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Structural Integrity using SMA Cutting edge Technology

An Innovative Method for Improving the Structural Integrity using SMA Cutting edge Technology

The information and views set out in this [report/study/article/publication] are those of the writer(s), nor necessarily reflect the official opinion of the European Union. Neither the European Union institutions and body nor anybody acting on their behalf may be held responsible for the use which may be made of the info contained therein.

No third-party textual or creative material is included in the publication without the copyright holder's preceding consent to help expand dissemination by other third get-togethers.

Reproduction is authorised provided the foundation is recognized.

  1. Introduction

A novelty in Horizon 2020 is the Open Research Data Pilot which aspires to boost and maximise usage of and re-use of research data made by funded jobs [1. 1]. Specific assignments funded under Horizon 2020, that are not covered by the opportunity of the Pilot discussed above, may take part in the pilot on the voluntary basis. The project InnoSmart has opted-in the Start Research Data Pilot system, which is therefore appreciated to give a Data Management Plan (DMP) following the first six months as well as in the later periods of the project lifetime. The Open up Research Data Pilot pertains to two types of data:

  • thedata, including associated metadataneeded to validate the results shown in scientific publications as soon as possible;
  • otherdata, including associated metadata, as specified and within the deadlines laid down in the data management plan - that is, according to the specific judgement by each job.

The purpose of the DMP is to provide an analysis of the key elements of the info management policy that'll be used by the applicants in regards to to all the datasets that'll be made by the job. The DMP is not really a fixed doc, but evolves during the lifespan of the task. The DMP should dwelling address the factors below on a dataset by dataset basis and really should reflect the existing status of reflection within the consortium about the data which will be produced [1. 2]:

  1. Data set reference and name: Identifier for the info established to be produced.
  1. Data set description: Description of the info that will be generated or gathered, its origins (in case it is collected), dynamics and scale and to whom it could be useful, and whether it underpins a scientific publication. Information on the life (or not) of similar data and the possibilities for integration and reuse.
  1. Standards and meta data: Mention of existing suitable specifications of the self-discipline. If these do not exist, an outline on how and what metadata will be created. Good data company is necessary to avoid duplication of documents and other mistakes, and constant naming and versioning steps ensure that the correct data can be retrieved simply, especially if multiple people are working on the same files. Make an effort to:
  • Give folders clear labels relating to their area/subject matter, not names of research workers.
  • Keep folder and record names short and concise; stay away from special heroes or places, as not all software can handle them.
  • If dates are important, use the standard format YYYY-MM-DD in the beginning of the document name.
  1. Datasharing: Information of how data will be shared, including access steps, embargo times (if any), outlines of technical mechanisms for dissemination and necessary software and other tools for permitting re-use, and explanation of whether access will be greatly open or restricted to specific groups. Id of the repository where data will be stored, if already existing and identified, indicating specifically the sort of repository (institutional, standard repository for the self-control, etc. ). In case the dataset can't be shared, the reasons for this should be talked about (e. g. honest, rules of personal data, intellectual property, commercial, privacy-related, security- related).
  1. Archiving and preservation (including storage area and backup): Description of the methods that will be put in place for long-term preservation of the info. Sign of how long the info should be conserved, what's its approximated end size, the actual associated costs are and how these are planned to be protected.

It also needs to be mentioned that participating in the Pilot will not necessarily mean opening up all research data. The target of the Pilot is on stimulating good data management as an essential factor of research best practice. Assignments taking part in the Pilot must comply with the following:

  • Participating projects are required to deposit the study data detailed above, ideally into a research data repository. 'Research data repositories' are online archives for research data. They can be subject-based/thematic, institutional or centralised.
  • As far as you can, tasks must then take options to allow for third people to access, mine, exploit, reproduce and disseminate (free of charge for any customer) this research.

At the same time, assignments should provide information via the chosen repository about tools and equipment at the removal of the beneficiaries and necessary for validating the results, for illustration specialised software or software code, algorithms, examination protocols, etc. Where possible, they should supply the tools and equipment themselves.

The project InnoSmart is split into seven work deals (WP1-WP7) where the most them include data gathering, processing and/or manipulation. The gathered data will be derived mainly from development and testing of the Smart Storage Alloys (SMA's) within laboratory conditions, trials of covering alloy structure and covering characteristics, laser beam profilometry, numerical structural diagnosis and prognostic models, and validation of mapping and numerical assessment software.

  1. Data derived from WP2: Development of the SMA elements
  1. Data set research and name

The dataset is related to the introduction of the Smart Storage area Alloys (SMA) and the laboratory trials for confirmation reasons. The reference name because of this dataset should be utilized as SMALabTrials.

  1. Data established description

This dataset will be produced from the work bundle WP2, mainly Job 2. 2: Lab trials for verification reasons.

In order to determine the physical aspect of the SMA elements for the coating request, the properties of SMA materials will be investigated to ensure the sustainment of the thermomechanical properties scheduled to processing. The SMAs will learn for obtaining a driven geometry and laboratory studies will be conducted to samples, such as plates where the physical characteristics of the SMAs will be examined for its efficiency.

The data will be reported for the handling conditions, the thermo-mechanical properties of the SMA plates, and their features.

  1. Standards and metadata

The metadata for every data school will comprise the following:

  • SMA handling conditions (processing temp, atmosphere, etc. )
  • Mechanical properties (hardness, toughness, tensile strength, flexible moduli, etc. )
  • Physical properties (electrical resitivity, sound velocities, density, thermal conductivity, etc. )
  • Thermomechanical characteristics in response to heat (IR thermography, etc. )
  • Microstructural characterization (Optical microscopy, electron microscopy, etc. )

2. 3. 1. File and folder names

The suggested folder names should include day, dataset name, dataset specifics, and dataset version. DateStamp_datasetName_datasetSpecifics_datasetVersion (e. g. "2015_12_09_SMALabTrial_Thermo_v01"). Each data file name inside a folder will include the all information on a belonging folder name, and any extra specifics of the document related to operating or environmental conditions.

Each folder will include a readme. txt data file containing metadata information and description of the dataset.

  1. Data sharing

The data product will be updated good experimental testing. The day of the update will be contained in the data file and you will be part of the data file name. During the project lifetime the data will be shared through publications in scientific publications or presentations in technological conferences.

During the project lifetime the data will be distributed publicly only after the patents have been submitted or shared on scientific journals, in order not to jeopardise the project objectives and deliverables.

  1. Data sharing repository

The dataset will be distributed on Zenodo research data sharing program (http://www. zenodo. org/) through the project lifetime as well as after. Zenodo enables researchers, scientists, European union projects and institutions to:

  • easily discuss the long tail of research ends in a wide variety of formats including content material, spreadsheets, audio, video tutorial, and images across all fields of technology,
  • display their research results and receive credit by making the research results citable and integrating them into existing reporting lines to financing firms like the Western european Commission, and
  • easily access and reuse shared research results.
  1. Licensing

The dataset will be produced publicly available under Creative Commons Non- commercial Share-alike permit version 4. 0 - CC BY-NC-SA 4. 0 - http://creativecommons. org/licenses/by-nc-sa/4. 0/. The specific license enables one to freely

  • share - duplicate and redistribute the materials in virtually any medium or format, and
  • adapt - remix, transform, and build upon the material.

The licensor cannot revoke these freedoms so long as one uses the license conditions. The following terms must be acknowledged:

  • Attribution - one must give appropriate credit, give a connect to the certificate, and point out if changes were made. One may do so in any reasonable manner, but not at all that suggests the licensor endorses you or your use.
  • NonCommercial - One might not use the material for commercial purposes.
  • ShareAlike - If one remixes, transforms, or develops upon the materials, one must send out the contributions under the same permit as the original.
  1. Archiving and preservation (including storage area and backup)

The dataset will be made publicly available for long-term using Zenodo research data sharing platform (http://www. zenodo. org/) or otherwise Cranfield University inner storage features. Both services enable data sharing free of charge so we are not expecting any extra costs following the project life-time.

Our intent is to help make the generated data available for use by educational community and companies designed for five years.

  1. Data derived from WP3: Creation and experimental research of the SMA coating
  1. Data set reference point and name

The dataset relates to the layer alloy composition, coating characteristics and properties data. The reference point for this dataset should be utilized as CoatingAlloy.

  1. Data established description

This dataset will be derived from the work program WP3, mainly tasks Task 3. 2: Experimental exploration of the covering, and Process 3. 3: Screening of the coating's expertise.

There are several directories for anatomist materials but almost all of these directories cover regular materials like plastics and steels. For SMA on the other hand only a restricted quantity of alloys is of anatomist interest which properties and their parameter dependence need to be handled to make the repository useful. During this project different coating alloy compositions as well as different layer deposition techniques will be examined.

Coating characteristics and properties data will contain coating characteristics and properties as effects of: period change properties conviction, mechanised and microstructural evaluation, adhesion assessments, anticorrosive properties assessments.

  1. Standards and metadata

Metadata for every single data class will comprise the next:

  • Coating characteristics (alloy structure, thickness, deposition strategy etc. ).
  • Phase change properties (resistivity, differential scanning calorimetry)
  • Mechanical (tensile properties, hardness and so on. . )
  • Microstructural (Electron microscopy: SEM/EDS, TEM- X-ray diffraction)
  • Adhesion lab tests ( bend test)
  • Anticorrosiveproperties(Linearsweepvoltammetry, electrochemical impedance spectroscopy).

Metadata will contain explanations of the info sources, test variables and types of procedures, specimen information, results etc.

All properties data for each coating with identified structure will be offered testing guidelines such as heat, variety of cycles, etc. Thermomechanical training, transformation temperature, compositions will be also available.

3. 3. 1. Fileandfoldernames

The data will be saved as text record corresponding to ASCII format. XML format for our metadata will be chosen in order to provide compatibility with international standards (xml format). The data will be accumulated as shown in Shape 3. 1:

Figure3. 1:Dataorganisation

The proposed folder names should include night out, dataset name, dataset specifics, and dataset version. DateStamp_datasetName_datasetSpecifics_datasetVersion (e. g. "2015_12_09_coatingAlloy_phaseChange_v01"). The dataset will be arranged into a given folder, which will be subdivided in various subfolder named C_SMA_1, C_SMA_2 C_SMA_n (where ncorresponds to the amount of the coating sample). Mechanical, period change, adhesion, microstructural and corrosion resistance properties will be examined and the outcomes of every test will be included in the associated subfolders, MEC_, PC_T, Advertising_T, EM, CR_T respectively.

Each data file name inside a folder should include the all details of a owed folder name, and any extra specifics of the data file related to operating or environmental conditions. Each folder will include a readme. txt document made up of metadata information and information of the dataset.

  1. Data sharing

Research data will be will most probably gain access to after publication of manuscripts predicated on the data we acquire. The distributed data are expected to be of interest to experts as well as to the layer industry. Data generated under the job will be disseminated in accordance to the Consortium Contract. Fresh data will be managed on hard drive and offered on need.

  1. Datasharingrepository

The dataset will be distributed on Zenodo research data posting system (http://www. zenodo. org/) through the project life-time as well as after. Zenodo enables researchers, scientists, European union projects and establishments to:

  • easily reveal the long tail of research ends up with a wide variety of formats including word, spreadsheets, audio, video, and images across all areas of research,
  • display their research results and receive credit by causing the study results citable and integrating them into existing reporting lines to funding companies like the Western Commission, and
  • easily gain access to and reuse distributed research results.

During the task lifetime the data will be distributed publicly only following the patents have been published or released on scientific publications, in order not to jeopardise the job goals and deliverables.

  1. Licensing

The dataset will be made publicly available under Creative Commons Non- commercial Share-alike permit version 4. 0 - CC BY-NC-SA 4. 0 - http://creativecommons. org/licenses/by-nc-sa/4. 0/. The specific license enables one to freely

  • share - copy and redistribute the material in any medium or format, and
  • adapt - remix, transform, and build upon the material.

The licensor cannot revoke these freedoms as long as one comes after the license terms. The following conditions must be recognized:

  • Attribution - one must give appropriate credit, give a connect to the certificate, and indicate if changes were made. One may do so in virtually any reasonable manner, however, not at all that implies the licensor endorses you or your use.
  • NonCommercial - One may well not use the material for commercial purposes.
  • ShareAlike - If one remixes, transforms, or creates upon the material, one must send out the contributions under the same permit as the original.
  1. Archiving and preservation (including safe-keeping and backup)

The dataset will be produced publicly designed for long-term using Zenodo research data showing program (http://www. zenodo. org/) or otherwise Cranfield University interior storage functions. Both services enable data sharing cost-free so we aren't expecting any extra costs after the project life time.

The data product will be modified in line with the experimental testing. The date of the revise will be included in the data file and you will be area of the data data file name. The daily and regular monthly backups of the data data will be maintained within an archive system.

Our purpose is to make the generated data available for use by academic community and business designed for five years.

  1. Data derived from WP4: Design, development and manufacture of the manipulating device, and WP6: System integration, lab and field trials
  1. Data set guide and name

The dataset is related to the design of the Laser beam Profilometer, its tests and validation, as well as screening of the prototype under laboratory conditions or using the real aircraft. The reference because of this dataset should be used as LPValid.

In addition, the dataset is related to the prototype design, its testing and validation within lab and field conditions. The reference for this dataset should be used as ProtoValid.

  1. Data place description

This dataset will be produced from the work program WP4, mainly Task 4. 2. : Production manipulating device including LP, and Activity 4. 3. Ensure that you validate manipulating device. On top of that, the same dataset will be produced from the work package deal related to prototype tests Task 6. 2. Laboratory testing, and Process 6. 3. Trial on genuine metallic structures.

LP will capture the 3D profiles of an thing by laser beam projection techniques, which is a method to make sinusoidal waves (oscillations) or optical patterns by a screen within the projector and immediate these waves on the surface. The connection of the waves on the surface triggers alternating lines of dark and light bands called as fringes. Fringe patterns of coated surfaces tend to resemble non-stationary 3D indicators, with depth along z axis and pixels stand for the space. The LP data will be used for fringe routine analysis, which has recently seen significant interest due to its widespread program in executive.

  1. Standards and metadata

The metadata for each and every data class will comprise the following:

  • Detailed condition of the analysed specimen (i. e. faultfree, broken, damage size, etc. )
  • Type of materials and type of coating

4. 3. 1. Fileandfoldernames

The proposed folder names will include day, dataset name, dataset details, and dataset version. DateStamp_datasetName_datasetSpecifics_datasetVersion (e. g. "2015_12_09_LPValid_Condition_v01"). Each data file name inside a folder should

include the all information on a owed folder name, and any additional specifics of the record related to operating or environmental conditions.

Each folder will contain a readme. txt record made up of metadata information and information of the dataset.

  1. Data sharing

The data product will be up to date based on the experimental checks. The day of the update will be included in the data file and you will be area of the data file name in conditions of particular date and version.

  1. Data sharing repository

The dataset will be shared on Zenodo research data writing program (http://www. zenodo. org/) through the project lifetime as well as after. Zenodo allows researchers, scientists, EU projects and establishments to:

  • easily promote the long tail of research ends in a multitude of formats including words, spreadsheets, audio, video, and images across all domains of knowledge,
  • display their research results and receive credit by causing the study results citable and integrating them into existing reporting lines to financing businesses like the Western Commission, and
  • easily access and reuse distributed research results.

During the task lifetime the data will be shared publicly only after the patents have been published or published on scientific journals, in order not to jeopardise the project targets and deliverables.

  1. Licensing

The dataset will be made publicly available under Creative Commons Non- commercial Share-alike certificate version 4. 0 - CC BY-NC-SA 4. 0 - http://creativecommons. org/licenses/by-nc-sa/4. 0/. The particular license enables one to freely

  • share - backup and redistribute the materials in any medium or format, and
  • adapt - remix, transform, and build after the material.

The licensor cannot revoke these freedoms as long as one comes after the license conditions. The following conditions must be recognized:

  • Attribution - one must give appropriate credit, provide a connect to the license, and point out if changes were made. One may do so in any reasonable manner, however, not at all that implies the licensor endorses you or your use.
  • NonCommercial - One might not exactly use the material for commercial purposes.
  • ShareAlike - If one remixes, transforms, or builds upon the materials, one must spread the contributions under the same permit as the initial.
  1. Archiving and preservation (including storage and back up)

The dataset will be made publicly designed for long-term using Zenodo research data sharing program (http://www. zenodo. org/) or on the other hand Cranfield University internal storage features. Both services permit data sharing free of charge so we are not expecting any extra costs after the project life-time.

Our intent is to make the generated data available for use by academic community and sectors available for five years.

  1. Data produced from Package WP5: Development of software for mapping and numerical deformation assessment
  1. Data set reference and name

Within the WP5 the following two data packages are expected to be derived:

  • StrucAssess- Numerical structural examination and prognostic models, and
  • ValidMap- Validate mapping and numerical examination software.

However, in a later stage of the InnoSMART job this file will be enriched in case necessary additional data pieces might be included.

  1. Data placed description
  1. Numerical structural assessment and prognostic models

The following data set refers to the work that'll be completed in Responsibilities 5. 2 and 5. 3. This work will involve the structural assessment of a component and it expected to be predicated on the following:

Numerical Structural Assessment

  • Finite Component (FE) Method - FE Software
  • Non - Linear Analysis (for allowing large displacements)
  • Geometric characteristics of the structural component
  • Geometric characteristics of the crack
  • Mechanical Material Properties
  • Boundary Conditions
  • Loading Conditions
  • Scripting code (macro-routine)

The research will be carried out using the Finite Aspect Method using a respected software that can satisfy the project's needs. Inside the FE software a non-linear research will be executed for considering large displacement. The final is essential specifically in the cases where imperfections, such as crack are present. The geometric characteristics of the structural element with the crack will be produced and eventually an FE model will be developed. Also, inputs about the material models, the boundary and the loading conditions will be required. It is worthwhile mentioning that all these aspects of the part will be defined by having a macro routine comprised by the scripting dialect of the FE software. This allows the parametric description of all variables of the research problem. To the end a simple insertion of changing the suggestions data can be accomplished.

Prognostic Models

  • Post processing capabilities of the FE software
  • Fracture mechanics
  • Failure standards for metallic materials based on Destruction Tolerance (DT)
  • Residual power of the component
  • Remaining life of the component
  • Scripting code (macro-routine)

The results of the fore stated numerical evaluation will be refined in line with the post-processing capabilities of the FE software. Therefore, fracture technicians features of the FE software have to be open to ensure reliability and validity for the results. Additionally, appropriate failure criteria are designed to be employed predicated on DT for examining the residual durability of the component. If fatigue examination is completed; conclusions regarding the staying life of the part will be also available. Once again, a macro workout will be written for executing the examination of the component and produce obviously results regarding the position of the investigated part (continue to operate, repair or discard/swap).

  1. Validate mapping and numerical assessment software

The following data set refer to the work which will be carried out in Responsibilities 5. 5. This work consists of the validation of mapping and numerical assessment software and it expected essentially to be transported through experimental trials. Therefore, this depends on the next:

Validation of Mapping Software

  • Experimental apparatus
  • Test samples (size of coupons) with known defects (size and location)
  • Sensors for measuring deformations
  • Data acquisition system

Validation of Numerical Analysis Software

  • All pointed out in the aforementioned list
  • Algorithm for assessing the numerical with the experimental results

The validation of the mapping and the numerical structural diagnosis software can be possibly completed through some experimental trials in cracked coupons where the size and the positioning of the destruction are known. Appropriate experimental equipment can potentially be utilised for doing the mechanical assessments to the samples. Employing appropriate sensors or optical methods the deformations/tension will be assessed by using a data acquisition system. Eventually, a representative deformation field will be accessible for contrast and validation purposes.

The same procedure can potentially be utilized for validating the numerical structural evaluation. The mechanical testing can be conducted up to failure for the sample. To the end bits of information regarding the actual status of the residual durability of the sample will be found. A possible useful end result is the strain where unstable split propagation occurs. Finally, an algorithm can be developed for effectively performing the comparability between your numerical and experimental results. Finally, conclusions regarding the validity of the numerical evaluation software will be drawn.

  1. Standards and metadata

Regarding Duties 5. 2, 5. 3 and 5. 5 no benchmarks will be utilized, since there are no relevant specifications for the activities which will be taken. At this initial stage of the task where no actual results/output have been generated from Task 5. 2, 5. 3 and 5. 5 no information regarding possible metadata can be documented hereafter. It really is expected to have more information in an updated version of the document.

  1. Fileandfoldernames

The suggested folder names should include day, dataset name, dataset details, and dataset version. DateStamp_datasetName_datasetSpecifics_datasetVersion (e. g. "2015_12_09_StrucAssess_Fracture_v01"). Each file name in the folder will include the all information on a owed folder name, and any extra details of the record related to operating or environmental conditions.

Each folder will contain a readme. txt document comprising metadata information and explanation of the dataset.

  1. Data sharing

The data which will be produced as can be seen from the information above will allow the associates to make several magazines in high impact journals. The publications come up from the project will most probably access to become exploit by not only the educational community by from the industry as this is the key goal of the consortium.

  1. Datasharingrepository

The dataset will be shared on Zenodo research data showing program (http://www. zenodo. org/) through the project life span as well as after. Zenodo enables researchers, scientists, European union projects and corporations to:

  • easily discuss the long tail of research leads to a multitude of formats including text message, spreadsheets, audio, video, and images across all fields of science,
  • display their research results and receive credit by making the study results citable and integrating them into existing reporting lines to financing organizations like the Western european Commission, and
  • easily access and reuse shared research results.

During the job lifetime the data will be shared publicly only following the patents have been submitted or released on scientific publications, in order never to jeopardise the task aims and deliverables.

  1. Licensing

The dataset will be produced publicly available under Creative Commons Non- commercial Share-alike certificate version 4. 0 - CC BY-NC-SA 4. 0 - http://creativecommons. org/licenses/by-nc-sa/4. 0/. The determined license enables one to freely

  • share - copy and redistribute the material in any medium or format, and
  • adapt - remix, transform, and build upon the material.

The licensor cannot revoke these freedoms so long as one practices the license conditions. The following terms must be acknowledged:

  • Attribution - one must give appropriate credit, provide a connect to the permit, and signify if changes were made. One may do so in any reasonable manner, however, not in any way that suggests the licensor endorses you or your use.
  • NonCommercial - One might not exactly use the material for commercial purposes.
  • ShareAlike - If one remixes, transforms, or creates upon the material, one must spread the contributions under the same certificate as the original.
  1. Archiving and preservation (including safe-keeping and back-up)

The dataset will be produced publicly designed for long-term using Zenodo research data writing program (http://www. zenodo. org/) or additionally Cranfield University inside storage capacities. Both services allow data sharing cost-free so we aren't expecting any additional costs after the project life-time.

Our purpose is to help make the generated data available for use by educational community and sectors available for five years.

  1. References

[1. 1] http://ec. europa. eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pi lot/h2020-hi-oa-pilot-guide_en. pdf

[1. 2] http://ec. europa. european union/research/participants/data/ref/h2020/grants_manual/hi/oa_pi lot/h2020-hi-oa-data-mgt_en. pdf

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