# Build Orientation Optimization Based on Weighted Analysis of Local Surface Region Curvature
## 2 Literature Review
The results of the method show significant improvements in the manufactured part's geometric accuracy and thus validate the performance of this approach. [PERSON] et al. [21] proposed a multi-object optimization of surface roughness, build time and support structures. Utility function approach is adopted to convert the optimization problem into a linear-weighted function. The optimal build orientation is determined by iteratively inputting the parameter. Although the multi-objective optimization algorithm uses multiple research objects and assigns a certain weight to each object to select the optimal build orientation to ensure that the final printing effect meets the expected settings, there are common shortcomings. The requirements of each research object on the build orientation are different or even opposite, which leads to a contradictory calculation process in the orientation optimization process. If the weight of a research object is increased or decreased, in fact, the multi-objective optimization algorithm reduces the dimensionality to a single-objective optimization algorithm. It is back to the problem of single objective optimization. With the rise of machine learning, machine learning (ML) has been applied in various aspects of 3D printing, such as design for 3D printing, material tuning, process optimization, in-situ monitoring, cloud serving and cybersecurity, to improve the whole design and manufacturing workflow especially in the era of industrial revolution 4.0 [22]. In recent years, many scholars have used machine learning to optimize 3D printing process parameters and part performance parameters, including the build orientation [23; 24; 25; 26]. [PERSON] et al. [23] proposed a generalized machine learning-based parameter optimization framework to determine optimal build orientation for FDM(Fused Deposition Modeling) components. The mechanical properties of the test examples selected are tested and simulated from the three main directions of XYZ, and then the training data are obtained through orthogonal experiments. Obviously the algorithm does not have universal applicability and is not applicable to other complex models. The simulation data of complex models is definitely more than three main directions for testing. [PERSON] et al. [24] proposed a method that applies a non-supervised machine learning method, K-means clustering with DaviesBouldin criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. Cluster analysis based on statistics takes a lot of time and it is difficult to converge for more complex models. CNNs is more precise at estimating all three studied factors than the baseline linear regression model for the training and evaluation conditions [25]. Machine learning algorithms have a great advantage for studying a class of models with similar characteristics. Given enough training data, it fits very well. When the model has anisotropic characteristics, either increase the training sample data or the new model needs to be re-learned to train the data. In addition, machine learning requires very high computer performance and it is more difficult to choose meta-parameters and network topology. Build orientation impacts surface quality, build time, support volume and mechanical properties [27]. Despite that multi-object and heuristic-based optimization can be used to determine an orientation and lots of researchers focus their attention on multi-object optimization, it cannot solve collective effect of multi-factors synchronously to the optimization process. Once the weight of each factor is determined, it essentially degrades into simple object optimization. The only benefit that draws from multi-object optimization is we can easily obtain the balanced optimal result from the factors defined by giving the specific weight factor. In this paper, we only focus the effect of different build orientations on the surface quality of regions with higher curvature in the fabricated part.