A deep learning model for controlling the galvanizing process in a production line

In the industry of steelmaking, the process of galvanizing is a treatment which is applied to protect the steel from corrosion. The air knife effect (AKE) occurs when nozzles emit a steam of air on the surfaces of a steel strip to remove excess zinc from it. In our work we formalized the problem to control the AKE and we implemented, with the R&D dept.of MarcegagliaSPA, a DL model able to drive the AKE. We call it controller. It takes as input the tuple : a tuple of the physical conditions of the process line (t,h,s) with the target value of the zinc coating (c); and generates the expected tuple of (pres and dist) to drive the mechanical nozzles towards the (c). According to the requirements we designed the structure of the network. We collected and explored the data set of the historical data of the smart factory. Finally, we designed the loss function as sum of three components: the minimization between the coating addressed by the network and the target value we want to reach; and two weighted minimization components for both pressure and distance. In our solution we construct a second module, named coating net, to predict the coating of zinc resulting from the AKE when the conditions are applied to the prod. line. Its structure is made by a linear and a deep nonlinear “residual” component learned by empirical observations. The predictions made by the coating nets are used as ground truth in the loss function of the controller. By tuning the weights of the different components of the loss function, it is possible to train models with slightly different optimization purposes. In the tests we compared the regularization of different strategies with the standard one in condition of optimal estimation for both; the overall accuracy is ± 3 g/m^2 dal target for all of them. Lastly, we analyze how the controller modeled the current solutions with the new logic: the sub-optimal values of pres and dist can be optimize of 50% and 20%.

Cure Induced Residual Stresses on Thick Composite Components Manufactured by Additive Process

This study investigates the effect of an additive process in manufacturing of thick composites. Airstone 780 E epoxy resin and 785H Hardener system is used in the analysis since it is widely used wind turbine blade, namely thick components. As a fiber, fabric by SAERTEX (812 g/m2) with a 0-90 degrees layup direction is used. Temperature overshoot is a major issue during the manufacturing of thick composites. A high temperature overshoot leads to an increase in residual stresses. These residual stresses are causing warping, delamination, dimensional instability, and undesired distortion of composite structures. A coupled thermo-mechanical model capable of predicting cure induced residual stresses have been built using the commercial FE software Abaqus®. The possibility of building thick composite components by means of adding a finite number of sub-laminates has been investigated. The results have been compared against components manufactured following a standard route. The influence of pre-curing of the sub-laminates has also been addressed and results compared with standard practice. As a result of the study, it is found that introducing additive process can prevent temperature overshoot to occur and benefits the residual stresses generation during the curing process. However, the process time required increases by 50%, therefore increasing the manufacturing costs. An optimized cure cycle is required to minimize process time and cure induced defects simultaneously.