Using Historic Materials Data from Assurance Testing to Optimise Future Manufacturing Processes of High Integrity Components

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Companies such as Element Materials Technology play a key role in providing independent assurance of materials performance for a range of high integrity applications across several sectors. As such Element has created a strong digital platform for recording and reporting of test results; this presents a great opportunity to explore the ideals of the flow of such data in an Industry 4.0 context and provides a robust feedback loop to the designer and manufacturer if enacted. Often the challenge of converting paper records to a digital platform prevents the use of this data. The existing digital data can be interrogated for trends as with any big data project but there is real benefit to industry in applying a statistical process control approach to this information, generating tools that can be used by Element Materials Technology and their customer base to monitor performance and provide preventative interventions in manufacturing prior to loss of control. Evidence of conforming to process may also preclude the need for future testing in some circumstances by defining the parameters to monitor that truly control manufacture. Fracture toughness data often represents two key types of mechanical behaviour; low resistance trans-granular cleavage associated with catastrophic failure of structures and high resistance micro-void coalescence that describes ductile rupture (see Figure 1). Many other effects of specimen geometry, materials mechanical properties and failure modes can also effect establishing appropriate estimates of performance. Identifying when these have happened is key to providing assurance of future performance.The Master Curve methodology has become the accepted engineering solution for processing fracture toughness data of low alloy steels in the transition region where large variability in recorded toughness values are observed (see Figure 2). This has been adopted into international standards as the backbone of assessment methodologies (2,3) and is dependent on assumed materials behaviour, as exemplified by set parameters for probability distributions. The stochastic nature of the failure process can result in large variations even within a single material; the Master Curve provides a framework for making estimates of performance on sparse data. In doing so, it has proved very successful for energy industry applications, affording life extensions to key infrastructure.This project will develop knowledge of assurance methodologies, metallurgy of the manufacturing processes involved and an in-depth understanding of the statistical methods that can be employed to assess the data correctly. A purely data driven approach could result in over specification of the manufacturing processes, costing time, material and resources through unnecessary rejection of suitable materials. As such, the project will be run in partnership between the Department of Materials Science and Engineering and the School of Mathematics and Statistics.