Miloš Madić

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.

Comparative modeling of CO2 laser cutting using multiple regression analysis and artificial neural network

In this paper, empirical modeling of surface roughness in CO2 laser cutting of mild steel using the multiple regression analysis (MRA) and artificial neural network (ANN) was presented. To cover wider range of laser cutting parameters such as cutting speed, laser power and assist gas pressure as well as to obtain experimental database for MRA and ANN model development, Taguchi’s L25 orthogonal array was implemented for experimental plan. The average surface roughness was chosen as a measure of surface quality. The mathematical models of surface roughness developed by MRA and ANN were expressed as explicit nonlinear functions of the selected input parameters. The comparison between experimental results and models predictions showed that ANN model provided more accurate predictions when compared with the MRA model. The use of MRA for surface roughness prediction in CO2 laser cutting was of limited applicability and reliability. Powerful modeling ability of the ANNs justified the use of the ANN models for accurate modeling of the complex processes with many nonlinearities and interactions such as CO2 laser cutting. Finally, based on the derived ANN equation, the effects of the laser cutting parameters on surface roughness were examined.

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.

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.

An approach for robust decision making rule generation: Solving transport and logistics decision making problems

Abstract The selection of an optimal, efficient and reliable transport and logistics policy is one of the most important factors in supply chain management and logistics planning. Given that decision making in transport and logistics involves the consideration of a number of opposite criteria and possible solutions, such a selection can be considered as a multi-criteria decision making (MCDM) problem. This study presents a new integrated approach to decision making, i.e. the generation of a robust decision making rule (RDMR) by combining different MCDM methods and Taguchis robust quality engineering principles. As a logical implication of the proposed approach, a conceptual model of an adaptive and interactive expert system is developed. Its purpose is to enhance the decision making process through enabling the decision maker to: (i) use higher level knowledge regarding the selection of criteria weights and MCDM methods, (ii) estimate the ranking of a new alternative, which can be added to the initial decision matrix after a posteriori analysis of the final rankings of alternatives, and, moreover, quantify its distance from the ideal and the anti-ideal solution. Five different case studies in the field of transport and logistics were considered in order to illustrate the proposed approach. The obtained results and the results of the other MCDM methods were compared using Kendalls tau-b and Spearmans rho tests. An analysis of the final rank stability with respect to the changes in criteria weights was also performed so as to assess the sensitivity of the alternative rankings obtained by the application of different MCDM methods and the proposed approach. To this aim, the Monte Carlo simulation was conducted covering 1000 different scenarios of criteria weights in three different cases. Finally, an additional procedure was introduced for an explicit representation of an RDMR using the design of experiments (DOE) principles.

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.

The effects of passivation parameters on pitting potential of biomedical stainless steel

Passivation is a chemical process where electrochemical condition of passivity is gained on the surface of metal alloys. Biomedical AISI 316LVM stainless steel (SS) can be passivized by means of nitric acid immersion in order to improve a protective oxide layer on the surface and consequently increase corrosion resistance of the SS in the physiological solutions. In this study, multiple regression analysis and artificial neural network (ANN) were employed for mathematical modeling of the AISI 316LVM SS passivation process after immersion in the nitric acid solution. Pitting potential, which represents the measure of pitting corrosion resistance, was chosen as the response while passivation parameters were nitric acid concentration, temperature and passivation time. The comparison between experimental results and models predictions showed that only the ANN model provided statistically accurate predictions with a high coefficient of determination and a low mean relative error. Finally, based on the derived ANN equation, the effects of the passivation parameters on pitting potential were examined. [Projekat Ministarstva nauke Republike Srbije, br. ON174004 i br. TR35034]

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.