Multi-response Problem Research Articles

Efficient optimization of the multi-response problem in the taguchi method through advanced data envelopment analysis formulations integration

This study introduces an innovative strategy to address the multi-response challenge inherent in the Taguchi Method, frequently encountered in the enhancement of production processes and productivity indices. By integrating the Taguchi Method with Data Envelopment Analysis (DEA), experimental trials are transformed into Decision Making Units (DMUs), with response variables categorized as inputs and outputs. Advanced DEA models, including simultaneous DEA and super-efficiency models, are employed to determine the DMUs’ relative and cross efficiencies, thus retaining the advantages of the Taguchi Design of Experiments (DoE) while offering an optimal solution for enhancing multiple quality responses. The superiority of this approach is validated by the Addictive Model for Factor Effects, demonstrating enhanced outcomes and reduced computational effort compared to existing empirical and scholarly solutions. These findings have significant implications for advancing manufacturing practices that require multi-response optimization, marking a notable contribution to the field.

Desirability-based post-hoc selection of Pareto solutions

ABSTRACT Generating a set of optimal solutions is a recommended practice when solving a multiresponse problem. However, it is known that some optimal solutions may yield unexpected outcomes when implemented in practice. Thus, to avoid wasting resources and time in implementing a theoretical optimal solution that does not produce the expected outcomes, a new approach to select a solution from the Pareto front is proposed. This approach employs a desirability-based function to aggregate all the desired response characteristics, namely the responses’ Bias, Resilience, Quality of Predictions, and Robustness. Two case studies illustrate the usefulness of the proposed approach.

Preparation of polyurethane concrete and its novel application in bridge expansion joint anchorage zone

The frequent damage and repair of bridge expansion joint anchorage zone has caused a great burden to the bridge maintenance work. The objective of this study is to prepare a steel fiber reinforced polyurethane concrete (SFPUC) with balanced multi-properties of workability, compressive strength, and flexural strength for the application in bridge expansion joint anchorage zone. A four-factor, five-level orthogonal experimental design was carried out to investigate the effects of the binder-aggregate ratio ( BA), cement content ( C), fine-coarse aggregate ratio ( FC), and steel fiber dosing ( SF) on the workability, compressive strength, and flexural strength, respectively. The multi-criteria decision-making (MCDM) method was used to transform the multi-response problem in the orthogonal experimental design into a single-response problem. Finally, a real engineering application and a finite element method were used to verify the feasibility of SFPUC for the expansion joint anchorage zone. The results show that the optimum proportion of SFPUC is BA (25%), C (20%), FC (20%), and SF (2.5%). The maximum principal stress in SFPUC anchorage zone is less than that in C50 steel fiber concrete anchorage zone under the same load. SFPUC is a highly promising material for bridge expansion joint anchorage zone repair.

Structural analysis of cylindrical composite structures for high velocity applications: A parametric optimization study

This research explores the significance of structural modifications on the energy absorption and ballistic response of a cylindrical composite structure exposed to high velocity impact. The composite structure comprises an aramid honeycomb core supported by cylindrically shaped aluminum facesheets on both sides. Ballistic impact tests were performed by firing cylindrical projectiles, namely conical and hemispherical, using a pneumatic gun. Three-dimensional models of the composite structure with aluminum facesheets and an aramid honeycomb core were created using ABAQUS/Explicit. By utilizing numerical models, impact phenomena such as facesheet failure, core crushing, facesheet debonding, etc. were analyzed, which showed a high degree of similarity with a real impact scenario, thus validating the model. Furthermore, a parametric analysis was carried out using this model on different parameters affecting the composite structure. The study also examined the effect of these parameters on the ballistic responses of the cylindrical composite structure against both projectiles using a grey-based Taguchi analysis method. The investigation focused on multi-response optimization of the cylindrical composite structure's characteristics using Minitab software. The proposed grey-based Taguchi method proved effective in solving multi-response problems.

MULTI-RESPONSE OPTIMIZATION OF METFORMIN -HYDROCHLORIDE DRUG VIA EXPERIMENTAL DESIGN TECHNIQUES APPLIED ON THE ADAPTIVE LOCAL LINEAR REGRESSION MODEL FOR RESPONSE SURFACE METHODOLOGY

The challenge encountered by industrial statisticians and engineers during statistical process control in other to build-up and formulates pharmaceutical optimization of drugs have prompted several choices of experimental design tool and regression models. The most commonly applied regression model is the second-order polynomial which may perform poorly due to model misspecification. In this paper, we present two experimental design methods namely; the Full Factorial Design (FFD) and the Circumscribed Central Composite Design (CCCD) applied to an existing Adaptive Local Linear Regression ( model to ameliorate the problem of boundary bias for a multi-response problem. The FFD do not make reference to the star points and as such could not address variability in the data, hence we also applied the CCCD to accommodate the star points in order to maintain rotatability and curvature in the data. In the application, we minimized Metformin Hydrochloride (Met-HCL) drug usage via FFD on and CCCD on and the results from the goodness-of-fit statistics, residual plots and optimization were obtained and analyzed. The that utilized CCCD outperformed that uses FFD in terms of the goodness-of-fit statistics, minimum residual plots as it relates to the zero residual line and optimization of Met-HCL for Response Surface Methodology (RSM) data.

A PDCA Framework towards a Multi-Response Optimization of Process Parameters Based on Taguchi-Fuzzy Model

Multi-response optimization problems investigation is a crucial element in initiatives designed to enhance quality and overall productivity for manufacturing processes. Since no particular algorithm can be employed for all multi-response problems, defining the method that is utilized as a problem-solving technique is a vital step in the process factors optimization. Identifying a formal procedure of implementing the improvement approach in a multi-criteria decision-making problem is a critical need to ensure the consistency and sustainability of the enhancement methods. In this study, a Plan–Do–Check–Act (PDCA) framework is implemented for a case study in the food industry under which a multi-response optimization problem is investigated. The design of experiment (DOE) is used to examine the effect of process parameters on the quality responses by using the Taguchi method to find the optimal setting for each parameter. An orthogonal array (OA) and signal-to-noise (SNR) ratio is employed to investigate the performance characteristics. Each performance characteristic is then converted into a signal-to-noise ratio, and all the ratios are then fed into a fuzzy model to produce a single comprehensive output measure (COM). The average COM values for various factor levels are calculated, and the level that maximizes the COM value for each factor is identified as the optimal level. Results indicated the effectiveness of the applied method to find the optimal factor levels for the multi-response optimization problem under study. The global optimal factor levels that are driven from the fuzzy logic for the studied parameters are 1250, 40, 7.5, and 1:2, for the speed, frying time, cooking time, and the coating ratio, respectively. Means of all the studied quality characteristics were closer to the target values when compared with the initial factors’ settings.

Multi Objective Optimization Of Burnishing To Eliminate Heat Treatment In Reamer Shank Manufacturing By Using Taguchi Coupled Principal Component Analysis (PCA)

ABSTRACT Burnishing is a well-known cold working technique for improving the surface integrity. It is cost-efficient and effective technique for enhancing surface finish as well as hardness. The use of burnishing to replace current heat treatment including grinding process on the reamer shank is offered as a unique strategy for reamer shank processing. This change in processing reduces cycle time and resources while also conserves energy required for processing during heat treatment and grinding. In this work, principal component analysis (PCA) is coupled with Taguchi optimisation technique to deal with multi-response problem to determine optimum parameter values of burnishing process to attain required surface finish and hardness in order to fulfill all functional requirements of shank. Test outcomes are convincing and show that suggested technique has ability to substitute current heat treatment along with grinding operation for reamer processing.

Optimasi Taguchi Menggunakan Metode Vikor dalam Pemilihan Ban Mobil

The main functions of tire are to withstand the vehicle’s load, continue the steering function and control the vehicle’s direction, transmit the driving force and vehicle’s braking on the road surface and reduce shock vibrations from the road surface. Therefore, optimal tire selection is something that needs to be considered for driving perfection. Taguchi methods is one of experimental design methods that is often used because of considerations of effectiveness and efficiency. However, Taguchi methods only can be used to solve a single response optimization problem. When dealing with multi-response problems, the responses are analyzed separately. This study aims to optimize Taguchi design using VIKOR method in production of car tires. The experimental design and responses used in this study were derived from car tire production data which analyzed using VIKOR methods and selected based on the optimum value to obtain the optimal combination of factors levels. The results of Taguchi's analysis are the first and third responses indicating the 16th experiment and second response indicating the 14th experiment which is the optimal combination. It can be concluded that Taguchi methods is inconsistent to producing optimal combination of levels of factors when dealing with multi-response problems, so additional methods are needed as Taguchi optimization methods in solving multi-response problems, including using VIKOR methods which results in the 2nd experiment being optimal combination.

Development of framework for ranking pervious concrete pavement mixtures: application of multi-criteria decision-making methods

ABSTRACT The major objective of this study was to evaluate the performance characteristics of control and sand-modified pervious concrete (PC) mixtures through the development of a hybrid multi-criteria decision-making (MCDM) tool to rank the materials and recommend the most optimal performing products. A full-factorial experimentation was adopted to analyse porosity, density, permeability, and compressive strength for 36 PC mixtures. A hybrid MCDM tool involving analytical hierarchy process, Entropy, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods was used to optimize the multi-response problem into a single output. Based on the results, the water-to-cement ratio may be restricted to a maximum of 0.30 and 0.33 for aggregates varying from 9.5 to 6.7 mm and 6.7 to 4.75 mm, respectively. Likewise, the cement-to-aggregate ratio may be restricted to a maximum of 1:3.75 and 1:4.25 for aggregate size in the range 9.5–6.7 mm and 6.7–4.75 mm, respectively. Further, the ranks for various PC mixtures obtained from TOPSIS and VIKOR were identical/ similar for about 80% of the mixtures. The proposed MCDM tools are envisioned to help the decision-makers in selecting the most optimum PC mix based on performance and site-specific requirements, a priori pavement design and construction.

Solving Multi-Response Problem Using Goal Programming Approach and Quantile Regression

Response surface methodology (RSM) is a group of mathematical and statistical techniques helpful for improving, developing and optimizing processes. It also has important uses in the design, development and formulation of new products. Moreover, it has a great help in the enhancement of existing products. (RSM) is a method used to discover response functions, which meet and fulfill all quality diagnostics simultaneously. Most applications have more than one response; the main problem is multi-response optimization (MRO). The classical methods used to solve the Multi-Response Optimization problem do not guarantee optimal designs and solutions. Besides, they take a long time and depend on the researcher's judgment. Therefore, some researchers used a Goal Programming-based method; however, they still do not guarantee an optimal solution. This study aims to form a goal programming model derived from a chance constrained approach using quantile regression to deal with outliers not normal and errors. It describes the relationship between responses and control variables at distinctive points in the response conditional distribution; it also considers the uncertainty problem and presents an illustrative example and simulation study for the suggested model.

A New Approach for Assessing the Reliability of Electronic Assemblies Using Fuzzy Logic for Multi-criteria Optimization

Abstract The reliability of electronic assemblies is a vital criterion used to assure product quality over its lifetime. Weibull distribution is the most common distribution utilized to describe the reliability data. Most of the studies use the Weibull scale parameter, or characteristic life, to compare alternatives and make a selective decision. This may not lead to achieving the optimal parameters which can be problematic because this method doesn’t consider the variability behavior of the fatigue life. In this study, a new approach for process parameters selection is proposed to find the optimal parameter values that improve the micro-optimality selection process based on reliability data. In this study, a new approach is proposed based on examining the solder joint reliability by using a multi criteria analysis. The fuzzy logic is utilized as a tool to solve the multi criteria problem that is presented from the proposed approach. The reliability of microelectronic connections in thermal cycling operating conditions is used as a validation case study. In the validation case study, the optimal process parameters are found for ball grid array electronic components. Two levels of the solder sphere materials, three levels of the surface finish, and 10 levels of solder paste alloys are studied as process parameters. Using the proposed approach, four quality responses are employed to assess the reliability data, including the scale parameter, the B10 (life at 10% of the population failure), mean-standard deviation response, and the signal to noise ratio (SNR). The fuzzy logic is applied to solve the multiresponse problem. An optimal process parameter setting that considers different quality characteristics was found for the validation case study. ENIG surface finish, SAC305 solder sphere, and material six were the optimal factor levels that are obtained for the aged CABGA208 component using the proposed approach.

Accurate fault location method for distribution network in presence of DG using distributed time‐domain line model

In this paper a novel technique is presented for fault location in distribution networks in the presence of distributed generators (DGs). The proposed approach uses recorded voltage and current at the beginning of feeder and DG terminals. This method is based on transient state data, and due to presence of high frequencies in this state, to achieve high accuracy it utilizes the distributed time-domain (DTD) line model. The fault location problem is formulated as an optimization problem and is solved using Genetic Algorithm (GA). Through modal transformation, in addition to locate the symmetrical three-phase to ground (3LG) faults, the location of asymmetrical faults including single line to ground (SLG) and double line (LL) faults can also be determined. The effects of fault distance, fault resistance and fault inception angle on the accuracy of the proposed algorithm have also been investigated. A solution is also proposed to overcome the multi-response problem. The simulations are conducted and the results are obtained via PSCAD and MATLAB softwares, respectively. The effectiveness of the proposed approach has been verified using the modified IEEE 34-node test feeder under different fault conditions. The simulation results demonstrate the high efficiency and accuracy of the proposed method.

Research of multi-response optimization of milling process of hardened S50C steel using minimum quantity lubrication of Vietnamese peanut oil

This study aims to build a regression model when surveying the milling process on S50C steel using Minimum Quantity Lubrication (MQL) of Vietnamese peanut oil-based on Response Surface Methodology. The paper analyses and evaluates the effect of cutting parameters, flow rates, and pressures in minimum quantity lubrication system on cutting force and surface roughness in the milling process of S50C carbon steel materials after heat treatment (reaching a hardness of 52 HRC). The Taguchi method, one of the most effective experimental planning methods nowadays, is used in this study. The statistical analysis software, namely Minitab 19, is utilized to build a regression model between parameters of the cutting process, flow rates and pressures of the minimum quantity lubrication system and the cutting force, surface roughness of the part when machining on a 5-axis CNC milling machine. Thereby analyzing and predicting the effect of cutting parameters and minimum quantity lubrication conditions on the surface roughness and cutting force during machining to determine the influence level them. In this work, the regression models of Ra and F were achieved by using the optimizer tool in Minitab 19. Moreover, the multi-response optimization problem was solved. The optimum cutting parameters and lubricating conditions are as follows: Cutting velocity Vc=190.909 m/min, feed rate fz=0.02 mm/tooth, axial depth of cut ap=0.1 and nozzle pressure P=5.596 MPa, flow rate Q=108.887 ml/h. The output parameters obtained from the above parameters are Ra=0.0586 and F=162.035 N, respectively. This result not only provides the foundation for future research but also contributes reference data for the machining process

Optimization of the n-type HPGe detector parameters using the “design of experiments” technique

This paper presents a new procedure to optimize the geometric parameters of a n-type coaxial HPGe detector. It is based on a statistical technique called “Design of Experiments” (DoE). This technique aims to identify the most influential parameters and to determine the optimal configuration. In this work, The effects of each parameter on the detector responses have been investigated by a fractional factorial design. Only the most influential factors contributing to the detector response have been selected. Precise modeling of these factors was then performed using a full factorial design. Based on the results obtained from this design, the full energy peak efficiencies according to the geometric parameters were modeled by a multiple-linear regression. These models have been statistically validated by analysis of variance (ANOVA). The optimal combination of the geometric parameters has been identified using the desirability function approach, which is a useful tool to optimize multi-response problems. A verification test was performed to validate the results obtained. It was observed that the relative deviation found between experimental and simulated values was less than 5%.

A DEA-based multi-response fusion model in the context of Taguchi method

Taguchi methods have been widely used to optimize machining parameters of a manufacturing process so that the product performances or responses are close to the desired targets and not sensitive to external noises. For single-response problems, the optimal machining parameters can be obtained based on Taguchi signal-to-noise ratio or response surface models. For multi-response problems, a multi-objective optimization approach or a fusion model is needed before obtaining the optimal parameters; and the latter is preferred due to its simplicity. This paper proposes a fusion model to combine multiple responses into a composite response variable. The proposed model first transforms the response data obtained from Taguchi experiments into the data with smaller-the-better or larger-the-better quality characteristics, and then combines the transformed data into the composite response variable in the way similar to Data Envelopment Analysis (DEA). Once the fusion model is built, the problem reduces into a single-response problem, which can solved using existing approaches. The proposed model can be easily implemented using a spreadsheet program; and one real-world example is included to illustrate its appropriateness.

A novel approach for non-normal multi-response optimisation problems

Various creative multi-response optimisation approaches have been developed in the literature. Most of these researches are based on the normality assumption of the response distribution. However, this assumption does not necessarily hold in some real cases, such as non-normal multiple responses. Also, the reproducibility of optimisation results does not hold in some practical applications due to the variability of predicted responses associated with model uncertainty. In this paper, a novel approach is proposed to address the issues for non-normal multi-response optimisation. The proposed method not only identifies significant effects for each response by incorporating factorial effect principles into the framework of the Bayesian generalised linear models (GLMs) but also takes into account the model uncertainty and the variability of predicted responses by using the Bayesian sampling technique and Pareto optimal strategy. Furthermore, the optimal parameter settings are found by using grey incidence analysis (GIA). Besides, two examples are used to illustrate the effectiveness of the proposed method. The results show that the proposed method not only effectively identify significant factors but also find more satisfactory parameter settings when the reliability and reproducibility of optimisation results are considered simultaneously.

Abrasive waterjet machining studies on Al6061/15%LaPO4 composite

To the Al6061 matrix composite, the monazite structure Lanthanum Phosphate with a weight ratio of 15% is added as the reinforcement. The machinability characteristics of the stir cast composite is predicted using Abrasive Waterjet Machine. Jet Pressure, Stand-Off Distance, and Cutting Speed are taken as independent parameters. Material Removal Rate, Kerf taper Angle, and Surface Profile Roughness are taken as the dependent responses. The influence of the machining parameters is investigated using the Taguchi's L27 orthogonal array through ANOVA table for both single response and multi responses problems by Gray Relational Approach. From the single response study, the jet pressure has significant impact on material removal and kerf angle whereas, the surface finish depends on the cutting speed. For the multi-response problems, stand-off distance shows the superior influences on the output responses followed by jet pressure and cutting speed.

Exploring Aluminum Alloy Metal Matrix Composites in EDM Using Coupled Factor-level-present Worth Analysis and Fuzzy Analytic Hierarchy Process

This paper targets two improvement aspects of the electrical discharge machining (EDM) process. First, it formulates the EDM problem as an economic issue incorporating the present worth analysis into the factor-level framework and solves it with the performance analysis flow diagram. Second, it conceptualizes the EDM process as imprecision and uncertainty and solves it with the fuzzy analytical hierarchy process (FAHP) approach. At present, these methods are not accessible to the EDM process engineer to machine the work material, AA6061/10%Al2O3AMMCs. In this study, the EDM process application using these methods on AA6061/10%Al2O3AMMCs is considered. This paper investigates the use of fuzzy AHP multi-criteria and the present worth method to produce a structured approach to model multi-response problem of EDM process parametric optimization concerning composite named AA6061/10%Al2O3AMMCs to obtain a robust design and the best parametric selection. The selected performance measures for the inputs to attain the performance flow analysis using the present worth method are MRR = 17.932 to 31.753 mg/min, TWR = 0.171 to 0.289 mg/min, SR = 8.228 to 12.680 mm and OV = 0.292 to 0.406 mm. The performance flow analysis reveals a present with a value of 1.604 based on the equivalence analysis of the flow diagram's positive and negative sides. The FAHP results showed the enhanced values of 0.1051, 0.2290, and 0.6658 for peak current, pulse on time, and duty factor, respectively. The approach is novel and has not been implemented elsewhere to solve the problem for the combination of materials studied.

Supply chain information sharing under consideration of bullwhip effect and system robustness

Supply chain system experiences variance amplification in order replenishment and inventory level, leading to severe inefficiencies of the system. Information distortion is universally known as a fundamental reason for the variance amplification phenomenon. The purpose of this paper is to study the effect of demand information sharing in reducing bullwhip effect and improving the robustness of supply chain systems. The “automatic pipeline inventory and order-based production control system, APIOBPCS” is adopted to model supply chains with different information-sharing strategies. The stochastic factors in the supply chain system lead to poor performance in system robustness. Taguchi design is adopted to find out the optimal setting of ordering parameters in the APIOBPCS model for a robust supply chain. An extension of Taguchi design is adopted to solve the multi-response problems. The weighted signal-to-noise ratio is used as the performance index of the overall performance of the supply chain, including inventory cost, customer service level, and inventory variance amplification. The results show that full demand information transparency helps to improve the overall performance of supply chain. Furthermore, the sensitivity analysis of stochastic lead times verifies the results. This research gives some insights to improve the overall performance of supply chain via information sharing.

Optimization of ultrasound-assisted extraction of anthocyanins and bioactive compounds from butterfly pea petals using Taguchi method and Grey relational analysis.

Ultrasound-assisted extraction (UAE) was used to extract anthocyanins, antioxidants and phenolic compounds from butterfly pea petals, as an alternative to traditional methods. Taguchi method with three factors: extraction time (30, 45, 60min), temperature (40, 60, 80°C) and liquid-solid ratio (5, 7.5, 10mL distilled water/mg butterfly pea) was used to obtain the high extraction yield. Grey relational analysis was employed to convert multi-response problem into single response optimization. The high extraction efficiency could be achieved at optimal parameter condition using 45min of extraction time, 40°C and 10ml distilled water/mg butterfly pea. Liquid-solid ratio exhibited the highest contribution for anthocyanin and total phenolic content. A high temperature of ultrasonication resulted in a negative effect on antioxidant capacity and total phenolic content. The findings from this study indicated that the UAE process optimization would be an efficient and sustainable method for the preparation of bioactive compounds from medical plants with saving of reaction time and cost in which extraction yields of antioxidant capacity and total phenolic content were also increased. The color response analysis results suggested that the gelatin film incorporated with butterfly pea extract can be potentially used as pH-indicator for detecting food spoilage for intelligent packaging.