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To meet the ever-increasing demands of the engineering industry, novel approaches to design optimization are essential, especially in fast-paced production environments. Conventional CAD and simulation tools often struggle to keep up with the complexity and speed required for designing critical components. In this context, leveraging Deep Learning technologies presents a promising solution by integrating knowledge from simulations and designs to drastically accelerate product development. With the drive for Electrification, conventional power electronics and systems are becoming more energy dense and hence requires compact and efficient thermal management solutions. Higher energy density is attributed to high power electrical components fitted in packs with shrinking characteristic dimensions and hence needs more efficient and compact thermal management solutions.

In amorphous solids such as fused quartz, the failure mechanism under cyclic loading is very different when compared to metals where this failure is attributable to dislocation movement and eventual slip band activity. Standard mechanical fatigue prediction methodologies, S-N or ε-N based, which have been historically developed for metals are rendered inapplicable for this class of material. The fatigue strength of Fused Silica or Fused Quartz (SiO2) material is known to be highly dependent on the stressed area and the surface finish. Stable crack growth in Region II of the V-K curve (Crack growth rate vs Stress intensity factor) is dependent on the competing and transitional effects of temperature and humidity, along that specific section of the stress intensity factor abscissa. Fused glass (under harsh environment conditions) finds usage in Automotive, Marine and Aerospace applications, where stress and load (both static and cyclic) can be severe.