Miroslav Radovanović

Software prototype for solving multi-objective machining optimization problems: Application in non-conventional machining processes

Abstract For an effective and efficient application of machining processes it is often necessary to consider more than one machining performance characteristics for the selection of optimal machining parameters. This implies the need to formulate and solve multi-objective optimization problems. In recent years, there has been an increasing trend of using meta-heuristic algorithms for solving multi-objective machining optimization problems. Although having the ability to efficiently handle highly non-linear, multi-dimensional and multi-modal optimization problems, meta-heuristic algorithms are plagued by numerous limitations as a consequence of their stochastic nature. To overcome some of these limitations in the machining optimization domain, a software prototype for solving multi-objective machining optimization problems was developed. The core of the developed software prototype is an algorithm based on exhaustive iterative search which guarantees the optimality of a determined solution in a given discrete search space. This approach is justified by a continual increase in computing power and memory size in recent years. To analyze the developed software prototype applicability and performance, four case studies dealing with multi-objective optimization problems of non-conventional machining processes were considered. Case studies are selected to cover different formulations of multi-objective optimization problems: optimization of one objective function while all the other are converted into constraints, optimization of a utility function which combines all objective functions and determination of a set of Pareto optimal solutions. In each case study optimization solutions that had been determined by past researchers using meta-heuristic algorithms were improved by using the developed software prototype.

APPLICATION OF THE PERFORMANCE SELECTION INDEX METHOD FOR SOLVING MACHINING MCDM PROBLEMS

Complex nature of machining processes requires the use of different methods and techniques for process optimization. Over the past few years a number of different optimization methods have been proposed for solving continuous machining optimization problems. In manufacturing environment, engineers are also facing a number of discrete machining optimization problems. In order to help decision makers in solving this type of optimization problems a number of multi criteria decision making (MCDM) methods have been proposed. This paper introduces the use of an almost unexplored MCDM method, i.e. performance selection index (PSI) method for solving machining MCDM problems. The main motivation for using the PSI method is that it is not necessary to determine criteria weights as in other MCDM methods. Applicability and effectiveness of the PSI method have been demonstrated while solving two case studies dealing with machinability of materials and selection of the most suitable cutting fluid for the given machining application. The obtained rankings have good correlation with those derived by the past researchers using other MCDM methods which validate the usefulness of this method for solving machining MCDM problems.

International standardization and organizations in the field of tribology

Wide tribology concept through the knowledge interaction from physics, chemistry and various engineering disciplines, has put tribology in a group with interdisciplinary sciences. This work gives information about the international standardization in the field of tribology, International Tribology Council, whose members consist of 36 national tribology society and associations from 34 countries worldwide and an overview of other international and regional organizations and institutions. Analysis of ISO, IEC and EN standards in various fields related to tribology and a view of the structure of most significant ISO Technical Committees related to standardization in tribology fields are presented.

Software prototype for validation of machining optimization solutions obtained with meta-heuristic algorithms

Optimization of machining processes is of primary importance for increasing machining efficiency and economics. Determining optimal values of machining parameters is performed by applying optimization algorithms to mathematical models of relationships between machining parameters and machining performance measures. In recent years, there has been an increasing trend of using empirical models and meta-heuristic optimization algorithms. The use of meta-heuristic optimization algorithms is justified because of their ability to handle highly non-linear, multi-dimensional and multi-modal optimization problems. Meta-heuristic algorithms are powerful optimization tools which provide high quality solutions in a short amount of computational time. However, their stochastic nature creates the need to validate the obtained solutions. This paper presents a software prototype for single and multi-objective machining process optimization. Since it is based on an exhaustive iterative search, it guarantees the optimality of determined solution in given discrete search space. The motivation for the development of the presented software prototype was the validation of machining optimization solutions obtained by meta-heuristic algorithms. To analyze the software prototype applicability and performance, six case studies of machining optimization problems, both single and multi-objective, were considered. In each case study the optimization solutions that had been determined by past researchers using meta-heuristic algorithms were either validated or improved by using the developed software prototype.

Selection Of Cutting Inserts For Aluminum Alloys Machining By Using MCDM Method

Abstract Machining of aluminum and its alloys requires the use of cutting tools with special geometry and material. Since there exists a number of cutting tools for aluminum machining, each with unique characteristics, selection of the most appropriate cutting tool for a given application is very complex task which can be viewed as a multi-criteria decision making (MCDM) problem. This paper is focused on multi-criteria analysis of VCGT cutting inserts for aluminum alloys turning by applying recently developed MCDM method, i.e. weighted aggregated sum product assessment (WASPAS) method. The MCDM model was defined using the available catalogue data from cutting tool manufacturers.

AN OPTIMIZATION APPROACH FOR PRODUCTION TIME MINIMIZATION IN LONGITUDINAL TURNING

One of the ways for increasing turning efficiency and economics is formulation and solving optimization problems through the use of mathematical models and optimization methods and algorithms. Ability to deal with complex and multi-dimensional optimization problems resulted in the use of a number of different optimization methods and algorithms for solving turning optimization problems, formulated either as single or multi-objective optimization problems with or without constraints. This study promotes the use of conceptually simple and parameter free optimization approach based on the use of exhaustive iterative search. To this aim optimization problem of single-pass turning process was considered. The proposed optimization approach was employed for determining optimal turning conditions, in terms of cutting speed, feed rate and depth of cut, so as to minimize total production time while considering four non-linear constraints. The obtained optimization solutions were compared with those obtained by the previous researchers using different meta-heuristic algorithms including genetic algorithms, simulated annealing, particle swarm optimization, differential evolution etc.

Pareto optimisation of certain quality characteristics in laser cutting by ANN-GA approach

Determining the optimal laser cutting conditions for simultaneous improvement of multiple cut quality characteristics is of great importance. The aim of the present research is to simultaneously optimise three cut quality characteristics such as surface roughness, kerf taper angle and burr height in CO2 laser cutting of stainless steel. The laser cutting experiment was conducted based on Taguchis experimental design using L27 experimental plan by varying four parameters such as laser power, cutting speed, assist gas pressure and focus position at three levels. Using the obtained experimental results three mathematical models for the prediction of cut quality characteristics were developed using artificial neural networks (ANNs). The developed response models for cut quality characteristics were taken as objective functions for the multi-objective optimisation based on the genetic algorithm. The obtained optimal solution sets were used to generate 2-D and 3-D Pareto fronts. The overall improvement of about 16% was registered in multiple cut quality characteristics.

Multi-objective optimization of cut quality characteristic in CO2 laser cutting stainless steel

Original scientific paper In this paper, multi-objective optimization of the cut quality characteristics in CO2 laser cutting of AISI 304 stainless steel was discussed. Three mathematical models for the prediction of cut quality characteristics such as surface roughness, kerf width and heat affected zone were developed using the artificial neural networks (ANNs). The laser cutting experiment was planned and conducted according to the Taguchi’s L27 orthogonal array and the experimental data were used to train single hidden layer ANNs using the Levenberg-Marquardt algorithm. The ANN mathematical models were developed considering laser power, cutting speed, assist gas pressure, and focus position as the input parameters. Multi-objective optimization problem was formulated using the weighting sum method in which the weighting factors that are used to combine cut quality characteristics into the single objective function were determined using the analytic hierarchy process method.

Use of Taguchi Method and Grey Relational Analysis for Optimizing a Ram Electrical Discharge Machining Process

There are various factors able to exert influence on the results of electrical discharge machining process. If there are many output factors of the machining process, one can formulate a problem of multicriterial optimization. It is necessary to find adequate values for the input factors so that the output factors have optimized values. The paper presents the results of a research aiming to optimize the material removal rate and the tool electrode wear rate, in the case of ram electrical discharge machining process. As input factors, one used the pulse on time, pulse off time and average peak current intensity. The Taguchi method was applied, in association with the Grey relational analysis. In this way, combinations of values corresponding to the input factors were determined, in order to obtain optimal results for the process output factors.

Taguchi Approach for the Optimization of Cutting Parameters in Finish Turning of Medical Stainless Steel

Optimization of cutting parameters in finish turning of medical stainless steel 316LVM with coated carbide tools using Taguchi method is proposed in this paper. Four cutting parameters namely, insert radius, depth of cut, feed and cutting speed are optimized with considerations of surface roughness as performance characteristic. The effects of cutting parameters on the surface roughness were experimentally investigated. Experimentation was conducted as per Taguchis orthogonal array. Four cutting parameters with three levels are arranged in L27 orthogonal array. The orthogonal array, measured values of surface roughness, signal-to-noise ratios and analysis of variance are employed to study the surface roughness. Based on the analysis, the optimal cutting parameter settings were determined. Through the confirmation test with optimal cutting parameter settings the effectiveness of the optimization approach are validated. The obtained results have shown that Taguchi method is suitable for optimizing the cutting parameter levels with the minimum number of experiments.