Time-efficient Prediction of the Surface Layer State after Deep Rolling using Similarity Mechanics Approach

Highly stressed components like turbine blades made of IN718 (ASTM: B637), crankshafts made of 42CrMo4 (ASTM: A322- 4140) or connecting rods made of GGG60 (ASTM: A536-80-55-06) have to satisfy stringent requirements regarding durability and reliability. The induction of compressive stresses and strain hardening in the surface layer of technical components has proven to be a promising method to significantly increase the fatigue resistance. These required surface layer properties can be achieved by deep rolling. The determination of optimal deep rolling process parameters still requires elaborate experimental set-up and subsequent time- and cost-intensive measurements. Therefore, this work provides a new approach to determine surface layer properties by applying similarity mechanics in combination with FE-simulation of the deep rolling process. Thereby, this approach provides an efficient estimation of process results in which time-costly and challenging FE-simulations become redundant.