Multi-response optimisation design of parameters for cold ring rolling process using fuzzy-grey method

Cold ring rolling (CRR) is a complex metal forming process with continuous and local three–dimension metal flow under coupled effects with multi–factors, which includes geometry sizes of rolls and ring blank, material properties, speed parameters, friction conditions, etc. In this paper, a multi–response optimisation design for the forming parameters of opened–pass plain CRR process is proposed to obtain the desired quality and precision of the deformed ring. Sixteen virtual orthogonal experimental arrays with five control factors (the radii RD and the rotational speed nD of the driving roll, the radii RM and the feed rate vM of the mandrel, and the friction coefficient µ) are designed based on Taguchi methodology, and four responses (the average spread AS, the fishtail coefficient FC, the equivalent plastic strain distribution SDP and the maximum rolling force MaxRF) are measured according to three–dimension finite element numerical simulation. Moreover, a novel fuzzy–grey optimisation design method, which can effectively deal with the uncertain nature of the measured data and observably reduce the analysis cost by parallel computing, is proposed by integrating grey relational analysis with fuzzy relational analysis. The investigation clearly shows that the proposed method can serve as a valuable guideline to optimise the forming parameters of the CRR process.