Taguchi Robust Design Research Articles

Development of geopolymer from gold mine tailings utilizing Taguchi robust design

Development of geopolymer from gold mine tailings utilizing Taguchi robust design

Taguchi Design of Experiments to Optimize Material Removal Rate in Turning AISI 4340 Steel

In this work, an effort is prepared to optimize turning process by maximising the material removal rate (MRR). For experimentation, CNC Spinner 15 lathe machine is used. The input factors are spindle speed, feed rate, depth of cut and type of tool under the 3 altered values of noise factor. Output parameter is MRR. The work piece material is AISI4340 steel. The Taguchi robust design is achieved by implementing spindle vibration as a noise factor. The orthogonal Array L9 is used for experimentation. The MRR is calculated by the defined formula. The data analysis is done by Taguchi technique. The Higher-the-better signal-noise ratio is adopted to compute the quality characteristics S by N fraction of MRR for every run of experiment. Response table is then computed. The main effect plot is the base to discover the best situation of the response parameter. ANOVA is used to guess the significant parameters. The best setting is A-1 B-2 C-2 D-3 and E-3) i.e. spindle speed 780rpm, feed rate = 2.42 mm/rev, coated CVD tool, nose radius=1.2 mm whereas depth of cut = 1.5 mm.

Orthogonal array and artificial neural network approach for sustainable cutting optimization machining of 17-4 PH steel under CNC wet turning operations

Sustainable manufacturing strives to increase output while minimizing resource usage, costs, and environmental impact. Tool longevity is crucial, considering material, power, and resource consumption. Challenges like chip removal, heat, and friction necessitate effective solutions. Cutting fluids, like rice bran oil, reduce temperatures, friction, and enhance tool life and surface quality. Research focuses on using rice bran oil with a TiAlN tipped tool to minimize friction and cutting forces. Taguchi's robust design and L-9 orthogonal array reduce experimental trials. Controllable factors include speed, feed, depth of cut, and rice bran oil flow rate, with surface roughness as the performance metric. Taguchi analysis improves turning performance, particularly with 17-4 PH stainless steel. Rice bran oil reduces frictional forces, enabling improved surface quality across all samples, promoting sustainability. Surface roughness (Ra) is notably influenced by feed rate and rice bran oil, contributing 58.06% and 49.5%, respectively. This research underscores the potential of optimizing cutting fluid and process parameters to enhance sustainability in manufacturing.

Experimental Study on Warpage Phenomenon of Wax Parts Manufactured by Fused Filament Fabrication.

Warpage is one of the prominent issues in Fused Filament Fabrication. The cause of this is the rapid cooling of the polymer during extrusion. The residual thermal stresses accumulated within the print part result in a shape distortion and subsequent detachment of the object from the print bed. In this study, both experimental and numerical approaches were used to identify the stresses due to thermal shrinking that occurs in soft polymers such as wax. A temperature sweep test was performed using a rotational rheometer to measure the magnitude of axial forces that are generated due to the thermal shrinking of a thin layer of 3D printable wax. The thermal stresses responsible for warpage were computed analytically and using the FEA. It was found that due to thermal processes, the stress magnitude can reach a value of 1.17 MPa. This value is enough to cause the plastic deformation in the wax part having a thin elongated shape. In addition, Taguchi's robust design has identified two major FFF parameters that impact the warpage in amorphous soft polymers. They are the printing speed and the print bed temperature. To achieve a low level of warpage, it is important to make sure that the layer deposition occurs at medium speeds and the print bed temperature is moderately high according to the findings of this study.

Influential factor analysis of slag-based engineered cementitious composites using Taguchi robust method

ABSTRACT This study presents experimental and analytical investigations of the mechanical properties and cracking patterns of slag-based engineered cementitious composites (S-ECCs) incorporating polyethylene fiber (PE). The Taguchi robust design (TRD) method was adopted to design the variables and systematically analyze the results. Various factors including slag, crumb rubber particles, water, and PE fiber contents were considered to evaluate the positive and negative influences on compressive strength, tensile properties, and cracking patterns of S-ECCs. In the experimental results, most of the composites showed highly ductile behavior. Amounts of crumb rubber particles and water were two prominent factors that had strong influences on the mechanical properties of S-ECCs. An analytical model based on multiple regression analysis for obtaining high-strength and high-ductility S-ECCs provided best-performing S-ECC mixture and differences between the experimental and predicted values were less than 5%.

Evaluation and optimization of mechanical properties of laterized concrete containing fly ash and steel fiber using Taguchi robust design method

The present study employs the S/N ratio and multiple regression analysis to evaluate the mechanical properties of laterized concrete with fly ash and steel fiber. The study considers compressive, split tensile, and flexural strengths as responses at 7 and 28 days. The Taguchi L16 (4 *4) orthogonal array experimental design is applied to optimize the performance of mechanical properties of concrete by using the design of experiments. The replacement of 25% of the granite aggregate with laterite aggregate resulted in a 12.44% increase in the strength of M30-grade concrete after 28 days. The composite's compressive strength decreases when the replacement level exceeds 25%. The split tensile and flexural strengths of the composite showed an increase of 14.54% and 16.93%, respectively, compared to the control mix. Furthermore, the test outcomes for durability factors like drying shrinkage and rapid chloride penetration in steel fiber reinforced fly ash-based lateritic concrete mixes with a 50% replacement rate adhere to the prescribed standards, verifying their designed performance over the designated structural lifespan. However, after multiple regression analysis, it is found that the error related to the correlation between experimental and analytical value strength is about 5%. Microstructural studies of the ideal mix were performed at 28 days to further understand the morphology of the laterized concrete containing fly ash and steel fiber.

Enhancing surface quality of en31 steel using Taguchi robust design

Enhancing surface quality of en31 steel using Taguchi robust design

Ensemble model for predicting chronic non-communicable diseases using Latin square extraction and fuzzy-artificial neural networks from 2013 to 2019

BackgroundThe presented study tracks the increase or decrease in the prevalence of seventeen different chronic non-communicable diseases in Serbia. This analysis considers factors such as region, age, and gender and is based on data from two national cross-sectional studies conducted in 2013 and 2019. The research aims to accurately identify the regions with the highest percentage of affected individuals, as well as their respective age and gender groups. The ultimate goal is to facilitate organized, free preventive screenings for these population categories within a very short time-frame in the future. Materials and methodsThe study analyzed two cross-sectional studies conducted between 2013 and 2019, using data obtained from the Institute of Public Health of Serbia. Both studies involved a total of 27801 participants. The study compared the performance of Decision Tree and Support Vector Regressor models with artificial neural network (ANN) models that employed two encoding functions. The new methodology for the ANN-L36 model was based on artificial neural networks constructed using a Latin square (L36) design, incorporating Taguchi's robust design optimization. ResultsThe results of the analysis from three different models have shown that cardiovascular diseases are the most prevalent illnesses among the population in Serbia, with hypertension as the leading condition in all regions, particularly among individuals aged 64 to 75 years, and more prevalent among females. In 2019, there was a decrease in the percentage of the leading disease, hypertension, compared to 2013, with a decrease from 34.0% to 32.2%. The ANN-L36 model with Fuzzy encoding function demonstrated the highest precision, achieving the smallest relative error of 0.1%. ConclusionTo date, no studies have been conducted at the national level in Serbia to comprehensively track and identify chronic diseases in the manner proposed by this study. The model presented in this research will be implemented in practice and is set to significantly contribute to the future healthcare framework in Serbia, shaping and advancing the approach towards addressing these conditions. Furthermore, experimental evidence has shown that Taguchi's optimization approach yields the best results for identifying various chronic non-communicable diseases.

Molybdenum Carbide/Ni Nanoparticles Embedded into Carbon Nanofibers as an Effective Non-Precious Catalyst for Green Hydrogen Production from Methanol Electrooxidation.

Molybdenum carbide co-catalyst and carbon nanofiber matrix are suggested to improve the nickel activity toward methanol electrooxidation process. The proposed electrocatalyst has been synthesized by calcination electrospun nanofiber mats composed of molybdenum chloride, nickel acetate, and poly (vinyl alcohol) under vacuum at elevated temperatures. The fabricated catalyst has been characterized using XRD, SEM, and TEM analysis. The electrochemical measurements demonstrated that the fabricated composite acquired specific activity for methanol electrooxidation when molybdenum content and calcination temperature were tuned. In terms of the current density, the highest performance is attributed to the nanofibers obtained from electrospun solution having 5% molybdenum precursor compared to nickel acetate as a current density of 107 mA/cm2 was generated. The process operating parameters have been optimized and expressed mathematically using the Taguchi robust design method. Experimental design has been employed in investigating the key operating parameters of methanol electrooxidation reaction to obtain the highest oxidation current density peak. The main effective operating parameters of the methanol oxidation reaction are Mo content in the electrocatalyst, methanol concentration, and reaction temperature. Employing Taguchi's robust design helped to capture the optimum conditions yielding the maximum current density. The calculations revealed that the optimum parameters are as follows: Mo content, 5 wt.%; methanol concentration, 2.65 M; and reaction temperature, 50 °C. A mathematical model has been statistically derived to describe the experimental data adequately with an R2 value of 0. 979. The optimization process indicated that the maximum current density can be identified statistically at 5% Mo, 2.0 M methanol concentration, and 45 °C operating temperature.

Fuel consumption optimization for a plug-in hybrid electric bus during the vehicle-following scenario

In this paper, a robust SOC (state of charge of battery) trajectory planning method is proposed for a plug-in hybrid electric bus (PHEB) in the vehicle-following scenario. Firstly, the trajectory areas of SOC are planned based on the Taguchi robust design (TRD) considering the noises of the vehicle mass and driving cycles. Moreover, the reliability of the result of TRD is verified based on the Monte Carlo simulation (MCS). Then, the longitudinal driving control of PHEB is transformed into a multi-objective optimal problem that incorporates driving safety, fuel economy and ride comfort in the vehicle-following scenario. Finally, based on a novel nonlinear model predictive control (NMPC), the electric motor, engine and brake system are collaboratively optimized to comprehensively adjust the dynamic and economy of PHEB. The results show that the planning trajectory areas of SOC effectively improved the fuel economy compared with the pre-planning. Furthermore, based on the novel NMPC, the fuel consumption of the proposed method is lower than the method that just adds an energy management strategy to adaptive cruise control (ACC + EMS). The hardware-in-loop test results show that the proposed method can be implemented into the vehicle controller, and the results between the HIL and offline simulations are largely coincident.

Practical hyperparameters tuning of convolutional neural networks for EEG emotional features classification

EEG-based emotional states recognition is an active research direction, rushed up by recent emerging applications of intelligent systems and smart environments. However, accurate EEG-based emotion recognition is still challenging even with the use of the most efficient recent approaches like deep learning. Whereas many studies showed that convolutional neural networks can achieve good results in this field, they did not offer a general guide to subsequent researchers in designing dedicated networks. For practical deployment of deep neural networks in emotion-aware systems, an efficient design method, in terms of hyperparameters selection facilities, is needed. This paper investigates the simplification of the design process of a convolutional neural network applied to a binary and subject-dependent emotional valence classification. The proposed approach is based on two key solutions which are network architecture modularity and procedural tuning method. The proposed tuning approach estimates, in a given search space, the hyperparameter tunability defined as its main effect on the classification accuracy. The search space is depicted by selected hyperparameters levels and a fractional orthogonal array according to the Taguchi robust Design of Experiments method. In this investigation, we include twelve hyperparameters related to the neural network architecture and the training process. The significance of the estimated hyperparameter tunability is validated by statistical variance analysis. Experimental results allowed the determination of the best performance hyperparameters combination, confirmed the well-established effect of some training hyperparameters and revealed the importance of the pooling layer type in our case.

Optimization of Electropolishing Process Using Taguchi Robust Design for UNS N08367 in a Mixed Solution of Sulfuric Acid and Phosphoric Acid

The purpose of this investigation was to determine the optimal conditions for UNS N08367 electropolishing using the Taguchi method. The investigated factors were the electrolyte composition ratio, applied current density, and electrolyte temperature. Each factor was tested at three levels. Electropolishing was optimized using analysis of variance (ANOVA), signal-to-noise ratio (the smaller the better the characteristics), and surface analysis. The ANOVA results showed that among the three factors, only the electrolyte composition ratio was effective in surface planarization. The optimal conditions for electropolishing determined according to the signal-to-noise ratio were a sulfuric acid-to-phosphoric acid ratio of 2:8, a current density of 400 mA/cm2, and an electrolyte temperature of 75 °C.

Optimization of fly ash-based polyethylene fiber-reinforced engineered cement-free composites with low-density and ultra-ductility using Taguchi robust design method

This paper presents an experimental and analytical investigation on the density and mechanical properties of fly ash-based engineered cement-free composites (F-ECFC) reinforced by polyethylene fiber. Three key factors, i.e., curing temperature, curing time, and fiber volume, were investigated to determine their prominent effects on the density, compressive strength, and tensile properties of F-ECFCs using the Taguchi robust design (TRD) method. The test results showed that the F-ECFCs had widely varying compressive strength and tensile properties according to the test variables. Interestingly, some mixtures showed higher tensile strength than compressive strength. Using ANOVA, curing temperature was the most pivotal parameter in terms of influencing the density, compressive strength, and tensile performance of the F-ECFCs. Furthermore, multiple regression analysis was adopted to acquire the optimal mixture of the F-ECFCs with low-density and ultra-ductility. The optimized F-ECFC showed a density of 1.6 g/cm3, a compressive strength of 25.9 MPa, a tensile strength of 8.3 MPa, and a tensile strain capacity of 11.0%.

Istraživanje o obradivosti aluminijuma 7075 u suvom okruženju

Aluminum is a light and soft material that is difficult to machine. It is the most produced non-ferrous metal and undergoes extensive machining for the development of a wide range of products. Advances in industry inspire the need to find sustainable ways of machining aluminum and its alloys using conventional machining processes. In the study reported in this paper, two sets of experiments were conducted to investigate the machinability of aluminum 7075 using a plain carbide tool under a dry environment, i.e., no lubrication. In the first set, four rough experiments were conducted where three important machining parameters, i.e., cutting speed CS (115-495 RPM), depth of cut DOC (0.8-1.5 mm), and Feed rate FR (0.08-0.2 mm/rev) have been varied at two levels each to check the behavior of responses or machinability indicators, i.e., surface roughness and tool wear, at machining parameters' highest and lowest values. Based on the results of the first set of experiments, the ranges and levels of parameters have been fixed in the second set for a detailed study of the machinability of aluminum. A total of nine experiments based upon Taguchi's robust design of experiment technique with orthogonal array L9 have been conducted where an additional machining parameter, i.e., machining time MT, has been introduced. The effect of machining parameters on tool wear and surface roughness has been studied in detail, and it is found that the dry machining of aluminum is possible without the early failure of the tool. Dry machining with low values of CS, DOC, FR, and medium MT is desirable for better machinability, i.e., minimum roughness and tool wear, an optimum combination of machining parameter cutting speed-115 RPM, depth of cut-0.8 mm, feed rate-0.12 mm/rev, and machining time-90 seconds. The findings of the present work will assist engineers and researchers in attaining quality, productivity, and sustainability while manufacturing parts and components from aluminum to be used in the automotive, defense, and aerospace sectors.

The Optimization of Spheroidizing Heat Treatment Parameters on SCM440 Alloy Steel Wires Using Taguchi Robust Design

In the fastener industry, SCM440 alloy steel wire is widely used in the production of cold forging parts, such as high-strength steel fasteners, auto parts, hardware supplies, etc. The wire manufacturers use an intercritical process for spheroidized annealing the alloy steel wires. The quality of a spheroidized annealed wire affects the cold forging quality of fasteners. A series of experimental tests on SCM440 alloy steel wires is carried out in a commercial hydrogen furnace. Various parameters such as spheroidized annealing temperature (A), prolonged heating time (E), holding temperature and time (B, C), cooling rate and temperature (D, F) affect the quality characteristics of wires, such as tensile strength, hardness and ductility. The effects of intercritical spheroidized annealing treatment on mechanical properties are investigated using Taguchi robust design to obtain optimal spheroidized annealing conditions to improve the mechanical properties of alloy steel wires for cold forming. It is experimentally revealed that the spheroidized annealing temperature (A), cooling rate and temperature (D, F) significantly affect the quality of spheroidizing annealed SCM440 alloy steel wires. The optimal spheroidized annealing conditions for SCM440 alloy steel wire are spheroidized annealing temperature of 771°C and prolonged heating for 5.0 h, then holding the temperature of 725°C for 1.0 h, and slowly cooling to the temperature of 713°C at the rate of -9°C /h. Therefore, the optimal mean tensile strength of 520.0 MPa, optimal mean hardness of 80.8 HRB, and optimal mean ductility of 0.488 are obtained. The variations of the properties are significantly reduced to achieve a much even quality for annealed wires. The optimal spheroidizing heat treatment parameter settings evidently improve the performance measures and the cold formability of SCM440 alloy steel wires is effectively improved.

The Tribological Behaviour of Silumin Reinforced with Carbonized Plantain Fibre Ash Particulate for Possible Motorbike Parts Fabrication

The management of waste products may reduce pollution and dumping sites. Therefore, one of the main goals of contemporary research is to recover these wastes through conversion into a more environmentally acceptable material that applies to fabrication engineering. This study demonstrated that waste products such as plantain waste are good reinforcement in the development of composite materials because it was found to be environmentally responsible and economically inventive. Materials made from agricultural waste appear amazing, showing immense potential and boosting green engineering. This research investigates the mechanical behaviours of silumin alloy reinforced with carbonized plantain fibre (CPF) for developing motorbike clutch lever. The ISO standards sieve series specifications of 565 and 3310/1 were employed under the requirements of the American Society for Testing and Materials (ASTM). The different weight percentages of 2.5-10% of CPF were used to advance the investigated matrix composites. The morphology of the surface damage behaviour of the composites was investigated via scanning electron microscopy (SEM). The results of the mechanical property showed an increase in wear behaviour. The best signal-to-noise (SN) ratio, a criterion for evaluating the superiority features, was attained through Taguchi's robust design process and was validated using Minitab18 software. The optimal wear value of 50.20g/min and the control factors are established for the composite.

Improved Gamma-Decalactone Synthesis by Yarrowia lipolytica Yeast Using Taguchi Robust Design Method

The production of gamma-decalactone (GDL) by Yarrowia lipolytica is mainly based on the biotransformation of ricinoleic acid, derived from castor oil triglycerides. The main difficulty in this process is the multitude of factors that determine the growth rate of microorganisms, and thus affect the efficiency of lactone synthesis. In order to improve the technological aspects of GDL biosynthesis in batch culture, the influence of three factors was determined: substrate concentration, medium mixing intensity, and its pH, using the Taguchi solid design method (based on orthogonal array design). On the basis of four bioreactor batch cultures, the most favorable culture conditions in terms of GDL synthesis were selected using the statistical Taguchi method. The statistical method of experimental planning has shown that the optimal parameters of lactone biosynthesis are a constant pH at the level of 7, a variable mixing speed in the range of 200–500 rpm, and a substrate concentration at the level of 75 g/L. Using these parameters, about 2.93 ± 0.33 g/L of aroma was obtained. The intensity of mixing turned out to be the most important factor influencing the increase in GDL concentration in the medium.

Multi-response optimization of AISI H11 using Taguchi and Grey relational analysis

Tool & Die is one of the important department in the manufacturing industries that takes care of proper designing and fabrication of tools and dies required for the production. In this sector, tool steels are used as the primary source of materials. These tool steels belongs to the family of carbon and alloy steels. Mostly used alloying elements are chromium, tungsten, molybdenum and vanadium and are heat-treated. The objective of the work involves machinability study of AISI H11 chromium hot-worked steel extensively used for tool & die making. It deals with the analysis of machining parameters and its influences on the responses considered. Here the controlling parameters considered are cutting speed (Cs), feed rate (Fr) and depth of cut (Dc) and responses as surface roughness (Ra) and material removal rate (Mrr). Each controlling parameters are assigned with 3 levels and experimental runs were executed as per taguchi robust design. To determine multi-objective optimal solution, grey relational analysis (GRA) is employed. GRA is used as it provides a feasible platform for converting a multi-objective function into single-objective function. The experimental runs were performed as per L27 orthogonal array sequence in CNC end-milling. The responses recorded are then analyzed using analysis of variance (ANOVA) and optimal solutions are validated through confirmatory runs. The entire machinability study of AISI H11 is performed in two conditions involving rough machining and finish machining. This has been addressed based on the machining scenario followed in industries taking up job orders. The confirmatory results for rough machining recorded was found to be 0.7871 microns against predicted value of 0.7654 microns resulting with a deviation error of 2.88%. Similarly, for finish machining, confirmatory runs recorded 0.8579 microns against predicted value of 0.8357 microns resulting with a deviation error of 2.66%. The deviation level indicated above between predicted and observed values are minimum, which shows the reliability of the optimal solutions arrived in.

Application of artificial neural network models to predict ultrasonic welding parameters for joining copper to aluminium sheet

Ultrasonic welding is a method of combining solid-state components that does not require the use of heat or flux and instead relies on vibration to hold the components together. When connecting copper wires to an equipment alternator in electrical and automotive applications, a solid-state joining method is utilized as the preferred connection method. This situation calls for the utilization of ultrasonic welding of metal. In businesses that use ultrasonic metal welding processes, issues with weld quality and strength appear to be the most frequently encountered difficulties. These issues are frequently the result of inadequate parameter selection, which may be traced back to the welding process. To produce ultrasonically welded joints with a thickness of 0.3 mm, the welding characteristics of copper to aluminum connections, such as welding pressure, weld duration, and vibration amplitude, are considered in this study. Compared to the size of this weld, alternative joint construction methods could potentially cause damage to the joint. To conduct the tests, a suitable experimental design was conceived and put into action by employing Taguchi's robust design process in a manner that was tailored to the specifics of the situation. With the help of a synthetic neural network, the answer was predicted, and its accuracy was checked using the mean square relative error, the Nash-Sutcliffe efficiency, and the mean absolute percentage error.

Construction and Praxis of Six Sigma DMAIC for Bearing Manufacturing Process

The process capability improvement of a bearing manufacturing process using the Six Sigma-DMAIC approach was performed. A progressive method was adopted by the utilization of statistical techniques such as design of experiments for optimizing process capability. We report the draconian improvement in process capability with present approach. This has led to a diminution in high resource spoilage and rework. In the Define-phase, rejection data was used to identify the critical factors responsible for rejection. Process capability indices were used to evaluate the process performance in the Measure-phase. In the Analyze-phase, the vital root causes responsible for low process capability were identified. Using the Taguchi robust design technique, the Improve-phase focuses on optimizing the critical root causes. Analysis of variance was used to examine the results of the experiment. In the control-phase, Cp and Cpk values were improved from 1.170 and 0.976 to 2.780 and 2.230.