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Micro-EDM is an important process in the field of micro-machining. Especially, the μEDM is one of the technologies widely used for manufacture of micro-parts, micro-tools and micro-components, etc. The accuracy and repeatability of the μEDM process is still highly dependent on the μWEDG process. The electrode generation and regeneration is considered a key enabling technology for improving the performance of the μEDM process. Many engineers considered the Taguchi technique as engineering judgment during multiple response optimizations. This paper aims to focus on the use of micro-WEDG process to generate a micro-tool (electrode) with minimum surface roughness and higher metal removal rate (MRR).

In this research work, the Taguchi quality loss function analysis is used to examine and explain the influences of three process parameters (feed rate, capacitance and voltage) on the output responses such as MRR and surface roughness. Further, the optimized machining parameters were determined considering the multiple response objective using Taguchi multi-response signal-to-noise ratio.

Based on the experimental result, it was concluded that the Taguchi technique is suitable for the optimization of multi-response problem.

This paper presents an alternative approach using Taguchi's quality loss function. In most of the modern technological situations, more than one response variable is pertinent to the success of an industrial process. In this research work, the influence of feed rate, capacitance and voltage on the MRR and surface roughness (multiple responses) is investigated.

There is growing evidence of a knowledge gap in the association of maintenance with production activities in bottling plants. Indeed, insights into how to jointly optimise these activities are not clear. In this paper, two optimisation models, Taguchi schemes and response surface methodology are proposed.

Borrowing from the “hard” total quality management elements in optimisation and prioritisation literature, two new models were developed based on factor, level and orthogonal array selection, signal-to-noise ratio, analysis of variance and optimal parametric settings as Taguchi–ABC and Taguchi–Pareto. An additional model of response surface methodology was created with analysis on regression, main effects, residual plots and surface plots.

The Taguchi S/N ratio table ranked planned maintenance as the highest. The Taguchi–Pareto shows the optimal parametric setting as A4B4C1 (28 h of production, 30.56 shifts and 37 h of planned maintenance). Taguchi ABC reveals that the planned maintenance and number of shifts will influence the outcome of production greatly. The surface regression table reveals that the production hours worked decrease at a value of planned maintenance with a decrease in the number of shifts.

This is the first time that joint optimisation for bottling plant will be approached using Taguchi–ABC and Taguchi–Pareto. It is also the first time that response surface will be applied to optimise a unique platform of the bottling process plant.

The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.

The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.

Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.

The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.

The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.

No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

The purpose of this paper is to obtain a better understanding on robust performance of a hardening and tempering process producing component worm shaft used in the steam power plant. This research is capable to explaining the variation of process capability in terms of robustness.

This paper proposed a methodology (a combination of simulation, regression modelling and robust design technique) to study robustness of a hardening and tempering process producing component worm shaft used in the steam power plant and process capability acts as a surrogate measure of robustness. In each experimental run, the values of responses and the corresponding multivariate process capability indices across the outer array are determined. The variation of process performance (process capability values) due to random noise variation is studied using a general purpose process control chart (R-chart).

The results provide useful information in term of insensitiveness of the process against the noise (raw material and process noise) variation where the process capability acts as a surrogate measure of process robustness and explains the variation of process capability in term of robustness.

This paper adds to the body of knowledge on robustness of a manufacturing process. This paper may be of particular interest to practicing engineers as it suggests what factors should be more emphasis to achieve robust (consistent) performance from the process.

The originality of this paper lies within the context in which this study is to address key relationships between process robustness and process capability in a manufacturing industry.

This paper aims to determine the rational operating regime of a rotary percussive drilling machine under optimal conditions.

An orthogonal array of Taguchi, signal-to-noise (S/N) ratio, ANOVA Pareto analysis and regression analysis are used to investigate the effect of drilling operational factors on the penetration rate. A series of experiments based on orthogonal arrays L₂₇ was carried out, and the results were collected and analyzed using the statistical software Minitab.

The statistical analysis (ANOVA Pareto) of the results showed that among all setting parameters, air pressure is the most essential element that affects the penetration rate. The rational operating regime of the rotary percussive drilling machine was determined with optimum air pressure values of 17 bar (Level 3), rotation speed of 60 rpm (Level 3) and a thrust of 825 kgf (Level 2), which maximize the penetration rate. A quadratic regression model was developed for the penetration rate. The predicted values are compared with the experimental data and are considered to be in good agreement.

The study uses the orthogonal array of Taguchi, S/N ratio, ANOVA Pareto analysis and regression analysis to investigate the effect of drilling operational factors on the penetration rate.

In a multistage process, the final quality in the last stage not only depends on the quality of the task performed in that stage but is also dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. One of the most efficient statistical approaches used to model the multistage problems is the response surface method (RSM). However, it is necessary to optimize each response in all stages so to achieve the best solution for the whole problem. Robust optimization can produce very accurate solutions in this case.

In order to model a multistage problem, the RSM is often used by the researchers. A classical approach to estimate response surfaces is the ordinary least squares (OLS) method. However, this method is very sensitive to outliers. To overcome this drawback, some robust estimation methods have been presented in the literature. In optimization phase, the global criterion (GC) method is used to optimize the response surfaces estimated by the robust approach in a multistage problem.

The results of a numerical study show that our proposed robust optimization approach, considering both the sum of square error (SSE) index in model estimation and also GC index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method.

To the best of the authors’ knowledge, there are few papers focusing on quality-oriented designs in the multistage problem by means of RSM. Development of robust approaches for the response surface estimation and also optimization of the estimated response surfaces are the main novelties in this study. The proposed approach will produce more robust and accurate solutions for multistage problems rather than classical approaches.

The purpose of this study is to realize the multi-objective optimization for MRR and surface roughness in micro-milling of Inconel 718.

Taguchi method has been applied to conduct experiments, and the cutting parameters are spindle speed, feed per tooth and depth of cut. The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis. Genetic algorithm has been utilized to implement multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718.

This paper implemented the multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. And some conclusions can be summarized. Depth of cut is the major cutting parameter influencing surface roughness. Feed per tooth is the major cutting parameter influencing MRR. A number of cutting parameters have been obtained along with the set of pareto optimal solu-tions of MRR and surface roughness in micro-milling of Inconel 718.

There are a lot of cutting parameters affecting surface roughness and MRR in micro-milling, such as tool diameter, depth of cut, feed per tooth, spindle speed and workpiece material, etc. However, to the best our knowledge, there are no published literatures about the multi-objective optimization of surface roughness and MRR in micro-milling of Inconel 718.

The purpose of this paper is based on the response surface method, the authors determined the conditions for achieving the optimum rubber-sealing performance by using the maximum contact stress as the response value.

A two-dimensional model of a compression packer rubber was established by finite-element analysis software. Under the single axial load of 53.85 MPa, the four single factors of the end-face inclination angle, subthickness, height of rubber and friction coefficient of the rubber were analyzed.

Results show that the optimum sealing performance of the rubber tube is achieved when the end-face angle is equal to 45º and the thickness of the rubber tube is 9 mm. The response surface designed by Box–Behnken shows that the sealing performance of the rubber tube is the optimum when the end-face inclination angle is 48.1818°, the subthickness is 9 mm, the height of rubber is 90 mm and the friction coefficient is 0.1. Verification test results show that the model is reliable and effective.

Packer operations are performed downhole, and research on real experiments is limited. In this work, the feasibility of such experiments is determined by comparing finite-element modeling with actual experiments, and the results have guiding significance for actual downhole operations.

Researchers heavily investigated the use of industrial robots to enhance the quality of spray-painted surfaces. Despite its advantages, automating process is not always economically feasible. The manual process, on the other hand, is cheaper, but its quality is prone to the mental and physical conditions of the worker making it difficult to operate consistently. This research proposes a mathematical cost model that integrates human factors in determining optimal process settings.

Taguchi's robust design is used to investigate the effect of fatigue, stability of worker's hand and speed on paint consumption, surface quality, and processing time. A crossed array experimental design is deployed. Regression analysis is then used to model response variables and formulate cost model, followed by a multi-response optimization.

Results reveal that noise factors have a significant influence on painting quality, time, and cost of the painted surface. As a result, a noise management strategy should be implemented to reduce their impact and obtain better quality and productivity results. The cost model can be used to determine optimal setting for different applications by product and by industry.

Hardly any research considered the influence of human factors. Most focused on robot trajectory and its effect on paint uniformity. In proposed research, both cost and quality are integrated into a single objective. Quality is measured in terms of uniformity, smoothness, and surface defects. The interaction between trajectory and flow rate is investigated here for the first time. A unique approach integrating quality management, statistical analysis, and optimization is used.