Abstract
Tensile strength, warping degree, and surface roughness are important indicators to evaluate the quality of fused deposition modeling (FDM) parts, and their accurate and stable prediction is helpful to the development of FDM technology. Thus, a quality prediction method of FDM parts based on an optimized deep belief network was proposed. To determine the combination of process parameters that have the greatest influence on the quality of FDM parts, the correlation analysis method was used to screen the key quality factors that affect the quality of FDM parts. Then, we use 10-fold cross-validation and grid search (GS) to determine the optimal hyperparameter combination of the sparse constrained deep belief network (SDBN), propose an adaptive cuckoo search (ACS) algorithm to optimize the weights and biases of the SDBN, and complete the construction of prediction model based on the above work. The results show that compared with DBN, LSTM, RBFNN, and BPNN, the ACS-SDBN model designed in this article can map the complex nonlinear relationship between FDM part quality characteristics and process parameters more effectively, and the CV verification accuracy of the model can reach more than 95.92%. The prediction accuracy can reach more than 96.67%, and the model has higher accuracy and stability.
Highlights
Additive manufacturing (AM) is a rapid prototyping technology born in the 1980s, which realizes the conversion from a 3D digital model to a physical model by continuously adding layers of materials
The mechanical properties of raw materials, changes in the forming temperature field, and process forming parameters all have an impact on the quality of the parts [6,7,8,9]. e main quality problems of Fused deposition modeling (FDM) parts include poor tensile strength, warpage deformation, and insufficient surface accuracy
E main contributions and innovations of this study are as follows: firstly, we construct a new quality prediction model, which can predict the roughness, warpage, and tensile strength of FDM parts at the same time; secondly, the optimal learning rate, the number of hidden layers, and the number of hidden elements of the sparse constrained deep belief network (SDBN) model are determined by grid search and cross-validation, and this method can realize the automatic selection of model parameters; an adaptive cuckoo search algorithm is proposed by introducing the cosine diminishing strategy, and the algorithm is used as the optimizer of the model to improve the prediction accuracy of the model
Summary
Additive manufacturing (AM) is a rapid prototyping technology born in the 1980s, which realizes the conversion from a 3D digital model to a physical model by continuously adding layers of materials. E main contributions and innovations of this study are as follows: firstly, we construct a new quality prediction model, which can predict the roughness, warpage, and tensile strength of FDM parts at the same time; secondly, the optimal learning rate, the number of hidden layers, and the number of hidden elements of the SDBN model are determined by grid search and cross-validation, and this method can realize the automatic selection of model parameters; an adaptive cuckoo search algorithm is proposed by introducing the cosine diminishing strategy, and the algorithm is used as the optimizer of the model to improve the prediction accuracy of the model. The second term is the likelihood term and the third term is the regularization term. lS represents the Lorentz metric of sparsity, S represents the control factor of the sparsity of activation probability, BP h1
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