  During the strip rolling process, a considerable amount of the forces of the
material pressure cause elastic deformation on the work-roll, i.e., the
deflection process. The uncontrollable amount of the work-roll deflection leads
to the high deviations in the permissible thickness of the plate along its
width. In the context of the Austenitic Stainless Steels (ASS), due to the
instability of the Austenite phase in a cold temperature, cold deformation
leads to the production of Strain-Induced Martensite (SIM), which improves the
mechanical properties. It leads to the hardening of the ASS 316L during the
cold deformation, which causes the Strain-Stress curve of the ASS 316L to
behave non-linearly, which distinguishes it from other categories of steels. To
account for this phenomenon, we propose to utilize a Machine Learning (ML)
method to predict more accurately the flow stress of the ASS 316L during the
cold rolling. Furthermore, we conduct various mechanical tensile tests in order
to obtain the required dataset, Stress316L, for training the neural network.
Moreover, instead of using a constant value of flow stress during the
multi-pass rolling process, we use a Finite Difference (FD) formulation of the
equilibrium equation in order to account for the dynamic behavior of the flow
stress, which leads to the estimation of the mean pressure, which the strip
enforces to the rolls during deformation. Finally, using the Finite Element
Analysis (FEA), the deflection of the work-roll tools will be calculated. As a
result, we end up with a generalized model for the calculation of the roll
deflection, specific to the ASS 316L. To the best of our knowledge, this is the
first model for ASS 316L which considers dynamic flow stress and SIM of the
rolled plate, using FEM and an ML approach, which could contribute to the
better design of the tolls.
