Application Of 3D Printing Technology In Traditional Handicraft Production

The existing 3D printing manufacturers hope to do a data analysis of 3D printing process to obtain the "optimal process" of 3D printing.Due to the uneven theoretical learning and experience of existing 3D process engineers,3D process is mainly "trial method",which strongly relies on the experience of different engineers,resulting in different product quality and poor product precision stability.In this paper,machine learning is applied to the optimization of 3D printing parameters.Two process parameters,forming direction and layer thickness,are intelligently optimized.Data simulation based on genetic algorithm is used to find the solution set of "optimal process".Firstly,the research background and purpose of 3D printing technology are analyzed,and the limitations of 3D printing technology are pointed out.The existing 3D processing model software and process optimization are discussed,and the repeatability of 3D printing is proposed.Genetic algorithm is used to construct the multi-objective decision-making model of 3D printing process,and the relevant parameters in the model are optimized to give 3D printing the ability of learning process parameters optimization reasoning and cognitive strategy.Secondly,the working principle of the current mainstream 3D printing technology is discussed.Based on the mainstream printing technology,various kinds of 3D modeling CAD software are analyzed,and the influence of various factors of 3D printing process on the product forming process is described in detail.Based on the data function representation of forming quality,forming precision and forming efficiency,the volume difference and forming time are used to quantify the forming quality,forming precision and forming efficiency,and the optimization criteria are established,and the 3D printing process optimization model is constructed.Then,the 3D printing process optimization model is constructed with the forming direction and layer thickness as the process optimization parameters,and the forming quality,forming precision and forming efficiency are taken as the objectives.The genetic algorithm is used to solve the model and a group of non inferior solutions are obtained.Finally,the program of 3D printing process optimization model is programmed by using simulation software,and the optimal iteration times of the model are found by using the population change of different iteration times.On this basis,the optimal solution is found to meet the different combination of molding quality,molding precision and molding efficiency.In this way,the 3D printing process optimization model can effectively solve the balance problem of forming quality,forming precision and forming efficiency according to the needs of different customers,and provide the decision of forming direction and layer thickness for inexperienced 3D process engineers.The model proposed in this paper can be applied to the molding of parts with different shapes in the UV curing process,and can also be extended to the field of 3D process parameter optimization of different materials and processes,which has a certain reference significance.