Roll wear modeling using genetic programming – industry case study

Abstract

Using the continuous rolling line (10 stands-6 horizontal, 4 vertical), all the rolled dimensions, including round (more than 80 nominal diameters), flat (more than 650 shapes and dimensions) and square bars (13 different sizes), can be rolled each month. The purpose of the research was to identify the parameters affecting the working roll wear in the hot-rolling process. For this purpose, we collected data during the 2013 annual production on the first stand of the continuous roll mill for rolling of diameters from f20 mm to f58 mm for which data of the groove shape and surface, roll diameter, contact time, carbon equivalent, rolling temperature and quantity of the rolled material are available. After roll wear-out they are machined using a turning operation. The root cause "why the rolls were machined" was not collected. To evaluate the roll wear-out, the quantity of rolled material before the machining of rolls was used. Prediction of the quantity of rolled material before the machining of rolls was conducted using linear regression and genetic programming. The developed models were validated using the data from 2014. The validation showed that in the case of excluding the fatigue cracks from collected data the prediction could be improved drastically. The results of the research can be used in practice for predicting roll wear and consequently roll maintenance on the basis of rolling schedule quantities.

Influences of thermal crown and wear crown of work roll on strip shape in tandem cold rolling using a novel 3D multi-pass FE model

“…Song et al 15 constructed a WR wear model in skin-pass rolling of hot strip, which was optimised by the differential evolution algorithm and three-population particle swarm optimisation. Furthermore, the neural network 16 and genetic programming 17 were also used to predict the WR wear.…”

Modelling the surface roughness of steel after laser hardening by using 2D visibility network, convolutional neural networks and genetic programming

“…The output of the last layer (output) predicts target variables, in our case, the surface roughness. -Genetic programming (GP) is one of the most efficient and universal methods for solving problems developers use to face [14][15][16], such as symbolic regression, data mining, design optimization [17], and research of the behavior of developing populations (emergent behavior) in biological communities. GP belongs to a class of methods known as evolutionary algorithms since they are based on the concepts of natural selection and evolution (as evident in [18]).…”