Taguchi Robust Design Method Research Articles

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.

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.

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%.

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.

Taguchi robust design for adaptive energy management of plug-in fuel cell electric bus

This paper presents an adaptive energy management strategy (EMS) based on Pontryagin's Minimum Principle (PMP), considering both historical driving cycles and stochastic vehicle mass. First, an improved battery state of charge (SOC) feedback control method based on Proportion-Integration (PI) controller is proposed for adaptive energy management. Second, a Taguchi robust design (TRD) method is proposed by considering the noises of historical driving cycles and stochastic vehicle mass. The main purpose of the TRD is to find the robust parameters (P and I) that can counter to the above noise disturbance, thereby neglecting the influence of noise disturbance in adaptive energy management control and minimizing the mean hydrogen consumption. The results show that the TRD is reasonable and applicable in the robust design of plug-in fuel cell electric buses (PFCEBs); the fuel economy of the proposed strategy can be averagely improved by 18.36%, compared to the rule-based energy management strategy.

Optimization of sol–gel synthesis condition of Y2BaCuO5 nanoparticles using Taguchi robust design method and investigation of its magnetic behavior

Optimization of sol–gel synthesis condition of Y2BaCuO5 nanoparticles using Taguchi robust design method and investigation of its magnetic behavior

Integrating Taguchi Method and Gray Relational Analysis for Auto Locks by Using Multiobjective Design in Computer-Aided Engineering

In automobiles, lock parts are matched with inserts, and this is a crucial quality standard for the dimensional accuracy of the molding. This study employed moldflow analysis to explore the influence of various injection molding process parameters on the warpage deformation. Deformation of the plastic part is caused by the nonuniform product temperature distribution in the manufacturing process. Furthermore, improper parameter design leads to substantial warpage and deformation. The Taguchi robust design method and gray correlation analysis were used to optimize the process parameters. Multiobjective quality analysis was performed for achieving a uniform temperature distribution and reducing the warpage deformation to obtain the optimal injection molding process parameters. Subsequently, three water cooling system designs—original cooling, U-shaped cooling, and conformal cooling—were tested to modify the temperature distribution and reduce the warpage. Taguchi gray correlation analysis revealed that the main influencing parameter was the mold temperature followed by the holding pressure. Moreover, the results indicated that the conformal cooling system improved the average temperature distribution.

Design and Numerical Analysis of Electric Vehicle Li-Ion Battery Protections Using Lattice Structure Undergoing Ground Impact

Improvement in electric vehicle technology requires the lithium-ion battery system’s safe operations, protecting battery fire damage potential from road debris impact. In this research a design of sandwich panel construction with a lattice structure core is evaluated as the battery protection system. Additive manufacturing technology advancements have paved the way for lattice structure development. The sandwich protective structure designs are evaluated computationally using a non-linear dynamic finite element analysis for various geometry and material parameters. The lattice structure’s optimum shape was obtained based on the highest Specific Energy Absorption (SEA) parameter developed using the ANOVA and Taguchi robust design method. It is found that the octet-cross lattice structure with 40% relative density provided the best performance in terms of absorbing impact energy. Furthermore, the sandwich panel construction with two layers of lattice structure core performed very well in protecting the lithium-ion NCA battery in the ground impact loading conditions, which the impactor velocity is 42 m/s, representing vehicle velocity in highway, and weigh 0.77 kg. The battery shortening met the safety threshold of less than 3 mm deformation.

Cone calorimetric fire properties and thermal analysis of polyester-banana peduncle fibre composite fused with bio-fire retardant additive

BackgroundLately, thermoplastic and thermoset polymers are integrated with natural fillers to harvest composites. Due to an excellent property profile, these composites find wide applications in engineering fields. This research work aimed to investigate the parameters that influence the thermal degradation of Polyester-Banana peduncle fibre-reinforced composite incorporated with cow horn ash particle (CHAp) as a fire retardant additive and optimize the total heat produced for the smaller the better. The major instruments used in this study were cone calorimeter and the thermogravimetric analyser.ResultThe parameters considered were time, reinforcement type, and weight per cent of reinforcement material. The composites were made by varying the ratios of CHAp and BPF in the polyester matrix from 0:0, 2.5:2.5, 5:5 and 7.5:7.5 and 10:10 weight per cent. The flammability properties of the developed composites were examined, using a cone calorimeter. The controlling parameters were analysed using the Taguchi robust design method. The ANOVA showed that time had the greatest influence on the total heat release rate (81.72%). The weight per cent reinforcement type (10.37%) and reinforcement type (5.28%) had smaller influences on the heat release rate. The S/N ratio obtained, using optimal testing shows that time had the greatest influence on the total heat release rate, followed by weight per cent of reinforcement type, while reinforcement type had the least influence. The corresponding multiple regression models for total heat released revealed that the total heat release rate increased with an increase in time and reinforcement type and decreased with an increase in weight per cent of the fire retardant additive material.ConclusionThis work indicated that the parameter design of the Taguchi method provided an efficient methodology for the analyses of the effects of thermal degradation parameters of composites. The controlling parameters of time, the weight of reinforcement material and the type of reinforcement material had significant contributions to the value of heat production during composites thermal decomposition. Time had the greatest contribution, followed by the weight of reinforcement type, and type of reinforcement material.

Optimal Design of Novel Blade Profile for Savonius Wind Turbines

Since the affirming of global warming, most wind energy projects have focused on the large-scale Horizontal Axis Wind Turbines (HAWTs). In recent years, the fast-growing wind energy sector and the demand for smarter grids have led to the use of Vertical Axis Wind Turbines (VAWTs) for decentralized energy generation systems, both in urban and remote rural areas. The goals of this study are to improve the Savonius-type VAWT’s efficiency and oscillation. The main concept is to redesign a Novel Blade profile using the Taguchi Robust Design Method and the ANSYS-Fluent simulation package. The convex contour of the blade faces against the wind, creating sufficient lift force and minimizing drag force; the concave contour faces up to the wind, improving or maintaining the drag force. The result is that the Novel Blade improves blade performance by 65% over the Savonius type at the best angular position. In addition, it decreases the oscillation and noise accordingly. This study achieved its two goals.

Design optimisation of submersible permanent magnet synchronous motor by combined DOE and Taguchi approach

This study presents an optimisation procedure for a submersible permanent magnet synchronous motor (PMSM) to achieve the maximum efficiency and minimum cogging torque simultaneously. Numerous geometric parameters can be used to define submersible PMSM. To identify the most significant parameters for optimisation, the fractional factorial design out of design of experiment (DOE) is employed for the screening activity, considering the interaction effects into account. The manufacturing process of the motor causes dimension tolerances, shape and position tolerances, installation errors and deviations of material properties. To ensure a good robustness for the performance of the submersible PMSM, Taguchi's robust design method is employed. The screened significant parameters are taken as the control factors. The noise factors include inner diameter of stator, the air-gap length, the remanence of the magnets and the dimension tolerances of magnets. Numerical results demonstrate that this method is an effective optimisation design procedure to improve the performance of submersible PMSM, which is finally proved by the prototype test.

A multicriteria optimization model for cloud service provider selection in multicloud environments

SummaryMulticloud computing is a strategy that helps customers to reduce reliance on any single cloud provider (known as the vendor lock‐in problem). The value of such strategy increases with proper selection of qualified service providers. In this paper, a constrained multicriteria multicloud provider selection mathematical model is proposed. Three metaheuristics algorithms (simulated annealing [SA], genetic algorithm [GA], and particle swarm optimization algorithm [PSO]) were implemented to solve the model, and their performance was studied and compared using a hypothetical case study. For the sake of comparison, Taguchi's robust design method was used to select the algorithms' parameters values, an initial feasible solution was generated using analytic hierarchy process (AHP)—as the most used method to solve the cloud provider selection problem in the literature, all three algorithms used that solution and, in order to avoid AHP limitations, another initial solution was generated randomly and used by the three algorithm in a second set of performance experiments. Results showed that SA, GA, PSO improved the AHP solution by 53.75%, 60.41%, and 60.02%, respectively, SA and PSO are robust because of reaching the same best solution in spite of the initial solution.

Robust Co-Design for Energy Saving of Plug-in Hybrid Electric Bus With Existing Components

The energy saving of Plug-in hybrid electric bus (PHEB) can be maximized by co-design method (integrated design of topology, component sizing and energy management). In many cases, the components have been manufactured, so it is a shortcut to realize the co-design with existing components. Motivated by this, a Taguchi robust design (TRD) method is proposed for the robust co-design of the PHEB. The main innovation is that the noises of driving cycles and stochastic vehicle mass are considered in the TRD, and the TRD is formulated as a smaller-the-better (STB) problem. Moreover, the signal-to-noise ratio (SNR) is taken as the analysis index, where the fuel economy together with its robustness can be simultaneously enhanced. In specific, the dynamic programming (DP) is firstly taking as the fuel consumption calculation module of the TRD. Then, a series of historical driving cycles and the stochastic vehicle mass are designed as noise factors; the components are designed as control factors. Finally, a receding horizon control (RHC) strategy is deployed to realize adaptive energy management control using the robust component match and the same DP. The TRD results demonstrate that the proposed co-design method is applicable and reasonable; the simulation results show that the RHC strategy can realize adaptive control, and averagely improve the fuel economy by 10.85% compared to a rule-based control strategy.

Rockets, gauges, and pendulums: applying engineering principles to cell biology.

From flight to radar to Velcro, biological form and function have inspired engineers for centuries. It is equally valuable to consider whether concepts in engineering might provide insights into core biological processes. To explore this idea, cell cycle checkpoints, biological clocks, and signaling pathways are viewed here from an engineering perspective. Engineering concepts covered include gauge error, the distinction between precision and accuracy, and the Taguchi method of robust design. Also discussed is the Pareto principle, which describes the observation that, in complex systems, a minority of the components (or inputs) are responsible for a majority of the outputs. These concepts enable engineers to manage complexity, both in system design and in operation. Thus, with new techniques and large data sets revealing ever-increasing levels of biological complexity, an engineering mindset may be particularly valuable for the study of living systems.

Hybrid genetic algorithm for multi-objective flow shop scheduling problem with sequence dependent setup time: parameter design using Taguchi's robust design method

This paper determines optimal parameters of the proposed hybrid genetic algorithm for solving the multi-objective flow shop scheduling problem with sequence dependent setup time. The objectives considered in this study are minimisation of makespan and mean tardiness. In order to achieve these objectives, genetic algorithm is used in combination with a local search method to obtain Pareto-optimal solutions. The best parameters of the proposed algorithm are determined using the Taguchi's robust design method and the concept of utility index value. The set of parameters corresponding to the highest utility value is selected as the optimal parameters for the proposed algorithm. The analysis of results reveals that crossover type is the most influential parameter. The other parameters in the order of importance are mutation probability, crossover probability, mutation type and initial population. The hybrid genetic algorithm is applied to the benchmark problems of flow shop scheduling with sequence dependent setup time.

Hybrid genetic algorithm for multi-objective flow shop scheduling problem with sequence dependent setup time: parameter design using Taguchi's robust design method

Hybrid genetic algorithm for multi-objective flow shop scheduling problem with sequence dependent setup time: parameter design using Taguchi's robust design method

A novel dynamic assignment rule for the distributed job shop scheduling problem using a hybrid ant-based algorithm

Distributed scheduling problems are among the most investigated research topics in the fields of Operational Research, and represents one of the greatest challenges faced by industrialists and researchers today. The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories and with determining the sequence of operations on each machine in distributed manufacturing environments. The objective is to minimize the global makespan over all the factories. Since the problem is NP-hard to solve, one option to cope with this intractability is to use an approximation algorithm that guarantees near-optimal solutions quickly. Ant based algorithm has proved to be very effective and efficient in numerous scheduling problems, such as permutation flow shop scheduling, flexible job shop scheduling problems and network scheduling, etc. This paper proposes a hybrid ant colony algorithm combined with local search to solve the Distributed Job shop Scheduling Problem. A novel dynamic assignment rule of jobs to factories is also proposed. Furthermore, the Taguchi method for robust design is adopted for finding the optimum combination of parameters of the ant-based algorithm. To validate the performance of the proposed algorithm, intensive experiments are carried out on 480 large instances derived from well-known classical job-shop scheduling benchmarks. Also, we show that our algorithm can process up to 10 factories. The results prove the efficiency of the proposed algorithm in comparison with others.

Design optimisation of high sensitivity MEMS piezoresistive intracranial pressure sensor using Taguchi approach

MEMS piezoresistive pressure sensors have been contemporarily used to measure intracranial pressure. Since an intracranial signal is of the pulsating type, the microsensor must be very sensitive to detect these changes. The sensitivity of the existing MEMS piezoresistive intracranial pressure sensors are in the range of 2 µV/V/mmHg to 0.17 mV/V/mmHg. Factors influencing the sensitivity and linearity of the sensor include the diaphragm thickness, the shape and placement of the piezoresistors, and the doping concentration. This paper will discuss the incorporation of these factors, which were tested to obtain higher sensitivity silicon-based piezoresistive intracranial pressure sensor, while maintaining the linearity of the sensor. In order to achieve this objective, the Taguchi robust design method of L27 orthogonal array was employed. The sensing outputs of these designs, with different combinations of factors were determined through simulations using COMSOL Multiphysics. The results indicated that the diaphragm thickness and perpendicular piezoresistors play important roles in the sensitivity performance of the MEMS piezoresistive intracranial pressure sensor. The findings also showed that the doping concentration of the piezoresistors have significant effect on the linearity performance of the sensor. Consequently, the design that consolidated the 3-turns (perpendicular) and 0-turn (parallel) meander shaped piezoresistors of 1017 cm−3 dopant concentration on a 2 μm diaphragm thickness was found to be the optimum design, with sensitivity of 0.1272 mV/V/mmHg and linearity of 99%. This design has been proven to be an improved version for the small diaphragm piezoresistive intracranial pressure sensor.

Improvement of the Mechanical Properties of Spheroidized 10B21 Steel Coil Using Taguchi Method of Robust Design

Improvement of the Mechanical Properties of Spheroidized 10B21 Steel Coil Using Taguchi Method of Robust Design