3) Reporting. The process of monitoring the data archives, and the ability to
pull out Key Performance Indicators that can inform decision making for
further improvements in the Data Management system

#  Architecture Process Reporting

• Define data • Define data • Define repository archiving monitoring and

structure processes feedback strategies

A plan has been developed to provide detail in each of these areas as follows
:

  1. **_Architecture_ **

  

  

Due to the high volume of data that is expected to be generated during the
project, all of the data will be required to be stored locally at each
partner’s site, but results of significance will be shared internally using a
common HIMALAIA repository and externally using standard online repositories,
as shown in Figure 2.

_Figure 2_

_–_

_Architecture of data storage in HIMALAIA_

Online

repository

Journals, FigS

hare, OpenAire

Local repository

1

Partner

Local repository

Partner

2

Local repository

Partner

3

HIMALAIA

repository

DB

Online Platform,

An index of the data available will be stored in a software database that will
be found within the HIMALAIA repository. This will give a snapshot of the
entirety of data generated as well as providing a searchable index and links
to the location of the actual data resource. This will also provide a useful
tool for monitoring the amount of generated data and significance for
reporting purposes.

  2. **_Process_ **

  

  

The management of HIMALAIA data requires standardised and timely reporting of
data which can be shared within the project and with external stakeholders.
The standard process for handling new data generated during the project is
defined in Figure 1. This process will ensure that all data generated is
captured and can be made available to both internal and external stakeholders
if required. Stakeholders will also be able to see that data has been recorded
in advance of processing and analysis and can prepare activities in advance of
the publication of results.

Generate New Data in WP activity

Create new WHITE entry in data

Store in secure local archive

monitoring DB

Analyse data. Are the Results

significant?

Elevate to GREEN status. Copy to

HIMILAIA archive.

Update data

monitoring DB entry.

Yes

Are the Results significant to the

international community/worthy of publication?

Yes

Elevate to GOLD status. Copy to online archive. Publish on OpenAire. Update
data monitoring DB entry.

_Figure 3 – Workflow for newly generated data_

**3._Reporting_ **

Database monitoring tools will be used for extracting key KPIs from the
extensive data sets in the archive.

**II. HIMALAIA expected data streams and related data management**

It is important that the various data sets generated throughout the project
can be easily identified to facilitate the evaluation of the key
functionalities that result from each process chain. In order to achieve this,
there will be a fundamental identification code for each of the mould inserts
generated in WP2, and this code will serve as the first parameter in a unique
identification code for each set of data generated as the project evolves. The
code will be based on the date of production of the insert, the laser
parameters and the functionality.

For example:

**YYYYMMDD-xxxxxxxxxxx-AB**

The first 8 digits are the date of manufacture and the remaining numbers are
the laser manufacturing parameters. The letters represents the end user
application ( AB means antibacterial).

Further identification information will be appended to this root code for each
data stream as specified in this section.

##  A. 2.1 Material Data

###  2.1.1 Interest of and use of materials data

Material data consists of reference data (processing settings, MSDS, physical
properties) and measured data (rheology curves, Differential Scanning
Calorimetry data, Thermogravimetric analysis data). These will be used for
defining initial process conditions, investigating the influence of material
properties on replication performance and as input data for simulation
studies.

###  2.1.2 Data format, availability and management

Typically single figure parameter values and x,y curves. These have no
significant storage requirement.

ID format:

MatID_TestID_ **YYYYMMDD** _RunNumber Where:

MatID is a four-digit alphanumeric code indicating the material Grade and
Batch Number

TestID is a 3-digit alphanumeric code identifying the test or data sheet type

**YYYYMMDD** is the date the data was recorded

RunNumber is a two-digit numeric code indicating the specific run number.

##  B. 2.2 Processing parameters data

###  2.2 Interest of and use of processing parameters data

Processing parameter data consists of machine settings and configurations used
in a process chain. In HIMALAIA such data will be generated extensively for
the surface structuring processes and the injection moulding trials.

####  2.2.2 Data format, availability and management

Typically, single figure values will be defined for each parameter. These have
no significant storage requirement.

ID format:

InsertCode_ProcessID_ **YYYYMMDD** _RunNumber Where:

InsertCode is as defined earlier in this section.

ProcessID is a two-digit alphanumberic code where the first digit indicates
the machine used to perform the process and the second digit indicates the
process variant.

**YYYYMMDD** is the date on which the process was perfomed.

RunNumber is a two-digit numeric code indicating the specific run number.

##  C. 2.3 Process Characterisation data

###  2.3.1 Interest of and use of processing characterisation data

Process characterisation data consists of sensor and machine response
measurements that measure the processing environment with great accuracy and
provide much more sensitive data than process parameters alone for
understanding a manufacturing process. The data can be used as input data into
models that can predict process performance.

###  2.3.2 Data format, availability and management

Typically single figure parameter values and arrays of x,y curves. These
typically have a medium range storage requirement.

ID format:

InsertCode_ProcessID_YYMMDD_RunNumber Where:

InsertCode, ProcessID, RunNumber and YYMMDD are defined earlier in this
section.

##  D. 2.4 High speed image data

###  2.4.1 Interest of and use of processing image data

High speed visible and IR camera acquisitions of processes and experiments in
the HIMALAIA project give an increased understanding of fundamental behaviour
and enable comparison with simulation results.

###  2.4.2 Data format, availability and management

Very large file sizes require a significant amount of data storage.

ID format:

CamID_InsertCode_ProcessID_YYMMDD_RunNumber Where:

CamID is a two digit alphanumeric code indicating camera type (IR, VS)

InsertCode, ProcessID, RunNumber and YYMMDD are defined earlier in this
section.

##  E. 2.5 Surface Characterisation Data

###  2.5 Interest of and use of surface characterisation data

Surface characterisation data is key for the success of the HIMALAIA project
as it assesses the surface topology of the manufactured components that will
exhibit the advanced functionalities. The data will be used throughout the
project to explore the links between functionality, on the one hand, and
materials, tooling and processes on the other.

**_2.5.2 Data format, availability and management_ ** x,y,z data sets, can be
quite large for large areas. These have a medium storage requirement.

ID format:

SurfID_InsertCode_ProcessID_YYMMDD_RunNumber

SurfID is a two digit alphanumeric code indicating surface measurement type.

InsertCode, ProcessID, RunNumber and YYMMDD are defined earlier in this
section.

##  F. 2.6 Antimicrobial functionality data

###  2.6.1 Interest of and use of antimicrobial functionality data

Antimicrobial efficacy data is key for the success of the HIMALAIA project
-and in particular for the industrial partners that their applications relate
to this; EO, ALBEA- as it assesses how the surface topology of the
manufactured components affect bacterial adhesion, viability and biofilm
formation on the manufactured components and whether certain topology can
bring about advanced functionalities. The data will be used throughout the
project to explore the links between antimicrobial efficacy and materials
topology towards advanced functional surfaces.

###  2.6.2 Data format, availability and management

These x,y,z data sets, can be quite large for large areas and have medium
storage requirement.

ID format:

AMID_InsertCode_ProcessID_YYMMDD_RunNumber

AMID is a two-digit alphanumeric code indicating antimicrobial efficacy
testing type.

InsertCode, ProcessID, RunNumber and YYMMDD are defined earlier in this
section.

##  G. 2.7 Anti-squeak and anti-scratch functionality data

###  2.7.1 Interest of and use of anti-squeak functionality data

Anti-squeak and anti-scratch functionalities are key for the success of the
HIMALAIA project, notably for the automotive applications. Anti-squeak tests
assess how the surface topology of the manufactured components affect the
noisiness of the parts when they rub against adjacent parts (stick-slip
motion). The anti-scratch tests determine to what extent surfaces are
scratched, scuffed or marred under various circumstances.

###  2.6.2 Data format, availability and management

These tests are typical performed on limited areas and have no significant
storage requirement.

ID format:

ASID_InsertCode_ProcessID_YYMMDD_RunNumber

ASID is a two digit alphanumeric code indicating which antisqueak or anti-
scratch test was applied.

InsertCode, ProcessID, RunNumber and YYMMDD are defined earlier in this
section.

##  H. 2.8 Optical appearance data

###  2.8 Interest of and use of processing image data

Intriguing optical functionalities are key for the aesthetic appeal of
packaging, notably for cosmetic applications in Himalaia. Optical tests assess
how the surface topology of the manufactured components affects the visual
effects of the parts (butterfly wing effects, holograms, other intriguing
effects).

####  2.8.2 Data format, availability and management

These tests provide x,y,z data sets and are typical performed on limited
areas. They have a medium storage requirement.

ID format:

OID_InsertCode_ProcessID_YYMMDD_RunNumber

OID is a two digit alphanumeric code indicating which optical test was
applied.

InsertCode, ProcessID, RunNumber and YYMMDD are defined earlier in this
section.  
  
<table>  
<tr>  
<th>

**III.**

</th>  
<th>

**3**

</th>  
<th>

**Conclusions**

</th> </tr> </table>

This document outlines the system for data management in the HIMALAIA
programme. It defines a standard for metadata that enables reuse and
understanding of Himalaia datasets. It also outlines the architecture for the
storage of data locally, via the shared online project database and via open
platforms as well as a process for evaluating which type of data is to be
stored where.

**Appendix 1. Deliverables and milestones focused on data in HIMALAIA.**

**WP**

WP1

WP2 **Objectives** **related to data**

  * Surface-functionalision 

  

  * Replication platform 

  

  * In-line, model-based functional characterisation 

  

  * Development of laser-based technologies for producing replication inserts with functional micron and sub-micron surface structures/patterns; 

  

  * Advancement and adaptation of the use of hexagonal closed packed (HCP) microsphere arrays as laser near-field microlenses, called “photonic nanojet”, for functional texturing/patterning of replication inserts; 

  

  * Development of reliable and reproducible laser patterning/texturing of large 3D surfaces on replication inserts by equipping existing multi-axis laser machining platforms with necessary CAD/CAM and system-level solutions for integrating them into tool-making process chains; 

  

  * Adaptation and development of further metal surface engineering technologies to ensure (i) the durability of the mould inserts and (ii) efficient demoulding behaviour; 

  

  * Synergistical combination of the latest advances in laser surface patterning/texturing and metal surface engineering to address open issues concerning the durability, yield and cost in produce functional surface structures/features on replication inserts for serial manufacture of thermoplastic parts; 

  

  * Provision of micron /sub-micron structured mould inserts for testing. 

  

**Deliverables focused on data** **Milestones focused on data**

Reports related to end user Consistency between endspecifications users
requirements and

envisioned platform

capabilities

Reports related to • 3D surface patterning

  * Surface engineering technologies validated technologies for producing • System-level tools for patterned/textured large 3D surfaces 

  

replication inserts

patterning integrated in

  * Laser-based technologies 

  

GFMS equipment

for producing mould inserts with patterned/textured functional surfaces

  * System-level tools for laser processing large 3D surfaces 

  

  
<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

WP3

</td>  
<td>

•

•

• •

•

</td>  
<td>

Tests of the applicability of HIMALAIA laser patterned replication inserts for
injection moulding

Delivery of optimised processing routes for low cost, repeatable manufacturing
of 3D functional surfaces in thermoplastics

Recording of process data via an advanced data collection system Monitoring
production process quality and define the relationship between the process
environment, pattern replication quality and pattern physical functionalities
in tandem with WP4

Informing the strategy for the proposed industrial demonstrator manufacturing
processes in WP5

</td>  
<td>

Reposts based on

  * Multifunction tool and planar insert design 

  

  * Material selection 

  

  * Replication process 

  

  * Optimised injection moulding-based replication parameters 

  

  * Simulated data 

  

  * Simulations for replication efficiency 

  

</td>  
<td>

•

•

</td>  
<td>

Availability of the process building blocks for injection mouldingbased
replication of 3D and large surfaces Availability of simulation data

</td> </tr>  
<tr>  
<td>

WP4

</td>  
<td>

• •

•

•

</td>  
<td>

Quantify the functional surface behaviours

Relate the effectiveness of the functionalities with the surface metrology
parameters

Build pattern classifiers (models) for discriminating ‘acceptable’ from ‘non
acceptable’ surfaces according to their functional

properties and pre-defined tolerance levels

Develop an injection moulding flow solver subroutine to predict the
effectiveness of replication and corresponding surface functionality for user
defined input process conditions

</td>  
<td>

Reports based on :

  * Developed testing methods 

  

  * In-line metrology for 3D patterned / textured large surfaces 

  

  * Nano-safety by design 

  

  * Model-based inspection methods for linking functionality with surface parameters 

  

  * User subroutine and documentation for prediction of surface functionality 

  

</td>  
<td>

•

•

</td>  
<td>

Functional characterisation methods for 3D patterned / textured surfaces are
validated In-line metrology tool is available with the targeted performances

</td> </tr> </table>  
<table>  
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<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

WP5

</td>  
<td>

•

•

•

•

</td>  
<td>

Define the layout of the manufacturing platform and the interoperability
requirements between the different building blocks

Physically implement the technologies and tools developed in the previous WPs
on the manufacturing platform

Enable efficient data communication between the implemented technologies and
tools in order to achieve an optimal process control

Develop and implement Zero Defect Strategies on the platform

</td>  
<td>

Reports based on :

  * Platform layout 

  

  * Platform with integrated hardware 

  

  * Platform automation and process control 

  

  * Generic validation procedure 

  

  * Zero Defect strategy 

  

  * Best Practices for IM-based Platform 

  

  * New paradigms and ICT tools for HIMALAIA’s technological integration with scenario for broad industrial implementation 

  

</td>  
<td>

•

•

</td>  
<td>

Fully integrated platform

Generic validation procedure defined

</td> </tr>  
<tr>  
<td>

WP6

</td>  
<td>

•

•

</td>  
<td>

Demonstrate the production of large and/or 3D parts on the HIMALAIA
manufacturing platform

Evaluate all demonstrators via both technological and economical assessments.

</td>  
<td>

Reports based on :

  * Life Cycle Assessment 

  

  * Total Costs and Benefits of Ownership 

  

  * Automotive in-cabin components demonstrator production 

  

  * Orthodontics demonstrator production 

  

  * Cosmetic tubes - packaging demonstrator production 

  

  * HIMALAIA platform and validation of the demonstrators 

  

</td>  
<td>

•

</td>  
<td>

Demonstrators available for validation and creation of data

</td> </tr> </table>