Multi-performance Characteristic Index Research Articles

A fuzzy-regression-PSO based hybrid method for selecting welding conditions in robotic gas metal arc welding

PurposeThis paper aims to propose fuzzy-regression-particle swarm optimization (PSO) based hybrid optimization approach for getting maximum weld quality in terms of weld strength and bead depth of penetration.Design/methodology/approachThe prediction of welding quality to achieve best of it is not possible by any single optimization technique. Therefore, fuzzy technique has been applied to predict the weld quality in terms of weld strength and weld bead geometry in combination with a multi-performance characteristic index (MPCI). Then regression analysis has been applied to develop relation between the MPCI output value and the input welding process parameters. Finally, PSO method has been used to get the optimal welding condition by maximizing the MPCI value.FindingsThe predicted weld quality or the MPCI values in terms of combined weld strength and bead geometry has been found to be highly co-related with the weld process parameters. Therefore, it makes the process easy for setting of weld process parameters for achieving best weld quality, as there is no need to finding the relation for individual weld quality parameter and weld process parameters although they are co-related in a complicated manner.Originality/valueIn this paper, a new hybrid approach for predicting the weld quality in terms of both mechanical properties and weld geometry and optimizing the same has been proposed. As these parameters are highly correlated and dependent on the weld process parameters the proposed approach can effectively analyzing the ambiguity and significance of each process and performance parameter.

Multi-response Optimization and Surface Texture Characterization for CNC Milling of Inconel 718 Alloy

The objectives of any modern machining process are to continuously enhance both volume and rate of production with best utilization of the resources. Attempts are always made to innovate, explore, plan and use resources such that the machining work becomes more and more effective, efficient, economically viable and environment-friendly. This requires knowledge or awareness of the factors which govern machining economy. In this research work, a fuzzy-based desirability function analysis has been considered for selection of input parameters of a CNC milling process, which will simultaneously optimize the responses like surface roughness, axial cutting force and material removal rate for decision making. A multi-response optimization for CNC milling of Inconel 718 alloy has been performed. The results showed that observation 12 has the highest value of multi-performance characteristic index (MPCI) and optimum combination of machining parameters is speed (3500 rpm), feed (100 mm/rev) and depth of cut (0.25 mm). In addition, most influencing factors for highest value of MPCI are feed rate. Digital image processing technique is used to measure the pixel count within two successive scratches. This technique illustrates that maximum and minimum distances between two successive scratches are 45.3 mm and 3.74 mm, respectively.

Multi-objective optimal design of motion precision for fork robot arm in LCD panel manufacturing process system

This paper tried to study the robot arm applied to the LCD panel manufacturing process. There is a fork structure in the robot arm used to perform the stretch into/back the glass-base board cassette. The box beam structure is used as the arm structure, and the carbon-fiber composite material is used to realize the feature of light weight. Multi-objective optimal design based on multi-performance characteristic index and response surface method is adopted to achieve two objective functions: static moving forward precision (SMFP) as well as dynamic moving forward and backward precision (DMFBP). Additionally, three control factors and two noise factors associated with the robot arm structure are selected to illustrate the first order statistical model with the RSM method. Case 1 studied the optimal static moving forward precision (SMFP); Case 2 studied the optimal dynamic moving forward and backward precision (DMFBP); Case 3 studied both SMFP and DMFBP simultaneously. The findings indicate that the optimal belt tension values are obtained for the robot arm to perform the optimal precision under static and dynamic operation; what's more, it is verified that the optimal statistical model can provide a good reference for creating a cloud model to improve the manufacturing process in the era of industry 4.0.

Optimization of user experience in mobile application design by using a fuzzy analytic-network-process-based Taguchi method

Mobile application (app) design is an expanding research area, with user experience (UX) as its core. UX encompasses all aspects of human–computer interaction, and thus the optimization of UX has multiple objectives. Quality characteristics related to UX are subjective and even subconscious; moreover, there exists interdependence among UX quality characteristics. However, very little attention has been focused on these issues when optimizing UX based on multiple objectives. In this paper, a fuzzy analytic network process (ANP)-based Taguchi method is proposed for optimizing UX in mobile app design. First, design patterns and UX quality characteristics are determined. Subsequently, a Taguchi experiment is designed and carried out, and then signal-to-noise (S/N) ratios are calculated. A fuzzy ANP is adopted to derive the preference weights for the UX quality characteristics. Based on these weights, the S/N ratios are converted into a multiperformance characteristic index (MPCI) by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Finally, according to the MPCI, the significant design patterns are identified by using the analysis of variance, and the optimal design is obtained by using the response table and response graph. A mobile health app design was presented to illustrate the proposed approach. The results suggest that the proposed approach can effectively manage the interdependence among the subjective and even subconscious UX quality characteristics in the optimization process, and be used as a universal robust design approach to optimize UX in mobile app design.

Optimization of process parameters of direct metal laser sintering process using fuzzy-based desirability function approach

CL50WS material has been mainly used in tooling industries for making die, tool insert, mould or casting die. As the manufacturing of the CL50WS material parts from the DMLS machine differs from the conventional machining process, it is very essential to understand of DMLS behaviour and determine the proper optimal process parameter. Hence, the present study highlights the application of Box–Behnken method (BBD) of response surface methodology with fuzzy-based desirability function approach in order to optimize the multiple process parameters of direct metal laser sintering. The study also focused to investigate the effects of process parameters, viz. the laser power, scanning speed, layer thickness and hatch spacing, on the performance characteristics such as surface roughness, hardness and impact strength for the fabricated specimen using response surface plot. Fuzzy inference system has been applied in order to aggregate the aforementioned performance characteristics into a single objective response, i.e. multi-performance characteristic index. The optimum values of process parameters such as laser power 130 W, scan speed 550 mm/sec, layer thickness 0.03 mm and hatch spacing of 0.010 mm have been observed in the present study.

Application of Fuzzy-Based Hybrid Taguchi Method for Multiobjective Optimization of Product Form Design

In today’s competitive market, industrial product form design is moving towards being consumer centric. Affective responses relate to customers’ affective needs and are receiving increasing attention. To design a product form that can appeal to consumers, designers should consider multiple affective responses (MARs). This paper proposes a robust design approach that uses a fuzzy-based hybrid Taguchi method to derive the optimal product form design concerning MARs. First, design analysis is used to identify design variables and MARs. According to the results, a Taguchi experiment is designed in which fuzzy sets are used to measure the MARs; then, signal-to-noise (S/N) ratios are calculated. Subsequently, a fuzzy questionnaire with multiple answers is employed to acquire consumers’ preference weights for MARs, following which Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is adopted to transform the multiple S/N ratios into a multiperformance characteristic index (MPCI). On the basis of the MPCI, the effects of design variables are identified through analysis of variance and the response table and the response graph are obtained. Consequently, the optimal form design is achieved. A car profile design was used as an example to demonstrate the proposed approach. The results indicate that this approach can effectively improve consumers’ affective response qualities and can be used as a robust design approach to optimize product form design.

Optimization of user experience in interaction design through a Taguchi‐based hybrid approach

AbstractUser experience (UX), which encompasses all aspects of a user’s interaction with an interactive product, has been recognized as central to interaction design. This paper proposes a Taguchi‐based hybrid approach to realize the optimal UX design. In this approach, design analysis is first used to identify design patterns and UX characteristics. According to the results, a Taguchi experiment is conducted and the signal‐to‐noise ratios are computed. Subsequently, the preference weights for the UX characteristics are obtained by using analytical hierarchy process‐based group decision making. A multiperformance characteristic index is then defined based on the gray relational grade (GRG) obtained through gray relational analysis. On the basis of the GRG, the optimal design can be obtained. A mobile health application design was used to demonstrate the proposed approach. The results show that this approach can effectively enhance UX quality and be used as a universal design approach for optimizing UX.

Optimization of Process Parameters of EDM Process Using Fuzzy Logic and Taguchi Methods for Improving Material Removal Rate and Surface Finish

The main indent of this work is to optimization of multiple responses of Electric discharge machining (EDM) using Fuzzy method coupled with Taguchi is attempted. The work piece material was AISI 304 Stainless Steel and a cylindrical copper electrode with side impulse flushing was used. The effect of machining parameters, i.e., discharge current,pulse on time, discharge voltage and Inter Electrode Gap (IEG) on the Material Removal Rate (MRR), Tool Wear Rate and Surface Roughness (Ra) in EDM are examined. L9 orthogonal array was used to design the experiment and the effect of the factors on the responses were studied. As the responses are conflicting in nature, factors of a single combination will not be treated as best machining performance for all responses. The multiple responses were converted into a single characteristic index by using Fuzzy logic known as Multi Performance Characteristic Index (MPCI). Finally, MPCIs were optimized by using Taguchi.

Optimisation of product form design using fuzzy integral-based Taguchi method

ABSTRACTThe trend of industrial product design is changing towards consumer-centered. Affective responses that concern consumers’ affective needs have been recognised as invaluable in industrial product form design and have received increasing attention. To design a product form that appeals to consumers, designers should consider multiple affective responses (MARs). This paper proposes a robust design approach that uses a fuzzy integral-based Taguchi method to obtain the optimal product form design related to MARs. Design analysis is first used to identify design variables and MARs. According to the results, a Taguchi experiment is conducted and the signal-to-noise (S/N) ratios are computed. A multiperformance characteristic index (MPCI) is then defined based on the fuzzy integral value (FIV), and thus, the S/N ratios are converted into a single performance index. According to the FIV, the effects of design variables are identified through analysis of variance (ANOVA), and the response table and response graph are derived. Consequently, the optimal form design is obtained. A car profile design experiment is conducted to illustrate the proposed approach. The result shows that this approach can effectively enhance consumers’ affective response qualities, and be used as a universal robust design approach for optimising product form design.

Integrating Principal Component Analysis, Fuzzy Linguistic Reasoning and Taguchi Philosophy for Quality-Productivity Optimization

The present work demonstrates a multi-response optimization problem for selection of optimal cutting parameters (optimal process environment) for machining (turning) of nylon 6; by using Principal Component Analysis (PCA) followed by fuzzy linguistic reasoning in combination with Taguchi’s robust design technique. In this study, three controllable process parameters: cutting speed, feed, and depth of cut have been considered for obtaining desired Material Removal Rate (MRR) of the process and favorable multiple surface roughness features for the machined product; based on L9 orthogonal array experimental design. The study has been aimed to search an appropriate process environment for simultaneous optimization of quality-productivity favorably. Various surface roughness parameters of statistical importance (of the machined product) have been considered as product quality characteristics whereas; MRR has been treated as productivity measure for the said machining process. To avoid assumptions, limitations, uncertainties and imprecision in application of existing multi-response optimization techniques; Principal Component Analysis (PCA) has been proposed to convert correlated responses into uncorrelated quality indices (called individual principal components); next, a fuzzy inference system (FIS) has been proposed for meaningful and feasible aggregation of individual principal components into an equivalent single quality index, thereby, converting such a multi-objective optimization problem into an equivalent single objective optimization situation. A Multi-Performance Characteristic Index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method. The study exhibits application feasibility of the proposed approach with satisfactory result of confirmatory test.

Optimization of MRR, Surface Roughness, and Maximum Tool-Tip Temperature during Machining of CFRP Composites

Recently, machining of carbon fiber reinforced polymer (CFRP) composites has become a vital concern for the modern manufacturing companies due to the widespread applications of CFRP machined parts especially in automobile as well as aerospace industries. Owing to the necessity of mass-production of CFRP component parts, it is indeed essential to optimize the machining process parameters in order to improve process performance yields in terms of product quality economically. Overall performance of the machining (turning) process is influenced by different process parameters such as spindle speed, feed rate and depth of cut. In machining of CFRP composites, Material Removal Rate (MRR), surface roughness and tool-tip temperature are generally considered as the output responses. In the present work, the extent of process performance has been evaluated in turning of CFRP composites using PVD coated carbide tool. For optimization of multiple responses, a Fuzzy Inference System (FIS) has been used to convert multiple responses into an equivalent single response known called as Multi-Performance Characteristic Index (MPCI). A non-linear regression model has been developed in expressing MPCI as a function of the selected process parameters. The said regression model has been considered as the fitness function and finally optimized by two evolutionary techniques known as Harmony Search (HS) algorithm and Teaching-Learning-Based Optimization (TLBO) algorithm. The effectiveness of the aforesaid algorithm has been compared to that of Taguchi’s robust optimization philosophy.

Multi-objective optimization in drilling of CFRP (polyester) composites: Application of a fuzzy embedded harmony search (HS) algorithm

Widespread application of carbon fiber reinforced polymer (CFRP) composites in automobile, structural and aerospace engineering leads to vital concern for attaining usable shapes with reasonable accuracy through machining and moulding processes. Machining of CFRP composites needs careful planning and estimation of adequate process parameters as it is substantially different from conventional machining of metallic materials. Performance characteristics in machining (drilling) of CFRP composites are greatly influenced by various process parameters such as drill speed, feed and drill diameter. Generally, thrust force, torque, surface roughness and delamination factor (both at entry and exit) are considered as the output performance characteristics in composite drilling. In the present work, the extent of process performance has been evaluated in drilling of CFRP composites using TiAlN coated solid carbide drill bit. Multiple performance characteristics are converted into an equivalent single performance characteristic known as Multi Performance Characteristic Index (MPCI) using a Fuzzy Inference System (FIS). A non-linear regression model has been developed to express MPCI as a function of the selected process parameters. The regression model has been considered as the fitness function and finally optimized by a latest evolutionary technique known as harmony search (HS) algorithm which is inspired by the improvisation process of musicians. The effectiveness of the proposed algorithm has been compared with that of genetic algorithm (GA) as well as Taguchi’s robust optimization philosophy. The results indicate that HS algorithm is quite efficient in searching optimal process parameters at less computational effort as compared to genetic algorithm due to diversity in search mechanism.

Taguchi approach followed by fuzzy linguistic reasoning for quality‐productivity optimization in machining operation

PurposeThe study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.Design/methodology/approachIn this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L9 orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.FindingsThe study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.Originality/valueIn the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.

Application of a Fuzzy Inference System for the Optimization of Material Removal Rate and Multiple Surface Roughness Characteristics in the Machining of GFRP Polyester Composites

This paper presents a multi-objective extended optimization methodology applied in the machining of a randomly oriented GFRP rod. Design of Experiment (DOE) has been selected based on a L9 orthogonal array design with varying process control parameters like: spindle speed, feed rate and depth of cut. Multiple surface roughness parameters of the machined FRP product along with the Material Removal Rate (MRR) of the machining process have been optimized simultaneously. The Fuzzy Inference System (FIS) has been proposed for providing feasible means for the meaningful aggregation of multiple objective functions into an equivalent single performance index (MPCI). This Multi-Performance Characteristic Index (MPCI) has been optimized using the Taguchi method. The approach adapted here is capable of overcoming limitations/ assumptions of existing optimization methodologies available in the literature.

Principal component analysis and fuzzy embedded Taguchi approach for multi-response optimisation in machining of GFRP polyester composites: a case study

This paper proposes an extended multi-objective optimisation philosophy applied in a case study of machining (turning) of randomly oriented glass fibre-reinforced plastic polyester composites. Design of experiment has been selected based on Taguchi’s L9 orthogonal array design with varying process control parameters, such as spindle speed, feed rate and depth of cut. Multiple surface roughness parameters of the machined fibre-reinforced polymer product along with material removal rate of the machining process have been optimised simultaneously. A principal component analysis coupled with fuzzy inference system has been proposed for providing feasible means for meaningful aggregation of multiple objective functions into an equivalent single performance index. This multi-performance characteristic index has been optimised using Taguchi method.

The fuzzy inference system approach to a multi-performance characteristic index for surface quality improvement in CNC end milling

Utility embedded fuzzy approach has been adopted for multiple surface quality optimisation of 6061 T4 Aluminium in CNC end milling operation. The purpose is to evaluate the most favourable process environment consisting of selected process parameters viz. spindle speed, feed and depth of cut in order to satisfy multiple requirements of surface integrity. Various surface roughness parameters have been taken into account with a focus to minimise all of them simultaneously. Utility concept has been adopted to convert individual surface roughness parameters into corresponding utility value which have been fed to a fuzzy inference system to obtain a multi-performance characteristic index (MPCI). MPCI has been optimised finally using Taguchi method. Detailed methodology of the proposed approach has been presented with an illustrative example followed by satisfactory result of confirmatory test.