Ayyıldız, Mustafa2020-04-302020-04-3020190260-22881758-6828https://doi.org/10.1108/SR-02-2019-0051https://hdl.handle.net/20.500.12684/3702WOS: 000482447100011Purpose This paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot. Design/methodology/approach In ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R-2) for the experimental data were determined based on conjugate gradient back propagation, Levenberg-Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagation feed forward back propagation training algorithm with logistic transfer function. Findings In the ANN architecture established for the surface roughness (Ra), three neurons [cutting speed (V), feed rate (f) and depth of cut (a)] were contained in the input layer, five neurons were included in its hidden layer and one neuron was contained in the output layer (3-5-1).Trials showed that LM learning algorithm was the best learning algorithm for the surface roughness. The ANN model obtained with the LM learning algorithm yielded estimation training values R-2 (97.5 per cent) and testing values R-2 (99 per cent). The R-2 for multiple regressions was obtained as 96.1 per cent. Originality/value The result of the surface roughness estimation model showed that the equation obtained from the multiple regressions with quadratic model had an acceptable estimation capacity. The ANN model showed a more dependable estimation when compared with the multiple regression models. Hereby, these models can be used to effectively control the milling process to reach a satisfactory surface quality.en10.1108/SR-02-2019-0051info:eu-repo/semantics/closedAccessMillingArtificial neural networkSurface roughnessMultiple regressionArticle395716723WOS:000482447100011Q4