  Citation: Kosarac, A.; Mladjenovic, C.; Zeljkovic, M.; Tabakovic, S.; Knezev, M. Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset. Materials 2022, 15, 700. https://doi.org/10.3390/ma15030700 Academic Editor: Mirko Ficko Received: 25 December 2021 Accepted: 13 January 2022 Published: 18 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). materials Article Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset Aleksandar Kosarac 1 , Cvijetin Mladjenovic 2, * , Milan Zeljkovic 2 , Slobodan Tabakovic 2 and Milos Knezev 2 1 Faculty of Mechanical Engineering, University of East Sarajevo, 71123 Istoˇ cno Sarajevo, Bosnia and Herzegovina; aleksandar.kosarac@ues.rs.ba 2 Department of Production Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia; milanz@uns.ac.rs (M.Z.); tabak@uns.ac.rs (S.T.); knezev@uns.ac.rs (M.K.) * Correspondence: mladja@uns.ac.rs; Tel.: +381-21-485-2345 Abstract: Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of deﬁning the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artiﬁcial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artiﬁcial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training. Keywords: artiﬁcial neural networks; surface roughness; design of experiment; small dataset 1. Introduction Surface quality is one of the most important indicators of the quality of machined parts [1]. The arithmetic mean roughness (Ra) represents a measure of the surface qual- ity [2]. The arithmetic mean roughness is inﬂuenced by machining parameters and tool geometry. The analytical method of deﬁning the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. The design of machine parts very often focuses on dimensional and form tolerances. In cases where the quality of the surface has signiﬁcant importance and requires an indicator, the arithmetic mean roughness (Ra) is often used. Some researchers have investigated the inﬂuence of cutting parameters (cutting speed, feed rate, axial and radial depth of the cut) on the arithmetic mean roughness (Ra) [3–9]. It can be noted that cutting speed, feed rate and the depth of the cut are the most dominant factors in these studies, even though some researchers have used other factors which can inﬂuence surface roughness, such as vibration or tool wear [10–12]. Some of the researchers have examined the inﬂuence of different cooling/lubricating techniques as factors inﬂuencing the arithmetic mean roughness (Ra) [13] or have analyzed Materials 2022, 15, 700. https://doi.org/10.3390/ma15030700 https://www.mdpi.com/journal/materials