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Research On Drawing Quality Of Sheet Metals In Variable Forming Speed Stamping

Posted on:2016-04-17Degree:MasterType:Thesis
Country:ChinaCandidate:Q W ZhangFull Text:PDF
GTID:2271330470984678Subject:Materials Processing Engineering
Abstract/Summary:PDF Full Text Request
304 austenitic stainless steel has good mechanical properties at room temperature, processing property and corrosion resistance as well as high economic efficiency. Besides,304 stainless steel possesses strain rate sensitivity, so it is a kind of good stamping steel and has broad application prospects such as in automotive field. Rectangular box is a kind of typical stamping part in sheet metal deep drawing, so it is obvious to find that there is great theoretical and practical significance to study the forming law of such parts. Hydro-mechanical deep drawing, as a new soft mold forming method, can simplify the mould structure, reduce the cost of production and improve the quality of forming parts; as a result, hydro-mechanical deep drawing is highly recognized and closely concerned by stamping industry experts at home and abroad. On the other hand, we adopted the variable forming speed pattern according to the characteristics of different forming stages. Through controlling material flow and strain rate, changing the states of stress and strain in deformation zone, it is available to enhance the formability of sheet metal and reduce the defects of crack and wrinkle. In this paper,304 stainless steel was chosen for the research material and the impact of variational forming speed on the typical stamping rectangular box in the hydro-mechanical deep drawing process was studied, which can provide technical support and reference for laying down parameters in rectangular box hydro-mechanical deep drawing process. The research contents can be concluded as follows:(1) The uniaxial tensile test of 304 stainless steel sheet was carried out at room temperature, and the impact of strain rate on the microstructure and properties of 304 stainless steel was studied intensively. The results show that the changes of strain rate can cause the change of martensite content in 304 stainless steel, thereby, the martensite content will effect the ultimate mechanical properties.(2) The rectangular box deep drawing process was analyzed, and the evaluation standard for the quality of rectangular box hydro-mechanical deep drawing was published. The quality of rectangular box stamping was estimated by the comprehensive evaluation standard y, which includes the maximum principal strain(xi), the maximum thickness thinning(x2), the maximum equivalent stress(x3), the sum of variation of node’s thickness(x4), the negative ratio of security node(xs). The smaller the value of y, the better the quality of rectangular box.(3) Combined with CAE technology and the orthogonal experiment design method, we can draw a conclusion of the influence extent of deep drawing speed in different stages on the forming quality through the analysis of range:V1>V3>V4>V2>V5. The simulation results indicate that the quality of rectangular box was improved in the rectangular box hydro-mechanical deep drawing numerical simulation which adopted the optimized speed curve through orthogonal experiment, the speed curve was 8mm/s-3mm/s-3mm/s-13mm/s-3mm/s, and the comprehensive evaluation standard y was reduced to 0.1203.(4) The data samples of forming quality under different speeds were trained by the intelligent forecast model of BP (Backward Propagation) neural network, at the same time, the maximum error should be ensured within 5%. Then the optimal forming speed was obtained through GA (Genetic Algorithm) calculating, the speed curve was 3mm/s-7mm/s-11mm/s-14mm/s-8mm/s. The simulation results show that the forming quality was improved after the majorization using BP neural network and GA, and y was reduced to 0.0827 furtherly.(5) The 100T Partitioned VBHF (Variable Blank Holder Force) hydro-mechanical hydraulic press was reformed to obtain variable speeds in one process. And the validation experimentations were carried out, thus validating the reliability of BP neural network and GA to acquire the optimal forming speed. The experimental results show that we can adopt the first increasing and then decreasing type forming speed with stable changing trend to improve the forming quality of rectangular box furtherly in the actual production of hydro-mechanical deep drawing.
Keywords/Search Tags:304 stainless steel, Rectangular box, Variable forming speed curve, Hydro-mechanical deep drawing, BP neural network, GA
PDF Full Text Request
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