Abstract
In recent years, fused deposition molding (FDM) has attracted much attention as one of the most common and promising 3D printing technologies. Forming accuracy is one of the most concerned quality characteristics in the FDM process and is influenced by many factors. Based on the fact that the temperature gradient affects the molding accuracy, this paper presents a method for optimizing the accuracy of fused deposition molded parts based on least square support vector regression (LS-SVR), which considers a functional input: the printing speed varies continuously in the printing process, thus reducing the temperature gradients. Some parameters that can affect the temperature and cooling of the part such as nozzle temperature, hotbed temperature, and filling rate are also included in the study. Integrating the characteristics of a functional input and the principle of experimental design, we propose to model the printing speed curve using a Bézier curve and use the curve control points together with the scalar inputs as the variables to be optimized. Then, the sample set is obtained experimentally using stratified Latin hypercube sampling for experimental point selection. The regression modeling of the sample data is performed using LS-SVR with an improved kernel function, where the kernel function is improved by the Fréchet distance. Finally, the entire model is optimized by means of the genetic algorithm. The results show that the dimensional accuracy of the parts is significantly optimized by the proposed method. A comparison with existing methods demonstrates the efficiency and practicality of the proposed method.
Highlights
Many quality characteristics can be studied and improved for products formed by fused deposition molding (FDM), such as dimensional accuracy and mechanical properties.8 Some studies have shown that these quality characteristics are strongly influenced by the input settings.dimensional accuracy is an important quality characteristic of the FDM manufacturing process.9 Since polymer materials undergo thermal shrinkage during the printing process, the resulting parts always deviate more or less from the original design
Considering the FDM process with functional inputs, we propose a method based on the least square support vector regression (LS-Support vector regression (SVR)) model for modeling and use a genetic algorithm to find the optimal combination of factors
We refer to the proposed method as method X, the Response surface methodology (RSM) as method Y, and the LS-SVR with scalar inputs as method Z
Summary
Many quality characteristics can be studied and improved for products formed by FDM, such as dimensional accuracy and mechanical properties. Some studies have shown that these quality characteristics are strongly influenced by the input settings. The researchers used methods such as the gray Taguchi method and artificial neural networks to optimize the accuracy of parts.10,11 These studies were aimed at the case where the inputs are scalar, and in today’s manufacturing process, there is another case where the inputs are functional data. The temperature gradient will cause the accumulation of residual stresses, which is one of the most important reasons for affecting the molding accuracy and other quality characteristics.23 Based on this fact, we believe that the molding accuracy can be improved by reducing the temperature gradient in some way, and in this study, some way means adjusting the printing speed curve throughout the process. Traditional experimental design and modeling methods are only for processes where the input variables are scalar, so in this paper, we refer to the functional data basis function expansion and first determine the design form of functional input variables, such as Bézier curves. We construct the outer layer model using the improved kernel function LS-SVR
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