Research Article

Surface roughness prediction of FFF-fabricated workpieces by artificial neural network and Box–Behnken method

Valaya Alongkorn Rajabhat University, 1 Moo 20 Paholyothin Rd. Klong-Luang District, 13180 Pathum Thani, Thailand

^* Corresponding author: karin@vru.ac.th

Received: 16 August 2020
Accepted: 3 June 2021

Abstract

Fused Filament Fabrication (FFF) or Fused Deposition Modelling (FDM) or three-dimension (3D) printing are rapid prototyping processes for workpieces. There are many factors which have a significant effect on surface quality, including bed temperature, printing speed, and layer thickness. This empirical study was conducted to determine the relationship between the above-mentioned factors and average surface roughness (Ra). Workpieces of cylindrical shape were fabricated by an FFF system with a Polylactic acid (PLA) filament. The surface roughness was measured at five different positions on the bottom and top surface. A response surface (Box-Behnken) method was utilised to design the experiment and statistically predict the response. The total number of treatments was sixteen, while five measurements (Ra₁, Ra₂, Ra₃, Ra₄ and Ra₅) were carried out for each treatment. The settings of each factor were as follows: bed temperature (80, 85, and 90 °C), printing speed (40, 80 and 120 mm/s), and layer thickness (0.10, 0.25 and 0.40 mm). The prediction equation of surface roughness was then derived from the analysis. The same set of data was also used as the inputs for a machine learning method, an artificial neural network (ANN), to construct the prediction equation of surface roughness. Rectified linear unit (ReLU) was utilised as the activation function of ANN. Two training algorithms (resilient backpropagation with weight backtracking and globally convergent resilient backpropagation) were applied to train multi-layer perceptrons. Moreover, the different number of neurons in each hidden layer was also studied and compared. Another interesting aspect of this study is that the ANN was based on a limited number of training samples. Finally, the prediction errors of each method were compared, to benchmark the prediction performance of the two methods: Box-Behnken and ANN.