Geometry Generation And Partitioning For Commodity 3D Printing

3D printing is also known as Additive Manufacturing(AM).It has changed the traditional subtractive manufacturing.Vigorously developing the 3D printing industry can bring about changes in manufacturing processes and production models,and effectively promote the development of technology and research in 3D digitalization.With the development and increasing popularity,the personalized customization of 3D printing towards common consumers has become the most important trend.In the 3D printing customization,common users customize the 3D model and conduct the printing process in a low-end consumer 3D printer.However,common users usually have limited 3D modeling skills and the consumer 3D printers have limited hardware capabilities,which worsens the convenience of the whole customized 3D printing process and has become the biggest obstacle on the road to the further popularization of 3D printing.The commodity 3D printing proposed in this dissertation seeks to offer the common users light-weight solutions to conduct fast and economy 3D model generation and printing,without the need of professional training and high-end machine setups,breaking the constraints of limited skills and hardware capabilities.The emergence of commodity 3D printing would contribute significantly in the popularization of the 3D printing technique to the common customers.Therefore,this dissertation is oriented to the field of 3D printing,and researches on some problems related to commodity 3D printing.This dissertation is oriented to commodity 3D printing,addressing the problem raised by the lack of professional skills in 3D model generation,the problem of using low-end 3D printers in printing large objects and the high cost problem of low-end 3D printers.It specifically studies the problem of intelligent completion of incomplete shapes in the generation of 3D models,the decomposition and pack of large 3D models problem and the 3D model decomposition problem for hollow-and-fill in the geometric processing of 3D models.Addressing these problems,combined with the specific requirements and constraints of 3D printing,this dissertation proposes corresponding commodity 3D printing solutions.The innovations and contributions of this dissertation include the following aspects:(1)A multimodal shape completion method via conditional generative adversarial networksThis dissertation introduces the conditional generative adversarial network model to the 3D shape completion task required in the fast and convenient 3D model generation process.In order to overcome the difficulty in obtaining the training data required to supervise the network training process in completing incomplete shape data generated from different situations,a generative modeling network is used to reason the missing regions in a generative modeling manner.So there is no need for paired data to train the deep neural network;in addition,when completing incomplete shapes,especially in the middle of user modeling,the completion of the missing parts is often ambiguous,the completion results may have different forms,therefore,the multimodal distribution of all possible shape completion results are modeled,and the multimodal distribution is used as a condition to produce the completion results,yielding multiple different completion results for a single incomplete input.(2)A commodity 3D printing method for large-scale 3D models via decompose-and-packIn order to solve the problem of the incapacity of consumer 3D printers in printing large 3D models,the method proposed in this dissertation decomposes and packs large 3D models into the printing space for efficient printing.The optimization algorithm for the decompose-and-pack of 3D models can adapt to different types of printers.With the constraints of different printers and the goal of commodity 3D printing,the decomposition and pack of 3D models are jointly optimized.The algorithm proposed in this paper does not optimize the decomposition and the pack individually,but couples the decomposition and pack problems and jointly optimizes for a better global optimal solution.(3)A commodity 3D printing method with universal building blocks as filling substituteIn order to solve the inefficiency of applying 3D printing to solid printing,this dissertation innovatively proposes the use of universal building blocks that can be cheaply,batch pre-manufactured to substitute the filling of the large internal space of the 3D model,improving 3D printing efficiency.An algorithm is proposed specifically to decompose the 3D model into a shell and an internal core.The internal core can be quickly assembled by universal building blocks to fill a large amount of space inside the 3D model,and the shell will be further decomposed for efficient 3D printing into pyramidal parts that are printing-friendly and do not require(or require only a small amount of)support waste.By using a pre-manufactured universal building blocks to fill a large amount of internal space of the 3D model,and only 3D printing a small number of pyramidal shell parts,the efficiency of 3D printing is greatly improved and the cost is significantly reduced.