Paint appearance is an important factor in the overall product quality of steel sheet, especially in the automotive industry. The present paper deals with the functional behaviour of the surface texture according to paint appearance. The main results on the improvement of paint appearance of outer car body panels, obtained with coated and uncoated cold rolled steel, textured with EBT-rolls, is reviewed. There are different methods to relate steel sheet surface texture to the appearance after painting. From the literature it is found that waviness has a detrimental effect on the surface appearance after painting. Different roughness and waviness parameters are compared in order to find a good relation with paint appearance. The following surface texture parameters are evaluated in this study: roughness parameters Ra, peak count Pc, waviness parameters Wca and amplitude between 0.5 – 5mm (Fourier analysis) and envelope parameters motif W and Ra-macro, measured with a spherical stylus of radius 1.5mm. Classical parameters such as Ra and Pc are only able to evaluate paint appearance for one single texturing process, and within very limited range. The ability to predict paint appearance by Ra-macro and Wca also seems to be limited, because of their dependency on the roll texturing technique. Based on extensive testing, it is suggested that Fourier analysis might be the best approach to predict the appearance after painting, and this independently of the texturing process. The results clearly show the improved appearance of Sibetex textured sheets compared to conventional textured sheets. This is due to the fact that, with Sibetex sheet, a reduction in waviness can be achieved.

Assessment of steel surface roughness and waviness in relation with paint appearance.

Abstract The accurate measurement of acid concentration, including hydrogen chloride (HCl) and ferrous chloride concentrations (FeCl 2 ), is a critical part of ensuring the quality of strip steel pickling. In this study, a multi-task attention convolutional long short-term memory (MACL) neural network model was proposed to predict hydrogen ion and ferrous ion concentrations simultaneously. Firstly, in order to extract significant information from the input sequence, an attention mechanism was added to the model to calculate the correlation between the input features and the acid concentration at each time step. Next, multi-task learning explores the connections between the two tasks and exploits hidden constraints to improve prediction accuracy. Finally, deep features were extracted through convolutional long short-term memory (CLSTM) neural network, and the acid concentration was predicted. The proposed MACL model was compared with other popular prediction models. The experimental results show that proposed MACL model generally outperforms other models, indicating that proposed model has excellent predictive performance and effectiveness. 

A multi-task prediction method for acid concentration based on attention-CLSTM

To produce cold-rolled steel strips with specific mechanical properties and surface roughness typically temper rolling is adopted. In most cases, a uniform roughness pattern on the strip surface is mandatory. Due to the wear of the textured work rolls, their surface roughness ( R a ) continuously reduces during the process, which should be accounted for process control. However, conventional temper rolling systems fail to guarantee a uniform surface roughness. In this work, the influence of strip tension on the imprinting of surface roughness during temper rolling is analyzed based on a multi-scale FE modeling concept to explore new ways for surface roughness control. This is done in simulation where, a macroscopic rolling model incorporating strip tension is coupled to a mesoscopic imprinting model and both models are validated using copper rolling trials. The influence of different thickness reductions, strip tensions and incoming strip's surface roughness on imprinting is modeled and compared. The numerical results reveal that a higher strip tension decreases the roughness transfer, which presents potential to control the roughness transfer ratio without changing other process parameters like the prescribed thickness reduction in the future.

Modeling and exploiting the strip tension influence on surface imprinting during temper rolling of cold-rolled steel

Model-based Control of the Strip Roughness in Cold Rolling

As recent trends in manufacturing engineering disciplines show a clear development in the sustainable as well as economically efficient design of forming processes, monitoring techniques have been gaining in relevance. In terms of monitoring of product properties, most processes are currently open-loop controlled, entailing that the microstructure evolution, which determines the final product properties, is not considered. However, a closed-loop control that can adjust and manipulate the process actuators according to the required product properties of the component will lead to a considerable increase in efficiency of the processes regarding resources and will decrease postproduction of the component. For most forming processes, one set of component dimensions will result in a certain set of product properties. However, to successfully establish closed-loop property controls for the processes, a systematic understanding of the reciprocity of the dimensions after forming and final product properties must be established. This work investigates the evolution of dimensional accuracy as well as product properties for a series of forming processes that utilize different degrees of freedom for process control.

Christopher Schulte

Papers

Modeling and exploiting the strip tension influence on surface imprinting during temper rolling of cold-rolled steel

Model-based Control of the Strip Roughness in Cold Rolling

Monitoring the evolution of dimensional accuracy and product properties in property-controlled forming processes