Optimal Levels Of Process Parameters Research Articles

An experimental investigation of process parameters on material removal and surface roughness improvement in abrasive flow machining

There is a huge demand for mechanical components with long life cycles and superior surface finishes as a result of the Industrial Revolution. Several methods for nano-finishing complex structures, including abrasive flow machining (AFM), have been developed in response to the need for superior surface finish. In AFM, abrasion only happens in locations where flow is restricted. This property makes AFM useful in polishing the interior, inaccessible cavities and recesses of the metallic parts using a semi-liquid paste. This paper focuses on investigating the impact of process parameters on material removal, and percentage improvement in surface roughness on cylindrical brass workpieces using Taguchi L9 orthogonal array. The number of cycles, extrusion pressure, and grit size of abrasives have been selected as process parameters. The abrasive medium employed in this investigation is composed of a blend of polymer, hydrocarbon gel, and aluminium oxide abrasive particles with varying grit sizes. The minimum value of roughness after AFM has been achieved 4.25 microns, and the maximum %ΔRa has been achieved 28%. The number of cycles that have the largest percentage contribution to material removal was 83.74%. Whereas, the percentage contributions of the abrasive particle grit size and extrusion pressure are 2.03%, and 14.16%, respectively. N2P2G3 is the optimal level of process parameters used for material removal. Similarly, the number of cycles has the largest percentage contribution of 83.48% in surface roughness. Whereas extrusion pressure and grit size contribute 7.38%, and 8.88% in surface roughness. The optimal level of process parameters that have been attained in the case of surface roughness is N3P2G3.

Operational excellence of the steel industry using the Lean Six Sigma approach: a case study

PurposeThis article aims to focus on implementing Lean Six Sigma (LSS) in steel manufacturing to enhance productivity and quality in the galvanizing process line. In recent trends, manufacturing organizations have expressed strong interest in the LSS since they attempt to enhance its overall operations without imposing significant financial burdens.Design/methodology/approachThis article used lean tools and Six Sigma's DMAIC (Define, Measure, Analyze, Improve and Control) with Yin's case study approach. This study tried to implement the LSS for the steel galvanizing process in order to reduce the number of defects using various LSS tools, including 5S, Value stream map (VSM), Pareto chart, cause and effect diagram, Design of experiments (DoE).FindingsResults revealed a significant reduction in nonvalue-added time in the process, which led to improved productivity and Process cycle efficiency (PCE) attributed to applying lean-Kaizen techniques. By deploying the LSS, the overall PCE improved from 22% to 62%, and lead time was reduced from 1,347 min to 501 min. DoE results showed that the optimum process parameter levels decreased defects per unit steel sheet.Practical implicationsThis research demonstrated how successful LSS implementation eliminates waste, improves process performance and accomplishes operational distinction in steel manufacturing.Originality/valueSince low-cost/high-effect improvement initiatives have not been adequately presented, further research studies on adopting LSS in manufacturing sectors are needed. The cost-effective method of process improvement can be considered as an innovation.

Optimisation of electrochemical oxidation process with boron doped diamond (BDD) for removing COD, colour, ammonium, and phosphate in landfill leachate

ABSTRACT This study investigated the electrooxidation (EO) of mature landfill leachate from the Brady Road Resource Management Facility, Winnipeg (Canada). EO using boron-doped diamond (BDD) electrodes were applied to treat real landfill leachate using a batch reactor. Response surface methodology (RSM) was used to determine the optimum process parameter levels. This research mainly focused on how different current densities (64, 95, and 125 mA/cm2) and operational time (30 min, 1, 1.5, 2, 2.5, and 3 hr.) influenced the optimisation of parameters such as chemical oxygen demand (COD), colour, ammonium, and phosphate removal in mature landfill leachate at varied pH. To attain a high percentage of removal for the parameters mentioned above, the optimal conditions were found to be a current density (J) of 125 mA/cm2 and a pH of 8. The optimum conditions resulted in removal percentages of 95.47%, 80.27%, 71.15%, and 47.15% for colour, NH4 +, COD, and PO4 3- respectively, with an energy consumption of 0.05 kWh/dm3. The removal is related to a mechanism of the decomposition of water molecules to hydroxyl radicals and by direct anodic oxidation where the pollutants are transformed to CO2 and H2O. The novelty of this research lies in the optimisation of BDD electrode-based treatment for the simultaneous removal of COD, ammonium, phosphate, and colour from mature leachate collected from a severely cold climatic region of Canada. The BDD electrode showed excellent removal efficiencies for the targeted contaminants with lower energy consumption, making it a feasible method for on-site landfill leachate treatment.

Optimization of sound absorbing material by using Taguchi and ANOVA

At present, target on eco-friendly acoustic material is decorated to diminish impact on environment due to adoption of synthetic material in acoustic application. In this paper, Taguchi method is enforced to find optimum parameters for sound absorbing coefficient of coconut fiber-based sound absorbing material. The influence of the various process parameters, i.e. Thickness, density, air gap and fiber percentage (%) on sound absorption coefficient (SAC) was studied. The settings of the parameters were determined by using Taguchi’s experimental design method. Orthogonal arrays (L25) of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA), and regression analyses were employed to find the optimal process parameter levels and to analyze the effect of these parameters on sound absorption coefficient values. Confirmation test with the optimal levels of parameters was carried out in order to illustrate the effectiveness of the Taguchi optimization method. Optimum parameters were found by using Taguchi method are which have 50 mm thickness, 0.0002539 g/mm3 density, 70% fiber percentage and 0 mm air gap. Also significant parameters are found which are thickness and density having p-value less than 0.05 and followed by fiber percentage and air gap. By using optimum parameters sound absorption coefficient is increased to 0.98 at frequency 2800–3500 Hz.

Numerical simulation and optimization of AC electrothermal microfluidic biosensor for COVID-19 detection through Taguchi method and artificial network.

Microfluidic biosensors have played an important and challenging role for the rapid detection of the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Previous studies have shown that the kinetic binding reaction of the target antigen is strongly affected by process parameters. The purpose of this research was to optimize the performance of a microfluidic biosensor using two different approaches: Taguchi optimization and artificial neural network (ANN) optimization. Taguchi L8(25) orthogonal array involving eight groups of experiments for five key parameters, which are microchannel shape, biosensor position, applied alternating current voltage, adsorption constant, and average inlet flow velocity, at two levels each, are performed to minimize the detection time of a biosensor excited by an alternating current electrothermal force. Signal to noise ratio ( ) and analysis of variance were used to reach the optimal levels of process parameters and to demonstrate their percentage contributions, in terms of improved device response time. The principal results of this study showed that the Taguchi method was able to identify that the kinetic adsorption rate is the most influential parameter at 93% contribution, and the reaction surface position is the least influential parameter at 0.07% contribution. Also, the ANN model was able to accurately predict the optimal input values with a very low prediction error. Overall, the major conclusion of this study is both the Taguchi and ANN approaches can be effectively utilized to optimize the performance of a microfluidic biosensor. These advances have the potential to revolutionize the field of biosensing.

Determining the range of process parameters for friction stir welding using steepest ascent approach

In the present research work, friction stir welding (FSW) of dissimilar aluminium alloy series (i.e., 7xxx and 8xxx) is performed at various rotational speeds (1900–2500 rpm), welding speeds (60–80 mm min−1), and tilt angle (1.8°–2.4°) to find out the optimal ranges of process parameters for main experiments. The experiments are designed as per the steepest ascent approach by using three factors two-level fractional factorial design. The tensile strength is considered as an output parameter for deciding the optimal levels of process parameters. The graphical analysis of the steepest ascent provides the optimal ranges of process parameters, i.e., 1818 to 2104 rpm for rotation speed, 42 to 63 mm min−1 for tool travel speed and 0.9 to 1.8° for tool tilt angle. Furthermore, microstructural and fractography analyses are also carried out to study the morphology and nature of joints. The microstructure shows the different welding zones such as the nugget zone, thermomechanical affected zone, and heat affected zone. The fractography analysis reveals the ductile nature of joint.

Process parameter optimisation of fused deposition modelling for augmenting the strength of nylon-aramid PMC’s

In mechanical parts and components, the strength of the material plays a vital role for its effective functioning. For certain individual materials, the enhancement on its quality of strength must be performed at the initial development stage and can rarely be performed using post processing techniques. 3-D printed Polymer Matrix Composites (PMC’s) are one such material. The current research focuses on augmenting the strength of nylon-aramid PMC’s, using Fused Deposition Modelling (FDM) 3-D printing technique. The 3-D printed material was tested according to ASTM standards for mechanical properties such as compression, tensile, flexural and impact strength. Since the association amongst the mechanical properties and the processing parameters of FDM is challenging to verify, an effort was attempted to obtain an empirical formula using the Taguchi Technique of Optimisation. The influencing processing parameters in the FDM technique, such as infill part, layer thickness, print temperature, raster angle, infill pattern style and density were optimised using Taguchi analysis combined with Multi-Response Performance Index (MPI), to enhance the Tensile, Flexural, Impact and Compression Strength, thereby augmenting the total strength of the FDM component. Thedevelopment of the FDM component used these optimised levels again. The mechanical properties were validated according to the ASTM standards of testing. The optimisation technique using T-DoE indicated that the similarity between the computed values from the regression equation and the measured with minimalistic deviations. The research concluded that for a print temperature of 300 °C, layer thickness of 0.4 mm, raster angle of 90°, infill part density of 90 %, and having a rectilinear style of infill pattern, provided an optimal levels of process parameters for augmenting the strength of 3-D printed Nylon-Aramid PMC’s.

Machinability analysis and optimisation of EDM in AA6082/3 wt% BN/1 wt% MoS2 hybrid composites using entropy method weights integrated with complex proportional assessment (COPRAS) method

An investigation was made on composites containing AA6082 as matrix and boron nitride/molybdenum disulphide (BN/MoS2) as reinforcement, which was a composite materials hybrid at 3 wt% of BN and 1% MoS2 and fabricated by stir casting methodology. Using electrical discharge machining (EDM) equipment, machinability studies were undertaken to achieve an optimal condition on process parameters. Taguchi (L27 orthogonal array)-based design of experiments was employed to examine the influence of process parameters like pulse on time (Ton), peak current (IP), and gap voltage (V) on material removal rate (MRR), circularity (CIRC), electrode wear rate (EWR), and cylindricity (CYLD). Multi-criteria decision-making (MCDM) methodology was employed for the process parameters while machining AA6082/3 wt% BN/1% MoS2 hybrid composites. An entropy-based objective weights method was integrated with the complex proportional assessment (COPRAS) approach to assess the most excellent optimal level of process parameters. The entropy (Wj) weights were 0.1285, 0.1002, 0.5707, and 0.2005 for MRR, EWR, CIRC, and CYLD. Optimal conditions are obtained from the COPRAS method of relative significance score, and quantitative utility assessment score in the nineteenth experiment having the 15 A, 50 μs, and 30 V with the relative significance score of 0.0735 and a higher quantitative utility score of 100% being achieved. Using an SEM, the machined surface is analysed for the micro-structural examination. At higher peak current (IP), micro-voids and craters with poor surface finish were located.

Effect of process parameters on surface roughness, dimensional accuracy and flatness of VeroBlue RGD840 rigid opaque materials

Abstract In the present scenario, additive manufacturing is considered to produce the functional parts in low volume. This investigation deals with the effect of process parameters viz., raster angle, orientation and type of surface on dimensional accuracy, flatness, shrinkage and surface roughness of VeroBlue RGD840 rigid opaque materials, produced by the polyjet process. A Taguchi array (L 8) is used to design the experiment. Surface roughness parameters are measured by using an optical profilometer. Dimensional accuracy and flatness are measured by using coordinate measuring machines. Analysis of variance is employed to evaluate the effect of individual process parameters. An empirical model is developed to predict the dimensional accuracy, flatness and surface roughness. Results revealed that the optimum level of process parameters are different for the dimensional accuracy and flatness. A multi-objective optimization technique is employed to get the best level of process parameters. The belt level of process parameters are raster angle of 60°, orientation flat and glossy type of surface finish. The best value of surface roughness R a = 28 nm, percentage error in dimensional accuracy of 12.13 and flatness of 0.0503 mm are observed. Confirmation tests are performed at the best level of process parameters to verify the results.

EXPERIMENTAL RESEARCH OF PARAMETRIC OPTIMIZATION ON WIRE ELECTRICAL DISCHARGE MACHINING OF AA7075/ZRB2 IN SITU COMPOSITES

The AA7075 composites reinforced with 3, 6, and 9 volume fractions (vol.%) of ZrB2 particles have been successfully fabricated using in situ fabrication method. In this technique, the inorganic salts K2ZrF6 and KBF4 were added to the molten aluminum and retained the melting at 900∘C to form the required vol.% of ZrB2 particles. The aluminum-reinforced metal matrix composites (MMCs) are commonly known as aluminum matrix composites (AMCs). This work carries the effects of various wire electrical discharge machining (WEDM) input process parameters like pulse-on-time ([Formula: see text]), pulse-off-time ([Formula: see text]), wire feed rate (WFR), and gap voltage (GV) on the output responses such as material removal rate (MRR), surface roughness (SR), and Kerf-width ([Formula: see text]) of the fabricated AA7075/(3, 6, and 9) vol.% ZrB2 in situ AMCs. Based on Taguchi’s L9 orthogonal array method, the design of experiments (DoE) was used to carry out the operations on the fabricated AMCs. The optimal level of process parameters was found by the main effects plot for signal-to-noise (S/N) ratio, contour plots, and the analysis of variance (ANOVA). The effect of these process parameters on MRR and SR was analyzed and it is found that the maximum MRR was attained in the optimal parametric combination of GV [Formula: see text][Formula: see text]V; [Formula: see text][Formula: see text][Formula: see text]s; [Formula: see text][Formula: see text][Formula: see text]s; WFR [Formula: see text][Formula: see text]mm/s; Reinforcement [Formula: see text] vol.%, and the minimum SR was attained in the optimal parametric combination of GV [Formula: see text][Formula: see text]V; [Formula: see text][Formula: see text][Formula: see text]s; [Formula: see text][Formula: see text][Formula: see text]s; WFR [Formula: see text][Formula: see text]mm/s; Reinforcement [Formula: see text] vol.%.

Multi response optimization for compressive strength, porosity and dimensional accuracy of binder jetting 3D printed ceramic bone scaffolds

Additively manufactured bone scaffolds have been gaining attention of researchers for past couple of years. Besides biocompatibility, bone scaffolds are expected to exhibit a good combination of porosity, mechanical strength and dimensional accuracy for success of the bone tissue engineering. This paper presents a multi objective optimization for additively manufactured ceramic porous bone scaffold prototypes using hybrid Taguchi based Grey relational analysis approach. Five process parameters namely, layer thickness (LT), build orientation (BO), build position (BP), binder saturation (BS) and delay time (DT) of binder jetting 3D printing process have been optimized for three response parameters, namely compressive strength (CS), measured porosity (MP) and dimensional accuracy (DA) of fabricated porous scaffolds. Initially, experiments are designed and performed as per the Taguchi's L27 orthogonal array and signal-to-noise (S/N) ratios for all the experiments have been calculated. Then Grey relational grades (GRGs) of S/N ratios are computed. Main effect plots for means and S/N ratios of GRGs reveals LT1, BO1, BP2, DT1 and BS2 as optimum levels of process parameters for CS, MP and DA. Analysis of variance of GRGs suggests that BO (40.42%) the most, LT (29.81%) the second most and DT (5.19%) the least significant parameter in affecting the response parameters. The value of GRG at optimum levels of the parameters have been predicted using the mean GRG model and liner regression model. Finally, a confirmation test is performed and result has been compared with the predicted values. A deviation of 3.93% (mean GRG model) and 3.89% (regression model) have been observed between experimental and predicted values of GRGs.

Experimental Investigation of Peak Temperature and Microhardness in Friction Stir Processing of AA6082 T6 using Taguchi GRA

Friction stir processing demonstrated tremendous potential for improving mechanical properties and its success relies on the optimal level of process parameters that result in a flawless stir zone. The present study is an experimental investigation of performance characteristics peak temperature and microhardness on FSPed AA6082-T6. The experiments are designed using the Taguchi L16 orthogonal array by considering four different levels of rotational speed, traverse speed, shoulder diameter, and tilt angle. The grey relational analysis is used for multiple responses to optimise the FSP process parameters and analysis of variance is utilised to establish the relevance of parameters for these responses. According to the findings, the shoulder diameter and traverse speed are the most important determinants for peak temperature and microhardness. It is observed from the experimental analysis that the main reasons for defect formation are either excessive or inadequate heat generation. The multi-response optimal condition is attained at a rotating speed of 1400 rpm, transverse speed of 112 mm/min, shoulder diameter of 25 mm, and tilt angle of 1º.

Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models.

Optimization of Machining Parameters for Wire EDM of AMCs (LM5/ZrO2) using Taguchi Technique

Aluminium alloy with high toughness, hardness, and impact resistance, is increasingly in demand in the aerospace and mechanical industries. Machine tools have seen rapid growth over the past two decades, but they are still not commonly used to their maximum potential. The inability to use machine tools at their peak output thresholds is at the core of the problem. Practicing engineers and researchers were intrigued by the difficulty of reaching a meticulous analysis of effective parameters. AMCs-LM5/3,6,9% ZrO2 fabricated by stir casting process are machined in Wire EDM to choose the optimum parameters by changing the Pulse on Time, Pulse off Time, Gap Voltage and Wire Feed. Achieving a good surface finish, as well as a better MRR and less kerf in the WEDM machining is of paramount importance. The experiments were conceded using the Taguchi’s L27 OA to obtain the desired effects. ANOVA determines the parameters that affect the Wire EDM, and also the relative contribution of machining parameters. The optimum levels of process parameters, for maximum MRR, minimum SR and minimum kerf were calculated. S/N ratio analysis determines the parameter that has the most impact on the response. A confirmation experiment was done for the best combination of parameters.

Parametric optimization of friction stir welding process parameters of dissimilar welded joints using grey relational analysis and desirability function approach

Purpose The main purpose of the present work is to study the multi response optimization of dissimilar friction stir welding (FSW) process parameters using Taguchi-based grey relational analysis and desirability function approach (DFA). Design/methodology/approach The welded sheets were fabricated as per Taguchi orthogonal array design. The effects of tool rotational speed, transverse speed and tool tilt angle process parameters on ultimate tensile strength and hardness were analyzed using grey relational analysis, and DFA and optimum parameters combination was determined. Findings The tensile strength and hardness values were evaluated from the welded joints. The optimum values of process parameters were estimated through grey relational analysis and DFA methods. Similar kind of optimum levels of process parameters were obtained through two optimization approaches as tool rotational speed of 1150 rpm, transverse speed of 24 mm/min and tool tilt angle of 2° are the best process parameters combination for maximizing both the tensile strength and hardness. Through these studies, it was confirmed that grey relational analysis and DFA methods can be used to find the multi response optimum values of FSW process parameters. Research limitations/implications In the present study, the FSW is performed with L9 orthogonal array design with three process parameters such as tool rotational speed, transverse speed and tilt angle and three levels. Practical implications Aluminium alloys are widely using in automotive and aerospace industries due to holding a high strength to weight property. Originality/value Very limited work had been carried out on multi objective optimization techniques such as grey relational analysis and DFA on friction stir welded joints made with dissimilar aluminium alloys sheets.

Friction stir welding and friction stir spot welding of polymethyl methacrylate (PMMA) to other materials: A review

Polymers are widely used in packaging and automobile industries because of their desirable properties such as lightweight, transparency, strength to weight ratio, good hardness and stiffness, low water absorption, ultraviolet rays' protection. Keeping Polymethyl methacrylate (PMMA) polymer in focus, this review research is being done here to collect the literature available best of our knowledge on the joining of PMMA to PMMA/Polymers/ Metal alloys by Friction stir welding/ Friction stir spot welding (FSW/FSSW). Friction stir welding is a solid-state joining process in which the temperature remains below the material's melting temperature. This friction stir welding technique is widely applied on materials that are difficult to weld by fusion welding, such as aluminium alloys, titanium alloys, and polymers. Friction stir welding is an ecological weld method and environmentally friendly as it does not produce harmful radiation toxic gases, requires less energy, produces less waste than conventional fusion welding processes. It was found out that by applying Friction stir welding/ Friction stir spot welding technique, the Polymethyl methacrylate can be welded to itself, to polymers, and metal alloys. In this review, efforts are made to point out the optimum level of process parameters of FSW concluded by researchers in their studies of PMMA joining. For each study, its parameter range and the optimum value of process parameters are presented in the form of tables and graphs, and explanations and discussions are made to better understand welding Polymethyl methacrylate polymer by Friction stir welding process.

Optimization of single point incremental forming process through experimental investigation on SS 304 DDQ steel

Sheet Metal Single Point Incremental Forming (SPIF) is a flexible novel forming process to meet the growing demands of the manufacturing industry and the development of latest sheet metal based products. The SPIF process is used for rapid prototyping and sheet metal forming in batch production and is more economical than traditional stamping processes. The current study looks into the effects of process variables such as rpm, feeds, and step size on forming time, surface quality, and formed part thinning. Octagonal shape of part is formed from SS 304 DDQ steel sheet of 1 mm thickness. This process is performed by the CNC Vertical Machining Center (VMC). For this task, the fixture used for forming and tools with hemispherical edges are designed and manufactured. Experiments are performed based on design of experiments using orthogonal L9 arrays with Taguchi methods. The ideal process factors on response, such as forming time, surface roughness, and formed part thinning, were determined using analysis of variance (ANOVA). To determine the optimal level of process parameters, the signal-to-noise ratio (S/N) is used. The results show that step size has the greatest impact on forming time, followed by feed rate and a minimal effect from rotational speed. The feed rate has been found to have the greatest impact on the part's surface roughness, followed by speed and step size. Furthermore, the most important parameter that affects thinning of the created portion is step size, which is followed by rate of feedand spindlespeed in controlling overall variability.

OPTIMIZATION OF MACHINING PARAMETERS OF LOW CARBON STEEL FOR TURNING OPERATION USING TAGUCHI ANALYSIS

Cutting fluids play an important role in machine operations, tool life, workpiece quality, and overall productivity, because of their ability to prevent workpiece and cutting tool excessive heating. This paper investigates the optimal parameters for turning low carbon steel using formulated orange seed oil cutting fluid. The three process parameters considered in this paper were spindle speed, feed rate, and depth of cut. Orthogonal arrays, the signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) were used to find the optimal process parameter levels and analyze the effect of these parameters on surface roughness and flank wear. Following that, a confirmation test was run to compare the predicted results to the experimental results. From the results, the best S/N ratio of the main effect plot for the surface roughness stood at 180 rpm spindle speed, 0.105 mm/rev feed rate and 1.5mm depth of cut with R-Sq = 95.64% R-Sq(adj) = 89.09%. The Taguchi method has revealed that the feed rate has a significant role to play in producing the best surface quality and this was achieved at 64.50% of the contribution, followed by the spindle speed at 29.10% of the contribution, and the depth of cut at 20% of the contribution has the least role to play in producing the best surface quality. For flank wear, the S/N ratio of the main effect plot stood at 180 rpm spindle speed, 0.105 mm/rev feed rate and 1.0 mm depth of cut with R2 = 96.43%, R2(adj) = 91.07%. The least flank wear was obtained at 60.38% contribution of feed rate, 35.62% contribution for spindle speed and 0.43% contribution for depth of cut.

Optimization of casting parameters for improved mechanical properties of eggshell reinforced composites

Abstract The tensile strength, hardness and toughness of any materials are the most important mechanical properties in the selection of materials for varied industrial applications. In the development of aluminum based composite material, tensile strength and hardness were significantly improved by adding various ceramic reinforcement particles. However, toughness was reduced. In this research work, an attempt was made to enhance tensile strength, hardness and toughness simultaneously by using carbonized eggshell as reinforcement material developed via electromagnetic stir casting. The process parameters used in this study are the matrix pouring temperature, wt.-% of the reinforcement; preheat temperature, stirring current and stirring time. Response surface methodology (RSM) is used for conducting the experiment. The multi-objective optimization technique utility theory is employed to optimize the combined mechanical properties viz. tensile strength, hardness and toughness. Microstructure results show that at the optimum level of process parameters, eggshells are uniformly distributed. Confirmation tests are conducted to validate the experimental results. Experimental results revealed that at optimum process parameters, hardness and tensile strength are significantly improved without affecting the toughness property of the composite. The optimum level of process parameters to enhance all mechanical properties (tensile strength, hardness and toughness) were found to be a reinforced preheating temperature of about 448.32 °C, a stirring current of about 11.64 A, a stirring time of about 63.64 s, a maximum pouring temperature of about 800.24 °C and a percentage of eggshells of about 9.16 %. The novelty of this work lies in the fact that no attempt was made to optimize these electromagnetic process parameters. Corrosion loss, thermal expansion behavior and a wear test were investigated to observe the effect of adding eggshell at optimum electromagnetic stir casting parameters.

WITHDRAWN: Optimization of process parameters by Hybrid Taguchi-Grey Relational Analysis for thermal behaviours of lubricant oil of worm gearbox

In this research work, the major focus is given on thermal behaviours of lubricant oil of worm gearbox. In worm gearbox due to sliding motion between teeth's of worm and worm wheel cause friction energy loss. The friction energy loss is converted into heat energy and that heat energy is dissipated into lubricant oil of worm gearbox. The performance of lubricant oil is decreasing due to the rise in temperature which causes decreases in the overall performance of gearbox. To observe thermal behaviour of lubricant oil of worm gearbox Lubricant Heating Time (time required to increase the temperature of the oil from 35 °C to 50 °C) and the Viscosity of oil (at 50 °C) is taken as response parameters while Load on the worm wheel (L), Volume of oil (V) and Type of oil (T) are taken as process parameters. Every process parameter is varied at three dissimilar levels. Taguchi optimization technique (L9) is used for the optimization of experiments for this Minitab19 software is used. Grey Relational Analysis (GRA) is used to determine the optimum levels of process parameters. The optimum levels of process parameters for maximum Lubricant Heating Time and maximum Viscosity are L1V2T2 i.e. the lowest level of Load on the worm wheel, intermediate level of Volume of oil and Type of oil B. ANOVA is performed to determine the relative contribution of process parameters on Lubricant Heating Time. ANOVA results show that Type of oil is the maximum influence parameter (98.26%) followed by Load on the worm wheel (0.99%) and Volume of oil (0.72%) on Lubricant Heating Time. There exist an inverse relationship between oil viscosity and lubricant heating time.