Vol.:(0123456789) 1 3 Journal of Bio- and Tribo-Corrosion (2021) 7:36 https://doi.org/10.1007/s40735-020-00469-1 Comparative Analysis of Response Surface Methodology and Artifcial Neural Network on the Wear Properties of Surface Composite Fabricated by Friction Stir Processing Lakshay Tyagi 1  · Ravi Butola 1  · Luckshaya Kem 1  · Ranganath M. Singari 1 Received: 26 July 2020 / Revised: 8 December 2020 / Accepted: 23 December 2020 / Published online: 23 January 2021 © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021 Abstract Aluminium surface composite having ceramic reinforcement is successfully developed using friction stir processing at dif- ferent tool rpm. Pin-on-disc test was performed at diferent sliding distances (300 m, 600 m, 900 m) and at diferent applied loads (20 N, 30 N, 40 N), to analyse wear behaviour of the fabricated composites. Response surface methodology (RSM) and Artifcial neural network (ANN) are used to successfully develop two diferent models and a comparative study was done of the predictive capacity of both the developed models. The comparative study shows that the predictive capacity of the ANN model is more efcient than the RSM model. RSM is also utilized to optimize the process parameter. Optimum condition predicted by the model is for the composite developed at 1200 tool rotational speed, applied with a load of 20 N for a sliding distance of 300 m. Scanning electron microscopy (SEM) and Energy dispersive spectroscopy (EDS) analysis of wear surface were done, revealing that adhesive wear is the major wear mechanism and oxide layer formation is present on the wear surface. Keywords Surface composite · Wear · Friction stir processing · Response surface methodology · Artifcial neural network 1 Introduction Aluminium is one of the widely available material in the earth’s crust and has wide applications. It possesses prop- erties like high strength, high toughness, and due to these characteristics it is preferred in the aerospace and automo- bile sectors [1–5]. Aluminium metal matrix composites (AMMC) are occupying great interest in industrial and manufacturing sectors due to its excellent properties like ductility, high strength, toughness, etc. [6]. Addition of reinforcement to matrix material enhances its mechanical and tribological properties. Some reinforcements like alu- mina (Al 2 O 3 ), Boron carbide (B 4 C), Silica (SiO 2 ), Silicon Carbide (SiC), Graphite (Gr), Tungsten carbide (WC) [7], and yttrium oxide are used to enhance the properties of the matrix material [8–10]. Various modelling and optimiza- tion techniques are being used nowadays in order to reduce the number of experiments performed and costs related to it. One of this type of modelling and optimization tech- nique is Artifcial neural network (ANN) [11]. ANN is the development of artifcial intelligence to predict the behav- iour of any material or a system [12, 13]. Various model- ling and optimization techniques are being used nowadays in order to reduce the number of experiments performed and costs related to it. Pramod et al. [14] studied Al7075- Al 2 O 3 composite and observed the wear behaviour, and analysed it using ANN. It was found that wear resistance improved in AA7075 reinforced with Al 2 O 3 . They also concluded that ANN is capable of predicting the wear loss. Atrian et al. [15] reinforced AA7075 with the nanoparticle of SiC and analysed ultimate tensile strength using neural network techniques like the stimulation of indentation test. Enhancement of about 300% was observed in ultimate ten- sile strength value. Mahanta et al. [16] reinforced Al7075 with 1.5wt% B 4 C and (0.5, 1.0, 1.5wt%) fy ash using the ultrasonic stir casting method. Scanning electron micro- scope (SEM) analysis revealed that oxidation and abrasion are the main constituents of wear. Kumar et al. [17] used response surface methodology (RSM) to study AA7075 and aluminium hybrid metal matrix composite. They concluded * Ravi Butola ravibutola33855@gmail.com 1 Mechanical Engineering Department, Delhi Technological University, New Delhi 110042, India