Element six.

Case Study

Chemically, physically and visually identical to their mined counterparts, synthetic diamonds and tungsten carbide super materials promise the same benefits to industry… plus that bit more.  

The planet’s hardest material, the diamond’s extreme and diverse properties give it the high tensile strength, chemical inertness, broad optical transmission and outstanding thermal conductivity. 

Throughout a range of industry applications, synthetic super materials are preferred, thanks to their unrivalled purity and hardness. Lab-grown diamonds, for example, are 10 times more durable than natural ones. 

In recent years, the technology behind lab-grown diamonds has made significant advances – allowing for high-quality diamonds to be grown more efficiently.  

the challenge

Element Six (E6) is a global leader in the design, development and production of synthetic diamond and tungsten carbide super materials.  

Since 1959, the company has worked to deliver extreme performance through the development of innovative, cutting-edge synthetic diamond and tungsten carbide solutions. 

E6 are always looking for ways to accurately predict synthetic diamond material properties to improve discovery and R & D. 

Recognising the importance of facilitating the development of machine learning models, the team approached T-DAB for a solution enabling the automatic collection, integration, and storage of data from experiments carried out by a collaborative robot (CoBot).  

the solution

Guided by the expertise and insights from the team at E6, T-DAB developed an easy-to-use tool engineered to automatically collect data from the CoBot; store and integrate it and allow for data analysis from individual and aggregated experiments.   

The platform utilises AWS and Azure for maximum reliability and it automatically harvests data collected from IoT sensors on the CoBot – saving material scientists time, while eliminating the risk of errors. 

Its ETL process, which ran as a Windows service during development, also allows for the loading of historic, manually collected data.  

Previously, experimental data was printed out as a PDF, to be manually followed by material scientists. By integrating the ETL process with E6’s legacy Lotus Notes database, this manual process could be presented as a webform – eliminating the need to re-enter, or duplicate, data.  

A powerful tool for analysis, the solution features an intuitive Power BI dashboard which allows E6 to leverage data from experiments. Quick and easy to use, it has reduced the time necessary for analysis aggregation time from as much as 4 hours to as little as a few seconds. 

Collaboration is key to a project’s success. T-DAB required the client to develop its in-house data science team to support this, backed by legacy services.  

As well as fully integrating E6’s junior data scientists and engineers into the project team to work alongside T-DAB, a programme of weekly technical help clinics was delivered. 

While the client junior engineer was introduced to data engineering principles including SQL servers and ETL, the client junior data scientist now has a deeper knowledge of machine learning methods, including Bayesian Statistics and Convolutional Neural Networks.  

the tools involved

The Benefits

7x Increase In Data Collection

Up To 10-15x Using The Cobot

3x Increase In Data Quality

hours to seconds

The collaboration helped E6 to reduce aggregation time for analysis between different tests from hours to seconds via a Power BI dashboard built using the clean data.

the result

T-DAB acted as domain experts throughout the project, with E6 providing the guidance and steering necessary for the project’s success 

This collaboration increased data collection efficiency by more than 700%. In operations involving the CoBot, that rises to as much as 1500%, while increasing data quality by 300%.  

Analysis is quicker and sharper, ensuring the R & D team can receive the vital information they need faster, with far greater accuracy and traceability.  

T-DAB’s effective knowledge-sharing ensured E6’s team could achieve the best results and use of the tool.  

As well as enhancing the R & D team’s data input, quality and storage, the project provides a foundation for further digital growth, while serving as a strong reminder of the benefits data can deliver when properly harnessed.   

Element Six acted as domain experts throughout the project with T-DAB providing guidance and steering the development of the project. This collaboration resulted in a 7x increase in data collection, with the potential of up to 10-15x using the CoBot, as well as a 3x increase in data quality

In terms of analysis, it helped E6 to reduce aggregation time for analysis between different tests from hours to seconds via a Power BI dashboard built using the clean data (the reduction in aggregation time of the data, from around 4 hours to 0 hours was the most prominent metric).

This in turn helped deliver vital testing information to the R&D team faster and with greater traceability and accuracy.

T-DAB always emphasise the importance of collaboration, particularly between domain and technical experts, and in the case of such a broad brief, teamwork was of particular importance.

A core requirement for E6 was to build up an in-house data science team. Therefore, to hep them reducing innovation cycles, while T-DAB lead the technical delivery of the project, we also provided lasting legacy services including:

  1. Integration of E6 junior data scientists and engineers to the project team, working alongside T-DAB. Full integration into daily stand ups, weekly reviews, and team collaborative working tools provided by T-DAB.
  2. Provision of weekly technical help clinics for E6 junior engineer; introducing Data Engineering principles including SQL server and ETL, which have since been used across digital transformation projects.
  3. Provision of weekly formal data science tutorial classes for the client junior data scientist; introducing the theory behind many Machine Learning methods, particularly Bayesian Statistics and Convolutional Neural Networks, which have been used to improve the accuracy of a CNN classifier by 5%.

T-DAB provided the technical expertise to drive and deliver this element of the project, in order to help them reducing innovation cycles, while ensuring the proper exchange of knowledge to allow E6 to gain understanding and make best use of the tool. This ensured immediate and lasting value. Close collaboration with the client combined with an agile development framework allowed us work with in-house support teams to deliver the project into production. There was a handover period where T-DAB continued to support the application, and E6 has now been completely self-sufficient for the last 6 months.

key outcome

Overall, the project greatly improved data input, quality and storage, and serves as a foundation for further digital additions, as well as setting a precedent for the benefits data can bring when harnessed properly.

"As an R&D centre, our data is often very varied. Thus storing our data accurately is important for traceability and analysis purposes. T-DAB were able to aid us with structuring our data, integrating it with our existing frameworks to provide bespoke software and database solutions.  As a result, we have seen an increase in both our data quality and volume, enabling us to gain a deeper understanding of our solutions and testing processes, as well as allowing a dashboard to be built. This has decreased the time taken to aggregate data on a daily basis and improved time efficiency in our R&D cycles"

Sanchit Nardekar | Data Scientist | Element Six

About The Data Analysis Bureau

The Data Analysis Bureau is data science and data engineering innovation company. We develop innovative, bespoke machine learning-driven solutions to allow anyone to infuse technology with the spark of predictive intelligence. 

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