ANN-Based Predictive Modelling for Fused Deposition Modelling: Material Consumption, Tensile Strength & Dimensional Accuracy

ANN-Based Predictive Modelling for Fused Deposition Modelling: Material Consumption, Tensile Strength & Dimensional Accuracy
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-11
Year of Publication : 2023
Author : Hani Nasuha Hadi Irazman, Mohd Sazli Saad, Mohamad Ezral Baharudin, Mohd Zakimi Zakaria, Azuwir Mohd Nor, Yuzairi Abdul Rahim
DOI : 10.14445/22315381/IJETT-V71I11P201

How to Cite?

Hani Nasuha Hadi Irazman, Mohd Sazli Saad, Mohamad Ezral Baharudin, Mohd Zakimi Zakaria, Azuwir Mohd Nor, Yuzairi Abdul Rahim, "ANN-Based Predictive Modelling for Fused Deposition Modelling: Material Consumption, Tensile Strength & Dimensional Accuracy," International Journal of Engineering Trends and Technology, vol. 71, no. 11, pp. 1-17, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I11P201

Abstract
Conventional modelling approaches fall short of accurately capturing the complexities of fused deposition modelling (FDM). This research proposes an Artificial Neural Network (ANN) model to predict the FDM process's material consumption, tensile strength, and dimensional accuracy. Inputs such as layer height, infill density, printing temperature, and printing speed are considered. A Face-Centered Central Composite Design (FCCCD) with 78 specimens is employed to design experiments (DOE). Material consumption is measured using a densimeter, while tensile strength is determined using a Universal Testing Machine (UTM). The performance of the ANN models is evaluated based on metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R 2 ). The optimal ANN structure for material consumption prediction is found to be 4-19-14-1, achieving a low MSE of 0.00096. For tensile strength prediction, the best ANN structure is determined as 4-16-15-12-1 with an MSE of 0.005274145. Furthermore, dimensional accuracy is successfully captured using a 4-12-12-1 network configuration, which attains the lowest overall MSE of 0.002898. The proposed ANN model provides accurate predictions for material consumption, tensile strength, and dimensional accuracy in the FDM process. This study contributes to the optimization and understanding of FDM manufacturing processes through the utilization of optimized network architectures. The findings demonstrate the efficacy of the ANN model in improving FDM process control and performance.

Keywords
Fused Deposition Modelling, Artificial Neural Network, Process modelling, Face-Centered Central Composite Design, Response surface methodology.

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