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There can be activities that cannot reduce times by conventional single minute exchange of die (SMED) tools. In this case more advanced tools are needed. The purpose of this paper is to integrate the fuzzy Taguchi method into the SMED method in order to improve the setup time. The reason for using fuzzy logic is the subjective evaluation of factor’s levels assessment by experts. Subjective assessment contains a certain degree of uncertainty and is vagueness. The fuzzy Taguchi method provides to determining optimal setup time parameters in an activity of SMED. So it is possible to reduce time more than the conventional SMED method.

In this study, the SMED method and the fuzzy Taguchi method are used.

In this study, it has been shown that the setup time is reduced (from 196 to 75 min) and the optimum value can be given at the intermediate value by the fuzzy Taguchi method.

In this limited literature research, the authors have not found a study using the fuzzy Taguchi method in the SMED method.

To provide a good insight into solving a multi‐response optimization problem using neuro‐fuzzy model and Taguchi method of experimental design.

Over the last few years in many manufacturing organizations, multiple response optimization problems were resolved using the past experience and engineering judgment, which leads to increase in uncertainty during the decision‐making process. In this paper, a four‐step procedure is proposed to resolve the parameter design problem involving multiple responses. This approach employs the advantage of both artificial intelligence tool (neuro‐fuzzy model) and Taguchi method of experimental design to tackle problems involving multiple responses optimization.

The proposed methodology is validated by revisiting a case study to optimize the three responses for a double‐sided surface mount technology of an electronic assembly. Multiple signal‐to‐noise ratios are mapped into a single performance statistic through neuro‐fuzzy based model, to identify the optimal level settings for each parameter. Analysis of variance is finally performed to identify parameters significant to the process.

The proposed model will be validated in future by conducting a real life case study, where multiple responses need to be optimized simultaneously.

It is believed that the proposed procedure in this study can resolve a complex parameter design problem with multiple responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready‐made neural and statistical software like Neuro Work II professional and Minitab.

This study adds to the literature of multi‐optimization problem, where a combination of the neuro‐fuzzy model and Taguchi method is utilized hand‐in‐hand.

The purpose of this paper is to develop a mathematical model for metal removal rate and surface roughness through Taguchi method and analyse the influence of the individual input machining parameters (cutting speed, feed rate, helix angle, depth of cut and wt% on the responses in milling of aluminium-titanium diboride metal matrix composite (MMC) with solid carbide end mill cutter coated with nano-crystals.

Taguchi OA is used to optimise the material removal rate (MRR) and Surface Roughness by developing a mathematical model. End Milling is used to create slots by combining various input parameters. Five factors, three-level Taguchi method is employed to carry out the experimental investigation. Fuzzy logic is used to find the optimal cutting factors for surface roughness (Ra) and MRR. The factors considered were weight percentage of TiB₂, cutting speed, depth of cut and feed rate. The plan for the experiments and analysis was based on the Taguchi L27 orthogonal array with five factors and three levels. MINITAB 17 software is used for regression, S/N ratio and analysis of variance. MATLAB 7.10.0 is used to perform the fuzzy logics systems.

Using fuzzy logics, multi-response performance index is generated, with which the authors can identify the correct combination of input parameters to get higher MRR and lower surface roughness value with the chosen range with 95 per cent confidence intervals. Using such a model, remarkable savings in time and cost can be obtained.

Machinability characteristics in Al-TiB₂ MMC based on the Taguchi method with fuzzy logic has not been analysed previously.

The purpose of this paper is to analyse and optimise the machinability behaviour of Carbon Fibre Reinforced Polymer (CFRP) composites with multiple performance characteristics using the Taguchi method with fuzzy logic.

A multi-response performance index (MRPI) was used for optimisation. The machining parameters, viz., tool geometry (helix angle of the endmill cutter), spindle speed, feed rate and depth of cut, were optimised with consideration of multiple performance characteristics, viz., machining force and material removal rate.

The results from confirmation runs indicated that the determined optimal combination of machining parameters improved the performance of the machining process.

The machinability behaviour of CFRP composites during milling of CFRP composites using Taguchi method with fuzzy logic has not been previously analysed.

The purpose of this paper is to analyze the wastewater discharge and a zero-wastewater-discharge (ZWD) reproduction plan is designed for a paper mill in Taiwan.

The proposed model of ZWD reproduction planning is established using the fuzzy comprehensive evaluation and Taguchi method to determine the overall wastewater recovery rate. Still the prior studies failed to address a systematic approach to optimize the waste water recovery rate.

The optimal solution for clean water is 500 tons, recovery electrodialysis reversal is 345 tons, the wastewater reuse performance is 1.3 and waste heat recycling performance is 0.8, the larger number is performed well. The results shows that the maximum overall waste water recovery rate is 97.8 percent.

A paper mill is strived for improving their sustainable development. In real situation, there is a need to address the qualitative information and qualitative data to carry out the optimal ZWD reproduction planning.

Glass fibre reinforced plastics (GFRP) contain two phases of materials with drastically distinguished mechanical and thermal properties, which brings in complicated interactions between the matrix and the reinforcement during machining. Surface quality and dimensional precision will greatly affect parts during their useful life especially in cases where the components will be in contact with other elements or materials during their useful life. The purpose of this paper is to discuss the application of the Taguchi method with fuzzy logic to optimise the machining parameters for machining of GFRP composites with multiple characteristics.

The machining tests were performed on a CNC milling machine using solid carbide (K10) End mill cutting tool with three different helix angles. Experiments were planned using Taguchi’s orthogonal array with the cutting conditions prefixed.

The machining parameters, namely, helix angle of the end mill cutter, spindle speed, feed rate, depth of cut, and work piece fibre orientation (specially applied to the GFRP composites) were optimised with considerations of multiple response characteristics, including machining force, material removal rate, and delamination. The results from confirmation runs indicated that the determined optimal combination of machining parameters improved the performance of the machining process.

Multi-response optimisation of machinability behaviour of GFRP composites using fuzzy logic has not been attempted previously.

In the present research work electrochemical grinding (ECG) process is applied to machine Al₂O₃/Al interpenetrating phase composite. The purpose of this paper is to present a new approach to optimize the ECG process parameters while machining alumina-aluminum (Al₂O₃ – Al) interpenetrating phase composites (IPC) used in automotive, aircraft and manufacture of space ships applying Taguchi-based experimental studies and fuzzy multi-criteria decision-making techniques.

The present work identifies the process variables that have significant consequences during ECG of Al₂O₃/Al IPC. The Taguchi L₉ orthogonal array is selected for design of experiments and the analysis is carried out following signal to noise ratio. The analysis of variance is carried out to establish the factors that significantly influence the responses. The present work also investigates the multi objective optimization of ECG process parameters using VIseKriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) and Grey relational analysis (GRA) to establish the reference ranking from a set of alternatives in the presence of conflicting criteria.

Material removal rate, surface finish, overcut and cutting force are shown to depend on the type of electrolyte, supply voltage, depth of cut and electrolyte flow rate. It is found that voltage and electrolyte concentration are important. The optimal machining parameter combination for ECG process is determined using fuzzy set theory, VIKOR and GRA. Substantial improvement in machining performance takes place.

A variety of manufacturing techniques are available for processing of Al₂O₃ – Al metal matrix composites. Generally manufacturers favor low cost modus operandi. Therefore ECG process is the best alternative for processing of MMCs in the present commercial sectors. The experimental investigation approach can act as useful and an efficient guideline for manufacturing.

The characteristic features of the ECG process are reflected through Taguchi design-based experimental studies with various process parametric combinations. Application of multi-response optimization technique for evaluation of best parametric combination for machining Al₂O₃ – Al IPC material using ECG process is a first-of-its-kind approach in literature.

Cost estimation based on expert's judgment is not an ideal approach, since human decisions are usually determined according to general attributes of limited and unstructured experience. The purpose of this paper is to develop a generic model of intelligence and cognitive science‐based method that can play an active role in process cost prediction within the shortest possible time.

In this paper, an intelligent system was conceived for prediction of total process cost of the product. The system is based on the concept of case‐based reasoning. It is a method for solving problems by making use of previous (source cases), similar situations and reusing information and knowledge about such situations. The source case data are generated by Taguchi technique and the cost function calculates the corresponding cost of each experiment in the economic time scale. The target case consists of the process variables whose cost needs to be determined. The cost for the source cases, consisting of the process variables of the already manufactured products are known in priori. The system calculates the similarities between the source cases and target cases and calculates the optimum cost. The fuzzy‐C‐means clustering method provides the model connecting the process parameters with total costs searched for.

The results show that the quality of predictions made by the intelligent system is comparable to the quality assured by the experienced expert. The proposed expert system is superior to traditional cost accounting system and assists inexperienced users in predicting the optimum process cost within the shortest possible time.

The research was limited to the traditional machining process.

The paper can be applied to any process industry and will have immense practical value.

This is the first time an expert system has been developed for the process industry that can calculate the process cost within a few days or a few hours before making an offer to a buyer.

The purpose of this study is to investigate the prediction and optimization of multiple performance characteristics in the face milling process of tool steel ASSAB XW-42.

The face milling parameters (cutting speed, feed rate and axial depth of cut) and flow rate (FR) of cryogenic cooling were optimized with consideration of multiple performance characteristics, i.e. surface roughness (SR), cutting force (F_c) and metal removal rate (MRR). FR of cryogenic cooling has two levels, whereas the three face milling parameters each have three levels. Using Taguchi method, an L₁₈ mixed-orthogonal array was selected as the design of experiments. The rough estimation of the optimum face milling parameters was determined by using grey fuzzy analysis. The global optimum face milling parameters were searched by applying the backpropagation neural network-based genetic algorithm (BPNN-GA) method.

The optimum SR, cutting force (F_c) and MRR could be obtained by setting FR, cutting speed, feed rate and axial depth of cut at 0.5 l/min, 280 m/min, 90 mm/min and 0.2 mm, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the multiple performance characteristics.

To the best of the authors’ knowledge, there were no publications available regarding multi-response optimization using the combination of grey fuzzy analysis and BPNN-based GA methods during cryogenically face milling process.

The purpose of this paper is to propose a simple methodology in solving multi‐response optimisation problems by employing Taguchi methods and a non‐parametric statistical technique.

There is a continuous interest in developing effective and statistically sound multi‐response optimisation methods such that they will provide a firm framework in global product and process improvement. A non‐parametric approach is proposed for the first time in a five‐step methodology that exploits Taguchi's fractional factorial designs and the concept of signal‐to‐noise ratio in data consolidation. The distinct feature of this method is the transformation of each response variable to a single rank variable. The subsequent incorporation of the squared ranks for each of the investigated responses issues a single master‐rank response suitably referred to conveniently as a “Super Rank” (SR) response, thus collapsing all dependent product characteristic information into a single non‐dimensional variable. This SR variable is handled by standard non‐parametric methods such as Wilcoxon's two‐sample, rank sum test or Mann‐Whitney's test eliminating at the same time multi‐distribution effects and small‐sample complications expected for this type of experimentation.

The proposed methodology is tested on already published data pertaining a design problem in the electronic assembly technology field. The case study requires six‐factor simultaneous optimisation of three response variables. A second example is analyzed by the proposed method focusing on the optimisation of a submerged arc‐welding process problem due to a group of five factors. The Mann‐Whitney's test contrasts the effects of factor settings one‐to‐one on the SR response in order to assign statistical significance to the optimal factor settings.

The application of this methodology is tested at the same time in a real three‐response optimisation case study where each response belongs to different optimisation category.

The methodology outlined in this work eliminates the need for sophisticated multi‐response data handling. In addition, small‐sample considerations and multi‐distribution effects that may be inherent do not restrict the applicability of the method presented herein by this type of experimentation.

This investigation provides a new angle to the published methods of multi‐response optimisation by supporting Taguchi's design of experiments methods through a multi‐ranking scheme that leads to non‐parametric factor resolution.