Practical Application of Taguchi and Hybrid Taguchi Methods for Optimization of Processing Parameters of Manufacturing Process for Improved Part Quality: A Review

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 6, Issue 7, July 2016)

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Practical Application of Taguchi and Hybrid Taguchi

Methods for Optimization of Processing Parameters of

Manufacturing Process for Improved Part Quality: A Review

Nitin Kumar Rathi

1

, Dr. Sunil Punjabi

2

, Dr. M. L. Jain

3

1_{Ph.D. Scholar (UEC, Ujjain), Associate Professor (MED, AITR, Indore), India} 2

Associate Professor (Mech), UEC Ujjain, India 3 _{Professor (Mech), SGSITS, Indore, India}

Abstract— The purpose of product or process development is to improve the performance characteristics of the product or process relative to customer needs and expectations. Process optimization is the discipline of adjusting a process so as to optimize some specified set of parameters without violating some constraint. The most common goals are minimizing cost and maximizing throughput and/or efficiency. This is one of the major quantitative tools in industrial decision making. When optimizing a process, the goal is to maximize one or more of the process specifications, while keeping all others within their constraints. This can be done by using an optimization tool/technique, discovering the critical activities and bottlenecks, and acting only on them. This paper aims to review the research of the practical use of Taguchi method in the optimization of processing parameters for various manufacturing process. Taguchi method has been employed with great success in experimental designs for problems with multiple parameters due to its practicality and robustness. However, it is realized that there is no single technique that appears to be superior in solving different kinds of problem. Improvements are to be expected by integrating the practical use of the Taguchi method into other optimization approaches to enhance the efficiency of the optimization process. The review will shed light on the standalone Taguchi method and integration of Taguchi method (Hybrid Taguchi) with various other approaches.

Keywords— Manufacturing or Machining Optimization, Taguchi Method, Quality Engineering

I. INTRODUCTION

Quality Engineering requires systematic experimentation with carefully developed prototypes whose performances are tested in actual field conditions. The objective of quality engineering is to discover optimum values of various design parameters to ensure the consistent performance of the product / process in actual use. Measuring a fraction of products outside the specified limits, a measure of the quality loss due to scrap, miserably fails as a predictor of customer satisfaction.

Reducing the specification limits, improving the on-line quality control to bring the specified units closer to the target and inspection of more samples in order to find the defective products before they reach the customers have been widely practiced in controlling the quality of various products. But such exercises are, often, not considered as good options since they address the symptoms and not the root cause of the problems. Many experimental designs have been demonstrated in the field of experimental statistics for characterization and optimization of various design factors. Typically, the performance of any process or product is affected by a multitude of factors. Since every performance cannot be predicted by theory, experimentation or prototyping is resorted for empirical optimization of the various process and products before launch.

Quality in Taguchi’s perspective

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Taguchi’s view on the nature of the quality loss function represents a fundamental paradigm shift in the way in which manufacturers consider product made according to print, with in permitted tolerances, is of high quality. The traditional approach employed has been to use “step function”. This is the “goalpost” syndrome. What are missing from this philosophy are the customer’s requirements. [42].Taguchi methodology states that even the best available manufacturing technology by itself is not an assurance that the final product will, actually, function in the hands of its users as desired and so strongly advocated for the engineered products with robust performance. Taguchi described entire concept in two basic ideas, namely, quality should be measured by the deviation from a special target value, rather than by conformance to preset tolerance limits and quality cannot be ensured through the inspection and rework, but must be built-in through the appropriate design of the process and product. The first concept underlines the basic difference between Taguchi methods and statistical process control (SPC) methodology. While SPC emphasizes the attainment of an attribute within tolerance range, Taguchi methods emphasize the attainment of the specified target value and the elimination of variation. [43].Taguchi’s contributions to quality engineering include loss function associated with a product or process, robust design and simplified statistical experiments using orthogonal arrays.

Loss function

The essence of loss function advocated by Taguchi can be stated as follows: deviation of a product from the target performance generates loss to the society that varies with reference to the extent of variation. The loss is proved to be minimum when performance coincides with target and increases gradually as it deviates from the target. The Taguchi’s loss function establishes a financial measure of the user dissatisfaction with a product’s performance as it deviates from a target value that is often, overlooked by other experimental designs. Taguchi believes that the customer becomes increasingly dissatisfied as the performance departs farther away from the target. According to Taguchi, the cost of quality in relation with the deviation from the target is not linear because the customer’s frustration increases at a faster rate as more defects are found on a particular product and a quadratic curve to represent the customer’s dissatisfaction with a product’s performance (Figure 1). Based on the Taylor series approximation, the loss function increases as the quality characteristics deviates on either sides of the target value.

Figure-1 Taguchi’s Loss Function

II. ABRIEF REVIEW OF THE WORK ALREADY DONE IN

THE FIELD &NOTEWORTHY CONTRIBUTIONS

Taguchi’s major contribution has involved combining engineering and statistical methods to achieve rapid improvements in cost and quality by optimizing product design and manufacturing processes. The success of many applications has demonstrated the power of Taguchi’s overall approach.

A.Optimisation of Plastic Injection Moulding Process

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It was found that there is a strict correlation between processing parameters and the quality of the injection moulded parts where an optimal combination of processing parameters can lead to significant improvement of the part quality.

Reference [21] reduced the warpage of an injection-moulded bus ceiling lamp base by 46.5% with the optimum values of processing parameters, including the mould temperature, melt temperature, packing pressure, packing pressure time, and cooling time. Another study with similar aim, which was conducted by [22], also indicated that the optimal combination of processing parameters managed to reduce the warpage of the initial thin shell plastic model significantly by 51%. In another study, [23] investigated the influence of processing parameters on shrinkage and warpage of a cellular phone casing. It was found that packing pressure is the most important parameter compared to other studied factors such as mould temperature, melt temperature, and injection speed. When the packing pressure was increased, the shrinkage and warpage were reduced drastically. In contrast, improper setting of processing parameters may induce devastating defects on the products, such as warpage, shrinkage, sink mark, and residual stress [24].

The purpose of this research [25] is to present an array of literature related to the broad use of Taguchi method in the determination of process parameters for plastic injection moulding. The review will shed light on the application of Taguchi method that, if taken into considerations, is expected to improve the effectiveness of the optimization by incorporating with other techniques. Prior studies are organized into two categories, namely, standalone Taguchi method and integration of Taguchi method with various approaches including numerical simulation; grey relational analysis (GRA), principal component analysis (PCA), artificial neural network (ANN), and genetic algorithm (GA).The features, advantages, and connection of these approaches are reviewed in this research.

B.Optimization of Rapid prototyping Process

In the study [27], the Taguchi method, a powerful tool to design optimization for quality, is used to find the optimal process parameters for fused deposition modeling (FDM) rapid prototyping machine that was used to produce acrylonitrile butadiene styrene (ABS) compliant prototype. An orthogonal array, main effect, the signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are employed to investigate the process parameters in order to achieve optimum elastic performance of a compliant ABS prototype so as to get maximum throwing distance from the prototype.

Through this study, not only can the optimal process parameters for FDM process be obtained, but also the main process parameters that affect the performance of the prototype can be found. Experiments were carried out to confirm the effectiveness of this approach. From the results, it is found that FDM parameters, i.e. layer thickness, raster angle and air gap significantly affect the elastic performance of the compliant ABS prototype. The optimum levels of parameters at different angle of displacement are also presented. In the present work [28], effect of five factors viz., layer thickness, part build orientation, raster angle, raster to raster gap (air gap)and raster width each at three levels together with the interaction of part build orientation with all the other factors is studied on the dimensional accuracy of FDM build part. Taguchi’s design of experiment is used to find the optimum factor levels and significant factors and interactions .Several attempts have been made to improve the part accuracy, surface finish, strength etc. by proper adjustment of process parameters by numerous researchers [29-31].

These works reveal that properties of RP parts can be significantly improved with proper adjustment of build parameters without incurring additional expenses for changing hardware and software. Further, literature suggests that studies on effect of process parameters in improving quality of FDM built parts, specifically, dimensional accuracy, have been devoted to a limited extent. Therefore, there is a need for in-depth study to understand process parameters and their interaction effects on responses like accuracy of dimensions in different directions of FDM built parts. Taguchi’s parameter design is adopted; not only to reduce the number of experiments but also identify the influencing factors and their interactions responsible for minimization of percentage change in dimensions of test parts.

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C.Optimisation of Milling Process

In order to build up a bridge between quality and productivity, the present study highlights optimization of CNC end milling process parameters to provide good surface finish as well as high material removal rate (MRR). The surface finish and material removal rate have been identified as quality attributes and are assumed to be directly related to productivity. An attempt has been made to optimize aforesaid quality attributes in a manner that these multi-criterions could be fulfilled simultaneously up to the expected level. This invites a multi-objective optimization problem which has been solved by PCA based Taguchi method.

To meet the basic assumption of Taguchi method; in the present work [32], individual response correlations have been eliminated first by means of Principal Component Analysis (PCA). Correlated responses have been transformed into uncorrelated or independent quality indices called principal components. The principal component, imposing highest accountability proportion, has been treated as single objective function for optimization (multi-response performance index). Finally Taguchi method has been adapted to solve this optimization problem. The aforesaid methodology has been found fruitful in the cases where simultaneous optimization of huge number of responses is required. In this paper [33], Taguchi method is applied to find optimum process parameters for end milling while hard machining of hardened steel. A L18 array, signal-to-noise

ratio and analysis of variance (ANOVA) are applied to study performance characteristics of machining parameters (cutting speed, feed, depth of cut and width of cut) with consideration of surface finish and tool life. Chipping and adhesion are observed to be main causes of wear. Results obtained by Taguchi method match closely with ANOVA and cutting speed is most influencing parameter. Multiple regression equations are formulated for estimating predicted values of surface roughness and tool wear.

D.Optimisation of Grinding Process

This research paper [34] has presented application of taguchi method to determine the optimal process parameters for cylindrical grinding process. The concept of ANNOVA and S/N ratio is used to determine the effect and influence of process parameters namely work speed, feed rate and depth of cut is studied on output responses, and found that the developed model is significant.

E.Optimisation of Gear Hobbing Process

The research paper [35] presents an overview of Taguchi experiments with multiple responses, a discussion of results from a case study on a gear hobbing machining process, an introduction to genetic algorithm based inference and determination of weighted SN ratios (WSN) using SN ratio and GA for the same case study and, finally, an analysis of variance to identify the most critical factors or parameters that influence the WSN.

F.Optimisation of Boring Process

This study [36] investigated the optimization of computer numerical control (CNC) boring operation parameters for aluminum alloy 6061T6 using the grey relational analysis (GRA) method. Nine experimental runs based on an orthogonal array of Taguchi method were performed. The surface properties of roughness average and roughness maximum as well as the roundness were selected as the quality targets. An optimal parameter combination of the CNC boring operation was obtained via GRA. By analyzing the grey relational grade matrix, the degree of influenced for each controllable process factor onto individual quality targets can be found. The feed rate is identified to be the most influence on the roughness average and roughness maximum, and the cutting speed is the most influential factor to the roundness. Additionally, the analysis of variance (ANOVA) was also applied to identify the most significant factor; the feed rate is the most significant controlled factor for the CNC boring operations according to the weighted sum grade of the roughness average, roughness maximum and roundness.

G.Optimisation of Turning Process

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Experiments are designed and conducted based on Taguchi’s L27 orthogonal array design. The turning parameters are cutting speed, feedrate, depth of cut and nose radius and the responses are surface roughness and power consumption. Taguchi’s signal-to-noise (S/N) ratio are determined based on their performance characteristics. A grey relation grade is obtained by using S/N ratio. Based on grey relational grade value, optimum levels of parameters have been identified by using response table and response graph and the significant contributions of controlling parameters are estimated using analysis of variances (ANOVA). Confirmation test is conducted for the optimal machining parameters to validate the test result. The proposed method is having prediction accuracy and competency. This method may be extended to other machining processes.

H.Optimisation of Abrasive waterjet machining (AWJM) Process

In the present work [44], the optimization of the abrasive water jet machining (AWJM) process parameters with multiple performance characteristics based on the orthogonal array with the grey relational analysis (GRA) has been studied. Optimization of multiple response characteristics is far more complex compared to optimization of single performance characteristic. A grey relational grade (GRG) calculated based on grey relational analysis is used to optimize the AWJM process with the multiple performance characteristics. In the present study, four machining parameters, namely hydraulic pressure, abrasive mass flow rate, standoff distance and traverse rate are optimized with consideration of multiple performance characteristics viz., surface waviness at four different heights of a Kevlar composite laminate. Experimental results have shown that machining performance in the AWJM process can be improved effectively using this approach.

This paper [45] discussed that; Abrasive water jet cutting (AWJC) is particularly useful for difficult-to-cut materials like composites, super alloys and ceramics. However, the surface finish produced is often poor, necessitating finishing operations like grinding, etc., which lead to delamination in case of polymer composite laminates. Therefore, optimum selection of process parameters is important in AWJC. This paper presents the influence of three process parameters (water jet pressure, abrasive flow rate and quality level) on surface roughness (Ra) in AWJC of Kevlar composites. The

optimal parameter setting was determined using a hybrid Taguchi and response surface method (HTRSM). The experimental results and ANOVA analysis indicate that quality level and water jet pressure have more significant effect on Ra.

A second order response surface model was developed using the central composite rotatable design (CCRD). Finally, confirmation experiments were conducted to verify the predicted and experimental results at optimal settings, and an excellent agreement was obtained between the two.

In this study [46], abrasive waterjet machining of the granite was experimentally investigated for various process parameters in terms of the cut depth and kerf width. The design philosophy of Taguchi was followed to conduct experiments. Analysis of variance was used to evaluate data obtained statistically. Major significant process factors affecting the cut depth and kerf width were determined. Additionally, effects of the process parameters on the cut depth and kerf width were presented by mean responses in detail. As a result of the study, it was determined that the highly effective parameters on the cut depth were the traverse speed, the abrasive flow rate and the abrasive size, although all the process parameters were found to be highly effective on the kerf width of the granite.

This research paper [47] addresses modeling and optimization of the process parameters for this machining technique. To model the process a set of experimental data has been used to evaluate the effects of various parameter settings in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. Depth of cut, as one of the most important output characteristics, has been evaluated based on different parameter settings. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. The pair wise effects of process parameters settings on process response outputs are also shown graphically. The proposed model is then embedded into a Simulated Annealing algorithm to optimize the process parameters. The optimization is carried out for any desired values of depth of cut. The objective is to determine proper levels of process parameters in order to obtain a certain level of depth of cut. Computational results demonstrate that the proposed solution procedure is quite effective in solving such multi-variable problems.

I. Optimisation of wire electrical discharge machining (WEDM) Process

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In this paper [39], the effects of various process parameters of WEDM like pulse on time (TON), pulse off time (TOFF), gap voltage (SV), peak current (IP), wire feed (WF) and wire tension (WT) have been investigated to reveal their impact on material removal rate of hot die steel (H-11) using one variable at a time approach. The optimal set of process parameters has also been predicted to maximize the material removal rate.The experimental studies were performed on ELECTRONICA SPRINTCUT WEDM machine. The material removal rate (MRR) directly increases with increase in pulse on time (TON) and peak current (IP) while decreases with increase in pulse off time (TOFF) and servo voltage (SV).

In this paper [40], wire electrical discharge machining of WC-Co composite is studied. Influence of taper angle, peak current, pulse-on time, pulse-off time, wire tension and dielectric flow rate are investigated for material removal rate (MRR) and surface roughness (SR) during intricate machining of a carbide block. In order to optimize MRR and SR simultaneously, grey relational analysis (GRA) is employed along with Taguchi method. Through GRA, grey relational grade is used as a performance index to determine the optimal setting of process parameters for multiple machining characteristics. Analysis of variance (ANOVA) shows that the taper angle and pulse-on time are the most significant parameters affecting the multiple machining characteristics. Confirmatory results, proves the potential of GRA to optimize process parameters successfully for multi-machining characteristics.

J. Optimisation of Welding Process

This paper reports [41] on an investigation of the effect and optimization of welding parameters on the tensile shear strength in the resistance spot welding (RSW) process. The experimental studies were conducted under varying electrode forces, welding currents, electrode diameters, and welding times. The settings of welding parameters were determined by using the Taguchi experimental design method. The level of importance of the welding parameters on the tensile shear strength is determined by using analysis of variance (ANOVA). The optimum welding parameter combination was obtained by using the analysis of signal-to-noise (S/N) ratio. The confirmation tests indicated that it is possible to increase tensile shear strength significantly by using the Taguchi method. The experimental results confirmed the validity of the used Taguchi method for enhancing the welding performance and optimizing the welding parameters in the resistance spot welding process.

K.Optimisation of Sand Casting Process

In this paper [48] an analysis of significant process parameters of green sand casting process is made. Green casting process involves many process parameters which affect the quality of casting produced. The parameters considered are green strength, moisture content, permeability and mould hardness. Using taguchi analysis the effect of various process parameters at different levels on casting quality is analysed and optimal settings of the various parameters have been accomplished. The outcome of this paper is the optimized process parameters of the green sand casting process which leads to improved process performance, reduced process variability and thus minimum casting defects. Also a neural network model is developed to map the complex non-linear relationship between process conditions and quality characteristics, namely castings defects.

III. CONCLUSION

The challenge of modern machining or manufacturing industries is mainly focused on the achievement of high quality, in terms of product characteristics as desired by the customers. Dimensional accuracy, form stability, surface smoothness, fulfillment of functional requirements in prescribed area of application etc. are important quality attributes of the product. Manufacturing industries have long depended on the skill and experience of shop-floor machine-tool operators for optimal selection of cutting conditions and cutting tools. Considerable efforts are still in progress on the use of handbook based conservative cutting conditions and cutting tool selection at the process planning level. The most adverse effect of such a not-very scientific practice is decreased productivity due to sub-optimal use of machining capability.

Taguchi method is a powerful tool for the design of high quality systems. It provides simple, efficient and systematic approach to optimize designs for performance, quality and cost. Taguchi method is efficient method for designing process that operates consistently and optimally over a variety of conditions. To determine the best design or process it requires the use of a strategically designed experiment. Taguchi approach to design of experiments is easy to adopt and apply for users with limited knowledge of statistics hence gained wide popularity in the engineering and scientific community.

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