Research On Stream Of Variation And Cost Prediction Mathematical Model And Locating Algorithm In Multistage Machining Process

In mechanical manufacturing industries, it often requires multiple machining stages to complete a complex component. As a result, Multistage machining process(MMP) is widely used in the manufacturing process. The basic requirement of complex components manufacturing is high efficiency with high speed and high precision, precision and ultra precision machining technology has become a key technology to promote market competition capability. Product competition, to a large degree, can be attributed to the quality, cost and efficiency of the product. To improve the machining quality and reduce the manufacturing cost of complex components, in this paper, the control of error and cost in MMP has been studied, and the following work has been completed.Firstly, the error sources of MMP are analyzed, and the generation and annihilation rules of error are reflected by utilizing the representation of part characteristics in different coordinate systems, and then studied the transformation and accumulation phenomenon of workpiece dimensional deviation. Finally, the method of homogeneous coordinate transformation is used to build the mathematical variation propagation model in MMP. The model comprehensively considered the effect of datum error, fixture error and machining error on the location and direction deviation of machined part, reflecting the mapping relationship of process variable to workpiece deviation. It can be used to guide the fixture layout optimization, as well as the separation and identification of errors. Thus reducing the error sources, and improving the part machining accuracy.Secondly, according to the stream of variation model built at the first part, acquiring the relationship of error sources and part dimensional deviation, and then using the relation of part dimensional deviation and tolerance as well as the existing cost and tolerance function, a cost prediction model for MMP is established at last. In the model, it considers the impact of the interaction relationship between each machining stages and the number of clamping and tool change positioning on manufacturing cost, which makes the prediction results fit the fact more precisely. The model can be used to estimate and predict the subsequent machining cost in the early design stage, thus to modify the design or process variables which causing the cost too high. So as to realize the control of post machining cost in the design phase and as a result, effectively reducing the manufacturing cost.Finally, the part automatic locating technology is studied. First, the least squares model of part localization is built, then using dictionary learning based sparse matrix representation method of image, proposing a new workpiece locating algorithm. The algorithm is on the basis of alternating iteration optimization, updating the transformation from the measurement data to ideal geometric model through aligning the column vector of Euclidean transformation matrix individually, to determine the configuration of design frame with respect to measurement frame. Good results have been achieved in convergence speed and computing time and it can converge to the global optimal solution, improve the precision of part automatic localization. In the end, the above models and methods were verified through constructing the experimental platform of experiments.