Grey-Taguchi-Based Optimization of Wire-Sawing for a Slicing Ceramic

Slicing ceramic (SC) is well-known as difficult-to-cut material. It is a hard and brittle material. The Grey-Taguchi method, which converts multiple response problems into a single response, is used to determine the effect of the process parameters for wire-sawing on multiple quality characteristics. The wire-sawing parameters include the wire tension (T), the slurry concentration (C), mixed grains mesh size (G), the wire speed (S), and the working load (P). The machining quality characteristics include a material removal rate (MRR), machined surface roughness (SR) of SC, kerf width (KW), wire wear (WW), and flatness (FT). An analysis of variance (ANOVA) is used to identify the mixed grains and slurry concentration that have a significant effect on multiple quality characteristics. The results of the ANOVA using the Grey-Taguchi method show that the optimum conditions are T2C1G1S2P1 (wire tension of 24 N, slurry concentration of 10% wt., mixed grains of #600 + #1000 mesh size, wire speed of 2.8 m/s, and working load of 1.27 N). The respective improvement in MRR, machined SR of SC, KW, WW, and FT is 2.43%, 2.36%, 1.08%, 2.33%, and 14.27%. The addition of #600 + #1000 mixed grains mesh size to the slurry improves the machined SR of SC, KW, and WW. An increase in wire speed and working load and the use of appropriate mixed grains mesh size and slurry concentration increases the MRR for wire-saw machining.

Enhanced multi-objective solution approach for multiple quality characteristics optimisation problems considering predictive uncertainties

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces. Practical implications 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. Originality/value 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.

Mathematical Modeling of Multiple Quality Characteristics of a Laser Microdrilling Process Used in Al7075/SiCp Metal Matrix Composite Using Genetic Programming

The conventional method for machining metal matrix composites (MMCs) is difficult on account of their excellent characteristics compared with those of their source materials. Modern laser machining technology is a suitable noncontact method for machining operations of advanced engineering materials due to its novel advantages such as higher productivity, ease of adaptation to automation, minimum heat affected zone (HAZ), green manufacturing, decreased processing costs, improved quality, reduced wastage, removal of finishing operations, and so on. Their application includes hole drilling in an aircraft engine components such as combustion chambers, nozzle guide vanes, and turbine blades made up of MMCs which meet quality standards that determine their suitability for service use. This paper presents a derived mathematical model based on evolutionary computation methods using multivariate regression fitting for the prediction of multiple characteristics (circularity, taper, spatter, and HAZ) of neodymium: yttrium aluminum garnet laser drilling of aluminum matrix/silicon carbide particulate (Al/SiCp) MMCs using genetic programming. Laser drilling input factors such as laser power, pulse frequency, gas pressure, and pulse width are utilized. From a training dataset, different genetic models for multiple quality characteristics were obtained with great accuracy during simulated evolution to provide a more accurate prediction compared to empirical correlations.

A Solution Framework for Response Surface-Based Multiple Quality Characteristics Optimization

A common problem generally encountered during manufacturing process improvement involves simultaneous optimization of multiple ‘quality characteristics’ or so-called ‘responses’ and determining the best process operating conditions. Such a problem is also referred to as ‘multiple response optimization (MRO) problem’. The presence of interaction between the responses calls for trade-off solution. The term ‘trade-off’ is an explicit compromised solution considering the bias and variability of the responses around the specified targets. The global exact solution in such types of nonlinear optimization problems is usually unknown, and various trade-off solution approaches (based on process response surface (RS) models or without using process RS models) had been proposed by researchers over the years. Considering the prevalent and preferred solution approaches, the scope of this paper is limited to RS-based solution approaches and similar closely related solution framework for MRO problems. This paper contributes by providing a detailed step-by-step RS-based MRO solution framework. The applicability and steps of the solution framework are also illustrated using a real life in-house pin-on-disc design of experiment study. A critical review on solution approaches with details on inherent characteristic features, assumptions, limitations, application potential in manufacturing and selection norms (indicative of the application potential) of suggested techniques/methods to be adopted for implementation of framework is also provided. To instigate research in this field, scopes for future work are also highlighted at the end.