Statistical Monitoring and Optimization of Electrochemical Machining using Shewhart Charts and Response Surface Methodology

The response surface methodology (RSM) and Shewhart control charts have been widely used in manufacturing to reduce variation, improve quality and optimize the output. This article proposes an application of individuals & moving range chart (I&MR) and RSM in electrochemical machining. The Shewhart-type I&MR control chart and RSM are combined together in an effective way to successfully guarantee the statistical control of the surface roughness (Ra) of the items produced by wire electrochemical turning, and meanwhile optimize Ra by exploring the optimal values of the machining parameters including applied voltage, wire feed rate, wire diameter, rotational speed and overlap distance. The conducted experiments reveal that the optimal values of the aforementioned factors are 23.67, 0.5, 0.2, 900 and 0.02, respectively.  A second-order regression model is also developed to predict the output (Ra) at different combinations of the input parameters. The developed regression model can predict the output values with a determination coefficient (R2) of 96.9%. The proposed combined scheme of Shewhart charts and RSM can be employed in other manufacturing processes and even in different service sectors to efficiently enhance the performance and reduce the cost.

[1]  Siuli Mukhopadhyay,et al.  Response surface methodology , 2010 .

[2]  Bijoy Bhattacharyya,et al.  Investigation into electrochemical micromachining (EMM) through response surface methodology based approach , 2008 .

[3]  Douglas C. Montgomery,et al.  Research Issues and Ideas in Statistical Process Control , 1999 .

[4]  Kerrie Mengersen,et al.  Principles of Experimental Design for Big Data Analysis. , 2017, Statistical science : a review journal of the Institute of Mathematical Statistics.

[5]  K. Thirugnanasambandham,et al.  Response surface modelling and optimization of treatment of meat industry wastewater using electrochemical treatment method , 2015 .

[6]  Abdur Rahim,et al.  Design of economic X̄ chart for monitoring electric power loss through transmission and distribution system , 2020 .

[7]  William H. Woodall,et al.  The Difficulty in Designing Shewhart X̄ and X Control Charts with Estimated Parameters , 2015 .

[8]  Di Zhu,et al.  Micro wire electrochemical machining with an axial electrolyte flow , 2012 .

[9]  Edson Antonio da Silva,et al.  Optimization of multiple-effect evaporation in the pulp and paper industry using response surface methodology , 2016 .

[10]  S. Gilmour,et al.  Robustness of subset response surface designs to missing observations , 2010 .

[11]  A. Noorul Haq,et al.  Optimisation of machining parameters of glass-fibre-reinforced plastic (GFRP) pipes by desirability function analysis using Taguchi technique , 2009 .

[12]  Yongbin Zeng,et al.  Wire electrochemical machining using reciprocated traveling wire , 2014 .

[13]  Salah Haridy,et al.  An application of fractional factorial design in wire electrochemical turning process , 2014 .

[14]  Chong Nam Chu,et al.  Micro Electrochemical Machining of 3D Micro Structure Using Dilute Sulfuric Acid , 2005 .

[15]  João Cirilo da Silva Neto,et al.  Intervening variables in electrochemical machining , 2006 .

[16]  S. Mukhopadhyay,et al.  Determining sample size to evaluate and compare the accuracy of binary diagnostic tests in the presence of partial disease verification , 2008 .

[17]  Douglas C. Montgomery,et al.  Process monitoring for multiple count data using generalized linear model-based control charts , 2003 .

[18]  Wataru Natsu,et al.  Investigation of Influence of Low-level Voltage on Machining Characteristics in Pulse Wire ECM , 2017 .

[19]  Salah Haridy,et al.  Effect of sample size on the performance of Shewhart control charts , 2017 .

[20]  Murat Sarıkaya,et al.  Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL , 2014 .

[21]  J. Benneyan,et al.  Statistical process control as a tool for research and healthcare improvement , 2003, Quality & safety in health care.

[22]  Connie M. Borror,et al.  Response surface design evaluation and comparison , 2009 .

[23]  John S. Oakland,et al.  Statistical Process Control , 2018 .

[24]  Gianni Campatelli,et al.  Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel , 2014 .

[25]  Nadia Bhuiyan,et al.  Continuous improvement of injection moulding using Six Sigma: case study , 2019, International Journal of Industrial and Systems Engineering.

[26]  İlhan Asiltürk,et al.  Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods , 2016 .

[27]  Bijoy Bhattacharyya,et al.  Non-traditional Micromachining Processes: Opportunities and Challenges , 2017 .

[28]  Chong Nam Chu,et al.  Analysis of the side gap resulting from micro electrochemical machining with a tungsten wire and ultrashort voltage pulses , 2008 .