Due to its highly favorable physical and chemical properties,titanium and titanium alloy are widely used in a variety of industries.Because of the low output of a single batch,plate cold rolling without tension is the most common rolling production method for titanium alloy.This method is lack of on-line thickness closed-loop control,with carefully thickness setting models for precision.A set of high-precision thickness setting models are proposed to suit the production method.Because of frequent variations in rolling specification,a model structural for the combination of analytical models and statistical models is adopted to replace the traditional self-learning method.The deformation resistance and friction factor,the primary factors which affect model precision,are considered as the objectives of statistical modeling.Firstly,the coefficient fitting of deformation resistance analytical model based on over-determined equations set is adopted.Additionally,a support vector machine(SVM)is applied to the modeling of the deformation resistance and friction factor.The setting models are applied to a 1450 plate-coiling mill for titanium alloy plate rolling,and then thickness precision is found consistently to be within 3%,exceeding the precision of traditional setting models with a self-learning method based on a large number of stable rolling data.Excellent application performance is obtained.The proposed research provides a set of high-precision thickness setting models which are well adapted to the characteristics of titanium alloy plate cold rolling without tension. Due to its highly favorable physical and chemical properties, titanium and titanium alloys are widely used in a variety of industries. Because of the low output of a single batch, plates cold rolling without tension is the most common rolling production method for titanium alloy. method is lack of on-line thickness closed-loop control, with careful thickness setting models for precision. A set of high-precision thickness setting models are to suit the production method. Abstract of frequent variations in rolling specification, a model structural for the combination of analytical models and statistical models is adapted to replace the traditional self-learning method. The deformation resistance and friction factor, the primary factors which affect model precision, are considered as the objectives of statistical modeling. Firstly, the coefficient fitting of deformation resistance analytical model based on over-determined equations set is adopted. Additionally, a support vector machine (SVM) is applied to the modeling of the deformation resistance and friction factor. The setting models are applied to a 1450 plate-coiling mill for titanium alloy plate rolling, and then thickness precision is found consistently to be within 3%, exceeding the precision of traditional setting models with a self-learning method based on a large number of stable rolling data. Excellent application performance is obtained. The proposed research provides a set-high-precision thickness setting models which are well adapted to the characteristics of titanium alloy plate cold rolling. tension.