G Mohanty et al./Materials Research Bulletin 43(2008)1814-1828 organics. The driving force for the electrophoretic mobility of the particles is mainly the magnitude of surface charge/ zeta potential of the particle in suspension. But there are also other parameters which can be grouped into two broad categories[22-26]:(i) those related to the suspension and substrate and (ii) those related to the process. The parameters related to the suspension and substrate include particle size, dielectric constant of the solvent, conductivity of the suspension, viscosity of the suspension and zeta potential and conductivity of the substrate. The parameters related to the process include particle mass concentration in the suspension, separation between the electrodes, applied voltage, and deposition time. But once the solvent, particle and substrates are fixed, the variable parameters which can be effectively controlled to optimize EPD are the process parameters. In a classical approach, optimization is done by varying one-factor-at-a-time while keeping the others constant. This method consists of successively varying each factor over its range with the other factors are held constant. This experimentation strategy does not provide any idea about the individual contribution of each factor towards the response and fails to consider any interactional effect of two or more variables which is more likely in an actual operating environment. The statistical optimization technique using full factorial design of experiments is an useful tool which a allows one to obtain appropriate data that can be analyzed to arrive at an objective conclusions and determine the optimum conditions through a relatively smaller number of systematic experiments [27]. Using a proper design matrix and systematically varying different variables one can obtain regression equations, which highlights the effect of ndividual parameters and their relative importance in given operation/process. In the conventional experimentation method of one-factor-at-a-time, only one factor is varied over its range with the other factors held nteraction effect of two or more variables cannot be determined using this approach. The primary advantage of statistical methods is that the interactional effects of two or more variables can also be known. It also adds objectivity to the decision-making process. They allow us to measure the likely error in a conclusion or to attach a level of confidence to a statement. When the problem involves data that are subject to experimental errors, statistical methodology is the only objective approach to analysis. In this paper we present a systematic investigation on the use of statistical design of experiments to optimize and develop quantitative understanding on the effect of process variables on the yield of electrophoretic deposition of alumina on steel substrates and highlight the methodologies and significance of each analysis in arriving at the optimized condition. The effects of individual parameters as well as their nteractional effects have also been highlighted. 2.. Materials The calcined alumina powder( Grade CT 3000SG)used for electrophoretic deposition in the present investigation was supplied by Alcoa, India. The powder had a mean particle size of 0. 7 um, BET surface area of 7.0 m g and sintered density of 3.9 g/cm. The organic solvent, propan-2-ol( C3HgO), used as the dispersing medium, was supplied by SD Fine Chemicals, Mumbai. The solvent was 99.5% pure with 0. 1%o residual water content in it. Stainless steel lates(20.5 mm x 20.5 mm x 5 mm) were used as the substrates for electrophoretic deposition. a stainless steel strip of the same dimension was used as the counter electrode. The substrates and the counter electrodes were thoroughly cleaned before use 2.2. Methods The suspension for electrophoretic deposition was prepared by dispersing alumina powder in the iso-propanol solvent media. The suspension was first magnetically stirred(REMI EQUIPMENTS) at moderate speed for 10 min followed by ultrasonication for 20 min by Vibronic Ultrasonic Processor(Model P2)at 200 V. The surface charge of alumina suspension measured by particle charge detector(PCD-03-pH, Mutek, Germany)was -0 18 C/g Conductivity of the suspension varied between 1.64 uS at 10%(w/v) particle loading to 3. 34 uS for 30%(w/v) particle loading Deposition experiments were conducted in a setup(Fig. 1)similar to that used by Besra et al. [28]. It consists of two lectrode holders made of Teflon as the principal components. One of the electrode holders is fixed and the other is movable and can slide along two parallel rods at the bottom so that the distance between the electrodes can be adjusted to desirable position. The electrodes are fixed onto the holders such that they face each other. Adequate clearance was provided beneath the setup to accommodate a magnetic bead. Each of the electrode holders has a square windoworganics. The driving force for the electrophoretic mobility of the particles is mainly the magnitude of surface charge/ zeta potential of the particle in suspension. But there are also other parameters which can be grouped into two broad categories [22–26]: (i) those related to the suspension and substrate and (ii) those related to the process. The parameters related to the suspension and substrate include particle size, dielectric constant of the solvent, conductivity of the suspension, viscosity of the suspension and zeta potential and conductivity of the substrate. The parameters related to the process include particle mass concentration in the suspension, separation between the electrodes, applied voltage, and deposition time. But once the solvent, particle and substrates are fixed, the variable parameters which can be effectively controlled to optimize EPD are the process parameters. In a classical approach, optimization is done by varying one-factor-at-a-time while keeping the others constant. This method consists of successively varying each factor over its range with the other factors are held constant. This experimentation strategy does not provide any idea about the individual contribution of each factor towards the response and fails to consider any interactional effect of two or more variables which is more likely in an actual operating environment. The statistical optimization technique using full factorial design of experiments is an useful tool which allows one to obtain appropriate data that can be analyzed to arrive at an objective conclusions and determine the optimum conditions through a relatively smaller number of systematic experiments [27]. Using a proper design matrix and systematically varying different variables one can obtain regression equations, which highlights the effect of individual parameters and their relative importance in given operation/process. In the conventional experimentation method of one-factor-at-a-time, only one factor is varied over its range with the other factors held constant. The interaction effect of two or more variables cannot be determined using this approach. The primary advantage of statistical methods is that the interactional effects of two or more variables can also be known. It also adds objectivity to the decision-making process. They allow us to measure the likely error in a conclusion or to attach a level of confidence to a statement. When the problem involves data that are subject to experimental errors, statistical methodology is the only objective approach to analysis. In this paper we present a systematic investigation on the use of statistical design of experiments to optimize and develop quantitative understanding on the effect of process variables on the yield of electrophoretic deposition of alumina on steel substrates and highlight the methodologies and significance of each analysis in arriving at the optimized condition. The effects of individual parameters as well as their interactional effects have also been highlighted. 2. Experimentals 2.1. Materials The calcined alumina powder (Grade CT 3000SG) used for electrophoretic deposition in the present investigation was supplied by Alcoa, India. The powder had a mean particle size of 0.7 mm, BET surface area of 7.0 m2 /g and sintered density of 3.9 g/cm3 . The organic solvent, propan-2-ol (C3H8O), used as the dispersing medium, was supplied by SD Fine Chemicals, Mumbai. The solvent was 99.5% pure with 0.1% residual water content in it. Stainless steel plates (20.5 mm 20.5 mm 5 mm) were used as the substrates for electrophoretic deposition. A stainless steel strip of the same dimension was used as the counter electrode. The substrates and the counter electrodes were thoroughly cleaned before use. 2.2. Methods The suspension for electrophoretic deposition was prepared by dispersing alumina powder in the iso-propanol solvent media. The suspension was first magnetically stirred (REMI EQUIPMENTS) at moderate speed for 10 min followed by ultrasonication for 20 min by Vibronic Ultrasonic Processor (Model P2) at 200 V. The surface charge of alumina suspension measured by particle charge detector (PCD-03-pH, Mutek, Germany) was 0.18 C/g. Conductivity of the suspension varied between 1.64 mS at 10% (w/v) particle loading to 3.34 mS for 30% (w/v) particle loading. Deposition experiments were conducted in a setup (Fig. 1) similar to that used by Besra et al. [28]. It consists of two electrode holders made of Teflon as the principal components. One of the electrode holders is fixed and the other is movable and can slide along two parallel rods at the bottom so that the distance between the electrodes can be adjusted to desirable position. The electrodes are fixed onto the holders such that they face each other. Adequate clearance was provided beneath the setup to accommodate a magnetic bead. Each of the electrode holders has a square window G. Mohanty et al. / Materials Research Bulletin 43 (2008) 1814–1828 1815