In an interview from early December 2021, the Castolin Eutectic Head of Research and Development from the UK, Dr. Spyros Kamnis, explains the company’s current R & D projects involving machine learning and IoT. These initiatives aim to enhance industrial asset performance, lower running costs and provide longer component lifespan.
Data is the lifeblood of Castolin Eutectic’s business, and machine learning can be the key to unlocking the value of available corporate data in the fields of material design and operational excellence. The R & D efforts strive to shift the paradigm from applying the material customers have to engineering the material customers need.
«It is our ability to combine real-time data, physical dependency models, and intelligence from different platforms in order to simulate, predict, and improve industrial assets and maintenance processes, » says Dr. Spyros Kamnis.
« We have devised this hybrid R&D approach, combining high throughput experimentation and neural networks that would allow to sift through more than a million possibilities in search of promising new alloys tailored always to the customer’s operating environment. This new approach is used to discover, manufacture and deploy advanced materials twice as fast and at the fraction of cost compared to conventional methods, » he adds.
Over the years, the company has built a strong relationship with its customers, who are now willing to participate in R&D programs where plans are set for products and features that are still in the incubation stage. In this way, customers can provide feedback and potentially influence the direction and quality of the Castolin Eutectic products and services. « In fact, they become our design partners, beta testers and early adopters, » summarizes Dr. Spyros Kamnis.
Having joined the company in 2014, Spyros Kamnis is a mechanical engineer with a PhD in thermal spray processes and a special focus on applied metallurgy, numerical modeling and machine learning.
RELATED NEWS
