High manganese austenitic twinning induced plasticity steels: A review of the microstructure properties relationships

A review: Fibre metal laminates, background, bonding types and applied test methods

Driven by the increasing requirements from aircraft producers, Hoogovens Aluminium Rolled Products GmbH, together with Hoogovens Research & Development, has enhanced the property combinations of their aircraft materials. For these types of material, optimised processing routes as well as new alloy chemistries have been investigated. Whilst retaining the strength levels required by the aerospace industry, new processing routes offer major improvements in ductility, toughness, fatigue performance and in reduction of residual stress in large dimension plate and sheet products. A further goal of investigating new alloy chemistries is the trend towards new joining techniques such as welding and brazing for aircraft structures. These new joining techniques require different property combinations compared to the conventional aerospace alloys. In parallel to these improved processing routes and new alloy developments, new ultrasonic inspection techniques have been developed, which are able to predict fatigue performance and residual stress in thick plate products.

Recent development in aluminium alloys for aerospace applications

Impact resistance of fiber-metal laminates: A review

https://repository.tudelft.nl/islandora/object/uuid%3Ae22dcf36-b9be-4b7a-b7fa-4626e8d5f393/datastream/OBJ/download

Rinze Benedictus

Papers

Recent development in aluminium alloys for aerospace applications

Impact resistance of fiber-metal laminates: A review

Methods for the prediction of fatigue delamination growth in composites and adhesive bonds: A critical review

A review of T-stress and its effects in fracture mechanics

Misinterpreting the Results: How Similitude can Improve our Understanding of Fatigue Delamination Growth