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

The review outlines modern trends in theoretical developments, novel designs and modern applications of sandwich structures. The most recent work published at the time of writing of this review is considered, older sources are listed only on as-needed basis. The review begins with the discussion on the analytical models and methods of analysis of sandwich structures as well as representative problems utilizing or comparing these models. Novel designs of sandwich structures is further elucidated concentrating on miscellaneous cores, introduction of nanotubes and smart materials in the elements of a sandwich structure as well as using functionally graded designs. Examples of problems experienced by developers and designers of sandwich structures, including typical damage, response under miscellaneous loads, environmental effects and fire are considered. Sample applications of sandwich structures included in the review concentrate on aerospace, civil and marine engineering, electronics and biomedical areas. Finally, the authors suggest a list of areas where they envision a pressing need in further research.

Review of current trends in research and applications of sandwich structures

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Formulas For Natural Frequency And Mode Shape

In this letter, a class of honeycomb acoustic metamaterial possessing lightweight and yet sound-proof properties is designed, theoretically proven, and then experimentally verified. It is here reported that the proposed metamaterial having a remarkably small mass per unit area at 1.3 kg/m2 can achieve low frequency (<500 Hz) sound transmission loss (STL) consistently greater than 45 dB. Furthermore, the sandwich panel which incorporates the honeycomb metamaterial as the core material yields a STL that is consistently greater than 50 dB at low frequencies. The proposed metamaterial is promising for constructing structures that are simultaneously strong, lightweight, and sound-proof.

A lightweight yet sound-proof honeycomb acoustic metamaterial

The purpose of this work is to use magnetorheological elastomers (MREs) as field-dependent springs within three vibration absorber configurations, and to determine their vibration absorption characteristics. Magnetorheological elastomers are fabricated from silicone gel and iron microparticles, and implemented as tunable springs in three vibration absorber configurations, which excited the MREs in shear, squeeze mode, and compression. Each vibration absorber configuration exploits different magneto-mechanical properties, achieving very different results. The MRE iron concentration is varied to find the largest natural frequency shift for the squeeze-mode absorber due to an applied magnetic field. Absorbers with MREs containing 35% iron by volume exhibits the largest natural frequency shift, 507%. MREs containing 35% iron are placed into shear and longitudinal mode vibration absorber devices, which exhibit 470% and 180% frequency increases, respectively.

Performance of MRE-based Vibration Absorbers:

https://hal.archives-ouvertes.fr/hal-03006382/document

Computation of acoustic properties and design guidelines of periodic Biot-modeled foams

Flinovia-flow induced noise and vibration issues and aspects-II: A focus on measurement, modeling, simulation and reproduction of the flow excitation and flow induced response

Problems involving vibrations occur in many fields of engineering. Thus, it is necessary to increase the knowledge of the damping, offered by new structural configurations and materials in order to...

Sergio De Rosa

Papers

Computation of acoustic properties and design guidelines of periodic Biot-modeled foams

Flinovia-flow induced noise and vibration issues and aspects-II: A focus on measurement, modeling, simulation and reproduction of the flow excitation and flow induced response

Damping evaluation on eco-friendly sandwich panels through reverberation time (RT60) measurements

Formulation and validation of the shift cell technique for acoustic applications of poro-elastic materials described by the Biot theory

Similitude laws for the sound radiation of flat orthotropic flexural panels