Roughness Optimization of Electroless Ni-B Coatings Using Taguchi Method

Abstract

In this paper, the authors present an experimental study of roughness characteristics of electroless Ni-B coatings and optimization of the coating process parameters based on L27 Taguchi orthogonal design. Three coating process parameters are considered viz. bath temperature, reducing agent concentration, and nickel source concentration. It is observed that concentration of reducing agent together with bath temperature play a vital role in controlling the roughness characteristics of the coatings. The analysis yields the optimum coating parameter combination for minimum roughness. A reduction of about 15% is observed in roughness at the optimal condition compared to the initial condition. The microstructure, composition, and the phase content of the coating are also studied with the help of scanning electron microscopes energy dispersive X-ray analysis, and X-ray diffraction analysis, respectively.

Optimization Studies on Electroless Nickel Coatings

“…Grey analysis is employed together with Taguchi method for the optimization and it was found that concentration of the reducing agent and its interaction with concentration of the nickel source solution, have significant influence in controlling the roughness characteristics of the coating. Similar study is carried out for Ni-B coating (Das & Sahoo, 2011h) and concentration of reducing agent and nickel source is observed to have good amount of significance on the roughness. Incorporation of tungsten ion is found to influence the roughness characteristics of Ni-P coatings significantly (Roy & Sahoo, 2012b).…”

“…Taguchi technique 28 (Arun & Kumar, 2013;Cai,Yan, & Shi,2012;Das & Sahoo, 2011a;Das & Sahoo, 2011b;Das & Sahoo, 2012;Das & Sahoo, 2011d;Das & Sahoo, 2011e;Das & Sahoo, 2011j;Deng & Moller, 1992;Farzaneh, Ehteshamzadeh, & Mohammadi, 2011;Farzaneh, Ehteshamzadeh Ghorbani, & Mehrabani, 2010;Hosseini & Bodaghi, 2013, Hou, Xiao, Guo, Zhou, & Wang, 2013Jiang, Xiao, Hu, Peng, Zhang, & Wang, 2009;Jiang & Xiao, 2011;Jiang, Kan, Yuen, & Wong, 2008;Li & Liu, 2011;Lin, Xuanmin, Jian, & Shixi, 2008;Liu, Zhang, Yu, & Ning, 2011;Panja & Sahoo, 2014;Perumalraj & Dasaradan, 2011;Qu, Huang, & Cai, 2011;Roy & Sahoo, 2013a;Sahoo, 2008a;Sahoo, 2008b;Sahoo, 2008c;Sahoo, 2009;Wang, Hu & Li, 2012) Response surface methodology 5 (Georgieva & Armyanov, 2007;Guo, Jiang, Yuen, Ng & Lan 2013;Oraon, Majumdar & Ghosh, 2006;Oraon, Majumdar & Ghosh 2007;Oraon, Majumdar & Ghosh, 2008) Grey relational analysis 13 (Das & Sahoo, 2011c;Das & Sahoo, 2011f;Das & Sahoo, 20...…”

“…Studying the microstructure of the coating in detail leads to better understanding of its macroscopic behaviour by observing the changes occurring at the microscopic level of the coating. Electroless nickel coatings are in general very smooth and lubricious in nature thanks to their unique nodular micro-structure resembling that of a cauliflower (Das & Sahoo, 2011e). But different surface treatments and incorporation of various particles and elements lead to a change of the conventional microstructure of the coating as illustrated in Fig.…”

“…root mean square slope of profile, root mean square wavelength, core roughness depth, reduced peak height, valley depth, peak area, valley area, etc. A study is conducted for electroless Ni-P coating by considering five roughness parameters together (Das & Sahoo, 2011h;Sahoo, 2008b). The five roughness parameters are centre line average roughness (R a ), root mean square roughness (R q ), skewness (R sk ), kurtosis (R ku ) and mean line peak spacing (R sm ).…”

Study on Slurry Paste Boronizing of 410 Martensitic Stainless Steel Using Taguchi Based Desirability Analysis (TDA)

“…The process of SPB of grade 410 martensitic stainless steel could be considered as a multi-input process with multiple outputs and parameter design can be performed by using techniques like data envelopment analysis, grey relational analysis (GRA), desirability analysis, principal component analysis (PCA), fuzzy logic and artificial neural networks (ANN) (Krishnaiah and Shahabudeen, 2012). The orthogonal arrays and signal-to-noise (S/N) ratio were adopted in Taguchi's optimization strategy (Das and Sahoo, 2011). However Taguchi techniques were employed for single response optimization problem, while a manufacturing situation generally requires the simultaneous optimization of multiple responses.…”