Injection molding is a very complex multi-factor coupling effect and a nonlinear dynamic process. Therefore, under the influence of nonlinear and multi-factor, injection molding goal is to effectively predict and guarantee the quality of injection molded parts. In this paper, the common methods used to predict the quality of injection molded parts are introduced, including: Taguchi method, artificial neural network, response surface method, radial basis function method and Kriging model method. Research progresses as well as application examples of forecasting methods at home and abroad is summarized. Besides, the development trend of the injection molding quality prediction is discussed.
Common digital differential analyzer(DDA)linear interpolation error is lower than a pulse equivalent, and the output pulse along each axis is not uniform. New DDA linear interpolation flow chart was obtained by combining a quick algorithm of DDA interpolation and interpolating algorithm for pulses uniformization with common DDA linear interpolation principle. The relationship between interpolation error and pulse equivalent was demonstrated in detail. As the results shows, the new DDA with high precision machining but simple algorithm, increased interpolating speed; the new algorithm make the generation of uniform pulse series come true, which is of great importance to keep stepping motor rotating steadily without missing steps; At coordinate origin, interpolation error is lower than 0.42 pulse equivalent, while the interpolation point is not at coordinate origin, interpolation error is lower than 0.5 pulse equivalent.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.