Study On The Internal Structure And Process Parameters Optimization Of The Additive Manufacturing Model

Additive manufacturing technology is widely used in various industries because of its excellent "bottom-up" molding characteristics and high degree of freedom in the process of design and manufacturing.At present,however,additive manufacturing technology still has some limitations.Firstly,the internal structure of additive manufacturing entity model is simple,which lacks the consideration of model characteristics.Secondly,after structural optimization,the internal structure of the model changes,which may result in the overhanging feature,resulting in the need for additional support structure in the molding process,which makes the final optimization effect poor.Finally,the process parameters have a great influence on the final molding effect,and the coupling effect of process parameters on the molding accuracy still needs to be explored.Therefore,it is very important for the development of additive manufacturing technology to consider the optimization of internal structure,the establishment of self-supporting structure and the optimization of process parameters in the forming process.Based on this background,this paper studies the internal structure optimization of the model and the process parameter optimization in the molding process.The main research work is as follows:In this paper,the physical model obtained through additive manufacturing is referred to as "solid model",and the additive manufacturing model is referred to as "model" for short.(1)An internal structure optimization method matching model features is proposed by using point cloud data processing.Aiming at the problem that there is no corresponding relationship between the internal structure of the model and the model features,based on the point cloud data processing method,the ellipsoid structure is selected as the optimization objective of the internal structure,and the model processing method is established.One or more internal ellipsoid structures corresponding to the characteristics of the model are successfully established in the solid model based on the characteristics of the model.In this study,an internal ellipsoid structure optimization method for model features was proposed,which completed the internal structure optimization processing of different feature models and realized the goal of building corresponding internal structures for different feature parts of the model.(2)A symmetric self-supporting algorithm based on umbrella search principle is proposed.In order to solve the problem of extra support caused by closed holes in the internal structure optimization process of the model,a virtual temperature field was established to identify whether there were closed holes in the model and the location of closed holes.Secondly,in the closed hole,the supporting area is analyzed and the circular supporting layer is created by the enclosing point method.The symmetrical self-supporting structure is established by shrinking the circular supporting layer layer by layer,so as to ensure the normal forming of the internal overhanging structure.Finally,through the molding experiment,it can be seen that the symmetrical self-supporting structure can realize the stable support of itself without generating additional support structure.The results show that the model with internal structure optimization and symmetric self-supporting algorithm can reduce the molding cost and the molding time by 12.8% and 12.3%.(3)The optimization method of multi-feature structural dimension melting deposition forming process parameters based on random walk Sparrow algorithm was proposed.In the process of melting deposition forming,the process parameters have an important effect on the accuracy of solid model.In order to improve the overall dimensional accuracy of the model,a random walk sparrow algorithm was used to select the best parameter combinations to obtain the optimal accuracy of the model.Firstly,the layering thickness,nozzle temperature,printing speed and filling rate of molten deposition forming were used as test variables to design a four-factor and four-level orthogonal experiment,and the relative errors of different characteristic structure sizes of the solid model were used as optimization objects.Secondly,Taguchi grey correlation method was used to process the experimental data,and the influence weights of each variable on the forming effect were obtained.Finally,the sparrow algorithm of random walk is used to calculate the optimal parameter scheme.Compared with the solid model obtained by Taguchi grey correlation method,the overall dimensional accuracy of the solid model formed with optimized process parameters is improved by 20%,and the grey correlation degree is improved by 27%.Based on the above research,this study completed the optimization of the internal structure of the additive manufacturing model.On this basis,the internal self-supporting structure was successfully established by combining the self-supporting algorithm with the internal structure optimization method.At the same time,the random walk sparrow search algorithm was used to optimize the process parameters of the molding process,which further improved the comprehensive dimensional accuracy of the solid model.