Researches On Cutting Tests And Cutting Parameter Optimations Of Two Hard-To-Cut Materials

Under the guidence of Low-Carbon Economic Strategy(LCES) thought, aimed at the problems of energy-saving, environmental protection and high efficiency cutting for hard-to-cut martieral,022Cr19Ni10Austenitic stainless steel (ASS) and Ti6A14V Titanium alloy (TA), which are common used in high level equipment manufacture in our country, a lot of experiment researches are carried out. Comparison tests are done for the two materials in three cooling conditions of dry, clean liquid and low temperature wind with fine oil and the cutting parameters obtained are optimazed. The optimatic cutting parameters, which are useful for instructing manufacture, are obtained.Main works in this thesis as following:(1) Cutting experimental scheme is designed by uniform design method and a pseudo-variable A is introduced to express cooling cutting conditions, and then mix horizontal cutting experiments including qualitative factors is carry out, the provided factor horizontal number and the factors range, therefore, are promoted, the number and the cost of cutting experiments are reduced. By means of this scheme, the regression effect of the dependent variable regression models built is very significant and the result differences of the variable model training in BP neural network are very fine, therefore it makes that posible to carry low-cost clean cutting experiment.(2) Aimed at the cutting process of ASS and TA, cutting force model, workpiece surface roughness model, cutting temperature pattern model and workpiece surface residual stress model of quadratic regression for the two materials are built up. At the meantime, workpiece surface roughness model and cutting temperature pattern model of BP neural network’s6-7-5-1structure as well as cutting temperature pattern model, workpiece surface residual stress model of6-7-1structure for the two materials are also built up. And the then, effects on the cutting force, workpiece surface roughness, cutting temperature and workpiece surface residual stress due to the respective variables and the independent variable interaction are analyzed respectively.(3) SCL^S tool life models of quadratic regression and BP neural network’s6-7-1for the ASS cutting are built respectively, and effects on the tool life SCL, S due to respective variables and the independent variable interaction are analyzed. Meanwhile, a Multi-objective Integrated Optimization model of cutting parameter is built and the model is tested and verified.Aimed at two type of hard-to-cut materials in three clean cooling cutting conditions, the active researches for the experimental design, the modeling dependent variable, modeling cutting parameter multi-objective optimization and the dependent variable factor analysis etc are carried in this thesis. A series of problems which appeared in the clean cutting promotion and application process, therefor, as solved based on the researches, and also a new approach for carrying out mix horizontal cutting experiments including qualitative factors at low cost are explored, the research ways are enriched and cutting tests means are completed.