Development and Demonstration of Digital and Data-Driven Technologies for Engineering Applications

In this section

Back to Core Research Programme AMorph – 4D Printing for Field Morphing Structures Automation of Composite and Hybrid Joining Process Coaxial Wire Additive Manufacturing Processes and In-Line Monitoring Tools Development of Coded Excitation Technique for Non-Metallic Pipes Inspection Development of Digital Twin technology as a demonstrator for real-time integrity assessment of crack growth in tensile specimens Development of Friction Stir Welding (FSW) for Hydrogen Fuel Storage Tanks Development of Leak-Before-Break Hydrogen Storage Composite Pressure Vessels With Polymeric Liner Establishing Assessment Methods for Determining Safe Service Limits of High-Temperature Materials in Extreme Environments Fracture toughness in aggressive environments: effect of crack monitoring techniques on test results In-process detection and non-destructive testing of electron beam weld imperfections Internal Bore Coatings for Applications in Corrosion and Hydrogen Environment Joining of Additively Manufactured Materials Long term behaviour of polymeric liner materials/coatings in hydrogen service Techniques for High Temperature Ultrasonic Inspection of Arc Welding Ultimate Leverage Fund

Project Code: 34239

Start date and planned duration: February 2021, 12 months

Objectives

For each of the project themes, implement the relevant data-driven technologies on past TWI project data to demonstrate the benefits of providing these capabilities.
Produce a review of available open-source software tools that can be used to implement data-driven technologies on the three applications.
Host a workshop at the end of the project with relevant industry representatives to demonstrate the three case study applications and stimulate future discussions around applications, extensions and enhancements of the project results.

Project Outline

Digital and data-driven technologies are shaping the future of engineering from the deployment of digital twins for structural integrity to the application of machine learning (ML) and artificial intelligence (AI) for the design of new alloys.

One of the biggest challenges for data-driven technologies is the availability of databases of sufficient breadth and depth. Robust digital technologies require extensive datasets to train, test, and validate their algorithms.

The overarching goal of this project is to develop and demonstrate emerging digital and data-driven technologies for engineering applications of high interest and relevance to industry. The concept is to demonstrate three different data-driven technologies on three separate technology themes:

Natural language processing (NLP) for failure investigations.
Automatic image processing for additive manufacturing micrographs.
Machine learning and big data analysis of mechanical properties.

None of the methods above (NLP, image processing, ML, etc) are new fields. These are established data science fields. More importantly, there are a wide range of open source, customisable software platforms that implement the methods underpinning these technologies. Similarly, the datasets upon which these tools will be applied already exist in TWI project archives. This will therefore be a highly focused, one-year project. The aim is to accelerate TWI’s adoption of digital technologies and demonstrate to industry that the exceptional depth and breadth of archived data from projects can be leveraged in an effective and secure way.

Industry Sectors

Oil and Gas

Power

Aerospace

Construction and Engineering

Benefits to Industry

Industry will benefit from the aggregation of cross-sectoral data available at TWI. A successful outcome will give confidence that TWI can add value for industry by exploiting archived data, while providing reassurance that the security of proprietary information is maintained.

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