The usefulness and application of fuzzy logic and fuzzy AHP in the materials finishing industry

It is suggested that fuzzy logic could occupy a more prominent role in the materials finishing industry. Whilst a number of applications have already been made to control finishing processes and help with decision making, there is clearly scope for extending the use of fuzzy logic in the industry. After surveying some of these applications, the background to fuzzy logic is described and its set theory explained. Finally, the steps involved in selecting an environmentally acceptable metal cleaning agent from possible alternatives using a fuzzy analytic hierarchy process (AHP) are described in detail. As illustration, two different sets of selection criteria ranking are considered for choosing (i) the best solvent for cleaning equipment to be used in oxygen service and (ii) for cleaning metal parts prior to further finishing treatment. This is an Accepted Manuscript of an article to be published by Taylor & Francis in Transactions of the IMF It will be available online:https://www.tandfonline.com/toc/ytim20/curren

Feasibility analysis of design for remanufacturing in bearing using hybrid fuzzy-topsis and taguchi optimization

The tremendous advancement in technology, productivity and improved standard of living has come at the cost of environmental deterioration, increased energy and raw material consumption. In this regard, remanufacturing is viable option to reduce energy usage, carbon footprint and raw material usage. In this manuscript, using computational intelligence techniques we try to determine the feasibility of remanufacturing in case of roller bearings. We collected used N308 bearings from 5 different Indian cities. Using Fuzzy-TOPSIS, we found that the roundness, surface roughness and weight play a vital role in design for remanufacturing of roller bearings. Change in diameter, change in thickness and change in width showed minimal influence.  We also used Taguchi analysis to reassess the problem. The roundness of inner and outer race was found to be the most influential parameters in deciding the selection of bearing for remanufacturing. The results suggest the bearing designer to design the bearing in such a way that roundness of both races will be taken cared while manufacturing a bearing. However, using Taguchi the weight of the rollers was found to be of least influence. Overall, the predictions of Taguchi analysis were found to be similar to Fuzzy-TOPSIS analysis

The Optimization of Packaging System Process Parameters Using Taguchi Method

Packaging systems constitute substantially to product cost, its safety, and optimization. Unfortunately, no previous optimization studies have examined the packaging system in a bottling process plant for the unique, developing country environment. Consequently, the Taguchi method is applied to optimize a process plant's packaging system in a Nigerian plant's real-life situation. Optimal combinations of packaging system parameters that minimize product waste are created. An L4 (23) Taguchi orthogonal array was selected to analyze the data, and signal-to-noise ratios were computed for each experiment's run. Since the aim was to minimize beer waste, the ‘the-smaller-the-better’ signal-to-noise ratio was chosen in the analysis. S/N ratio plots revealed the optimum settings to obtain minimal product waste, namely, A2, B1, and C2 from the main effects plot for signal-to-noise ratios. A two-way ANOVA was performed on the significant factors to determine their percentage contributions to the response (product waste). Through Taguchi's innovative approach, the feasibility of optimizing the packaging process parameters was demonstrated and validated

State of the art in simulation-based optimisation for maintenance systems

Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on preventive maintenance and optimising preventive maintenance frequency that will lead to the minimum cost. Discrete event simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and condition based maintenance especially in a production context

Interval-based ranking in noisy evolutionary multiobjective optimization

As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present ?-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standardmulti-objective evolutionary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experimental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization