Measurement 168 (2021) 108417 Available online 8 September 2020 0263-2241/© 2020 Elsevier Ltd. All rights reserved. Microstructural measurement and artifcial neural network analysis for adhesion of tribolayer during sliding wear of powder-chip reinforcement based composites Mayank Agarwal a, b, * , Manvandra Kumar Singh c , Rajeev Srivastava a , Rakesh Kumar Gautam d a Department of Mechanical Engineering, MNNIT, Allahabad, India b Pranveer Singh Institute of Technology, Kanpur, India c Amity School of Engineering and Technology, Amity University, Gwalior, India d Department of Mechanical Engineering, IIT (BHU), Varanasi, India A R T I C L E INFO Keywords: Semi-solid Casting XRD and EDS Tribolayer Surface Topography ANN Two Stage Nested Theory ABSTRACT The infuence of powder-chip based reinforced LM6 aluminum alloy fabricated by a consolidated effect of stirring and squeeze process in the semi-solid stage is reported for wear properties. Effect of oxide formation on the worn surfaces due to the processing was noticed and experimental results showing that powder-chip based rein- forcement with semi-solid slurry affects and gave excellent resistance against the adhesive wear. Evidences of protective tribo-layers observed from the worn surface investigation, proflometer analysis, EDS and XRD results which provides an appropriate explanation for the drop in the wear rate in alloys. Specifc wear rate reduction due to the effect of oxides in mixed tribolayer has been studied by artifcial neural network (ANN) with two stage nested analysis which refects only 1.11% Mean Square Error as compared to experimental values. This model provides better understanding to identify infuencing parameter for huge variable set of processing and validate with suffcient accuracy. 1. Introduction The mechanically mixed layer (MML) or Tribo-layer formation on dry sliding wear surfaces of a metal matrix composite generally consist transferred metal from the counter surface and bulk materials from the surface of the composite [1–3]. According to Deuis et al. [1] formation of a stable and compacted layer depends on the wear debris formed due to the effect of sliding speed, normal applied load and other processing parameters. These wear debris are generally transferred from one sur- face to another and observed protective in nature. Deuis et al. [1] re- ported that, this phenomenon adversely depends on the elevated temperature and wear scar which forms smooth tribo-layer. The mutual transfer of materials between rider and counter face is often acknowl- edged by a number of evidence [4–8]. This material transfer rate, compaction, accumulation and mechanical mixing is depends on the sliding surface materials, normal load, sliding speed and shear strain, generated by the frictional heat [5,6]. Formation of mechanically mixed layer is not only depends on the direct experimental parameters, but also on the environmental chemical reactions like oxidation, ploughing. D. Lu at al. [9] found that MML formation directly depends on the debris and broken asperities, which are generally fragmented on the MML under the combined effect of shear force and normal load. Y. S. Mao et al. [10] classifed the tribolayer into three categories according the counter-face material and processing conditions as. • Transfer layer by the similar composition of Counter-face. • MML formation against the hard and tough Counter-face and, • A thin composite layer, hard and brittle in nature against the hard and tough counter-face, formed at very high temperature. Y. S. Mao et al. [10] also classifed these layers according to nature and oxide formation. Nevertheless, Q. Y. Zhang et al. [11] classifed tribolayer frst, as no-oxide tribo-layer and second as tribo-oxide layer. According to Q. Y. Zhang et al. [11] these tribolayers eventually depends on the environmental conditions and they claimed that no-oxide tribo- layer have not ability in protective nature. M. Moazami-Goudarzi and F. * Corresponding author at: Department of Mechanical Engineering, MNNIT, Allahabad, India. E-mail addresses: mayankres@gmail.com (M. Agarwal), mksingh@gwa.amity.edu (M. Kumar Singh), rajmnnit@mnnit.ac.in (R. Srivastava), rkg.mec@itbhu.ac.in (R.K. Gautam). Contents lists available at ScienceDirect Measurement journal homepage: www.elsevier.com/locate/measurement https://doi.org/10.1016/j.measurement.2020.108417 Received 8 June 2017; Received in revised form 25 August 2020; Accepted 29 August 2020